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  • 1.
    Abdelraheem, Mohamed Ahmed
    et al.
    SICS Swedish ICT AB, SWE.
    Gehrmann, Christian
    SICS Swedish ICT AB, SWE.
    Lindström, Malin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Nordahl, Christian
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Executing Boolean queries on an encrypted Bitmap index2016In: CCSW 2016 - Proceedings of the 2016 ACM Cloud Computing Security Workshop, co-located with CCS 2016, Association for Computing Machinery (ACM), 2016, p. 11-22Conference paper (Refereed)
    Abstract [en]

    We propose a simple and efficient searchable symmetric encryption scheme based on a Bitmap index that evaluates Boolean queries. Our scheme provides a practical solution in settings where communications and computations are very constrained as it offers a suitable trade-off between privacy and performance.

  • 2.
    Cheddad, Abbas
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Nordahl, Christian
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Distance Teaching Experience of Campus-based Teachers at Times of Pandemic Confinement2022In: ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), 2022, p. 64-69Conference paper (Refereed)
    Abstract [en]

    Amidst the outbreak of the coronavirus (COVID-19) pandemic, distance education, where the learning process is conducted online, has become the norm. Campus-based programs and courses have been redesigned in a timely manner which was a challenge for teachers not used to distance teaching. Students' engagement and active participation become an issue; add to that the new emerging effects associated with this setup, such as the so-called "Zoom fatigue", a term coined recently by some authors referring to one's exhaustion feeling that stems from the overuse of virtual meetings. In realising this problem, solutions were suggested in the literature to help trigger students' engagement and enhance teachers' experience in online teaching. This study analyses these effects along with our teachers' experience in the new learning environment and concludes by devising some recommendations. To attain the above objectives, we conducted online interviews with six of our teachers, transcribed the content of the videos and then applied the inductive research approach to assess the results. © 2022 Owner/Author.

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    fulltext
  • 3.
    Nordahl, Christian
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Data Stream Mining and Analysis: Clustering Evolving Data2024Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Streaming data is becoming more prevalent in our society every day. With the increasing use of technologies such as the Internet of Things (IoT) and 5G networks, the number of possible data sources steadily increases. Therefore, there is a need to develop algorithms that can handle the massive amount of data we now generate.

    Data mining is an area of research where data is mined to gain an understanding of data and its underlying structure. When we move to streaming data, and the corresponding sub-domain data stream mining, restrictions are imposed on the algorithms that can be used. Data streams are possibly endless, and their instances arrive rapidly, can often only be processed once or a few times, and often evolve as the data is generated over time.

    This thesis explores data-driven techniques to model and analyze evolving data streams. We focus on slower data streams where incremental updates are not necessary, and the interest lies in analyzing its behavior over longer time periods. We aim to evaluate existing and develop novel algorithms and techniques suitable for analyzing these types of data streams. We use both supervised and unsupervised learning methods to model the user/system behaviors, and the methods and algorithms are evaluated on various datasets.

    Specifically, we investigate regression and clustering algorithms to mine streaming data for user/system behavior patterns. We also design an algorithm capable of modeling user/system behavior in a single evolving data stream, which is easy to use and capitalizes on prior knowledge from the history of the stream. Furthermore, we design a clustering algorithm that takes advantage of multiple data streams, where each stream represents a part of the entire system, to model various aspects of the user/system behavior. Finally, we review the current state-of-the-art methods for evaluating data stream clustering algorithms and identify aspects that should be considered for the future.

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  • 4.
    Nordahl, Christian
    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.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    MultiStream EvolveCluster2023In: The 36th Canadian Conference on Artificial Intelligence, 2023Conference paper (Refereed)
    Abstract [en]

    This paper proposes a novel multi-stream clustering algorithm, MultiStream EvolveCluster (MS-EC), that can be used for continuous and distributed monitoring and analysis ofevolving time series phenomena. It can maintain evolving clustering solutions separatelyfor each stream/view and consensus clustering solutions reflecting evolving interrelationsamong the streams. Each stream behavior can be analyzed by different clustering techniques using a distance measure and data granularity that is specially selected for it. Theproperties of the MultiStream EvolveCluster algorithm are studied and evaluated withrespect to different consensus clustering techniques, distance measures, and cluster evaluation measures in synthetic and real-world smart building datasets. Our evaluation resultsshow a stable algorithm performance in synthetic data scenarios. In the case of real-worlddata, the algorithm behavior demonstrates sensitivity to the individual streams’ data quality and the used consensus clustering technique.

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    fulltext
  • 5.
    Nordahl, Christian
    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.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    On Evaluation of Data Stream Clustering Algorithms: A SurveyManuscript (preprint) (Other academic)
  • 6.
    Nordahl, Christian
    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.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Netz Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    EvolveCluster: an evolutionary clustering algorithm for streaming data2022In: Evolving Systems, ISSN 1868-6478, E-ISSN 1868-6486, no 4, p. 603-623Article in journal (Refereed)
    Abstract [en]

    Data has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.

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    fulltext
  • 7.
    Nordahl, Christian
    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.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Netz Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Profiling of household residents’ electricity consumption behavior using clustering analysis2019In: Lect. Notes Comput. Sci., Springer Verlag , 2019, p. 779-786Conference paper (Refereed)
    Abstract [en]

    In this study we apply clustering techniques for analyzing and understanding households’ electricity consumption data. The knowledge extracted by this analysis is used to create a model of normal electricity consumption behavior for each particular household. Initially, the household’s electricity consumption data are partitioned into a number of clusters with similar daily electricity consumption profiles. The centroids of the generated clusters can be considered as representative signatures of a household’s electricity consumption behavior. The proposed approach is evaluated by conducting a number of experiments on electricity consumption data of ten selected households. The obtained results show that the proposed approach is suitable for data organizing and understanding, and can be applied for modeling electricity consumption behavior on a household level. © Springer Nature Switzerland AG 2019.

  • 8.
    Nordahl, Christian
    et al.
    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.
    Persson, Marie
    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.
    Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis.2018In: Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis, https://sites.google.com/view/arial2018/accepted-papersprogram , 2018Conference paper (Refereed)
    Abstract [en]

    We propose a cluster analysis approach for organizing, visualizing and understanding households’ electricity consumption data. We initially partition the consumption data into a number of clusters with similar daily electricity consumption profiles. The centroids of each cluster can be seen as representative signatures of a household’s electricity consumption behaviors. We evaluate the proposed approach by conducting a number of experiments on electricity consumption data of ten selected households. Our results show that the approach is suitable for data analysis, understanding and creating electricity consumption behavior models.

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    fulltext
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