Techniques for improving efficiency and scalability for the integration of information retrieval and databases

PhDThis thesis is on the topic of integration of Information Retrieval (IR) and Databases (DB), with particular focuses on improving efficiency and scalability of integrated IR and DB technology (IR+DB). The main purpose of this study is to develop efficient and scalable techniques for supporting integrated IR and DB technology, which is a popular approach today for handling complex queries over text and structured data. Our specific interest in this thesis is how to efficiently handle queries over large-scale text and structured data. The work is based on a technology that integrates probability theory and relational algebra, where retrievals for text and data are to be expressed in probabilistic logical programs such as probabilistic relational algebra or probabilistic Datalog. To support efficient processing of probabilistic logical programs, we proposed three optimization techniques that focus on aspects covered logical and physical layers, which include: scoring-driven query optimization using scoring expression, query processing with top-k incorporated pipeline, and indexing with relational inverted index. Specifically, scoring expressions are proposed for expressing the scoring or probabilistic semantics of implied scoring functions of PRA expressions, so that efficient query execution plan can be generated by rule-based scoring-driven optimizer. Secondly, to balance efficiency and effectiveness so that to improve query response time, we studied methods for incorporating topk algorithms into pipelined query execution engine for IR+DB systems. Thirdly, the proposed relational inverted index integrates IR-style inverted index and DB-style tuple-based index, which can be used to support efficient probability estimation and aggregation as well as conventional relational operations. Experiments were carried out to investigate the performances of proposed techniques. Experimental results showed that the efficiency and scalability of an IR+DB prototype have been improved, while the system can handle queries efficiently on considerable large data sets for a number of IR tasks

The relationship between IR and multimedia databases

Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system

Information Integration - the process of integration, evolution and versioning

At present, many information sources are available wherever you are. Most of the time, the information needed is spread across several of those information sources. Gathering this information is a tedious and time consuming job. Automating this process would assist the user in its task. Integration of the information sources provides a global information source with all information needed present. All of these information sources also change over time. With each change of the information source, the schema of this source can be changed as well. The data contained in the information source, however, cannot be changed every time, due to the huge amount of data that would have to be converted in order to conform to the most recent schema.\ud In this report we describe the current methods to information integration, evolution and versioning. We distinguish between integration of schemas and integration of the actual data. We also show some key issues when integrating XML data sources