Index structures and top-k join algorithms for native keyword search databases

For supporting keyword search on structured data, current solutions require large indexes to be built that redundantly store subgraphs called neighborhoods. Further, for exploring keyword search results, large graphs have to be loaded into memory. We propose a solution, which employs much more compact index structures for neighborhood lookups. Using these indexes, we reduce keyword search result exploration to the traditional database problem of top-k join processing, enabling results to be computed efficiently. In particular, this computation can be performed on data streams successively loaded from disk (i.e., does not require the entire input to be loaded at once into memory). For supporting this, we propose a top-k procedure based on the rank join operator, which not only computes the k-best results, but also selects query plans in a top-k fashion during the process. In experiments using large real-world datasets, our solution reduced storage requirements and also outperformed the state-of-the-art in terms of performance and scalability.