Ilya Markov defends Phd thesis on Distributed Information Retrieval

Today, Ilya Markov successfully defended his PhD thesis at the Università della Svizzera italiana in Lugano, Switzerland.

Uncertainty in Distributed Information Retrieval

by Ilya Markov

Large amounts of available digital information call for distributed processing and management solutions. Distributed Information Retrieval (DIR), also known as Federated Search, provides techniques for performing retrieval over such distributed data. In particular, it studies approaches to aggregating multiple searchable sources of information within a single interface.
DIR provides an efficient and low-cost solution to a distributed retrieval problem. As opposed to a centralized retrieval system, which acquires, stores and processes all available information locally, DIR delegates the search task to distributed sources. This way, DIR lowers the storage and processing costs and provides a user with up-to-date information even if this information is not crawlable (i.e. cannot be reached using hyperlinks).
DIR is usually based on a brokered architecture, according to which distributed retrieval is managed by a single broker. The broker-based DIR can be divided into five steps: resource discovery, resource description, resource selection, score normalization and results presentation. Among these steps, resource description, resource selection and score normalization are actively studied within DIR research, while the resource discovery step is addressed by the database community and results presentation is studied within aggregated search.
Despite the large volume of research on resource selection and score normalization, no unified framework of developed techniques exists, which makes difficult the application and comparison of available methods. The first goal of this dissertation is to summarize, analyze and evaluate existing resource selection and score normalization techniques within a unified framework. This should improve the understanding of available methods, reveal their underlying assumptions and limitations and describe their properties. This, in turn, will help to improve existing resource selection and score normalization techniques and to apply the right method in the right setting.
The second and the main contribution of this dissertation is in stating and addressing the problem of uncertainty in DIR. In Information Retrieval (IR) this problem has been recognized for a long time and numerous techniques have been proposed to deal with uncertainty in various IR tasks. This dissertation raises the question of uncertainty in DIR, outlines the sources of uncertainty on different DIR phases and proposes methods for measuring and reducing this uncertainty.