A Foundation for Spatio-Textual-Temporal Cube Analytics (Extended Version)

TL;DR

This paper introduces a novel spatio-textual-temporal cube data model and analytics framework that enables efficient, integrated analysis of large-scale spatial, textual, and temporal data, demonstrated on Twitter data.

Contribution

It formalizes the spatio-textual-temporal cube structure, introduces new measures and operators, and provides a scalable pre-aggregation framework for fast analytics.

Findings

Query response time reduced by 1-5 orders of magnitude.

Storage costs decreased by 97-99.9%.

Approximate results achieved 90-100% accuracy with significant speedup.

Abstract

Large amounts of spatial, textual, and temporal 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 spatial, textual, and temporal data together. In this paper, we define and formalize the Spatio-Textual-Temporal Cube structure to enable combined effective and efficient analytical queries over spatial, textual, and temporal data. Our novel data model over spatio-textual-temporal objects enables novel joint and integrated spatial, textual, and temporal insights that are hard to obtain using existing methods. Moreover, we introduce the new concept of spatio-textual-temporal measures with associated novel spatio-textual-temporal-OLAP operators. To allow for efficient large-scale analytics, we present a pre-aggregation framework for the exact and approximate computation of spatio-textual-temporal 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 Spatio-Textual-Temporal Cube 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.