FAST：用于内存连续过滤查询处理的频率感知空间文本索引
FAST: Frequency-Aware Spatio-Textual Indexing for In-Memory Continuous Filter Query Processing
摘要：
许多应用需要实时处理海量的空间文本数据流，以应对连续的空间文本查询。例如，在位置感知广告定向发布/订阅系统中，需要基于用户的位置和文本简档向数百万用户传播数百万广告和促销。本文研究了连续空间文本查询的索引问题。存在几种相关的空间-文本索引，它们通常将空间索引与文本索引集成。然而，这些索引通常对主内存有很高的要求，并假设预先知道关键字的整个词汇表。此外，这些索引不能成功地捕捉不同空间区域中关键字频率的变化，并以相同的方式处理频繁和不频繁的关键字。此外，现有索引不适应工作量随空间和时间的变化。例如，某些关键字可能在某些位置的某些时间成为趋势，并且这可能随着时间的推移而改变。这会显著影响现有索引的索引和搜索性能。在本文中，我们介绍FAST，一个频率感知的空间文本索引连续的空间文本查询。FAST是一种主存索引，它所需的内存最多是最先进索引所需内存的三分之一。FAST并不假定预先知道索引对象的整个词汇表。FAST自适应地考虑关键字在其相应空间区域内的频率差异，以自动选择最佳索引方法，从而优化插入和搜索时间。使用真实和合成数据集进行的广泛实验评估表明，FAST的搜索时间比最先进的索引快3倍，插入时间快5倍。
英文摘要：
Many applications need to process massive streams of spatio-textual data in real-time against continuous spatio-textual queries. For example, in location-aware ad targeting publish/subscribe systems, it is required to disseminate millions of ads and promotions to millions of users based on the locations and textual profiles of users. In this paper, we study indexing of continuous spatio-textual queries. There exist several related spatio-textual indexes that typically integrate a spatial index with a textual index. However, these indexes usually have a high demand for main-memory and assume that the entire vocabulary of keywords is known in advance. Also, these indexes do not successfully capture the variations in the frequencies of keywords across different spatial regions and treat frequent and infrequent keywords in the same way. Moreover, existing indexes do not adapt to the changes in workload over space and time. For example, some keywords may be trending at certain times in certain locations and this may change as time passes. This affects the indexing and searching performance of existing indexes significantly. In this paper, we introduce FAST, a Frequency-Aware Spatio-Textual index for continuous spatio-textual queries. FAST is a main-memory index that requires up to one third of the memory needed by the state-of-the-art index. FAST does not assume prior knowledge of the entire vocabulary of indexed objects. FAST adaptively accounts for the difference in the frequencies of keywords within their corresponding spatial regions to automatically choose the best indexing approach that optimizes the insertion and search times. Extensive experimental evaluation using real and synthetic datasets demonstrates that FAST is up to 3x faster in search time and 5x faster in insertion time than the state-of-the-art indexes.
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。
