QUERY PROCESSING OVER INCOMPLETE AUTONOMOUS WEB DATABASES by Hemal Khatri

Incompleteness due to missing attribute values (aka “null values”) is very common in autonomous web databases, on which user accesses are usually supported through mediators. Traditional query processing techniques that focus on the strict soundness of answer tuples often ignore tuples with critical missing attributes, even if they wind up being relevant to the user query. Ideally, the mediator is expected to retrieve such relevant uncertain answers and gauge their relevance by accessing their likelihood of being relevant answers to the query. The autonomous nature of the databases poses several challenges in realizing this idea. Such challenges include restricted access privileges, limited query patterns and cost sensitivity of database and network resource consumption in web environment. This thesis presents QPIAD – a framework for query processing over incomplete autonomous databases. QPIAD is able to retrieve relevant uncertain answers with high precision, high recall and manageable cost. Data integration over multiple autonomous data sources is an important task performed by a mediator. This thesis describes query rewriting techniques to perform data integration over multiple incomplete autonomous data sources on the web. Results of experimental evaluation on real-life databases demonstrate that our system retrieve relevant answers with high precision and manageable cost.