Increasing Performance of Boolean Retrieval Model by Data Parallelism Technique

Abstract

Information retrieval (IR) is to identify documents of non-uniform behaviorthat fulfill information requirements from the huge repository (maintained in computersystems). Different models have been defined to retrieve/fetch information. Forexample, the Boolean model, the Statistical model, which focuses on the vector spaceand probabilistic retrieval, and the Linguistic and Knowledge-based retrieval models.The Boolean model is defined as the “perfect match” model. If the queries are notaccurate, they retrieve/fetch some irrelevant documents. This is called the precision (p)rate, which is the proportion of the relevant retrieved documents. The Boolean methodprovides good techniques to elaborate or concise a query. The Boolean method workswell for the search process because of the clarity between the concepts. The Booleanretrieval model processes the queries in which terms of the queries are in the form ofBoolean expressions, that is, in which terms of the user query combined with AND(&),OR(||), and NOT(!) operators. The model views documents in the form of invertedindexes. The key concept of an inverted index is to maintain a dictionary of terms. Forevery term, there is a collection of documents in which the term occurs. Posting is acollection of documents in which a term occurs. The list is known as the postings list(or inverted list), and all the postings lists are collectively called postings. But as the number of documents is increased, the postings of documents are alsoincreased, and processing these documents becomes time-consuming; so to resolve thisproblem, a multithreaded model is proposed in which the postings list is broken downinto different chunks and processes, due to which Boolean operation between postingsin accordance with Boolean query becomes faster. Using this data parallelismtechnique, the performance of the Boolean Retrieval Model is increased.