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Scalable ordered indexing of streaming data
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (UDBL)
2012 (English)In: 3rd International Workshop on Accelerating Data Management Systems using Modern Processor and Storage Architectures, 2012, 11- p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
2012. 11- p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-185068OAI: oai:DiVA.org:uu-185068DiVA: diva2:570573
Conference
ADMS 2012, Istanbul, Turkey
Projects
eSSENCE
Available from: 2012-08-27 Created: 2012-11-19 Last updated: 2016-09-09Bibliographically approved
In thesis
1. Real-time data stream clustering over sliding windows
Open this publication in new window or tab >>Real-time data stream clustering over sliding windows
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In many applications, e.g. urban traffic monitoring, stock trading, and industrial sensor data monitoring, clustering algorithms are applied on data streams in real-time to find current patterns. Here, sliding windows are commonly used as they capture concept drift.

Real-time clustering over sliding windows is early detection of continuously evolving clusters as soon as they occur in the stream, which requires efficient maintenance of cluster memberships that change as windows slide.

Data stream management systems (DSMSs) provide high-level query languages for searching and analyzing streaming data. In this thesis we extend a DSMS with a real-time data stream clustering framework called Generic 2-phase Continuous Summarization framework (G2CS).  G2CS modularizes data stream clustering by taking as input clustering algorithms which are expressed in terms of a number of functions and indexing structures. G2CS supports real-time clustering by efficient window sliding mechanism and algorithm transparent indexing. A particular challenge for real-time detection of a high number of rapidly evolving clusters is efficiency of window slides for clustering algorithms where deletion of expired data is not supported, e.g. BIRCH. To that end, G2CS includes a novel window maintenance mechanism called Sliding Binary Merge (SBM). To further improve real-time sliding performance, G2CS uses generation-based multi-dimensional indexing where indexing structures suitable for the clustering algorithms can be plugged-in.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 33 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1431
Keyword
Data streaming; Sliding windows; Clustering;
National Category
Computer Systems
Research subject
Computer Science with specialization in Database Technology
Identifiers
urn:nbn:se:uu:diva-302799 (URN)978-91-554-9698-2 (ISBN)
Public defence
2016-11-23, ITC 2446, Lägerhyddsvägen 2, Uppsala, 10:00 (English)
Opponent
Supervisors
Available from: 2016-11-02 Created: 2016-09-09 Last updated: 2016-11-16

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Badiozamany, SobhanRisch, Tore

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