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331
Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects
"... The conventional Internet is acquiring a geo-spatial dimension. Web documents are being geo-tagged, and geo-referenced objects such as points of interest are being associated with descriptive text documents. The resulting fusion of geo-location and documents enables a new kind of top-k query that ta ..."
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Cited by 88 (16 self)
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The conventional Internet is acquiring a geo-spatial dimension. Web documents are being geo-tagged, and geo-referenced objects such as points of interest are being associated with descriptive text documents. The resulting fusion of geo-location and documents enables a new kind of top-k query that takes into account both location proximity and text relevancy. To our knowledge, only naive techniques exist that are capable of computing a general web information retrieval query while also taking location into account. This paper proposes a new indexing framework for locationaware top-k text retrieval. The framework leverages the inverted file for text retrieval and the R-tree for spatial proximity querying. Several indexing approaches are explored within the framework. The framework encompasses algorithms that utilize the proposed indexes for computing the top-k query, thus taking into account both text relevancy and location proximity to prune the search space. Results of empirical studies with an implementation of the framework demonstrate that the paper’s proposal offers scalability and is capable of excellent performance. 1.
Ed-Join: An Efficient Algorithm for Similarity Joins With Edit Distance Constraints
, 2008
"... There has been considerable interest in similarity join in the research community recently. Similarity join is a fundamental operation in many application areas, such as data integration and cleaning, bioinformatics, and pattern recognition. We focus on efficient algorithms for similarity join with ..."
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Cited by 72 (8 self)
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There has been considerable interest in similarity join in the research community recently. Similarity join is a fundamental operation in many application areas, such as data integration and cleaning, bioinformatics, and pattern recognition. We focus on efficient algorithms for similarity join with edit distance constraints. Existing approaches are mainly based on converting the edit distance constraint to a weaker constraint on the number of matching q-grams between pair of strings. In this paper, we propose the novel perspective of investigating mismatching q-grams. Technically, we derive two new edit distance lower bounds by analyzing the locations and contents of mismatching q-grams. A new algorithm, Ed-Join, is proposed that exploits the new mismatch-based filtering methods; it achieves substantial reduction of the candidate sizes and hence saves computation time. We demonstrate experimentally that the new algorithm outperforms alternative methods on large-scale real datasets under a wide range of parameter settings.
Performance of Compressed Inverted List Caching in Search Engines
- IN WWW
, 2008
"... Due to the rapid growth in the size of the web, web search engines are facing enormous performance challenges. The larger engines in particular have to be able to process tens of thousands of queries per second on tens of billions of documents, making query throughput a critical issue. To satisfy th ..."
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Cited by 58 (12 self)
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Due to the rapid growth in the size of the web, web search engines are facing enormous performance challenges. The larger engines in particular have to be able to process tens of thousands of queries per second on tens of billions of documents, making query throughput a critical issue. To satisfy this heavy workload, search engines use a variety of performance optimizations including index compression, caching, and early termination. We focus on two techniques, inverted index compression and index caching, which play a crucial rule in web search engines as well as other high-performance information retrieval systems. We perform a comparison and evaluation of several inverted list compression algorithms, including new variants of existing algorithms that have not been studied before. We then evaluate different inverted list caching policies on large query traces, and finally study the possible performance benefits of combining compression and caching. The overall goal of this paper is to provide an updated discussion and evaluation of these two techniques, and to show how to select the best set of approaches and settings depending on parameter such as disk speed and main memory cache size.
Packing bag-of-features
- in ICCV
, 2009
"... One of the main limitations of image search based on bag-of-features is the memory usage per image. Only a few million images can be handled on a single machine in reasonable response time. In this paper, we first evaluate how the memory usage is reduced by using lossless index compression. We then ..."
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Cited by 55 (9 self)
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One of the main limitations of image search based on bag-of-features is the memory usage per image. Only a few million images can be handled on a single machine in reasonable response time. In this paper, we first evaluate how the memory usage is reduced by using lossless index compression. We then propose an approximate representation of bag-of-features obtained by projecting the corresponding histogram onto a set of pre-defined sparse projection functions, producing several image descriptors. Coupled with a proper indexing structure, an image is represented by a few hundred bytes. A distance expectation criterion is then used to rank the images. Our method is at least one order of magnitude faster than standard bag-of-features while providing excellent search quality. 1.
Collective spatial keyword querying
- In SIGMOD
, 2011
"... With the proliferation of geo-positioning and geo-tagging, spatial web objects that possess both a geographical location and a textual description are gaining in prevalence, and spatial keyword queries that exploit both location and textual description are gaining in prominence. However, the queries ..."
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Cited by 53 (11 self)
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With the proliferation of geo-positioning and geo-tagging, spatial web objects that possess both a geographical location and a textual description are gaining in prevalence, and spatial keyword queries that exploit both location and textual description are gaining in prominence. However, the queries studied so far generally focus on finding individual objects that each satisfy a query rather than finding groups of objects where the objects in a group collectively satisfy a query. We define the problem of retrieving a group of spatial web ob-jects such that the group’s keywords cover the query’s keywords and such that objects are nearest to the query location and have the lowest inter-object distances. Specifically, we study two variants of this problem, both of which are NP-complete. We devise exact solutions as well as approximate solutions with provable approxi-mation bounds to the problems. We present empirical studies that offer insight into the efficiency and accuracy of the solutions. 1.
Mining query logs: Turning search usage data into knowledge
- Foundations and Trends in Information Retrieval
, 2008
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Effective pre-retrieval query performance prediction using similarity and variability evidence
, 2008
"... Query performance prediction aims to estimate the quality of answers that a search system will return in response to a particular query. In this paper we propose a new family of pre-retrieval predictors based on information at both the collection and document level. Pre-retrieval predictors are im ..."
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Cited by 42 (2 self)
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Query performance prediction aims to estimate the quality of answers that a search system will return in response to a particular query. In this paper we propose a new family of pre-retrieval predictors based on information at both the collection and document level. Pre-retrieval predictors are important because they can be calculated from information that is available at indexing time; they are therefore more efficient than predictors that incorporate information obtained from actual search results. Experimental evaluation of our approach shows that the new predictors give more consistent performance than previously proposed pre-retrieval methods across a variety of data types and search tasks.
