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.
Spatio-Textual Indexing. Recently, several spatio-textual indexes have been proposed to answer snap-shot queries over spatio-textual data. Examples of these queries include the filter, top-k, and collective group queries. @cite_22 surveys spatio-textual indexes and benchmarks their performance under various spatio-textual queries. The most relevant indexes are the IQ-tree @cite_8 and the R @math -tree @cite_13 . These indexes are mainly disk-based and have been outperformed by the AP-tree @cite_6 .
@cite_13 @cite_22 @cite_6 @cite_8
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Content based indexing is critical to the effective access of the multimedia data. To this end, visual data is often annotated with textual content for bridging the semantic gap. In this paper, we present a method to generate frame level fine grained annotations for a given video clip. Access to the frame level fine grained annotations lead to rich, dense and meaningful semantic associations between the text and video. This in turn makes the video retrieval systems more accurate. We demonstrate the use of probabilistic label consistent sparse coding and dictionary learning with a K-SVD algorithm to generate ‘fine grained’ annotations for a class of videos - lawn tennis. The algorithm simultaneously learns a classifier and a dictionary to generate the frame level annotations for the tennis videos using available textual descriptions. The utility of the proposed algorithm is demonstrated on a publicly available tennis dataset comprising of tennis match videos from Olympics games. In this paper, we investigate the selectivity estimation problem for streaming spatio-textual data, which arises in many social network and geo-location applications. Specifically, given a set of continuously and rapidly arriving spatio-textual objects, each of which is described by a geo-location and a short text, we aim to accurately estimate the cardinality of a spatial keyword query on objects seen so far, where a spatial keyword query consists of a search region and a set of query keywords. To the best of our knowledge, this is the first work to address this important problem. We first extend two existing techniques to solve this problem, and show their limitations. Inspired by two key observations on the "locality" of the correlations among query keywords, we propose a local correlation based method by utilizing an augmented adaptive space partition tree (A2SP-tree for short) to approximately learn a local Bayesian network on-the-fly for a given query and estimate its selectivity. A novel local boosting approach is presented to further enhance the learning accuracy of local Bayesian networks. Our comprehensive experiments on real-life datasets demonstrate the superior performance of the local correlation based algorithm in terms of estimation accuracy compared to other competitors. In this article, we study the problem of efficient top-k disjunctive query processing in a huge microblog dataset. In terms of compact indexing, we categorize the keywords into rare terms and common terms based on inverse document frequency (idf) and propose tailored block-oriented organization to save memory consumption. In terms of fast searching, we classify the queries into three types based on term category and judiciously design an efficient search algorithm for each type. We conducted extensive experiments on a billion-scale Twitter dataset and examined the performance with both simple and more advanced ranking functions. The results showed that with much smaller index size, our search algorithm achieves a factor of 2--3 times faster speedup over state-of-the-art solutions in both ranking scenarios. Given a set @math of @math strings of total length @math , our task is to report the "most relevant"strings for a given query pattern @math . This involves somewhat more advanced query functionality than the usual pattern matching, as some notion of "most relevant" is involved. In information retrieval literature, this task is best achieved by using inverted indexes. However, inverted indexes work only for some predefined set of patterns. In the pattern matching community, the most popular pattern-matching data structures are suffix trees and suffix arrays. However, a typical suffix tree search involves going through all the occurrences of the pattern over the entire string collection, which might be a lot more than the required relevant documents. The first formal framework to study such kind of retrieval problems was given by [Muthukrishnan, 2002]. He considered two metrics for relevance: frequency and proximity. He took a threshold-based approach on these metrics and gave data structures taking @math words of space. We study this problem in a slightly different framework of reporting the top @math most relevant documents (in sorted order) under similar and more general relevance metrics. Our framework gives linear space data structure with optimal query times for arbitrary score functions. As a corollary, it improves the space utilization for the problems in [Muthukrishnan, 2002] while maintaining optimal query performance. We also develop compressed variants of these data structures for several specific relevance metrics.