空间-文本-时间立方体分析基础（扩展版）
A Foundation for Spatio-Textual-Temporal Cube Analytics (Extended Version)
摘要：
每天都会产生大量的空间、文本和时间数据。这是包含非结构化组件（文本）、空间组件（地理位置）和时间组件（时间戳）的数据。因此，需要一种强大而通用的方法来同时分析空间、文本和时间数据。在本文中，我们定义和形式化的空间-文本-时间立方体结构，使空间，文本和时间数据相结合的有效和高效的分析查询。我们的新的数据模型的时空对象，使新的联合和集成的空间，文本和时间的见解，是很难获得使用现有的方法。此外，我们引入了新的概念，空间-文本-时间的措施与相关的新的空间-文本-时间-OLAP运营商。为了允许有效的大规模分析，我们提出了一个预聚合框架的精确和近似计算的时空文本的措施。我们在真实世界的Twitter数据集上进行的全面实验评估证实，与无物化基线相比，我们提出的方法将查询响应时间减少了1-5个数量级，并且与完全物化基线相比，存储成本降低了97%到99.9%，同时在空间-文本-时间立方体构建时间中只增加了微不足道的开销。此外，近似计算实现了90%和100%之间的准确性，同时减少查询响应时间的3-5个数量级相比，没有物化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
