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• W3081612900 abstract "Streaming spatio-textual data that contains geolocations and textual contents, e.g., geo-tagged tweets, is becoming increasingly available. Users can register continuous queries to receive up-to-date results continuously, or pose snapshot queries to receive results instantly. The large scale of spatio-textual data streams and huge amounts of queries pose great challenges to the current location-based services, and call for more efficient data management systems. In this paper, we present SSTD (Streaming Spatio-Textual Data), a distributed in-memory system supporting both continuous and snapshot queries with spatial, textual, and temporal constraints over data streams. Compared with existing distributed data stream management systems, SSTD has at least three novelty: (1) It supports more types of queries over streamed spatio-textual data ; (2)SSTD adopts a novel workload partitioning method termed QT (Quad-Text) tree, that utilizes the joint distribution of queries and spatio-textual data to reduce query latency and enhance system throughput. (3) To achieve load balance and robustness, we develop three new workload adjustment methods for SSTD to fit the changes in the distributions of data or queries. Extensive experiments on real-life datasets demonstrate the superior performance of SSTD." @default.
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• W3081612900 date "2020-08-01" @default.
• W3081612900 modified "2023-10-14" @default.
• W3081612900 title "SSTD" @default.
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• W3081612900 doi "https://doi.org/10.14778/3407790.3407825" @default.
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