Massively Distributed Time Series Indexing and Querying

Indexing is crucial for many data mining tasks that rely on efficient and effective similarity query processing. Consequently, indexing large volumes of time series, along with high performance similarity query processing, have became topics of high interest. For many applications across diverse dom...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 32; no. 1; pp. 108 - 120
Main Authors: Yagoubi, Djamel-Edine, Akbarinia, Reza, Masseglia, Florent, Palpanas, Themis
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Algorithms
Computer Science
Data mining
distributed querying
Domains
Euclidean distance
Indexing
Information Retrieval
parallel indexing
Queries
Query processing
Servers
Similarity
Task analysis
Time series
Time series analysis
Parallel Indexing
Index Terms-Time Series
Distributed Querying
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:Indexing is crucial for many data mining tasks that rely on efficient and effective similarity query processing. Consequently, indexing large volumes of time series, along with high performance similarity query processing, have became topics of high interest. For many applications across diverse domains though, the amount of data to be processed might be intractable for a single machine, making existing centralized indexing solutions inefficient. We propose a parallel indexing solution that gracefully scales to billions of time series, and a parallel query processing strategy that, given a batch of queries, efficiently exploits the index. Our experiments, on both synthetic and real world data, illustrate that our index creation algorithm works on four billion time series in less than five hours, while the state of the art centralized algorithms do not scale and have their limit on 1 billion time series, where they need more than five days. Also, our distributed querying algorithm is able to efficiently process millions of queries over collections of billions of time series, thanks to an effective load balancing mechanism.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2018.2880215