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  • 1.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    A Higher Order Mining Approach for the Analysis of Real-World Datasets2020In: Energies, E-ISSN 1996-1073, Vol. 13, no 21, article id 5781Article in journal (Refereed)
    Abstract [en]

    In this study, we propose a higher order mining approach that can be used for the analysis of real-world datasets. The approach can be used to monitor and identify the deviating operational behaviour of the studied phenomenon in the absence of prior knowledge about the data. The proposed approach consists of several different data analysis techniques, such as sequential pattern mining, clustering analysis, consensus clustering and the minimum spanning tree (MST). Initially, a clustering analysis is performed on the extracted patterns to model the behavioural modes of the studied phenomenon for a given time interval. The generated clustering models, which correspond to every two consecutive time intervals, can further be assessed to determine changes in the monitored behaviour. In cases in which significant differences are observed, further analysis is performed by integrating the generated models into a consensus clustering and applying an MST to identify deviating behaviours. The validity and potential of the proposed approach is demonstrated on a real-world dataset originating from a network of district heating (DH) substations. The obtained results show that our approach is capable of detecting deviating and sub-optimal behaviours of DH substations.

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  • 2.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Multi-view Clustering Analyses for District Heating Substations2020In: DATA 2020 - Proceedings of the 9th International Conference on Data Science, Technology and Applications2020, / [ed] Hammoudi S.,Quix C.,Bernardino J., SciTePress, 2020, p. 158-168Conference paper (Refereed)
    Abstract [en]

    In this study, we propose a multi-view clustering approach for mining and analysing multi-view network datasets. The proposed approach is applied and evaluated on a real-world scenario for monitoring and analysing district heating (DH) network conditions and identifying substations with sub-optimal behaviour. Initially, geographical locations of the substations are used to build an approximate graph representation of the DH network. Two different analyses can further be applied in this context: step-wise and parallel-wise multi-view clustering. The step-wise analysis is meant to sequentially consider and analyse substations with respect to a few different views. At each step, a new clustering solution is built on top of the one generated by the previously considered view, which organizes the substations in a hierarchical structure that can be used for multi-view comparisons. The parallel-wise analysis on the other hand, provides the opportunity to analyse substations with regards to two different views in parallel. Such analysis is aimed to represent and identify the relationships between substations by organizing them in a bipartite graph and analysing the substations’ distribution with respect to each view. The proposed data analysis and visualization approach arms domain experts with means for analysing DH network performance. In addition, it will facilitate the identification of substations with deviating operational behaviour based on comparative analysis with their closely located neighbours.

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    Multi-view Clustering Analyses for District Heating Substations
  • 3.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Johansson, Christian
    NODA Intelligent Systems AB, SWE.
    District Heating Substation Behaviour Modelling for Annotating the Performance2020In: Communications in Computer and Information Science / [ed] Cellier, P, Driessens, K, Springer , 2020, Vol. 1168, p. 3-11Conference paper (Refereed)
    Abstract [en]

    In this ongoing study, we propose a higher order data mining approach for modelling district heating (DH) substations’ behaviour and linking operational behaviour representative profiles with different performance indicators. We initially create substation’s operational behaviour models by extracting weekly patterns and clustering them into groups of similar patterns. The built models are further analyzed and integrated into an overall substation model by applying consensus clustering. The different operational behaviour profiles represented by the exemplars of the consensus clustering model are then linked to performance indicators. The labelled behaviour profiles are deployed over the whole heating season to derive diverse insights about the substation’s performance. The results show that the proposed method can be used for modelling, analyzing and understanding the deviating and sub-optimal DH substation’s behaviours. © 2020, Springer Nature Switzerland AG.

  • 4.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Brage, Jens
    NODA Intelligent Systems AB, SWE.
    Johansson, Christian
    NODA Intelligent Systems AB, SWE.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Lavesson, Niklas
    Jönköping University, SWE.
    Higher order mining for monitoring district heating substations2019In: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 382-391Conference paper (Refereed)
    Abstract [en]

    We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques. © 2019 IEEE.

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    Higher Order Mining for Monitoring DistrictHeating Substations
  • 5.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Gustafsson, Jörgen
    Ericsson AB.
    Shaikh, Junaid
    Ericsson AB.
    Outlier Detection for Video Session Data Using Sequential Pattern Mining2018In: ACM SIGKDD Workshop On Outlier Detection De-constructed, 2018Conference paper (Refereed)
    Abstract [en]

    The growth of Internet video and over-the-top transmission techniqueshas enabled online video service providers to deliver highquality video content to viewers. To maintain and improve thequality of experience, video providers need to detect unexpectedissues that can highly affect the viewers’ experience. This requiresanalyzing massive amounts of video session data in order to findunexpected sequences of events. In this paper we combine sequentialpattern mining and clustering to discover such event sequences.The proposed approach applies sequential pattern mining to findfrequent patterns by considering contextual and collective outliers.In order to distinguish between the normal and abnormal behaviorof the system, we initially identify the most frequent patterns. Thena clustering algorithm is applied on the most frequent patterns.The generated clustering model together with Silhouette Index areused for further analysis of less frequent patterns and detectionof potential outliers. Our results show that the proposed approachcan detect outliers at the system level.

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    fulltext
  • 6.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Ickin, Selim
    Ericsson, SWE.
    Gustafsson, Jörgen
    Ericsson, SWE.
    A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences2018In: 2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) / [ed] Wani M.A.,Sayed-Mouchaweh M.,Lughofer E.,Gama J.,Kantardzic M., IEEE, 2018, p. 1123-1130, article id 8614207Conference paper (Refereed)
    Abstract [en]

    Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.

  • 7.
    Abghari, Shahrooz
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    García Martín, Eva
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Johansson, Christian
    NODA Intelligent Systems AB, SWE.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Trend analysis to automatically identify heat program changes2017In: Energy Procedia, Elsevier, 2017, Vol. 116, p. 407-415Conference paper (Refereed)
    Abstract [en]

    The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.

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    fulltext
  • 8.
    Akser, M.
    et al.
    Ulster University, GBR.
    Bridges, B.
    Ulster University, GBR.
    Campo, G.
    Ulster University, GBR.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Curran, K.
    Ulster University, GBR.
    Fitzpatrick, L.
    Ulster University, GBR.
    Hamilton, L.
    Ulster University, GBR.
    Harding, J.
    Ulster University, GBR.
    Leath, T.
    Ulster University, GBR.
    Lunney, T.
    Ulster University, GBR.
    Lyons, F.
    Ulster University, GBR.
    Ma, M.
    University of Huddersfield, GBR.
    Macrae, J.
    Ulster University, GBR.
    Maguire, T.
    Ulster University, GBR.
    McCaughey, A.
    Ulster University, GBR.
    McClory, E.
    Ulster University, GBR.
    McCollum, V.
    Ulster University, GBR.
    Mc Kevitt, P.
    Ulster University, GBR.
    Melvin, A.
    Ulster University, GBR.
    Moore, P.
    Ulster University, GBR.
    Mulholland, E.
    Ulster University, GBR.
    Muñoz, K.
    BijouTech, CoLab, Letterkenny, Co., IRL.
    O’Hanlon, G.
    Ulster University, GBR.
    Roman, L.
    Ulster University, GBR.
    SceneMaker: Creative technology for digital storytelling2018In: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng. / [ed] Brooks A.L.,Brooks E., Springer Verlag , 2018, Vol. 196, p. 29-38Conference paper (Refereed)
    Abstract [en]

    The School of Creative Arts & Technologies at Ulster University (Magee) has brought together the subject of computing with creative technologies, cinematic arts (film), drama, dance, music and design in terms of research and education. We propose here the development of a flagship computer software platform, SceneMaker, acting as a digital laboratory workbench for integrating and experimenting with the computer processing of new theories and methods in these multidisciplinary fields. We discuss the architecture of SceneMaker and relevant technologies for processing within its component modules. SceneMaker will enable the automated production of multimodal animated scenes from film and drama scripts or screenplays. SceneMaker will highlight affective or emotional content in digital storytelling with particular focus on character body posture, facial expressions, speech, non-speech audio, scene composition, timing, lighting, music and cinematography. Applications of SceneMaker include automated simulation of productions and education and training of actors, screenwriters and directors. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

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    fulltext
  • 9.
    Ammar, Doreid
    et al.
    Norwegian Univ Sci & Technol, NOR.
    De Moor, Katrien
    Norwegian Univ Sci & Technol, NOR.
    Xie, Min
    Next Generat Serv, Telenor Res, NOR.
    Fiedler, Markus
    Blekinge Institute of Technology, Faculty of Computing, Department of Communication Systems.
    Heegaard, Poul
    Norwegian Univ Sci & Technol, NOR.
    Video QoE Killer and Performance Statistics in WebRTC-based Video Communication2016Conference paper (Refereed)
    Abstract [en]

    In this paper, we investigate session-related performance statistics of a Web-based Real-Time Communication (WebRTC) application called appear. in. We explore the characteristics of these statistics and explore how they may relate to users' Quality of Experience (QoE). More concretely, we have run a series of tests involving two parties and according to different test scenarios, and collected real-time session statistics by means of Google Chrome's WebRTC-internals tool. Despite the fact that the Chrome statistics have a number of limitations, our observations indicate that they are useful for QoE research when these limitations are known and carefully handled when performing post-processing analysis. The results from our initial tests show that a combination of performance indicators measured at the sender's and receiver's end may help to identify severe video freezes (being an important QoE killer) in the context of WebRTC-based video communication. In this paper the performance indicators used are significant drops in data rate, non-zero packet loss ratios, non-zero PLI values, and non-zero bucket delay.

  • 10.
    Ammar, Doreid
    et al.
    Emlyon business school, FRA.
    Moor, Katrien De
    NTNU - Norwegian University of Science and Technology, NOR.
    Skorin-Kapov, Lea
    University of Zagreb, HRV.
    Fiedler, Markus
    Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
    Heegaard, Poul E.
    NTNU - Norwegian University of Science and Technology, NOR.
    Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation2019In: Proceedings - Conference on Local Computer Networks, LCN / [ed] Andersson K.,Tan H.-P.,Oteafy S., IEEE Computer Society , 2019, p. 406-413Conference paper (Refereed)
    Abstract [en]

    We address the challenge faced by service providers in monitoring Quality of Experience (QoE) related metrics for WebRTC-based audiovisual communication services. By extracting features from various application-layer performance statistics, we explore the potential of using machine learning (ML) models to estimate perceivable quality impairments and to identify root causes. We argue that such performance-related data can be valuable and informative from a QoE assessment point of view, by allowing to identify the party/parties in a call that is/are experiencing quality impairments, and to trace the origins and causes of the problem. The paper includes case studies of multi-party videoconferencing that are established in a laboratory environment and exposed to various network disturbances and CPU limitations. Our results show that perceivable quality impairments in terms of video blockiness and audio distortions may be estimated with a high level of accuracy, thus proving the potential of exploiting ML models for automated QoE-driven monitoring and estimation of WebRTC performance. © 2019 IEEE.

  • 11.
    Angelova, Milena
    et al.
    Technical University of Sofia-branch Plovdiv, BUL.
    Vishnu Manasa, Devagiri
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Linde, Peter
    Blekinge Institute of Technology, The Library.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    An expertise recommender system based on data from an institutional repository (DiVA)2018In: Proceedings of the 22nd edition of the International Conference on ELectronic PUBlishing: From Projects to Sustainable Infrastructure, ELPUB 2018 / [ed] Chan L.,Mounier P., OpenEdition Press , 2018Conference paper (Refereed)
    Abstract [en]

    Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors inacademy.

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    fulltext
  • 12.
    Angelova, Milena
    et al.
    Technical University of sofia, BUL.
    Vishnu Manasa, Devagiri
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Linde, Peter
    Blekinge Institute of Technology, The Library.
    Lavesson, Niklas
    An Expertise Recommender System based on Data from an Institutional Repository (DiVA)2019In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan, Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
    Abstract [en]

    Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

  • 13.
    Bergenholtz, Erik
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Moss, Andrew
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Ilie, Dragos
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Casalicchio, Emiliano
    Finding a needle in a haystack: A comparative study of IPv6 scanning methods2019In: 2019 INTERNATIONAL SYMPOSIUM ON NETWORKS, COMPUTERS AND COMMUNICATIONS (ISNCC 2019), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    It has previously been assumed that the size of anIPv6 network would make it impossible to scan the network forvulnerable hosts. Recent work has shown this to be false, andseveral methods for scanning IPv6 networks have been suggested.However, most of these are based on external information likeDNS, or pattern inference which requires large amounts of knownIP addresses. In this paper, DeHCP, a novel approach based ondelimiting IP ranges with closely clustered hosts, is presentedand compared to three previously known scanning methods. Themethod is shown to work in an experimental setting with resultscomparable to that of the previously suggested methods, and isalso shown to have the advantage of not being limited to a specificprotocol or probing method. Finally we show that the scan canbe executed across multiple VLANs.

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    isncc2019-ipv6
  • 14.
    Boddapati, Venkatesh
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Petef, Andrej
    Sony Mobile Communications AB, SWE.
    Rasmusson, Jim
    Sony Mobile Communications AB, SWE.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Classifying environmental sounds using image recognition networks2017In: Procedia Computer Science / [ed] Toro C.,Hicks Y.,Howlett R.J.,Zanni-Merk C.,Toro C.,Frydman C.,Jain L.C.,Jain L.C., Elsevier B.V. , 2017, Vol. 112, p. 2048-2056Conference paper (Refereed)
    Abstract [en]

    Automatic classification of environmental sounds, such as dog barking and glass breaking, is becoming increasingly interesting, especially for mobile devices. Most mobile devices contain both cameras and microphones, and companies that develop mobile devices would like to provide functionality for classifying both videos/images and sounds. In order to reduce the development costs one would like to use the same technology for both of these classification tasks. One way of achieving this is to represent environmental sounds as images, and use an image classification neural network when classifying images as well as sounds. In this paper we consider the classification accuracy for different image representations (Spectrogram, MFCC, and CRP) of environmental sounds. We evaluate the accuracy for environmental sounds in three publicly available datasets, using two well-known convolutional deep neural networks for image recognition (AlexNet and GoogLeNet). Our experiments show that we obtain good classification accuracy for the three datasets. © 2017 The Author(s).

  • 15.
    Boeva, Veselka
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Angelova, M.
    Angelova, Milena
    Technical University of Sofia, BUL.
    Devagiri, Vishnu Manasa
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Tsiporkova, Elena
    EluciDATA Lab, Sirris, BEL.
    Bipartite Split-Merge Evolutionary Clustering2019In: Lect. Notes Comput. Sci., Springer , 2019, p. 204-223Conference paper (Refereed)
    Abstract [en]

    We propose a split-merge framework for evolutionary clustering. The proposed clustering technique, entitled Split-Merge Evolutionary Clustering is supposed to be more robust to concept drift scenarios by providing the flexibility to consider at each step a portion of the data and derive clusters from it to be used subsequently to update the existing clustering solution. The proposed framework is built around the idea to model two clustering solutions as a bipartite graph, which guides the update of the existing clustering solution by merging some clusters with ones from the newly constructed clustering while others are transformed by splitting their elements among several new clusters. We have evaluated and compared the discussed evolutionary clustering technique with two other state of the art algorithms: a bipartite correlation clustering (PivotBiCluster) and an incremental evolving clustering (Dynamic split-and-merge). © Springer Nature Switzerland AG 2019.

  • 16.
    Boeva, Veselka
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Angelova, Milena
    Technical University Sofia, BUL.
    Lavesson, Niklas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Rosander, Oliver
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Tsiporkova, Elena
    Collective Center for the Belgian Technological Industry, BEL.
    Evolutionary clustering techniques for expertise mining scenarios2018In: ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2 / [ed] van den Herik J.,Rocha A.P., SciTePress , 2018, Vol. 2, p. 523-530Conference paper (Refereed)
    Abstract [en]

    The problem addressed in this article concerns the development of evolutionary clustering techniques that can be applied to adapt the existing clustering solution to a clustering of newly collected data elements. We are interested in clustering approaches that are specially suited for adapting clustering solutions in the expertise retrieval domain. This interest is inspired by practical applications such as expertise retrieval systems where the information available in the system database is periodically updated by extracting new data. The experts available in the system database are usually partitioned into a number of disjoint subject categories. It is becoming impractical to re-cluster this large volume of available information. Therefore, the objective is to update the existing expert partitioning by the clustering produced on the newly extracted experts. Three different evolutionary clustering techniques are considered to be suitable for this scenario. The proposed techniques are initially evaluated by applying the algorithms on data extracted from the PubMed repository. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

  • 17.
    Boeva, Veselka
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Angelova, Milena
    Technical University Sofia Branch Plovdiv, BUL.
    Tsiporkova, Elena
    Sirris, Brussels, BEL.
    A split-merge evolutionary clustering algorithm2019In: ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence, SciTePress , 2019, Vol. 2, p. 337-346Conference paper (Refereed)
    Abstract [en]

    In this article we propose a bipartite correlation clustering technique that can be used to adapt the existing clustering solution to a clustering of newly collected data elements. The proposed technique is supposed to provide the flexibility to compute clusters on a new portion of data collected over a defined time period and to update the existing clustering solution by the computed new one. Such an updating clustering should better reflect the current characteristics of the data by being able to examine clusters occurring in the considered time period and eventually capture interesting trends in the area. For example, some clusters will be updated by merging with ones from newly constructed clustering while others will be transformed by splitting their elements among several new clusters. The proposed clustering algorithm, entitled Split-Merge Evolutionary Clustering, is evaluated and compared to another bipartite correlation clustering technique (PivotBiCluster) on two different case studies: expertise retrieval and patient profiling in healthcare. Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

  • 18.
    Boeva, Veselka
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Angelova, Milena
    TU of Sofia, BUL.
    Kohstall, Jan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Cluster Validation Measures for Label Noise Filtering2018In: 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings / [ed] JardimGoncalves, R; Mendonca, JP; Jotsov, V; Marques, M; Martins, J; Bierwolf, R, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 109-116Conference paper (Refereed)
    Abstract [en]

    Cluster validation measures are designed to find the partitioning that best fits the underlying data. In this paper, we show that these well-known and scientifically proven validation measures can also be used in a different context, i.e., for filtering mislabeled instances or class outliers prior to training in super-vised learning problems. A technique, entitled CVI-based Outlier Filtering, is proposed in which mislabeled instances are identified and eliminated from the training set, and a classification hypothesis is then built from the set of remaining instances. The proposed approach assigns each instance several cluster validation scores representing its potential of being an outlier with respect to the clustering properties the used validation measures assess. We examine CVI-based Outlier Filtering and compare it against the LOF detection method on ten data sets from the UCI data repository using five well-known learning algorithms and three different cluster validation indices. In addition, we study two approaches for filtering mislabeled instances: local and global. Our results show that for most learning algorithms and data sets, the proposed CVI-based outlier filtering algorithm outperforms the baseline method (LOF). The greatest increase in classification accuracy has been achieved by combining at least two of the used cluster validation indices and global filtering of mislabeled instances. © 2018 IEEE.

  • 19.
    Boeva, Veselka
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Inst Technol, Comp Sci & Engn Dept, Karlskrona, Sweden..
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Inst Technol, Comp Sci & Engn Dept, Karlskrona, Sweden..
    Kota, Sai M. Harsha
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sköld, Lars
    Telenor , SWE.
    Analysis of Organizational Structure through Cluster Validation Techniques Evaluation of email communications at an organizational level2017In: 2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2017) / [ed] Gottumukkala, R Ning, X Dong, G Raghavan, V Aluru, S Karypis, G Miele, L Wu, X, IEEE , 2017, p. 170-176Conference paper (Refereed)
    Abstract [en]

    In this work, we report an ongoing study that aims to apply cluster validation measures for analyzing email communications at an organizational level of a company. This analysis can be used to evaluate the company structure and to produce further recommendations for structural improvements. Our initial evaluations, based on data in the forms of emails logs and organizational structure for a large European telecommunication company, show that cluster validation techniques can be useful tools for assessing the organizational structure using objective analysis of internal email communications, and for simulating and studying different reorganization scenarios.

  • 20.
    Boeva, Veselka
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Kota, Sai M. Harsha
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. student.
    Sköld, Lars
    Telenor, SWE.
    Evaluation of organizational structure through cluster validation analysis of email communications2018In: JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE, ISSN 2432-2717, Vol. 1, no 2, p. 327-347Article in journal (Refereed)
    Abstract [en]

    In this work, we apply cluster validation measures for analyzing email communications at an organizational level of a company. This analysis can be used to evaluate the company structure and to produce further recommendations for structural improvements. Our evaluations, based on data in the forms of email logs and organizational structure for a large European telecommunication company, show that cluster validation techniques can be useful tools for assessing the organizational structure using objective analysis of internal email communications, and for simulating and studying different reorganization scenarios.

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  • 21.
    Boldt, Martin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Borg, Anton
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Ickin, Selim
    Ericsson Research, SWE.
    Gustafsson, Jörgen
    Ericsson Research, SWE.
    Anomaly detection of event sequences using multiple temporal resolutions and Markov chains2020In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 62, p. 669-686Article in journal (Refereed)
    Abstract [en]

    Streaming data services, such as video-on-demand, are getting increasingly more popular, and they are expected to account for more than 80% of all Internet traffic in 2020. In this context, it is important for streaming service providers to detect deviations in service requests due to issues or changing end-user behaviors in order to ensure that end-users experience high quality in the provided service. Therefore, in this study we investigate to what extent sequence-based Markov models can be used for anomaly detection by means of the end-users’ control sequences in the video streams, i.e., event sequences such as play, pause, resume and stop. This anomaly detection approach is further investigated over three different temporal resolutions in the data, more specifically: 1 h, 1 day and 3 days. The proposed anomaly detection approach supports anomaly detection in ongoing streaming sessions as it recalculates the probability for a specific session to be anomalous for each new streaming control event that is received. Two experiments are used for measuring the potential of the approach, which gives promising results in terms of precision, recall, F 1 -score and Jaccard index when compared to k-means clustering of the sessions. © 2019, The Author(s).

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  • 22.
    Borg, Anton
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Ahlstrand, Jim
    Telenor AB, SWE.
    Boldt, Martin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Improving Corporate Support by Predicting Customer e-Mail Response Time: Experimental Evaluation and a Practical Use Case2021In: Enterprise Information Systems / [ed] Filipe J., Śmiałek M., Brodsky A., Hammoudi S., Springer Science and Business Media Deutschland GmbH , 2021, p. 100-121Conference paper (Refereed)
    Abstract [en]

    Customer satisfaction is an important aspect for any corporations customer support process. One important factor keeping the time customers’ wait for a reply at acceptable levels. By utilizing learning models based on the Random Forest Algorithm, the extent to which it is possible to predict e-Mail time-to-respond is investigated. This is investigated both for customers, but also for customer support agents. The former focusing on how long until customers reply, and the latter focusing on how long until a customer receives an answer. The models are trained on a data set consisting of 51, 682 customer support e-Mails. The e-Mails covers various topics from a large telecom operator. The models are able to predict the time-to-respond for customer support agents with an AUC of 0.90, and for customers with an AUC of 0.85. These results indicate that it is possible to predict the TTR for both groups. The approach were also implemented in an initial trial in a live environment. How the predictions can be applied to improve communication efficiency, e.g. by anticipating the staff needs in customer support, is discussed in more detail in the paper. Further, insights gained from an initial implementation are provided. © 2021, Springer Nature Switzerland AG.

  • 23.
    Borg, Anton
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Ahlstrand, Jim
    Telenor AB, SWE.
    Boldt, Martin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Predicting e-mail response time in corporate customer support2020In: ICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems, SciTePress , 2020, p. 305-314Conference paper (Refereed)
    Abstract [en]

    Maintaining high degree of customer satisfaction is important for any corporation, which involves the customer support process. One important factor in this work is to keep customers' wait time for a reply at levels that are acceptable to them. In this study we investigate to what extent models trained by the Random Forest learning algorithm can be used to predict e-mail time-to-respond time for both customer support agents as well as customers. The data set includes 51,682 customer support e-mails of various topics from a large telecom operator. The results indicate that it is possible to predict the time-to-respond for both customer support agents (AUC of 0.90) as well as for customers (AUC of 0.85). These results indicate that the approach can be used to improve communication efficiency, e.g. by anticipating the staff needs in customer support, but also indicating when a response is expected to take a longer time than usual. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

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    Predicting e-Mail Response Time in Corporate Customer Support
  • 24.
    Borg, Anton
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boldt, Martin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Svensson, Johan
    Telenor Sverige AB, SWE.
    Using conformal prediction for multi-label document classification in e-Mail support systems2019In: ADVANCES AND TRENDS IN ARTIFICIAL INTELLIGENCE: FROM THEORY TO PRACTICE / [ed] Wotawa, F; Friedrich, G; Pill, I; KoitzHristov, R; Ali, M, Springer Verlag , 2019, Vol. 11536, p. 308-322Conference paper (Refereed)
    Abstract [en]

    For any corporation the interaction with its customers is an important business process. This is especially the case for resolving various business-related issues that customers encounter. Classifying the type of such customer service e-mails to provide improved customer service is thus important. The classification of e-mails makes it possible to direct them to the most suitable handler within customer service. We have investigated the following two aspects of customer e-mail classification within a large Swedish corporation. First, whether a multi-label classifier can be introduced that performs similarly to an already existing multi-class classifier. Second, whether conformal prediction can be used to quantify the certainty of the predictions without loss in classification performance. Experiments were used to investigate these aspects using several evaluation metrics. The results show that for most evaluation metrics, there is no significant difference between multi-class and multi-label classifiers, except for Hamming loss where the multi-label approach performed with a lower loss. Further, the use of conformal prediction did not introduce any significant difference in classification performance for neither the multi-class nor the multi-label approach. As such, the results indicate that conformal prediction is a useful addition that quantifies the certainty of predictions without negative effects on the classification performance, which in turn allows detection of statistically significant predictions. © Springer Nature Switzerland AG 2019.

  • 25.
    Casalicchio, Emiliano
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    A study on performance measures for auto-scaling CPU-intensive containerized applications2019In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543, Vol. 22, no 3, p. 995-1006, article id Special Issue: SIArticle in journal (Refereed)
    Abstract [en]

    Autoscaling of containers can leverage performance measures from the different layers of the computational stack. This paper investigate the problem of selecting the most appropriate performance measure to activate auto-scaling actions aiming at guaranteeing QoS constraints. First, the correlation between absolute and relative usage measures and how a resource allocation decision can be influenced by them is analyzed in different workload scenarios. Absolute and relative measures could assume quite different values. The former account for the actual utilization of resources in the host system, while the latter account for the share that each container has of the resources used. Then, the performance of a variant of Kubernetes’ auto-scaling algorithm, that transparently uses the absolute usage measures to scale-in/out containers, is evaluated through a wide set of experiments. Finally, a detailed analysis of the state-of-the-art is presented.

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  • 26.
    Casalicchio, Emiliano
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Cardellini, Valeria
    University of Rome, ITA.
    Interino, Gianluca
    University of Rome, ITA.
    Palmirani, Monica
    University of Bologna, ITA.
    Research challenges in legal-rule and QoS-aware cloud service brokerage2018In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 78, no Part 1, p. 211-223Article in journal (Refereed)
    Abstract [en]

    Abstract The ICT industry and specifically critical sectors, such as healthcare, transportation, energy and government, require as mandatory the compliance of ICT systems and services with legislation and regulation, as well as with standards. In the era of cloud computing, this compliance management issue is exacerbated by the distributed nature of the system and by the limited control that customers have on the services. Today, the cloud industry is aware of this problem (as evidenced by the compliance program of many cloud service providers), and the research community is addressing the many facets of the legal-rule compliance checking and quality assurance problem. Cloud service brokerage plays an important role in legislation compliance and QoS management of cloud services. In this paper we discuss our experience in designing a legal-rule and QoS-aware cloud service broker, and we explore relate research issues. Specifically we provide three main contributions to the literature: first, we describe the detailed design architecture of the legal-rule and QoS-aware broker. Second, we discuss our design choices which rely on the state of the art solutions available in literature. We cover four main research areas: cloud broker service deployment, seamless cloud service migration, cloud service monitoring, and legal rule compliance checking. Finally, from the literature review in these research areas, we identify and discuss research challenges.

  • 27.
    Casalicchio, Emiliano
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Iannucci, Stefano
    Mississippi State University, .
    The state-of-the-art in container technologies: Application, orchestration and security2020In: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 32, no 17, article id e5668Article in journal (Refereed)
    Abstract [en]

    Containerization is a lightweight virtualization technology enabling the deployment and execution of distributed applications on cloud, edge/fog, and Internet-of-Things platforms. Container technologies are evolving at the speed of light, and there are many open research challenges. In this paper, an extensive literature review is presented that identifies the challenges related to the adoption of container technologies in High Performance Computing, Big Data analytics, and geo-distributed (Edge, Fog, Internet-of-Things) applications. From our study, it emerges that performance, orchestration, and cyber-security are the main issues. For each challenge, the state-of-the-art solutions are then analyzed. Performance is related to the assessment of the performance footprint of containers and comparison with the footprint of virtual machines and bare metal deployments, the monitoring, the performance prediction, the I/O throughput improvement. Orchestration is related to the selection, the deployment, and the dynamic control of the configuration of multi-container packaged applications on distributed platforms. The focus of this work is on run-time adaptation. Cyber-security is about container isolation, confidentiality of containerized data, and network security. From the analysis of 97 papers, it came out that the state-of-the-art is more mature in the area of performance evaluation and run-time adaptation rather than in security solutions. However, the main unsolved challenges are I/O throughput optimization, performance prediction, multilayer monitoring, isolation, and data confidentiality (at rest and in transit). © 2020 John Wiley & Sons, Ltd.

  • 28.
    Casalicchio, Emiliano
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Shirinbab, Sogand
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Energy-Aware Adaptation in Managed Cassandra Datacenters2016In: Proceedings - 2016 International Conference on Cloud and Autonomic Computing, ICCAC / [ed] Gupta I.,Diao Y., IEEE, 2016, p. 60-71Conference paper (Refereed)
    Abstract [en]

    Today, Apache Cassandra, an highly scalable and available NoSql datastore, is largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra datacenters. As for all complex services, human assisted management of a multi-tenant cassandra datacenter is unrealistic. Rather, there is a growing demand for autonomic management solutions. In this paper, we present an optimal energy-aware adaptation model for managed Cassandra datacenters that modify the system configuration orchestrating three different actions: horizontal scaling, vertical scaling and energy aware placement. The model is built from a real case based on real application data from Ericsson AB. We compare the performance of the optimal adaptation with two heuristics that avoid system perturbations due to re-configuration actions triggered by subscription of new tenants and/or changes in the SLA. One of the heuristic is local optimisation and the second is a best fit decreasing algorithm selected as reference point because representative of a wide range of research and practical solutions. The main finding is that heuristic’s performance depends on the scenario and workload and no one dominates in all the cases. Besides, in high load scenarios, the suboptimal system configuration obtained with an heuristic adaptation policy introduce a penalty in electric energy consumption in the range [+25%, +50%] if compared with the energy consumed by an optimal system configuration.

  • 29.
    Casalicchio, Emiliano
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Shirinbab, Sogand
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Energy-aware Auto-scaling Algorithms for Cassandra Virtual Data Centers2017In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543, Vol. 20, no 3, p. 2065-2082Article in journal (Refereed)
    Abstract [en]

    Apache Cassandra is an highly scalable and available NoSql datastore, largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra Virtual Data Centers (VDCs). This paper address the problem of energy efficient auto-scaling of Cassandra VDC in managed Cassandra data centers. We propose three energy-aware autoscaling algorithms: \texttt{Opt}, \texttt{LocalOpt} and \texttt{LocalOpt-H}. The first provides the optimal scaling decision orchestrating horizontal and vertical scaling and optimal placement. The other two are heuristics and provide sub-optimal solutions. Both orchestrate horizontal scaling and optimal placement. \texttt{LocalOpt} consider also vertical scaling. In this paper: we provide an analysis of the computational complexity of the optimal and of the heuristic auto-scaling algorithms; we discuss the issues in auto-scaling Cassandra VDC and we provide best practice for using auto-scaling algorithms; we evaluate the performance of the proposed algorithms under programmed SLA variation, surge of throughput (unexpected) and failures of physical nodes. We also compare the performance of energy-aware auto-scaling algorithms with the performance of two energy-blind auto-scaling algorithms, namely \texttt{BestFit} and \texttt{BestFit-H}. The main findings are: VDC allocation aiming at reducing the energy consumption or resource usage in general can heavily reduce the reliability of Cassandra in term of the consistency level offered. Horizontal scaling of Cassandra is very slow and make hard to manage surge of throughput. Vertical scaling is a valid alternative, but it is not supported by all the cloud infrastructures.

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  • 30.
    Casalicchio, Emiliano
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Shirinbab, Sogand
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Optimal adaptation for Apache Cassandra2016In: SoSeMC workshop at 13th IEEE International Conference on Autonomic Computing / [ed] IEEE, IEEE Computer Society, 2016Conference paper (Refereed)
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  • 31.
    Casalicchio, Emiliano
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Lundberg, Lars
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Shirinbad, Sogand
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    An Energy-Aware Adaptation Model for Big Data Platforms2016In: 2016 IEEE International Conference on Autonomic Computing (ICAC) / [ed] IEEE, IEEE, 2016, p. 349-350Conference paper (Refereed)
    Abstract [en]

    Platforms for big data includes mechanisms and tools to model, organize, store and access big data (e.g. Apache Cassandra, Hbase, Amazon SimpleDB, Dynamo, Google BigTable). The resource management for those platforms is a complex task and must account also for multi-tenancy and infrastructure scalability. Human assisted control of Big data platform is unrealistic and there is a growing demand for autonomic solutions. In this paper we propose a QoS and energy-aware adaptation model designed to cope with the real case of a Cassandra-as-a-Service provider.

  • 32.
    Casalicchio, Emiliano
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Perciballi, Vanessa
    Spindox S.p.A, ITA.
    Auto-scaling of Containers: The Impact of Relative and Absolute Metrics2017In: 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017 / [ed] IEEE, IEEE, 2017, p. 207-214, article id 8064125Conference paper (Refereed)
    Abstract [en]

    Today, The cloud industry is adopting the container technology both for internal usage and as commercial offering. The use of containers as base technology for large-scale systems opens many challenges in the area of resource management at run-time. This paper addresses the problem of selecting the more appropriate performance metrics to activate auto-scaling actions. Specifically, we investigate the use of relative and absolute metrics. Results demonstrate that, for CPU intense workload, the use of absolute metrics enables more accurate scaling decisions. We propose and evaluate the performance of a new autoscaling algorithm that could reduce the response time of a factor between 0.66 and 0.5 compared to the actual Kubernetes' horizontal auto-scaling algorithm.

  • 33.
    Casalicchio, Emiliano
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Perciballi, Vanessa
    University of Rome, ITA.
    Measuring Docker Performance: What a Mess!!!2017In: ICPE 2017 - Companion of the 2017 ACM/SPEC International Conference on Performance Engineering, ACM , 2017, p. 11-16Conference paper (Refereed)
    Abstract [en]

    Today, a new technology is going to change the way platforms for the internet of services are designed and managed. This technology is called container (e.g. Docker and LXC). The internet of service industry is adopting the container technology both for internal usage and as commercial offering. The use of container as base technology for large-scale systems opens many challenges in the area of resource management at run-time, for example: autoscaling, optimal deployment and monitoring. Specifically, monitoring of container based systems is at the ground of any resource management solution, and it is the focus of this work. This paper explores the tools available to measure the performance of Docker from the perspective of the host operating system and of the virtualization environment, and it provides a characterization of the CPU and disk I/O overhead introduced by containers.

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  • 34.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Machine Learning in Healthcare: Breast Cancer and Diabetes Cases2021In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Science and Business Media Deutschland GmbH , 2021, Vol. 12585, p. 125-135Conference paper (Refereed)
    Abstract [en]

    This paper provides insights into a workflow of different applications of machine learning coupled with image analysis in the healthcare sector which we have undertaken. As case studies, we use personalized breast cancer screenings and diabetes research (i.e., Beta-cell mass quantification in mice and diabetic retinopathy analysis). Our tools play a pivotal role in evidence-based process for personalized medicine and/or in monitoring the progression of diabetes as a chronic disease to help for better understanding of its development and the way to combat it. Although this multidisciplinary collaboration provides only succinct description of these research nodes, relevant references are furnished for further details. © 2021, Springer Nature Switzerland AG.

  • 35.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    On Box-Cox Transformation for Image Normality and Pattern Classification2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 154975-154983, article id 9174711Article in journal (Refereed)
    Abstract [en]

    A unique member of the power transformation family is known as the Box-Cox transformation. The latter can be seen as a mathematical operation that leads to finding the optimum lambda (λ) value that maximizes the log-likelihood function to transform a data to a normal distribution and to reduce heteroscedasticity. In data analytics, a normality assumption underlies a variety of statistical test models. This technique, however, is best known in statistical analysis to handle one-dimensional data. Herein, this paper revolves around the utility of such a tool as a pre-processing step to transform two-dimensional data, namely, digital images and to study its effect. Moreover, to reduce time complexity, it suffices to estimate the parameter lambda in real-time for large two-dimensional matrices by merely considering their probability density function as a statistical inference of the underlying data distribution. We compare the effect of this light-weight Box-Cox transformation with well-established state-of-the-art low light image enhancement techniques. We also demonstrate the effectiveness of our approach through several test-bed data sets for generic improvement of visual appearance of images and for ameliorating the performance of a colour pattern classification algorithm as an example application. Results with and without the proposed approach, are compared using the AlexNet (transfer deep learning) pretrained model. To the best of our knowledge, this is the first time that the Box-Cox transformation is extended to digital images by exploiting histogram transformation.

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  • 36.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Structure Preserving Binary Image Morphing using Delaunay Triangulation2017In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 85, p. 8-14Article in journal (Refereed)
    Abstract [en]

    Mathematical morphology has been of a great significance to several scientific fields. Dilation, as one of the fundamental operations, has been very much reliant on the common methods based on the set theory and on using specific shaped structuring elements to morph binary blobs. We hypothesised that by performing morphological dilation while exploiting geometry relationship between dot patterns, one can gain some advantages. The Delaunay triangulation was our choice to examine the feasibility of such hypothesis due to its favourable geometric properties. We compared our proposed algorithm to existing methods and it becomes apparent that Delaunay based dilation has the potential to emerge as a powerful tool in preserving objects structure and elucidating the influence of noise. Additionally, defining a structuring element is no longer needed in the proposed method and the dilation is adaptive to the topology of the dot patterns. We assessed the property of object structure preservation by using common measurement metrics. We also demonstrated such property through handwritten digit classification using HOG descriptors extracted from dilated images of different approaches and trained using Support Vector Machines. The confusion matrix shows that our algorithm has the best accuracy estimate in 80% of the cases. In both experiments, our approach shows a consistent improved performance over other methods which advocates for the suitability of the proposed method.

  • 37.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Towards Query by Text Example for pattern spotting in historical documents2016In: Proceedings - CSIT 2016: 2016 7th International Conference on Computer Science and Information Technology, IEEE Computer Society, 2016, article id 7549479Conference paper (Refereed)
    Abstract [en]

    Historical documents are essentially formed of handwritten texts that exhibit a variety of perceptual environment complexities. The cursive and connected nature of text lines on one hand and the presence of artefacts and noise on the other hand hinder achieving plausible results using current image processing algorithm. In this paper, we present a new algorithm which we termed QTE (Query by Text Example) that allows for training-free and binarisation-free pattern spotting in scanned handwritten historical documents. Our algorithm gives promising results on a subset of our database revealing ∌83% success rate in locating word patterns supplied by the user.

  • 38.
    Cheddad, Abbas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Kusetogullari, Hüseyin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Object recognition using shape growth pattern2017In: Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, ISPA, IEEE Computer Society Digital Library, 2017, p. 47-52, article id 8073567Conference paper (Refereed)
    Abstract [en]

    This paper proposes a preprocessing stage to augment the bank of features that one can retrieve from binary images to help increase the accuracy of pattern recognition algorithms. To this end, by applying successive dilations to a given shape, we can capture a new dimension of its vital characteristics which we term hereafter: the shape growth pattern (SGP). This work investigates the feasibility of such a notion and also builds upon our prior work on structure preserving dilation using Delaunay triangulation. Experiments on two public data sets are conducted, including comparisons to existing algorithms. We deployed two renowned machine learning methods into the classification process (i.e., convolutional neural network-CNN- and random forests-RF-) since they perform well in pattern recognition tasks. The results show a clear improvement of the proposed approach's classification accuracy (especially for data sets with limited training samples) as well as robustness against noise when compared to existing methods.

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  • 39.
    Cheddad, Abbas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Kusetogullari, Hüseyin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Hilmkil, Agrin
    Peltarion AB,SWE.
    Sundin, Lena
    Independent Researcher, SWE.
    Yavariabdi, Amir
    KTO Karatay Univ, TUR.
    Aouache, Mustapha
    Dev Technol Avancees CDTA, Div Telecom, DZA.
    Hall, Johan
    Arkiv Digital AD AB, SWE.
    SHIBR-The Swedish Historical Birth Records: a semi-annotated dataset2021In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058, Vol. 33, no 22, p. 15863-15875Article in journal (Refereed)
    Abstract [en]

    This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms' performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child's first name, birth date, date of baptism, father's first/last name, mother's first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.

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  • 40. Danielsson, Max
    et al.
    Sievert, Thomas
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Rasmusson, Jim
    Sony Mobile Communications AB.
    Feature Detection and Description using a Harris-Hessian/FREAK Combination on an Embedded GPU2016Conference paper (Refereed)
    Abstract [en]

    GPUs in embedded platforms are reaching performance levels comparable to desktop hardware, thus it becomes interesting to apply Computer Vision techniques. We propose, implement, and evaluate a novel feature detector and descriptor combination, i.e., we combine the Harris-Hessian detector with the FREAK binary descriptor. The implementation is done in OpenCL, and we evaluate the execution time and classification performance. We compare our approach with two other methods, FAST/BRISK and ORB. Performance data is presented for the mobile device Xperia Z3 and the desktop Nvidia GTX 660. Our results indicate that the execution times on the Xperia Z3 are insufficient for real-time applications while desktop execution shows future potential. Classification performance of Harris-Hessian/FREAK indicates that the solution is sensitive to rotation, but superior in scale variant images.

  • 41.
    Dasari, Siva Krishna
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge Institute of Technology.
    Andersson, Petter
    GKN Aerospace Engine Systems, SWE.
    Predictive Modelling to Support Sensitivity Analysis for Robust Design in Aerospace Engineering2020In: Structural and multidisciplinary optimization (Print), ISSN 1615-147X, E-ISSN 1615-1488, Vol. 61, no 5, p. 2177-2192Article in journal (Refereed)
    Abstract [en]

    The design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study of uncertain variables’ effect on system performance. The aim of this study is to support sensitivity analysis for a robust design in aerospace engineering. For this, an approach is presented in which random forests (RF) and multivariate adaptive regression splines (MARS) are explored to handle linear and non-linear response types for response surface modelling. Quantitative experiments are conducted to evaluate the predictive performance of these methods with Turbine Rear Structure (a component of aircraft) case study datasets for response surface modelling. Furthermore, to test these models’ applicability to perform sensitivity analysis, experiments are conducted using mathematical test problems (linear and non-linear functions) and their results are presented. From the experimental investigations, it appears that RF fits better on non-linear functions compared with MARS, whereas MARS fits well on linear functions.

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  • 42.
    Dasari, Siva Krishna
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Andersson, Petter
    GKN Aerospace Engine Systems, SWE.
    Random Forest Surrogate Models to Support Design Space Exploration in Aerospace Use-case2019In: IFIP Advances in Information and Communication Technology, Springer-Verlag New York, 2019, Vol. 559Conference paper (Refereed)
    Abstract [en]

    In engineering, design analyses of complex products rely on computer simulated experiments. However, high-fidelity simulations can take significant time to compute. It is impractical to explore design space by only conducting simulations because of time constraints. Hence, surrogate modelling is used to approximate the original simulations. Since simulations are expensive to conduct, generally, the sample size is limited in aerospace engineering applications. This limited sample size, and also non-linearity and high dimensionality of data make it difficult to generate accurate and robust surrogate models. The aim of this paper is to explore the applicability of Random Forests (RF) to construct surrogate models to support design space exploration. RF generates meta-models or ensembles of decision trees, and it is capable of fitting highly non-linear data given quite small samples. To investigate the applicability of RF, this paper presents an approach to construct surrogate models using RF. This approach includes hyperparameter tuning to improve the performance of the RF's model, to extract design parameters' importance and \textit{if-then} rules from the RF's models for better understanding of design space. To demonstrate the approach using RF, quantitative experiments are conducted with datasets of Turbine Rear Structure use-case from an aerospace industry and results are presented.

  • 43.
    Dasari, Siva Krishna
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Palmquist, Jonatan
    Gkn Aerospace Engine Systems Sweden, Process Engineering Department, SWE.
    Melt-Pool Defects Classification for Additive Manufactured Components in Aerospace Use-Case2020In: 2020 7th International Conference on Soft Computing and Machine Intelligence, ISCMI 2020, Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 249-254, article id 9311555Conference paper (Refereed)
    Abstract [en]

    One of the crucial aspects of additive manufacturing is the monitoring of the welding process for quality assurance of components. A common way to analyse the welding process is through visual inspection of melt-pool images to identify possible defects in manufacturing. Recent literature studies showed the potential use of prediction models for defects classification to speed up the manual verification criteria since a huge data is generated from the additive manufacturing. Although a huge image data is available, the data needs to be labelled manually by experts which results in small sample datasets. Hence, to model small sample sizes and also to acquire the importance of parameters, we opted a traditional machine learning method, Random Forests (RF). For feature extraction, we opted for the Polar Transformation to explore its applicability using the melt-pool image dataset and a publicly available shape image dataset. The results show that RF models with Polar Transformation performed the best on our case study datasets and the second-best for the public dataset when compared to the Histogram of Oriented Gradients, HARALICK, XY-projections of an image, and Local Binary Patterns methods. As such, the Polar Transformation can be considered as a suitable compact shape descriptor. © 2020 IEEE.

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  • 44.
    Demir, Muhammed Fatih
    et al.
    Karatay Üniversitesi, TUR.
    Cankirli, Aysenur
    Karatay Üniversitesi, TUR.
    Karabatak, Begum
    Turkcell, Nicosia, CYP.
    Yavariabdi, Amir
    Karatay Üniversitesi, TUR.
    Mendi, Engin
    Karatay Üniversitesi, TUR.
    Kusetogullari, Hüseyin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Real-Time Resistor Color Code Recognition using Image Processing in Mobile Devices2018In: 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Proceedings / [ed] JardimGoncalves, R; Mendonca, JP; Jotsov, V; Marques, M; Martins, J; Bierwolf, R, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 26-30Conference paper (Refereed)
    Abstract [en]

    This paper proposes a real-time video analysis algorithm to read the resistance value of a resistor using a color recognition technique. To achieve this, firstly, a nonlinear filtering is applied to input video frame to smooth intensity variations and remove impulse noises. After that, a photometric invariants technique is employed to transfer the video frame from RGB color space to Hue-Saturation-Value (HSV) color space, which decreases sensitivity of the proposed method to illumination changes. Next, a region of interest is defined to automatically detect resistor's colors and then an Euclidean distance based clustering strategy is employed to recognize the color bars. The proposed method provides a wide range of color classification which includes twelve colors. In addition, it utilizes relatively low computational time which makes it suitable for real-time mobile video applications. The experiments are performed on a variety of test videos and results show that the proposed method has low error rate compared to the other resistor color code recognition mobile applications. © 2018 IEEE.

  • 45.
    Devagiri, Vishnu Manasa
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Abghari, Shahrooz
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    A Multi-view Clustering Approach for Analysis of Streaming Data2021In: IFIP Advances in Information and Communication Technology / [ed] Maglogiannis I., Macintyre J., Iliadis L., Springer Science and Business Media Deutschland GmbH , 2021, p. 169-183Conference paper (Refereed)
    Abstract [en]

    Data available today in smart monitoring applications such as smart buildings, machine health monitoring, smart healthcare, etc., is not centralized and usually supplied by a number of different devices (sensors, mobile devices and edge nodes). Due to which the data has a heterogeneous nature and provides different perspectives (views) about the studied phenomenon. This makes the monitoring task very challenging, requiring machine learning and data mining models that are not only able to continuously integrate and analyze multi-view streaming data, but also are capable of adapting to concept drift scenarios of newly arriving data. This study presents a multi-view clustering approach that can be applied for monitoring and analysis of streaming data scenarios. The approach allows for parallel monitoring of the individual view clustering models and mining view correlations in the integrated (global) clustering models. The global model built at each data chunk is a formal concept lattice generated by a formal context consisting of closed patterns representing the most typical correlations among the views. The proposed approach is evaluated on two different data sets. The obtained results demonstrate that it is suitable for modelling and monitoring multi-view streaming phenomena by providing means for continuous analysis and pattern mining. © 2021, IFIP International Federation for Information Processing.

  • 46.
    Devagiri, Vishnu Manasa
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Tsiporkova, Elena
    EluciDATA Lab, BEL.
    Split-merge evolutionary clustering for multi-view streaming data2020In: Procedia Computer Science / [ed] Cristani M.,Toro C.,Zanni-Merk C.,Howlett R.J.,Jain L.C.,Jain L.C., Elsevier, 2020, Vol. 176, p. 460-469Conference paper (Refereed)
    Abstract [en]

    In this study, we propose a new multi-view stream clustering approach, called MV Split-Merge Clustering. The proposed approach is an extension of an existing split-merge evolutionary clustering algorithm (entitled Split-Merge Clustering) to multi-view data applications. The extended version can be used to integrate data from multiple views in a streaming manner and discover cluster structure for each data chunk. The MV Split-Merge Clustering can be applied for grouping distinct chunks of multi-view streaming data so that a global integrated clustering model is built on each data chunk. At each time window, an updated clustering solution (local model) is initially produced on each view of the current data chunk by applying the Split-Merge Clustering algorithm. Formal Concept Analysis is then used in order to integrate information from the multiple views (local clustering models) and generate a global model (formal concept lattice) that reveals the correlations among the clusters of the local models. The proposed MV Split-Merge Clustering has been initially evaluated on a publicly available data set. Our results show that the approach is able to identify a clustering structure and relationships among the different views comparable to those produced in a batch scenario. © 2020 The Authors. Published by Elsevier B.V.

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  • 47.
    Devagiri, Vishnu Manasa
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Cheddad, Abbas
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Splicing Forgery Detection and the Impact of Image Resolution2017In: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTERS AND ARTIFICIAL INTELLIGENCE - ECAI 2017, IEEE , 2017Conference paper (Refereed)
    Abstract [en]

    With the development of the Internet, and the increase in the online storage space, there has been an explosion in the volume of videos and images circulating online. An important part of the digital forensics' tasks is to scrutinise part of these images to make important decisions. Digital tampering of images can impede reliability of these decisions. Through this paper we attempt to improve the detection rate of splicing forgery. We also examine how well the examined splicing forgery detection algorithm works on low-resolution images. In this paper, the aim is to enhance the accuracy of an existing algorithm. One tailed Wilcoxon signed rank test was utilised to compare the performance of the different algorithms.

  • 48.
    Dhont, Michiel
    et al.
    Sirris, BEL.
    Tsiporkova, Elena
    Sirris, BEL.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Layered integration approach for multi-view analysis of temporal data2020In: Lecture Notes in Computer Science, Springer Science and Business Media Deutschland GmbH , 2020, Vol. 12588, p. 138-154Conference paper (Refereed)
    Abstract [en]

    In this study, we propose a novel data analysis approach that can be used for multi-view analysis and integration of heterogeneous temporal data originating from multiple sources. The proposed approach consists of several distinctive layers: (i) select a suitable set (view) of parameters in order to identify characteristic behaviour within each individual source (ii) exploit an alternative set (view) of raw parameters (or high-level features) to derive some complementary representations (e.g. related to source performance) of the results obtained in the first layer with the aim to facilitate comparison and mediation across the different sources (iii) integrate those representations in an appropriate way, allowing to trace back similar cross-source performance to certain characteristic behaviour of the individual sources. The validity and the potential of the proposed approach has been demonstrated on a real-world dataset of a fleet of wind turbines. © Springer Nature Switzerland AG 2020.

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  • 49.
    Eghbalian, Amirmohammad
    et al.
    Blekinge Institute of Technology. Student.
    Abghari, Shahrooz
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Basiri, Farhad
    Iquest Ab, SWE.
    Multi-view Data Mining Approach for Behaviour Analysis of Smart Control Valve2020In: Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020 / [ed] Wani M.A.,Luo F.,Li X.,Dou D.,Bonchi F., Institute of Electrical and Electronics Engineers Inc. , 2020, p. 1238-1245, article id 9356190Conference paper (Refereed)
    Abstract [en]

    In this study, we propose a multi-view data analysis approach that can be used for modelling and monitoring smart control valve system behaviour. The proposed approach consists of four distinctive steps: (i) multi-view interpretation of the available data attributes by separating them into several representations (views), e.g., operational parameters, contextual factors, and performance indicators; (ii) modelling different control valve system operating modes by clustering analyses of the operational data view; (iii) annotating each operating mode (cluster) by using the remaining views (i.e., contextual and system performance data); (iv) context-aware monitoring of the control valve system operating behaviour by applying the built model. In addition, the data points (daily profiles) observed during the monitoring can be annotated by comparing them with the known typical behavioural modes. This information can be further analysed and used for continuous updating and improvement of the model.The potential of the proposed approach has been evaluated and demonstrated on real-world sensor data originating from a company in the smart building domain. The obtained results show the robustness of the proposed approach in modelling, analysing, and monitoring the control valve system behaviour. © 2020 IEEE.

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  • 50.
    Fiedler, Markus
    Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
    Performance Analytics by Means of the M5P Machine Learning Algorithm2019In: Proceedings of the 31st International Teletraffic Congress, ITC 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 104-105Conference paper (Refereed)
    Abstract [en]

    Machine Learning (ML) has shown its capability to analyse, classify, and make predictions based on large data sets. Network performance analysis and evaluation focuses on finding and expressing qualitative, quantitative and formal relationships between performance-related parameters, with specific interest in asymptotic behaviours. This work introduces the notion of performance analytics as performance modeling with help of ML. In particular, it demonstrates the applicablibility of the ML algorithm M5P to such performance analytics, as the parameters of the generated model trees allow for identifying approximations together the applicable parameter sub-spaces in a straightforward approach. We present a set of examples with focus on post-analysis of analytically obtained performance results for asymptotic behaviour. © 2019 ITC Press.

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