What happened then and there: Top-k spatio-temporal keyword query
What happened then and there: Top-k spatio-temporal keyword query
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DOI：
10.1016/j.ins.2018.04.057
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
X. Liu;Changxuan Wan;N. Xiong;Dexi Liu;Guoqiong Liao;Song Deng
中科院分区：
文献类型：
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作者：
X. Liu;Changxuan Wan;N. Xiong;Dexi Liu;Guoqiong Liao;Song Deng
社交媒体数据，例如推文，通常被地理标记，嵌入创建或发布时间，并与文本相关联。如今，有越来越多的需要查询这样的时空文本数据。在这项工作中，我们提出了一种新的类型的查询，top-kspatio-temporal关键字查询（简称k-STKQ），在Twitter的社交媒体数据。Ak-STKQ以位置、时间戳和一组关键字作为参数，并返回位置附近、时间戳附近和关键字相关的top-k对象。k-STKQ的一个例子是搜索最近从附近一些地方发送的提到“车库销售”的推文。社交媒体数据的大量和动态性质是有效处理k-STKQ的主要障碍。为了有效地返回答案，我们提出了一种新的索引，TiST，用于处理k-STKQ。TiST将传入数据划分为子集，并在每个子集上构建R树索引。对象的时间戳和文本也与R树集成在一起。为了进一步提高索引性能，我们提出了基于唤醒R-树的R-树插入方法，这是由观察到的许多对象集在它们的位置是相似的启发。对于对象的文本，我们提出了一种混合的基于位图的索引，这是节省空间，并支持相关性计算。基于TiST索引，给出了查询处理算法。我们进行了大量的实验，以证明我们的解决方案是能够提供优良的索引性能和良好的查询性能。
Social media data, e.g. Tweets, are usually geo-tagged, embedded with creation or posting time, and associated with texts. Nowadays, there is an increasing need for querying such spatio-temporal-text data. In this work, we propose a new type of query, top-kspatio-temporal keyword query (k-STKQ in short), over Twitter-like social media data. Ak-STKQ takes a location, a timestamp and a set of keywords as argument, and returns top-kobjects that are near the location, close to the timestamp, and relevant to the set of keywords. An example ofk-STKQ is to search the tweets mentioning “garage sale” recently sent from some places nearby. The massive amount and dynamic nature of social media data are the primary obstacles towards efficient processing ofk-STKQs. In order to return the answers efficiently, we propose a novel index, TiST, for the processing ofk-STKQs. TiST partitions the incoming data into subsets, and builds an R-tree index on each subset. The timestamps and texts of the objects are also integrated with the R-trees. To further boost the indexing performance, we propose arouting R-treebased R-tree insertion method, which is inspired by the observation that many sets of objects are similar in their locations. For the texts of objects, we propose a hybrid bitmap-based index, which is space-saving and supports relevance computation. The query processing algorithm is also presented based on the TiST index. We conduct extensive experiments to demonstrate that our solution is capable of providing excellent indexing performance and good query performance.