Information integration has been widely addressed over the last several decades. However, it is far from solved due to the complexity of resolving schema and data heterogeneities. In this paper, we propose out attempt to alleviate such difficulty by realizing keyword search functionality for integrating information from heterogeneous databases. Our solution does not require predefined global schema or any mappings between databases. Rather, it relies on an operator called keyword join to take a set of lists of partial answers from different data sources as input, and output a list of results that are joined by the tuples from input lists based on predefined similarity measures as integrated results. Our system allows source databases remain autonomous and the system to be dynamic and extensible. We have tested our system with real dataset and benchmark, which shows that our proposed method is practical and effective. / Singapore-MIT Alliance (SMA)

In this paper, we present a P2P-based database sharing system that provides information sharing capabilities through keyword-based search techniques. Our system requires neither a global schema nor schema mappings between different databases, and our keyword-based search algorithms are robust in the presence of frequent changes in the content and membership of peers. To facilitate data integration, we introduce keyword join operator to combine partial answers containing different keywords into complete answers. We also present an efficient algorithm that optimize the keyword join operations for partial answer integration. Our experimental study on both real and synthetic datasets demonstrates the effectiveness of our algorithms, and the efficiency of the proposed query processing strategies. / Singapore-MIT Alliance (SMA)