A foundation for spatio-textual-temporal cube analytics

Mohsin Iqbal, Matteo Lissandrini, Torben Bach Pedersen

Research output: Contribution to journalConference article in JournalResearchpeer-review

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Abstract

Large amounts of spatial, textual, and temporal (STT) data are being produced daily. This is data containing an unstructured component (text), a spatial component (geographic position), and a time component (timestamp). Therefore, there is a need for a powerful and general way of analyzing STT data together. In this paper, we define and formalize the Spatio-Textual-Temporal Cube (STTCube) structure to enable combined effective and efficient analytical queries over STT data. Our novel data model over STT objects enables novel joint and integrated STT insights that are hard to obtain using existing methods. Moreover, we introduce the new concept of STT measures with associated novel STTOLAP operators. To allow for efficient large-scale analytics, we present a pre-aggregation framework for exact and approximate computation of STT measures. Our comprehensive experimental evaluation on a real-world Twitter dataset confirms that our proposed methods reduce query response time by 1-5 orders of magnitude compared to the No Materialization baseline and decrease storage cost between 97% and 99.9% compared to the Full Materialization baseline while adding only a negligible overhead in the STTCube construction time. Moreover, approximate computation achieves an accuracy between 90% and 100% while reducing query response time by 3-5 orders of magnitude compared to No Materialization.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2840
Pages (from-to)11-20
Number of pages10
ISSN1613-0073
Publication statusPublished - 2021
Event23rd International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, DOLAP 2021 - Nicosia, Cyprus
Duration: 23 Mar 202123 Mar 2021

Conference

Conference23rd International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, DOLAP 2021
Country/TerritoryCyprus
CityNicosia
Period23/03/202123/03/2021

Bibliographical note

Publisher Copyright:
© 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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