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.
This approach integrates DB and IR engines at the application level @cite_22 . Query evaluation and indexing is provided by the IR engine, while the DBMS manages the documents and other metadata. According to @cite_4 the basic drawback of this approach is the difficulty to synchronize the DBMS document contents and the IR's index.
@cite_4 @cite_22
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While information retrieval(IR) and databases(DB) have been developed independently, there have been emerging requirements that both data management and efficient text retrieval should be supported simultaneously in an information system such as health care systems, bulletin boards, XML data management, and digital libraries. Recently DB-IR integration issue has been budded in the research field. The great divide between DB and IR has caused different manners in index maintenance for newly arriving documents. While DB has extended its SQL layer to cope with text fields due to lack of intact mechanism to build IR-like index, IR usually treats a block of new documents as a logical unit of index maintenance since it has no concept of integrity constraint. However, towards DB-IR integration, a transaction on adding or updating a document should include maintenance of the postings lists accompanied by the document - hence per-document basis transactional index maintenance. In this paper, performance of a few strategies for per-document basis transaction for inserting documents -- direct index update, stand-alone auxiliary index and pulsing auxiliary index - will be evaluated. The result tested on the KRISTAL-IRMS shows that the pulsing auxiliary strategy, where long postings lists in the auxiliary index are in-place updated to the main index whereas short lists are directly updated in the auxiliary index, can be a challenging candidate for text field indexing in DB-IR integration. Databases (DB) and information retrieval (IR) have evolved as separate fields. However, modern applications such as customer support, health care, and digital libraries require capabilities for both data and text management. In such settings, traditional DB queries, in SQL or XQuery, are not flexible enough to handle applicationspecific scoring and ranking. IR systems, on the other hand, lack efficient support for handling structured parts of the data and metadata, and do not give the application developer adequate control over the ranking function. This paper analyzes the requirements of advanced text- and data-rich applications for an integrated platform. The core functionality must be manageable, and the API should be easy to program against. A particularly important issue that we highlight is how to reconcile flexibility in scoring and ranking models with optimizability, in order to accommodate a wide variety of target applications efficiently. We discuss whether such a system needs to be designed from scratch, or can be incrementally built on top of existing architectures. The results of our analyses are cast into a series of challenges to the DB and IR communities. This paper proposes evaluation methods based on the use of non-dichotomous relevance judgements in IR experiments. It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents. This is desirable from the user point of view in modem large IR environments. The proposed methods are (1) a novel application of P-R curves and average precision computations based on separate recall bases for documents of different degrees of relevance, and (2) two novel measures computing the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. We then demonstrate the use of these evaluation methods in a case study on the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance. The test was run with a best match retrieval system (In- Query I) in a text database consisting of newspaper articles. The results indicate that the tested strong query structures are most effective in retrieving highly relevant documents. The differences between the query types are practically essential and statistically significant. More generally, the novel evaluation methods and the case demonstrate that non-dichotomous relevance assessments are applicable in IR experiments, may reveal interesting phenomena, and allow harder testing of IR methods. With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. We also propose several efficient query processing methods for the new ranking method. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency.