On the mapping of index compression techniques on CSR information retrieval

Information retrieval is the selection of documents relevant to a query. The inverted index is the conventional way to store the index of the collection. Because of the large amounts of data, compression techniques are commonly used in information retrieval systems to reduce the size of the inverted index. We experimentally evaluate the result of the mapping of such techniques on the compressed sparse row (CSR) information retrieval (IR). Our experimental results, using some of these compression techniques such as Elias Gamma, Golomb, interpolative, and fixed length byte-aligned, demonstrate that such techniques can easily be applied to compress the index in CSR IR.