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Lundberg, Lars
Publications (10 of 144) Show all publications
Vishnubhotla, S. D., Mendes, E. & Lundberg, L. (2018). An insight into the capabilities of professionals and teams in agile software development: A systematic literature review. In: ACM International Conference Proceeding Series: . Paper presented at 7th International Conference on Software and Computer Applications, ICSCA 2018, Kuantan, Malaysia (pp. 10-19). Association for Computing Machinery
Open this publication in new window or tab >>An insight into the capabilities of professionals and teams in agile software development: A systematic literature review
2018 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2018, p. 10-19Conference paper, Published paper (Refereed)
Abstract [en]

Background: Previous studies investigated key characteristics of software engineers and factors influencing the performance of individuals, productivity of teams and project success within agile software development (ASD). They aided in the active investigation of human aspects in ASD. However, capability measurement and prediction with respect to agile workforce, owing to its importance, is an area that needs spotlight. Objective: The objective of this paper is to present the state of the art relating to capability measurement of software engineers and teams working in ASD projects. Method: We carried out a systematic literature review (SLR) focused on identifying attributes used for measuring and predicting the capabilities of individual software engineers and teams. Results: Evidence from 16 studies showed attributes that can measure capabilities of engineers and teams, and also attributes that can be used as capability predictors. Further, different instruments used to measure those attributes were presented. Conclusions: The SLR presented a wide list of attributes that were grouped into various categories. This information can be used by project managers as, for example, a checklist to consider when allocating software engineers to teams and in turn teams to a project. Further, this study indicated the necessity for an investigation into capability prediction models. © 2018 Association for Computing Machinery.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2018
Keywords
Agile software development, Capability measurement, Capability prediction, Competence, Individual capability, Systematic literature review, Team capability, Application programs, Engineers, Forecasting, Human resource management, Software design
National Category
Software Engineering
Identifiers
urn:nbn:se:bth-16644 (URN)10.1145/3185089.3185096 (DOI)2-s2.0-85048487301 (Scopus ID)9781450354141 (ISBN)
Conference
7th International Conference on Software and Computer Applications, ICSCA 2018, Kuantan, Malaysia
Available from: 2018-06-28 Created: 2018-06-28 Last updated: 2018-06-29Bibliographically approved
Sidorova, Y., Sköld, L., Rosander, O. & Lundberg, L. (2018). Optimizing utilization in cellular radio networks using mobility data. Optimization and Engineering, 1-28
Open this publication in new window or tab >>Optimizing utilization in cellular radio networks using mobility data
2018 (English)In: Optimization and Engineering, ISSN 1389-4420, E-ISSN 1573-2924, p. 1-28Article in journal (Refereed) Epub ahead of print
Abstract [en]

The main resource for any telecom operator is the physical radio cell network. We present two related methods for optimizing utilization in radio networks: Tetris optimization and selective cell expansion. Tetris optimization tries to find the mix of users from different market segments that provides the most even load in the network. Selective cell expansion identifies hotspot cells, expands the capacity of these radio cells, and calculates how many subscribers the radio network can handle after the expansions. Both methods are based on linear programming and use mobility data, i.e., data defining where different categories of subscribers tend to be during different times of the week. Based on real-world mobility data from a region in Sweden, we show that Tetris optimization based on six user segments made it possible to increase the number of subscribers by 58% without upgrading the physical infrastructure. The same data show that by selectively expanding less than 6% of the cells we are able to increase the number of subscribers by more than a factor of three without overloading the network. We also investigate the best way to combine Tetris optimization and selective cell expansion. © 2018 The Author(s)

Place, publisher, year, edition, pages
Springer New York LLC, 2018
Keywords
Cellular radio network, Linear programming, Mobility data, Optimization, Cytology, Radio, Cell expansion, Cellular radio networks, Hot-spot cells, Market segment, Mobility datum, Number of subscribers, Radio networks, Telecom operators, Cells
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16334 (URN)10.1007/s11081-018-9387-4 (DOI)2-s2.0-85047379164 (Scopus ID)
Available from: 2018-06-07 Created: 2018-06-07 Last updated: 2018-06-07Bibliographically approved
Sidorova, Y., Lundberg, L., Rosander, O., Grahn, H. & Skold, L. (2018). Recommendations for marketing campaigns in telecommunication business based on the footprint analysis: Who is a good client?. In: 2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017: . Paper presented at 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, Larnaca (pp. 1-6). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Recommendations for marketing campaigns in telecommunication business based on the footprint analysis: Who is a good client?
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2018 (English)In: 2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

A major investment made by a telecom operator goes into the infrastructure and its maintenance, while business revenues depend on how efficiently it is exploited. We present a data-driven analytic strategy based on combinatorial optimization and analysis of historical data. The data cover historical mobility in one region of Sweden during a week. Applying the proposed method in a case study, we have identified the optimal combination of geodemographic segments in the customer base, developed a functionality to assess the potential of a planned marketing campaign, and investigated how many and which segments to target for customer base growth. A comprehensible summary of the conclusions is created via execution of the queries with a fuzzy logic component. © 2017 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
Keywords
business intelligence, combinatorial optimization, fuzzy logic, geodemographic segments, mobility data, MOSAIC, Commerce, Competitive intelligence, Computer circuits, Investments, Footprint analysis, Historical data, Marketing campaign, Mobility datum, Optimal combination, Telecom operators, Marketing
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16539 (URN)10.1109/IISA.2017.8316396 (DOI)2-s2.0-85047937690 (Scopus ID)9781538637319 (ISBN)
Conference
8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, Larnaca
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18Bibliographically approved
Boeva, V., Lundberg, L., Kota, S. M. H. & Sköld, L. (2017). Analysis of Organizational Structure through Cluster Validation Techniques Evaluation of email communications at an organizational level. In: Gottumukkala, R Ning, X Dong, G Raghavan, V Aluru, S Karypis, G Miele, L Wu, X (Ed.), 2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2017): . Paper presented at 17th IEEE International Conference on Data Mining (ICDMW), NOV 18-21, 2017, New Orleans, LA (pp. 170-176). IEEE
Open this publication in new window or tab >>Analysis of Organizational Structure through Cluster Validation Techniques Evaluation of email communications at an organizational level
2017 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
IEEE, 2017
Series
International Conference on Data Mining Workshops, ISSN 2375-9232
Keywords
cluster validation measures, data analysis, human capital management, internal communication, organizational structure
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15992 (URN)10.1109/ICDMW.2017.28 (DOI)000425845700022 ()978-1-5386-3800-2 (ISBN)
Conference
17th IEEE International Conference on Data Mining (ICDMW), NOV 18-21, 2017, New Orleans, LA
Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-03-23Bibliographically approved
Boddapati, V., Petef, A., Rasmusson, J. & Lundberg, L. (2017). Classifying environmental sounds using image recognition networks. In: Toro C.,Hicks Y.,Howlett R.J.,Zanni-Merk C.,Toro C.,Frydman C.,Jain L.C.,Jain L.C. (Ed.), Procedia Computer Science: . Paper presented at 21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, (KES), Marseille (pp. 2048-2056). Elsevier B.V., 112
Open this publication in new window or tab >>Classifying environmental sounds using image recognition networks
2017 (English)In: 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, Published 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).

Place, publisher, year, edition, pages
Elsevier B.V., 2017
Keywords
Convolutional Neural Networks, Deep Learning, Environmental Sound Classification, GPU Processing, Image Classification, Classification (of information), Convolution, Deep neural networks, Image recognition, Knowledge based systems, Neural networks, Automatic classification, Classification accuracy, Classification tasks, Convolutional neural network, Environmental sound classifications, Environmental sounds, Image representations
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:bth-15478 (URN)10.1016/j.procs.2017.08.250 (DOI)000418466000216 ()2-s2.0-85032359938 (Scopus ID)
Conference
21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, (KES), Marseille
Available from: 2017-11-10 Created: 2017-11-10 Last updated: 2018-01-18Bibliographically approved
Podapati, S., Lundberg, L., Sköld, L., Rosander, O. & Sidorova, Y. (2017). Fuzzy recommendations in marketing campaigns. In: Darmont J.,Kirikova M.,Norvag K.,Wrembel R.,Papadopoulos G.A.,Gamper J.,Rizzi S. (Ed.), Communications in Computer and Information Science: . Paper presented at 21st European Conference on Advances in Databases and Information Systems, ADBIS, Nicosia (pp. 246-256). , 767
Open this publication in new window or tab >>Fuzzy recommendations in marketing campaigns
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2017 (English)In: Communications in Computer and Information Science / [ed] Darmont J.,Kirikova M.,Norvag K.,Wrembel R.,Papadopoulos G.A.,Gamper J.,Rizzi S., 2017, Vol. 767, p. 246-256Conference paper, Published paper (Refereed)
Abstract [en]

The population in Sweden is growing rapidly due to immigration. In this light, the issue of infrastructure upgrades to provide telecommunication services is of importance. New antennas can be installed at hot spots of user demand, which will require an investment, and/or the clientele expansion can be carried out in a planned manner to promote the exploitation of the infrastructure in the less loaded geographical zones. In this paper, we explore the second alternative. Informally speaking, the term Infrastructure-Stressing describes a user who stays in the zones of high demand, which are prone to produce service failures, if further loaded. We have studied the Infrastructure-Stressing population in the light of their correlation with geo-demographic segments. This is motivated by the fact that specific geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic is applied to create an interface between big data, numeric methods for its processing, and a manager who wants a comprehensible summary. © 2017, Springer International Publishing AG.

Keywords
Call detail records, Fuzzy membership function, Geo-demographic segments, Intelligent data mining, Marketing
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15307 (URN)10.1007/978-3-319-67162-8_24 (DOI)2-s2.0-85029801359 (Scopus ID)9783319671611 (ISBN)
Conference
21st European Conference on Advances in Databases and Information Systems, ADBIS, Nicosia
Available from: 2017-10-06 Created: 2017-10-06 Last updated: 2018-01-13Bibliographically approved
Shirinbab, S., Lundberg, L. & Casalicchio, E. (2017). Performance Evaluation of Container and Virtual Machine Running Cassandra Workload. In: Essaaidi, M Zbakh, M (Ed.), PROCEEDINGS OF 2017 3RD INTERNATIONAL CONFERENCE OF CLOUD COMPUTING TECHNOLOGIES AND APPLICATIONS (CLOUDTECH): . Paper presented at 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Rabat (pp. 24-31).
Open this publication in new window or tab >>Performance Evaluation of Container and Virtual Machine Running Cassandra Workload
2017 (English)In: PROCEEDINGS OF 2017 3RD INTERNATIONAL CONFERENCE OF CLOUD COMPUTING TECHNOLOGIES AND APPLICATIONS (CLOUDTECH) / [ed] Essaaidi, M Zbakh, M, 2017, p. 24-31Conference paper, Published paper (Refereed)
Abstract [en]

Today, scalable and high-available NoSQL distributed databases are largely used as Big Data platforms. Such distributed databases typically run on a virtualized infrastructure that could be implemented using Hypervisorb ased virtualiz ation or Container-based virtualiz ation. Hypervisor-based virtualization is a mature technology but imposes overhead on CPU, memory, networking, and disk Recently, by sharing the operating system resources and simplifying the deployment of applications, container-based virtualization is getting more popular. Container-based virtualization is lightweight in resource consumption while also providing isolation. However, disadvantages are security issues and 110 performance. As a result, today these two technologies are competing to provide virtual instances for running big data platforms. Hence, a key issue becomes the assessment of the performance of those virtualization technologies while running distributed databases. This paper presents an extensive performance comparison between VMware and Docker container, while running Apache Cassandra as workload. Apache Cassandra is a leading NoSQL distributed database when it comes to Big Data platforms. As baseline for comparisons we used the Cassandra's performance when running on a physical infrastructure. Our study shows that Docker had lower overhead compared to the VMware when running Cassandra. In fact, the Cassandra's performance on the Dockerized infrastructure was as good as on the Non-Virtualized.

Keywords
Cassandra, Cloud computing, Containers, Docker, NoSQL databases, Virtual machine, VMware, Big Data, Performance evaluation
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16000 (URN)000426451400004 ()978-1-5386-1115-9 (ISBN)
Conference
3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), Rabat
Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-05-24Bibliographically approved
Sagar, S., Skold, L., Lundberg, L. & Sidorova, Y. (2017). Trajectory Segmentation for a Recommendation Module of a Customer Relationship Management System. In: Wu, Y Min, G Georgalas, N AlDubi, A Jin, X Yang, L Ma, J Yang, P (Ed.), Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017: . Paper presented at EEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter (pp. 1150-1155). IEEE
Open this publication in new window or tab >>Trajectory Segmentation for a Recommendation Module of a Customer Relationship Management System
2017 (English)In: Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017 / [ed] Wu, Y Min, G Georgalas, N AlDubi, A Jin, X Yang, L Ma, J Yang, P, IEEE , 2017, p. 1150-1155Conference paper, Published paper (Refereed)
Abstract [en]

In business analytics some industries rely heavily on commercial geo-demographic segmentation systems (MOSAIC, ACORN, etc.), which are a universally strong predictor of user's behavior: from diabetes propensity and purchasing habits to political preferences. A segment is defined with a postcode of the client's home address. Recent research suggests that a mature competitor to geo-demographic segmentation is about to emerge: segmentation based on user mobility is reported to be a reliable proxy of social well-being of the neighborhood. In this submission, we have completed a user segmentation model based on clustering of user trajectories from the Call Detail Records covering one week of activity of one region in Sweden. The new segmentation has been compared against MOSAIC in the recommendation module of a customer relationship management system and has revealed better business options with regard to network exploitation and potential revenues. The implementation is available from the corresponding author (JS or LL) on request.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
trajectory clustering, user segmentation, spectral clustering
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16057 (URN)10.1109/iThings-GreenCom-CPSCom-SmartData.2017.177 (DOI)000426972400177 ()
Conference
EEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-07-10Bibliographically approved
Casalicchio, E., Lundberg, L. & Shirinbad, S. (2016). An Energy-Aware Adaptation Model for Big Data Platforms. In: IEEE (Ed.), 2016 IEEE International Conference on Autonomic Computing (ICAC): . Paper presented at IEEE International Conference on Autonomic Computing (ICAC), Würzburg (pp. 349-350). IEEE
Open this publication in new window or tab >>An Energy-Aware Adaptation Model for Big Data Platforms
2016 (English)In: 2016 IEEE International Conference on Autonomic Computing (ICAC) / [ed] IEEE, IEEE, 2016, p. 349-350Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Big Data;fault tolerant computing;power aware computing;quality of service;resource allocation;Amazon SimpleDB;Apache Cassandra;Big Data platforms;Cassandra-as-a-Service provider;Dynamo;Google BigTable;Hbase;energy-aware adaptation model;human assisted control;infrastructure scalability;multitenancy;resource management;Adaptation models;Big data;Cloud computing;Optimization;Runtime;Scalability;Throughput;Apache Cassandra;Autonomic computing;Big Data;Cloud computing;Green computing
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-13669 (URN)10.1109/ICAC.2016.13 (DOI)000390681200054 ()978-1-5090-1654-9 (ISBN)
Conference
IEEE International Conference on Autonomic Computing (ICAC), Würzburg
Available from: 2016-12-26 Created: 2016-12-26 Last updated: 2018-01-13Bibliographically approved
Shirinbab, S., Lundberg, L. & Håkansson, J. (2016). Comparing Automatic Load Balancing using VMware DRS with a Human Expert. In: 2016 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING WORKSHOP (IC2EW): . Paper presented at IEEE International Conference on Cloud Engineering (IC2E), APR 04-08, 2016, TU Berlin, Berlin, GERMANY (pp. 239-246). IEEE
Open this publication in new window or tab >>Comparing Automatic Load Balancing using VMware DRS with a Human Expert
2016 (English)In: 2016 IEEE INTERNATIONAL CONFERENCE ON CLOUD ENGINEERING WORKSHOP (IC2EW), IEEE, 2016, p. 239-246Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, there has been a rapid growth of interest in dynamic management of resources in virtualized systems. Virtualization provides great flexibility in terms of resource sharing but at the same time it also brings new challenges for load balancing using automatic migrations of virtual machines. In this paper, we have evaluated VMware's Distributed Resource Scheduler (DRS) in a number of realistic scenarios using multiple instances of a large industrial telecommunication application. We have measured the performance on the hosts before and after the migration in terms of CPU utilization, and compared DRS migrations with human expert migrations. According to our results, DRS with the most aggressive threshold gave us the best results. It could balance the load in 40% of cases while in other cases it could not balance the load properly. DRS did completely unnecessary migrations back and forth in some cases.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Cloud Computing, Distributed Resource Scheduler (DRS), Virtual Machine Migration, Virtualization, VMware
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-13923 (URN)10.1109/IC2EW.2016.14 (DOI)000392269400047 ()978-1-5090-3684-4 (ISBN)
Conference
IEEE International Conference on Cloud Engineering (IC2E), APR 04-08, 2016, TU Berlin, Berlin, GERMANY
Available from: 2017-02-22 Created: 2017-02-22 Last updated: 2018-01-13Bibliographically approved
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