Parallel computing in information retrieval – an updated review

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. The progress of parallel computing in Information Retrieval (IR) is reviewed. In particular we stress the importance of the motivation in using parallel computing for Text Retrieval. We analyse parallel IR systems using a classification due to Rasmussen [1] and describe some parallel IR systems. We give a description of the retrieval models used in parallel Information PermanentProcessing.. We describe areas of research which we believe are needed.

Scalable Visual Analytics of Massive Textual Datasets

“…In the area of IR systems, several approaches have been developed that show scalability [2][3][4][5][6][7][8]. However, little work has been done with extending the use of the information retrieval to be applied to the data analytics.…”

Very Large Scale Information Retrieval

“…This is called document-id partitioning. [39] Each query is broadcast to all nodes in the cluster and each of them processes the query over the index for the piece of the collection for which they are responsible. The nodes may need to communicate with each other to exchange global statistical information such as df values.…”

Parallel methods for the generation of partitioned inverted files

“…These partitions are fragmented across physical disks. A fuller discussion of these partitioning methods can be found in (Jeong & Omiecinski, 1995;MacFarlane et al, 1997) and an example can be found in appendix 1. Two types of index build methods are used: Local and Distributed.…”