SSTR: Set Similarity Join over Stream Data

Lucas Pacífico; Leonardo Andrade Ribeiro

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Paper

SSTR: Set Similarity Join over Stream Data

Topics: Coupling and Integrating Heterogeneous Data Sources; Performance Evaluation and Benchmarking; Query Languages and Query Processing

In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, 52-60, 2020

Authors: Lucas Pacífico and Leonardo Andrade Ribeiro

Affiliation: Instituto de Informática, Universidade Federal de Goiás, Goiânia – Goiás Brazil

Keyword(s): Advanced Query Processing, Data Streams, Databases, Similarity Join

Abstract: In modern application scenarios, large volumes of data are continuously generated over time at high speeds. Delivering timely analysis results from such massive stream of data imposes challenging requirements for current systems. Even worse, similarity matching can be needed owing to data inconsistencies, which is computationally much more expensive than simple equality comparisons. In this context, this paper presents SSTR, a novel similarity join algorithm for streams of sets. We adopt the concept of temporal similarity and exploit its properties to improve efficiency and reduce memory usage. We provide an extensive experimental study on several synthetic as well as real-world datasets. Our results show that the techniques we proposed significantly improve scalability and lead to substantial performance gains in most settings.

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Paper citation in several formats:

Pacífico, L. and Ribeiro, L. (2020). SSTR: Set Similarity Join over Stream Data. In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-423-7; ISSN 2184-4992, SciTePress, pages 52-60. DOI: 10.5220/0009420400520060

@conference{iceis20,
author={Lucas Pacífico. and Leonardo Andrade Ribeiro.},
title={SSTR: Set Similarity Join over Stream Data},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2020},
pages={52-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009420400520060},
isbn={978-989-758-423-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - SSTR: Set Similarity Join over Stream Data
SN - 978-989-758-423-7
IS - 2184-4992
AU - Pacífico, L.
AU - Ribeiro, L.
PY - 2020
SP - 52
EP - 60
DO - 10.5220/0009420400520060
PB - SciTePress