With the rapidly increasing deployment of Internetconnected, location-aware mobile devices, very large and increasing amounts of geo-tagged and timestamped user-generated content, such as microblog posts, are being generated. We present indexing, update, and query processing techniques that are capable of providing the top-k terms seen in posts in a userspecified spatio-temporal range. The techniques enable interactive response times in the millisecond range in a realistic setting where the arrival rate of posts exceeds today's average tweet arrival rate by a factor of 4-10. The techniques adaptively maintain the most frequent items at various spatial and temporal granularities. They extend existing frequent item counting techniques to maintain exact counts rather than approximations. An extensive empirical study with a large collection of geo-tagged tweets shows that the proposed techniques enable online aggregation and query processing at scale in realistic settings.

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The web is increasingly being accessed from geo-positioned devices such as smartphones, and rapidly increasing volumes of web content are geo-tagged. In addition, studies show that a substantial fraction of all web queries has local intent. This development motivates the study of advanced spatial keyword-based querying of web content. Previous research has primarily focused on the retrieval of the top-k individual spatial web objects that best satisfy a query specifying a location and a set of keywords. This paper proposes a new type of query functionality that returns topk groups of objects while taking into account aspects such as group density, distance to the query, and relevance to the query keywords. To enable efficient processing, novel indexing and query processing techniques for single and multiple keyword queries are proposed. Empirical performance studies with an implementation of the techniques and real data suggest that the proposals are viable in practical settings.

Points of interest (PoI) data serves an important role as a foundation for a wide variety of location-based services. Such data is typically obtained from an authoritative source or from users through crowdsourcing. It can be costly to maintain an up-to-date authoritative source, and data obtained from users can vary greatly in coverage and quality. We are also witnessing a proliferation of both GPS-enabled mobile devices and geotagged content generated by users of such devices. This state of affairs motivates the paper's proposal of techniques for the automatic discovery of PoI data from geo-tagged microblog posts. Specifically, the paper proposes a new clustering technique that takes into account both the spatial and textual attributes of microblog posts to obtain clusters that represent PoIs. The technique expands clusters based on a proposed quality function that enables clusters of arbitrary shape and density. An empirical study with a large database of real geo-tagged microblog posts offers insight into the properties of the proposed techniques and suggests that they are effective at discovering real-world points of interest.

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