Many applications need to process massive streams of spatio-textual data in real-time against continuous spatio-textual queries. For example, in location-aware ad targeting publish subscribe systems, it is required to disseminate millions of ads and promotions to millions of users based on the locations and textual profiles of users. In this paper, we study indexing of continuous spatio-textual queries. There exist several related spatio-textual indexes that typically integrate a spatial index with a textual index. However, these indexes usually have a high demand for main-memory and assume that the entire vocabulary of keywords is known in advance. Also, these indexes do not successfully capture the variations in the frequencies of keywords across different spatial regions and treat frequent and infrequent keywords in the same way. Moreover, existing indexes do not adapt to the changes in workload over space and time. For example, some keywords may be trending at certain times in certain locations and this may change as time passes. This affects the indexing and searching performance of existing indexes significantly. In this paper, we introduce FAST, a Frequency-Aware Spatio-Textual index for continuous spatio-textual queries. FAST is a main-memory index that requires up to one third of the memory needed by the state-of-the-art index. FAST does not assume prior knowledge of the entire vocabulary of indexed objects. FAST adaptively accounts for the difference in the frequencies of keywords within their corresponding spatial regions to automatically choose the best indexing approach that optimizes the insertion and search times. Extensive experimental evaluation using real and synthetic datasets demonstrates that FAST is up to 3x faster in search time and 5x faster in insertion time than the state-of-the-art indexes.
Social content, such as Twitter updates, often have the quickest first-hand reports of news events, as well as numerous commentaries that are indicative of public view of such events. As such, social updates provide a good complement to professionally written news articles. In this paper we consider the problem of automatically annotating news stories with social updates (tweets), at a news website serving high volume of pageviews. The high rate of both the pageviews (millions to billions a day) and of the incoming tweets (more than 100 millions a day) make real-time indexing of tweets ineffective, as this requires an index that is both queried and updated extremely frequently. The rate of tweet updates makes caching techniques almost unusable since the cache would become stale very quickly. We propose a novel architecture where each story is treated as a subscription for tweets relevant to the story's content, and new algorithms that efficiently match tweets to stories, proactively maintaining the top-k tweets for each story. Such top-k pub-sub consumes only a small fraction of the resource cost of alternative solutions, and can be applicable to other large scale content-based publish-subscribe problems. We demonstrate the effectiveness of our approach on realworld data: a corpus of news stories from Yahoo! News and a log of Twitter updates. With the rapid progress of mobile Internet and the growing popularity of smartphones, location-aware publish subscribe systems have recently attracted significant attention. Different from traditional content-based publish subscribe, subscriptions registered by subscribers and messages published by publishers include both spatial information and textual descriptions, and messages should be delivered to relevant subscribers whose subscriptions have high relevancy to the messages. To evaluate the relevancy between spatio-textual messages and subscriptions, we should combine the spatial proximity and textual relevancy. Since subscribers have different preferences - some subscribers prefer messages with high spatial proximity and some subscribers pay more attention to messages with high textual relevancy, it calls for new location-aware publish subscribe techniques to meet various needs from different subscribers. In this paper, we allow subscribers to parameterize their subscriptions and study the location-aware publish subscribe problem on parameterized spatio-textual subscriptions. One big challenge is to achieve high performance. To meet this requirement, we propose a filter-verification framework to efficiently deliver messages to relevant subscribers. In the filter step, we devise effective filters to prune large numbers of irreverent results and obtain some candidates. In the verification step, we verify the candidates to generate the answers. We propose three effective filters by integrating prefix filtering and spatial pruning techniques. Experimental results show our method achieves higher performance and better quality than baseline approaches. Many applications require finding objects closest to a specified location that contains a set of keywords. For example, online yellow pages allow users to specify an address and a set of keywords. In return, the user obtains a list of businesses whose description contains these keywords, ordered by their distance from the specified address. The problems of nearest neighbor search on spatial data and keyword search on text data have been extensively studied separately. However, to the best of our knowledge there is no efficient method to answer spatial keyword queries, that is, queries that specify both a location and a set of keywords. In this work, we present an efficient method to answer top-k spatial keyword queries. To do so, we introduce an indexing structure called IR2-Tree (Information Retrieval R-Tree) which combines an R-Tree with superimposed text signatures. We present algorithms that construct and maintain an IR2-Tree, and use it to answer top-k spatial keyword queries. Our algorithms are experimentally compared to current methods and are shown to have superior performance and excellent scalability. The advance in wireless Internet and mobile computing brought the booming of intelligent Location-Based Services(LBS), which can actively push location-dependent information to mobile users according to their predefined interest. The successful development of push-based LBS applications relies on the existence of a publish subscribe middleware that can handle spatial relationship. This paper presents an efficient spatial publish subscribe system that can serve as the middleware for intelligent LBS applications. The basic models, including spatial event model, spatial subscription model and notification model, are introduced and the over-all architecture is presented. Two kinds of spatial predicate that can meet most common requirement of intelligent location aware applications are also discussed. Furthermore, we propose a novel spatial event processing approach that dispatches the spatial subscriptions to self-positioning mobile devices. By leveraging client-side computing resource and decreasing the communication times, the server-side workload is relieved and the communication cost is reduced. Experimental results clearly demonstrate the efficiency of our approach. This paper presents the Geo Feed system, a location-aware news feed system that provides a new platform for its users to get spatially related message updates from either their friends or favorite news sources. Geo Feed distinguishes itself from all existing news feed systems in that it takes into account the spatial extents of messages and user locations when deciding upon the selected news feed. Geo Feed is equipped with three different approaches for delivering the news feed to its users, namely, spatial pull, spatial push, and shared push. Then, the main challenge of Geo Feed is to decide on when to use each of these three approaches to which users. Geo Feed is equipped with a smart decision model that decides about using these approaches in a way that: (a) minimizes the system overhead for delivering the location-aware news feed, and (b) guarantees a certain response time for each user to obtain the requested location-aware news feed. Experimental results, based on real and synthetic data, show that Geo Feed outperforms existing news feed systems in terms of response time and maintenance cost.