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.
Publish Subscribe Systems. One main use case of FAST is in location-aware publish subscribe systems. Publish subscribe systems maintain subscriptions for long durations and match incoming messages against stored subscriptions. Publish subscribe systems can be categorized according to their matching approach into the following categories: (1) content-based @cite_19 , (2) TopK-similarity-based @cite_18 , and (3) location-aware @cite_2 . These publish subscribe systems do not simultaneously account for the spatial and textual properties of subscriptions and messages. Recently, several spatio-textual publish subscribe systems @cite_6 @cite_26 have been proposed. To the best of our knowledge, the AP-tree @cite_6 is the most relevant work for indexing continuous spatio-textual queries in a streaming environment.
@cite_18 @cite_26 @cite_6 @cite_19 @cite_2
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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. The publish subscribe (pub sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, extending a pub sub system in wireless networks has become a promising topic. However, most existing works focus on pub sub systems in infrastructured wireless networks. To adapt pub sub systems to mobile ad hoc networks, we propose DRIP, a dynamic Voronoi region-based pub sub protocol. In our design, the network is dynamically divided into several Voronoi regions after choosing proper nodes as broker nodes. Each broker node is used to collect subscriptions and detected events, as well as efficiently notify subscribers with matched events in its Voronoi region. Other nodes join their nearest broker nodes to submit subscriptions, publish events, and wait for notifications of their requested events. Broker nodes cooperate with each other for sharing subscriptions and useful events. Our proposal includes two major components: a Voronoi regions construction protocol, and a delivery mechanism that implements the pub sub paradigm. The effectiveness of DRIP is demonstrated through comprehensive simulation studies. Location-based services have become widely available on mobile devices. Existing methods employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish subscribe system to deliver publishers' messages to relevant subscribers.In this paper, we address the research challenges that arise in designing a location-aware publish subscribe system. We propose an rtree based index structure by integrating textual descriptions into rtree nodes. We devise efficient filtering algorithms and develop effective pruning techniques to improve filtering efficiency. Experimental results show that our method achieves high performance. For example, our method can filter 500 tweets in a second for 10 million registered subscriptions on a commodity computer. Users of content-based publish subscribe systems (CBPS) are interested in receiving data items with values that satisfy certain conditions. Each user submits a list of subscription specifications to a broker, which routes data items from publishers to users. When a broker receives a notification that contains a value from a publisher, it forwards it only to the subscribers whose requests match the value. However, in many applications, the data published are confidential, and their contents must not be revealed to brokers. Furthermore, a user's subscription may contain sensitive information that must be protected from brokers. Therefore, a difficult challenge arises: how to route publisher data to the appropriate subscribers without the intermediate brokers learning the plain text values of the notifications and subscriptions. To that extent, brokers must be able to perform operations on top of the encrypted contents of subscriptions and notifications. Such operations may be as simple as equality match, but often require more complex operations such as determining inclusion of data in a value interval. Previous work attempted to solve this problem by using one-way data mappings or specialized encryption functions that allow evaluation of conditions on ciphertexts. However, such operations are computationally expensive, and the resulting CBPS lack scalability. As fast dissemination is an important requirement in many applications, we focus on a new data transformation method called Asymmetric Scalar-product Preserving Encryption (ASPE) [1]. We devise methods that build upon ASPE to support private evaluation of several types of conditions. We also suggest techniques for secure aggregation of notifications, supporting functions such as sum, minimum, maximum and count. Our experimental evaluation shows that ASPE-based CBPS incurs 65 less overhead for exact-match filtering and 50 less overhead for range filtering compared to the state-of-the-art.