Block linked list index structure for large data full text retrieval

Yang, Yang; Ning, Hongyun

doi:10.1109/fskd.2017.8393099

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…Для полнотекстового поиска применяются инвертированные файлы [14,15]. Для каждого вхождения каждого слова в документе в индексе сохраняется словопозиция -запись вида (ID, P ), где ID -идентификатор документа, P -позиция слова в документе.…”

Section: число вхожденийunclassified

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Relevance ranking for proximity full-text search based on additional indexes with multi-component keys

Veretennikov

2021

Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki

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The problem of proximity full-text search is considered. If a search query contains high-frequently occurring words, then multi-component key indexes deliver improvement of the search speed in comparison with ordinary inverted indexes. It was shown that we can increase the search speed up to 130 times in cases when queries consist of high-frequently occurring words. In this paper, we are investigating how the multi-component key indexes architecture affects the quality of the search. We consider several well-known methods of relevance ranking; these methods are of different authors. Using these methods we perform the search in the ordinary inverted index and then in the index that is enhanced with multi-component key indexes. The results show that with multi-component key indexes we obtain search results that are very near in terms of relevance ranking to the search results that are obtained by means of ordinary inverted indexes.

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Section: число вхожденийunclassified

“…An inverted index is the data structure that is used for full-text search [14,15]. For each occurrence of each word in each document, a posting is stored in the index.…”

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confidence: 99%

Relevance ranking for proximity full-text search based on additional indexes with multi-component keys

Veretennikov

2021

Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki

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“…Set (position 15, key who), buffer 0 (3.1). Read the posting (21,20,15) Please note that W indowSize should be 64 for the better performance.…”

Section: Incremental Solutionmentioning

confidence: 99%

“…By a list of words, a user can obtain relevant documents that contain these words. Inverted files are used for this search [23,11,21,2]. Words occur in documents with different frequencies, and an example of the frequency distribution of words is represented by Zipf's law [22], which is presented in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Systems and Applications

2021

Advances in Intelligent Systems and Computing

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A search query consists of several words. In a proximity fulltext search, we want to find documents that contain these words near each other. This task requires much time when the query consists of highfrequently occurring words. If we cannot avoid this task by excluding high-frequently occurring words from consideration by declaring them as stop words, then we can optimize our solution by introducing additional indexes for faster execution. In a previous work, we discussed how to decrease the search time with multi-component key indexes. We had shown that additional indexes can be used to improve the average query execution time up to 130 times if queries consisted of high-frequently occurring words. In this paper, we present another search algorithm that overcomes some limitations of our previous algorithm and provides even more performance gain.

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An Improved Algorithm for Fast K-Word Proximity Search Based on Multi-component Key Indexes

Veretennikov

2020

Advances in Intelligent Systems and Computing

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Block linked list index structure for large data full text retrieval

Cited by 5 publications

References 7 publications

Relevance ranking for proximity full-text search based on additional indexes with multi-component keys

Relevance ranking for proximity full-text search based on additional indexes with multi-component keys

Intelligent Systems and Applications

An Improved Algorithm for Fast K-Word Proximity Search Based on Multi-component Key Indexes

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