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Data-Driven Techniques for Modeling and Analysis of User Behavior
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-7199-8080
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Our society is becoming more digitalized for each day. Now, we are able to gather data from individual users with higher resolution than ever. With the increased amount of data on an individual user level, we can analyze their behavior. This is of interest in many different domains, for example service providers wanting to improve their service for their customers. If they know how their service is used, they have more insight in how they can improve. But, it also imposes additional difficulties. When we reach the individual user, the irregularities in the regular behavior makes it harder to model the normal behavior.

In this thesis, we explore data-driven techniques to model and analyze user behaviors. We aim to evaluate existing as well as develop novel technologies to identify approaches that are suitable for use on an individual user level. We use both supervised and unsupervised learning methods to model the user behavior and evaluate the approaches on real world electricity consumption data.

Firstly, we analyze household electricity consumption data and investigate the use of regression to model the household's behavior. We identify consumption trends, how data granularity affects modeling, and we show that regression is a viable approach to model user behavior. Secondly, we use clustering analysis to profile individual households in terms of their electricity consumption. We compare two dissimilarity measures, how they affect the clustering analysis, and we investigate how the produced clustering solutions differ. Thirdly, we propose a sequential clustering algorithm to model evolving user behavior. We evaluate the proposed algorithm on electricity consumption data and show how the produced model can be used to identify and trace changes in the user's behavior. The algorithm is robust to evolving behaviors and handles both dynamic and incremental aspects of streaming data.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2019.
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 15
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18667ISBN: 978-91-7295-391-8 (print)OAI: oai:DiVA.org:bth-18667DiVA, id: diva2:1352329
Supervisors
Available from: 2019-11-15 Created: 2019-09-18 Last updated: 2019-11-21Bibliographically approved
List of papers
1. Detection of Residents' Abnormal Behaviour by Analysing Energy Consumption of Individual Households
Open this publication in new window or tab >>Detection of Residents' Abnormal Behaviour by Analysing Energy Consumption of Individual Households
2017 (English)In: Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW) / [ed] Gottumukkala, R; Ning, X; Dong, G; Raghavan, V; Aluru, S; Karypis, G; Miele, L; Wu, X, IEEE, 2017, p. 729-738Conference paper, Published paper (Refereed)
Abstract [en]

As average life expectancy continuously rises, assisting the elderly population with living independently is of great importance. Detecting abnormal behaviour of the elderly living at home is one way to assist the eldercare systems with the increase of the elderly population. In this study, we perform an initial investigation to identify abnormal behaviour of household residents using energy consumption data. We conduct an experiment in two parts, the first to identify a suitable prediction algorithm to model energy consumption behaviour, and the second to detect abnormal behaviour. This approach allows for an initial step for the elderly care that has a low cost, is easily deployable, and is non-intrusive.

Place, publisher, year, edition, pages
IEEE, 2017
Series
International Conference on Data Mining Workshops, ISSN 2375-9232
Keywords
Energy consumption, Predictive models, Smart meters, Correlation, Senior citizens
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15565 (URN)10.1109/ICDMW.2017.101 (DOI)000425845700096 ()978-1-5386-3800-2 (ISBN)
Conference
IEEE International Conference on Data Mining series (ICDM), New Orleans
Projects
BigData@BTH
Funder
Knowledge Foundation, 20140032
Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2019-09-18Bibliographically approved
2. Profiling of household residents’ electricity consumption behavior using clustering analysis
Open this publication in new window or tab >>Profiling of household residents’ electricity consumption behavior using clustering analysis
2019 (English)In: Lect. Notes Comput. Sci., Springer Verlag , 2019, p. 779-786Conference paper, Published 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.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Series
Lecture Notes in Computer Science ; 11540
Keywords
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
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18593 (URN)10.1007/978-3-030-22750-0_78 (DOI)2-s2.0-85068459816 (Scopus ID)9783030227494 (ISBN)
Conference
International Conference on Computational Science, ICCS, Faro, Algarve, 12 June 2019 through 14 June 2019
Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-10-17Bibliographically approved
3. Modeling Evolving User Behavior via Sequential Clustering
Open this publication in new window or tab >>Modeling Evolving User Behavior via Sequential Clustering
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we address the problem of modeling the evolution of clusters over time by applying sequential clustering. We propose a sequential partitioning algorithm that can be applied for grouping distinct snapshots of streaming data so that a clustering model is built on each data snapshot. The algorithm is initialized by a clustering solution built on available historical data. Then a new clustering solution is generated on each data snapshot by applying a partitioning algorithm seeded with the centroids of the clustering model obtained at the previous time interval. At each step the algorithm also conducts model adapting operations in order to reflect the evolution in the clustering structure. In that way, it enables to deal with both incremental and dynamic aspects of modeling evolving behavior problems. In addition, the proposed approach is able to trace back evolution through the detection of clusters' transitions, such as splits and merges. We have illustrated and initially evaluated our ideas on household electricity consumption data. The results have shown that the proposed sequential clustering algorithm is robust to modeling evolving behavior by being enable to mine changes and update the model, respectively.

Place, publisher, year, edition, pages
Springer, 2019
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18666 (URN)
Conference
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECMLPKDD, Würzburg,16th to the 20th of September
Note

In progress of being published in Springer Lecture Notes.

Available from: 2019-09-18 Created: 2019-09-18 Last updated: 2019-11-18Bibliographically approved
4. Monitoring Household Electricity Consumption Behaviour for Mining Changes
Open this publication in new window or tab >>Monitoring Household Electricity Consumption Behaviour for Mining Changes
2019 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

In this paper, we present an ongoing work on using a household electricity consumption behavior model for recognizing changes in sleep patterns. The work is inspired by recent studies in neuroscience revealing an association between dementia and sleep disorders and more particularly, supporting the hypothesis that insomnia may be a predictor for dementia in older adults. Our approach initially creates a clustering model of normal electricity consumption behavior of the household by using historical data. Then we build a new clustering model on a new set of electricity consumption data collected over a predefined time period and compare the existing model with the built new electricity consumption behavior model. If a discrepancy between the two clustering models is discovered a further analysis of the current electricity consumption behavior is conducted in order to investigate whether this discrepancy is associated with alterations in the resident’s sleep patterns. The approach is studied and initially evaluated on electricity consumption data collected from a single randomly selected anonymous household. The obtained results show that our approach is robust to mining changes in the resident daily routines by monitoring and analyzing their electricity consumption behavior model.

National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-18651 (URN)
Conference
3rd International Workshop on Aging, Rehabilitation and Independent Assisted Living (ARIAL), International Joint Conferenec on Artificial Intelligence (IJCAI), August 10-16, 2019, Macao, China.
Projects
Scalable resource-efficient systems for big data analytics
Available from: 2019-09-12 Created: 2019-09-12 Last updated: 2019-10-17Bibliographically approved

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Citation style
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