1 research outputs found
A Foundation for Spatio-Textual-Temporal Cube Analytics (Extended Version)
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.Comment: 14 pages, 11 figure