Toward DB-IR Integration: Per-Document Basis Transactional Index Maintenance

Kim, Jin-Suk; Jin, Du-Seok; Choi, Youngjoon; Jeong, Chang-Hoo; Kim, Kwangyoung; Choi, Sung-Pil; Cho, Minhee; Choe, Ho-Seop; Yoon, Ho‐Geun; Seo, Jeong-Hyun

doi:10.1109/alpit.2007.15

Cited by 3 publications

(5 citation statements)

References 22 publications

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“…Recently, several approaches to merge DB's structured data management and IR unstructured text search facilities have been proposed. According to [21], they can be classified in four different categories:…”

Section: Related Workmentioning

confidence: 99%

“…Query evaluation and indexing is provided by the IR engine, while the DBMS manages the documents and other metadata. According to [21] the basic drawback of this approach is the difficulty to synchronize the DBMS document contents and the IR's index. -DBMS extension by loose coupling Most DBMS offer extensible architectures using a high level interface, which can be used to integrate IR functionalities.…”

Section: Related Workmentioning

confidence: 99%

“…Systems based on this approach, include PowerDB-IR [14] (a scalable IR system for frequently changing data sets), QUIQ [20] (a collaborative customer support application, where a DBMS holds all the data and an external server maintains the index), TopX [28] (an Oracle based engine for XML and plain text data with top-k retrieval) and HySpirit [13] (a hypermedia retrieval engine using probabilistic Datalog). -DBMS extension by tight coupling In this approach, new data types and functionality for IR features are integrated into the core of the DBMS engine or the reverse (IRMS Information Retrieval & Management System) [21]. Tight coupled systems include Oddyseus [30], an engine build over an ORDBMS engine, and MonetDB/X100 [10], a column oriented storage management based system.…”

Section: Related Workmentioning

confidence: 99%

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Object-Relational Database Representations for Text Indexing

Papadakos,

Theoharis,

Marketakis

et al. 2009

Preprint

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One of the distinctive features of Information Retrieval systems comparing to Database Management systems, is that they offer better compression for posting lists, resulting in better I/O performance and thus faster query evaluation. In this paper, we introduce database representations of the index that reduce the size (and thus the disk I/Os) of the posting lists. This is not achieved by redesigning the DBMS, but by exploiting the non 1NF features that existing Object-Relational DBM systems (ORDBMS) already offer. Specifically, four different database representations are described and detailed experimental results for one million pages are reported. Three of these representations are one order of magnitude more space efficient and faster (in query evaluation) than the plain relational representation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Object-Relational Database Representations for Text Indexing

Papadakos,

Theoharis,

Marketakis

et al. 2009

Preprint

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“…In this method, all terms from the keyword dictionary are stored into and indexed by an open-source information retrieval engine, KRISTAL-IRMS [6]. Upon tagging request, the input document is used to query the dictionary database and to retrieve top-ranked documents (keywords in this case) using the vector space model supplied in the KRISTAL engine.…”

Section: Selection Of Candidate Abstractmentioning

confidence: 99%

“…In methods A and B, as mentioned above, the time complexity for comparing the input document and keyword dictionary is O(mN) since the basic pattern matching algorithm has O(m) complexity and it should be executed N times (Algorithm 1). In method C, an opensource Information Retrieval & Management System KRISTAL-IRMS [6] was used to store and index whole terms in the keyword dictionary, and to retrieve candidate terms using a vector space model by querying the whole text of the input document as a query (Algorithm 2). In method C, the inverted index contains, in sorted order, all the unique strings which appear in the text collection (all terms in the keyword dictionary in this study), together with a pointer to posting list.…”

Section: Automatic In-text Keyword Taggingmentioning

confidence: 99%

Automatic In-Text Keyword Tagging based on Information Retrieval

Kim¹,

Jin²,

Kim³

et al. 2009

Journal of Information Processing Systems

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As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) -if a pattern matching algorithm is used -can be reduced to O(mlogN) -if an Information Retrieval technique is adopted -while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 ~ 40 times compared with the fastest pattern matching algorithm.

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