Massive amounts of data that contain spatial, textual, and temporal information are being generated at a rapid pace. With streams of such data, which includes check-ins and geo-tagged tweets, available, users may be interested in being kept up-to-date on which terms are popular in the streams in a particular region of space. To enable this functionality, we aim at efficiently processing two types of general top-k term subscriptions over streams of spatio-temporal documents: region-based top-k spatial-temporal term (RST) subscriptions and similarity-based top-k spatio-temporal term (SST) subscriptions. RST subscriptions continuously maintain the top-k most popular trending terms within a user-defined region. SST subscriptions free users from defining a region and maintain top-k locally popular terms based on a ranking function that combines term frequency, term recency, and term proximity. To solve the problem, we propose solutions that are capable of supporting real-life location-based publish/subscribe applications that process large numbers of SST and RST subscriptions over a realistic stream of spatio-temporal documents. The performance of our proposed solutions is studied in extensive experiments using two spatio-temporal datasets.

中文翻译：

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地理文本数据流的前k个术语发布/订阅

包含空间，文本和时间信息的海量数据正在快速生成。有了包括签到和带有地理标签的推文在内的此类数据流，用户可能会对保持最新的术语感兴趣，以了解在空间的特定区域中流中流行的术语。为启用此功能，我们旨在有效处理时空文档流上的两种类型的常规top- k时事订阅：基于区域的top- k时空词（RST）订阅和基于相似度的top - k时空订阅。时间项（SST）订阅。RST订阅持续保持前k用户定义区域内最流行的趋势术语。SST订阅使用户无需定义区域，并基于结合了词频，词新近度和词接近度的排名功能来维护排名靠前的k个本地流行词。为了解决该问题，我们提出了一些解决方案，这些解决方案能够支持现实生活中基于位置的发布/订阅应用程序，这些应用程序在现实的时空文档流上处理大量的SST和RST订阅。我们的建议解决方案的性能在使用两个时空数据集的广泛实验中得到了研究。