Vector-space ranking with effective early termination

Anh, Vo Ngoc; Kretser, Owen de; Moffat, Alistair

doi:10.1145/383952.383957

Cited by 168 publications

(137 citation statements)

References 19 publications

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“…All algorithms were implemented in Java using the XXL library 1 . The nodes of the aR-trees, employed in S2I, have a block size of 4KB that is able to store between 42 and 85 entries.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Efficient Processing of Top-k Spatial Keyword Queries

Rocha-Junior

Gkorgkas

Jonassen

et al. 2011

Lecture Notes in Computer Science

184

126

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Abstract. Given a spatial location and a set of keywords, a top-k spatial keyword query returns the k best spatio-textual objects ranked according to their proximity to the query location and relevance to the query keywords. There are many applications handling huge amounts of geotagged data, such as Twitter and Flickr, that can benefit from this query. Unfortunately, the state-of-the-art approaches require non-negligible processing cost that incurs in long response time. In this paper, we propose a novel index to improve the performance of top-k spatial keyword queries named Spatial Inverted Index (S2I). Our index maps each distinct term to a set of objects containing the term. The objects are stored differently according to the document frequency of the term and can be retrieved efficiently in decreasing order of keyword relevance and spatial proximity. Moreover, we present algorithms that exploit S2I to process top-k spatial keyword queries efficiently. Finally, we show through extensive experiments that our approach outperforms the state-of-the-art approaches in terms of update and query cost.

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“…All algorithms were implemented in Java using the XXL library 1 . The nodes of the aR-trees, employed in S2I, have a block size of 4KB that is able to store between 42 and 85 entries.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Efficient Processing of Top-k Spatial Keyword Queries

Rocha-Junior

Gkorgkas

Jonassen

et al. 2011

Lecture Notes in Computer Science

184

126

View full text Add to dashboard Cite

show abstract

“…The author represented, a replacement inverted file structure mistreatment quantity weights that has superior retrieval effectiveness compared to standard inverted file structures once early termination heuristics area unit utilized. That is, we tend to area unit able to reach similar effectiveness levels with less procedure value, so offer a more robust cost/performance compromise than previous inverted file organizations [12]. The authors given results based mostly upon the trec net knowledge that show the mixture of those varied techniques to yield extremely competitive retrieval, in terms of each effectiveness and potency, for each short and long queries [13].…”

Section: Related Workmentioning

confidence: 99%

Big Data Analytics for Concurrent Data Processing

Samydurai¹,

Vijayakumaran²,

Kumaresan³

et al. 2015

IJCA

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Attractively voluminous data describes an immense volume of structured and unstructured data that is difficult to process utilizing traditional database techniques. The tremendous growth in arrival rates of data to support a large number of user queries creates complex problems in the traditional structured databases. In this paper, the input file is assigned to a master who has the ability to split and control the work flow with different workers. This will reduce the fault tolerance issues raised with nodes. They will evaluate the intermediate files and data items. Over again the processed data will be amalgamated and the required output will be immediately/middle file given to the user. Also the first solution for processing perpetual text queries efficiently to address the above challenges is given. The solution indexes the streamed documents in main recollection with a structure predicate on the principles of the inverted file, and processes document advent and expiration events with an incremental threshold-predicated method.

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“…If approximate results are acceptable, the TAAT approach can be made even more efficient using impact ordering [1,2,33]. The key idea of impact ordering is to precompute the TF for each document a term appears in.…”

Section: Term-at-a-time Processing (Taat)mentioning

confidence: 99%

Efficient in-memory top-k document retrieval

Culpepper

Petri

Scholer

2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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For over forty years the dominant data structure for ranked document retrieval has been the inverted index. Inverted indexes are effective for a variety of document retrieval tasks, and particularly efficient for large data collection scenarios that require disk access and storage. However, many efficiency-bound search tasks can now easily be supported entirely in-memory as a result of recent hardware advances.In this paper we present a hybrid algorithmic framework for inmemory bag-of-words ranked document retrieval using a self-index derived from the FM-Index, wavelet tree, and the compressed suffix tree data structures, and evaluate the various algorithmic trade-offs for performing efficient queries entirely in-memory. We compare our approach with two classic approaches to bag-of-words queries using inverted indexes, term-at-a-time (TAAT) and document-at-atime (DAAT) query processing. We show that our framework is competitive with state-of-the-art indexing structures, and describe new capabilities provided by our algorithms that can be leveraged by future systems to improve effectiveness and efficiency for a variety of fundamental search operations.

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Vector-space ranking with effective early termination

Cited by 168 publications

References 19 publications

Efficient Processing of Top-k Spatial Keyword Queries

Efficient Processing of Top-k Spatial Keyword Queries

Big Data Analytics for Concurrent Data Processing

Efficient in-memory top-k document retrieval

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