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Publikasjoner (8 av 8) Visa alla publikasjoner
Nordahl, C. (2024). Data Stream Mining and Analysis: Clustering Evolving Data. (Doctoral dissertation). Karlskrona: Blekinge Tekniska Högskola
Åpne denne publikasjonen i ny fane eller vindu >>Data Stream Mining and Analysis: Clustering Evolving Data
2024 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Karlskrona: Blekinge Tekniska Högskola, 2024. s. 231
Serie
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 1
Emneord
Data Stream Mining, Clustering, Data Streams, Data Mining
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
urn:nbn:se:bth-25539 (URN)978-91-7295-472-4 (ISBN)
Disputas
2024-01-24, Karlskrona, 09:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2023-11-17 Laget: 2023-11-03 Sist oppdatert: 2023-12-12bibliografisk kontrollert
Nordahl, C., Boeva, V. & Grahn, H. (2023). MultiStream EvolveCluster. In: The 36th Canadian Conference on Artificial Intelligence: . Paper presented at The 36th Canadian Conference on Artificial Intelligence, Montreal, 5-9 June 2023.
Åpne denne publikasjonen i ny fane eller vindu >>MultiStream EvolveCluster
2023 (engelsk)Inngår i: The 36th Canadian Conference on Artificial Intelligence, 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

Emneord
evolve clustering, data stream mining, consensus clustering
HSV kategori
Identifikatorer
urn:nbn:se:bth-25534 (URN)
Konferanse
The 36th Canadian Conference on Artificial Intelligence, Montreal, 5-9 June 2023
Tilgjengelig fra: 2023-11-01 Laget: 2023-11-01 Sist oppdatert: 2023-11-03bibliografisk kontrollert
Cheddad, A. & Nordahl, C. (2022). Distance Teaching Experience of Campus-based Teachers at Times of Pandemic Confinement. In: ACM International Conference Proceeding Series: . Paper presented at 5th International Conference on Education Technology Management, ICETM 2022, Virtual, Online, 16 December through 18 December 2022 (pp. 64-69). Association for Computing Machinery (ACM)
Åpne denne publikasjonen i ny fane eller vindu >>Distance Teaching Experience of Campus-based Teachers at Times of Pandemic Confinement
2022 (engelsk)Inngår i: ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), 2022, s. 64-69Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM), 2022
Emneord
COVID-19, Distance learning, Pandemic, Teacher's experience, Computer aided instruction, Coronavirus, E-learning, Students, Coronaviruses, Distance teaching, Distance-learning, Learning process, Student engagement, Teacher experience, Teachers', Teaching experience, Virtual meetings
HSV kategori
Identifikatorer
urn:nbn:se:bth-24693 (URN)10.1145/3582580.3582592 (DOI)2-s2.0-85159653289 (Scopus ID)9781450398015 (ISBN)
Konferanse
5th International Conference on Education Technology Management, ICETM 2022, Virtual, Online, 16 December through 18 December 2022
Tilgjengelig fra: 2023-06-02 Laget: 2023-06-02 Sist oppdatert: 2023-06-02bibliografisk kontrollert
Nordahl, C., Boeva, V., Grahn, H. & Netz Persson, M. (2022). EvolveCluster: an evolutionary clustering algorithm for streaming data. Evolving Systems (4), 603-623
Åpne denne publikasjonen i ny fane eller vindu >>EvolveCluster: an evolutionary clustering algorithm for streaming data
2022 (engelsk)Inngår i: Evolving Systems, ISSN 1868-6478, E-ISSN 1868-6486, nr 4, s. 603-623Artikkel i tidsskrift (Fagfellevurdert) Published
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.

sted, utgiver, år, opplag, sider
SPRINGER HEIDELBERG, 2022
Emneord
Evolving data stream; Clustering; Data stream clustering
HSV kategori
Identifikatorer
urn:nbn:se:bth-22395 (URN)10.1007/s12530-021-09408-y (DOI)000717906700001 ()2-s2.0-85119001929 (Scopus ID)
Forskningsfinansiär
Knowledge Foundation, 20140032
Merknad

open access

Tilgjengelig fra: 2021-11-26 Laget: 2021-11-26 Sist oppdatert: 2023-11-03bibliografisk kontrollert
Nordahl, C., Boeva, V., Grahn, H. & Netz Persson, M. (2019). Profiling of household residents’ electricity consumption behavior using clustering analysis. In: Lect. Notes Comput. Sci.: . Paper presented at International Conference on Computational Science, ICCS, Faro, Algarve, 12 June 2019 through 14 June 2019 (pp. 779-786). Springer Verlag
Åpne denne publikasjonen i ny fane eller vindu >>Profiling of household residents’ electricity consumption behavior using clustering analysis
2019 (engelsk)Inngår i: Lect. Notes Comput. Sci., Springer Verlag , 2019, s. 779-786Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer Verlag, 2019
Serie
Lecture Notes in Computer Science ; 11540
Emneord
Ambient Assisted Living, Non-intrusive remote monitoring, Assisted living, Clustering analysis, Clustering techniques, Electricity-consumption, Household level, Number of clusters, Remote monitoring, Electric power utilization
HSV kategori
Identifikatorer
urn:nbn:se:bth-18593 (URN)10.1007/978-3-030-22750-0_78 (DOI)000589285300076 ()2-s2.0-85068459816 (Scopus ID)9783030227494 (ISBN)
Konferanse
International Conference on Computational Science, ICCS, Faro, Algarve, 12 June 2019 through 14 June 2019
Tilgjengelig fra: 2019-09-09 Laget: 2019-09-09 Sist oppdatert: 2023-11-03bibliografisk kontrollert
Nordahl, C., Grahn, H., Persson, M. & Boeva, V. (2018). Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis.. In: Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis: . Paper presented at 2ND WORKSHOP ON AI FOR AGING, REHABILITATION AND INDEPENDENT ASSISTED LIVING (ARIAL) @IJCAI'18, Stockholm. https://sites.google.com/view/arial2018/accepted-papersprogram
Åpne denne publikasjonen i ny fane eller vindu >>Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis.
2018 (engelsk)Inngår i: Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis, https://sites.google.com/view/arial2018/accepted-papersprogram , 2018Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
https://sites.google.com/view/arial2018/accepted-papersprogram, 2018
HSV kategori
Identifikatorer
urn:nbn:se:bth-17439 (URN)
Konferanse
2ND WORKSHOP ON AI FOR AGING, REHABILITATION AND INDEPENDENT ASSISTED LIVING (ARIAL) @IJCAI'18, Stockholm
Tilgjengelig fra: 2018-12-19 Laget: 2018-12-19 Sist oppdatert: 2021-07-26bibliografisk kontrollert
Abdelraheem, M. A., Gehrmann, C., Lindström, M. & Nordahl, C. (2016). Executing Boolean queries on an encrypted Bitmap index. In: CCSW 2016 - Proceedings of the 2016 ACM Cloud Computing Security Workshop, co-located with CCS 2016: . Paper presented at 8th ACM Cloud Computing Security Workshop, CCSW,Vienna (pp. 11-22). Association for Computing Machinery (ACM)
Åpne denne publikasjonen i ny fane eller vindu >>Executing Boolean queries on an encrypted Bitmap index
2016 (engelsk)Inngår i: CCSW 2016 - Proceedings of the 2016 ACM Cloud Computing Security Workshop, co-located with CCS 2016, Association for Computing Machinery (ACM), 2016, s. 11-22Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM), 2016
Emneord
Bitmap index, Boolean query, Conjunctive search, Searchable symmetric encryption, Cloud computing, Economic and social effects, Bitmap indexes, Boolean queries, Practical solutions, Symmetric encryption, Symmetric encryption schemes, Trade off, Cryptography
HSV kategori
Identifikatorer
urn:nbn:se:bth-13648 (URN)10.1145/2996429.2996436 (DOI)000390888900003 ()2-s2.0-85001776518 (Scopus ID)9781450345729 (ISBN)
Konferanse
8th ACM Cloud Computing Security Workshop, CCSW,Vienna
Tilgjengelig fra: 2016-12-21 Laget: 2016-12-21 Sist oppdatert: 2018-01-13bibliografisk kontrollert
Nordahl, C., Boeva, V. & Grahn, H. On Evaluation of Data Stream Clustering Algorithms: A Survey.
Åpne denne publikasjonen i ny fane eller vindu >>On Evaluation of Data Stream Clustering Algorithms: A Survey
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
urn:nbn:se:bth-25538 (URN)
Tilgjengelig fra: 2023-11-03 Laget: 2023-11-03 Sist oppdatert: 2023-11-06bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0001-7199-8080