With the proliferation of Internet-connected, location-aware mobile devices, such as smartphones, we are also witnessing a proliferation and increased use of map-based services that serve information about relevant Points of Interest (PoIs) to their users.

With the rapidly increasing deployment of Internet-connected, 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 user-specified 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.

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 crowd sourcing. 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.

The notion of point-of-interest (PoI) has existed since paper road maps began to include markings of useful places such as gas stations, hotels, and tourist attractions. With the introduction of geopositioned mobile devices such as smartphones and mapping services such as Google Maps, the retrieval of PoIs relevant to a user's intent has became a problem of automated spatio-textual information retrieval. Over the last several years, substantial research has gone into the invention of functionality and efficient implementations for retrieving nearby PoIs. However, with a couple of exceptions existing proposals retrieve results at single-PoI granularity. We assume that a mobile device user issues queries consisting of keywords and an automatically supplied geo-position, and we target the common case where the user wishes to find nearby groups of PoIs that are relevant to the keywords. Such groups are relevant to users who wish to conveniently explore several options before making a decision such as to purchase a specific product. Specifically, we demonstrate a practical proposal for finding top- k PoI groups in response to a query. We show how problem parameter settings can be mapped to options that are meaningful to users. Further, although this kind of functionality is prone to combinatorial explosion, we will demonstrate that the functionality can be supported efficiently in practical settings.