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Abstract
In recent years, there has been a tremendous increase in the number of data transfer (information exchange) applications on mobile devices that form the bases for new research domains. Distributed information retrieval is one of the most prominent ways for building these data transfer applications. Despite a rich list of new characteristics and advantages, these new applications particularly for mobile devices, bring a variety of important challenges. As mobile devices are limited in resources, it poses a need to deal with the data transfer cost either by minimizing it or by avoiding it as much as possible.
Mobile content distribution networks, opportunistic routing for wireless networks, mobile social networks and overlay peer-to-peer networks are a few of those communication paradigms that rely on network link capacity. Therefore they usually suffer from a bottleneck at saturated links. In this current era, network capacity planning, increase in link capacity and reduction in the size of actual data transfer are a few of the proposed solutions to deal with the saturated link problem. However, not much attention has been given to domain specific solutions. The aim of this research is to fill this gap by investigating the domain specific solutions so that unnecessary network data transfer can be avoided.
This thesis targets both the push and pull based distributed information retrieval and the contribution is many fold. First, in contrast to state of the art data aggregation approaches, we investigate the data transfer optimization with the help of user profile aggregation and present an architecture for content-based networks. Next, we opt for the information store discovery (also known as resource selection) so that in order to reduce the network data transfer, only a selected set of information stores transfer the data for given user queries. We extend this research for systems where data duplication across information stores can not be avoided and propose data redundancy elimination algorithms. Our data redundancy elimination algorithms are based on summary exchange and make use of Bloom filter. To show the viability of these algorithms, we present a comprehension generation scenario for the mobile search engines and evaluate it with the existing semantic search engine. In order to build a better Bloom filter, we propose two new simplistic hashing algorithms. Next we integrate these hashing algorithms with our data redundancy elimination algorithms and empirically evaluate this combination. Finally, we describe how a semantic web dataset more efficiently can be queried with the query representation as a Bloom filter.