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Data Modeling for Outlier Detection
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.

Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.

We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2018.
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 4
Keywords [en]
data modeling, cluster analysis, stream data, outlier detection
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-16580ISBN: 978-91-7295-358-1 (print)OAI: oai:DiVA.org:bth-16580DiVA, id: diva2:1255525
Presentation
2018-11-09, Blekinge Tekniska Högskola, Karlskrona, 10:00 (English)
Opponent
Supervisors
Projects
Scalable resource-efficient systems for big data analytics
Funder
Knowledge Foundation, 20140032Available from: 2018-10-25 Created: 2018-10-12 Last updated: 2018-12-04Bibliographically approved
List of papers
1. Open Data for Anomaly Detection in Maritime Surveillance
Open this publication in new window or tab >>Open Data for Anomaly Detection in Maritime Surveillance
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2013 (English)In: Expert Systems with Applications, ISSN 0957-4174, Vol. 40, no 14, p. 5719-5729Article in journal (Refereed) Published
Abstract [en]

Maritime Surveillance has received increased attention from a civilian perspective in recent years. Anomaly detection is one of many techniques available for improving the safety and security in this domain. Maritime authorities use confidential data sources for monitoring the maritime activities; however, a paradigm shift on the Internet has created new open sources of data. We investigate the potential of using open data as a complementary resource for anomaly detection in maritime surveillance. We present and evaluate a decision support system based on open data and expert rules for this purpose. We conduct a case study in which experts from the Swedish coastguard participate to conduct a real-world validation of the system. We conclude that the exploitation of open data as a complementary resource is feasible since our results indicate improvements in the efficiency and effectiveness of the existing surveillance systems by increasing the accuracy and covering unseen aspects of maritime activities.

Place, publisher, year, edition, pages
Elsevier, 2013
Keywords
Open data, Anomaly detection, Maritime security, Maritime domain awareness
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-6807 (URN)10.1016/j.eswa.2013.04.029 (DOI)000321089200029 ()oai:bth.se:forskinfoD455168E88392FDDC1257B6200290B99 (Local ID)oai:bth.se:forskinfoD455168E88392FDDC1257B6200290B99 (Archive number)oai:bth.se:forskinfoD455168E88392FDDC1257B6200290B99 (OAI)
Available from: 2013-12-17 Created: 2013-05-05 Last updated: 2018-10-12Bibliographically approved
2. Trend analysis to automatically identify heat program changes
Open this publication in new window or tab >>Trend analysis to automatically identify heat program changes
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2017 (English)In: Energy Procedia, Elsevier, 2017, Vol. 116, p. 407-415Conference paper, Published 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.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
Energy Procedia, ISSN 1876-6102 ; 116
Keywords
District heating, Trend analysis, Change detection, Smart automated system
National Category
Computer Systems
Identifiers
urn:nbn:se:bth-12894 (URN)10.1016/j.egypro.2017.05.088 (DOI)000406743000039 ()
Conference
15th International Symposium on District Heating and Cooling (DHC2016), Seoul
Projects
BigData@BTH
Funder
Knowledge Foundation, 20140032
Note

Open access

Available from: 2016-09-26 Created: 2016-07-13 Last updated: 2018-10-12Bibliographically approved
3. Outlier Detection for Video Session Data Using Sequential Pattern Mining
Open this publication in new window or tab >>Outlier Detection for Video Session Data Using Sequential Pattern Mining
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2018 (English)In: ACM SIGKDD Workshop On Outlier Detection De-constructed, 2018Conference paper, Oral presentation only (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.

Keywords
Cluster Analysis, Data Stream Mining, Outlier Detection, Sequential Pattern Mining
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-16944 (URN)
Conference
ACM SIGKDD Workshop On Outlier Detection De-constructed, London,
Funder
Knowledge Foundation, 20140032
Available from: 2018-10-01 Created: 2018-10-01 Last updated: 2018-10-12Bibliographically approved
4. A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences
Open this publication in new window or tab >>A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences
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2018 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Clustering, Minimum spanning tree, Outlier detection, Sequential pattern mining
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-17100 (URN)10.1109/ICMLA.2018.00182 (DOI)000463034400174 ()9781538668047 (ISBN)
Conference
17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018; Orlando; United States; 17 December 2018 through 20 December
Funder
Knowledge Foundation, 20140032
Available from: 2018-10-09 Created: 2018-10-09 Last updated: 2019-06-28Bibliographically approved

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