|dc.description.abstract||One of the greatest challenges in information retrieval is to develop an intelligent system for user and machine interaction that supports users in their quest for relevant information. The dramatic increase in the amount of Web content gives rise to the need for a large-scale distributed information retrieval system, targeted to support millions of users and terabytes of data. To retrieve information from such a large amount of data in an efficient manner, the index is split among the servers in a distributed information retrieval system. Thus, partitioning the index among these collaborating nodes plays an important role in enhancing the performance of a distributed search engine. The two widely known inverted index partitioning schemes for a distributed information retrieval system are document partitioning and term partitioning. %In a document partitioned system, each of the server hosts a subset of the documents in the collection, and execute every query against its local sub-collection. In a term partitioned index, each node is responsible for a subset of the terms in the collection, and serves them to a central node as they are required for query evaluation.
In this thesis, we introduce the Document over Term inverted index distribution scheme, which splits a set of nodes into several groups (sub-clusters) and then performs document partitioning between the groups and term partitioning within the group. As this approach is based on the term and document index partitioning approaches, we also refer it as a Hybrid Inverted Index. This approach retains the disk access benefits of term partitioning and the benefits of sharing computational load, scalability, maintainability, and availability of the document partitioning. We also introduce the Document over Document index partitioning scheme, based on the document partitioning approach. In this approach, a set of nodes is split into groups and documents in the collection are partitioned between groups and also within each group. This strategy retains all the benefits of the document partitioning approach, but reduces the computational load more effectively and uses resources more efficiently.
We compare distributed index approaches experimentally and show that in terms of efficiency and scalability, document partition based approaches perform significantly better than the others. The Document over Term partitioning offers efficient utilization of search-servers and lowers disk access, but suffers from the problem of load imbalance. The Document over Document partitioning emerged to be the preferred method during high workload.||en