Cardinality estimation and dynamic length adaptation for Bloom filters
Distributed and Parallel Databases
Indexing and querying XML using extended Dewey labeling scheme
Data & Knowledge Engineering
Scaling XML query processing: distribution, localization and pruning
Distributed and Parallel Databases
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Due to the structural heterogeneity of XML, queries are often interpreted approximately. This is achieved by relaxing the query and ranking the results based on their relevance to the original query. Query relaxation over distributed XML repositories may incur large communication costs, since partial result lists from different sites need to be gathered and ranked to assembly the overall top-k results. To process such queries efficiently, we propose using a distributed clustered index to group documents based on their structural similarity. The clustered index proves to be very effective in reducing the sizes of the partial lists that need to be combined. Furthermore, it can be used as the basis of a pay-as-you-go approach, where clusters of documents are accessed gradually providing the user with increasingly improving results. To reduce the cost of constructing and maintaining the clustered index, we use a compact data structure that trades-off accuracy for storage and communication efficiency. The index is also used for selectivity estimation so that query relaxation is geared towards the most promising structural transformations. Our experimental results show that our approach significantly reduces the communication cost for retrieving the top-k results, while maintaining a low construction cost for the clustered index.