2015 QueryWorkloadsforDataSeriesInde

• (Zoumpatianos et al., 2015) ⇒ Kostas Zoumpatianos, Yin Lou, Themis Palpanas, and Johannes Gehrke. (2015). “Query Workloads for Data Series Indexes.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783382

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Query+Workloads+for+Data+Series+Indexes
• http://dl.acm.org/citation.cfm?id=2783258.2783382&preflayout=flat#citedby

Quotes

Author Keywords

• Content analysis and indexing; data series; general; indexing; similarity search; workloads

Abstract

Data series are a prevalent data type that has attracted lots of interest in recent years. Most of the research has focused on how to efficiently support similarity or nearest neighbor queries over large data series collections (an important data mining task), and several data series summarization and indexing methods have been proposed in order to solve this problem. Nevertheless, up to this point very little attention has been paid to properly evaluating such index structures, with most previous work relying solely on randomly selected data series to use as queries (with / without adding noise). In this work, we show that random workloads are inherently not suitable for the task at hand and we argue that there is a need for carefully generating a query workload. We define measures that capture the characteristics of queries, and we propose a method for generating workloads with the desired properties, that is, effectively evaluating and comparing data series summarizations and indexes. In our experimental evaluation, with carefully controlled query workloads, we shed light on key factors affecting the performance of nearest neighbor search in large data series collections.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 QueryWorkloadsforDataSeriesInde	Johannes Gehrke Yin Lou Themis Palpanas Kostas Zoumpatianos			Query Workloads for Data Series Indexes				10.1145/2783258.2783382		2015