Nils Grimsmo

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This article describes how to implement efficient memory resident path indexes for semi-structured data. Two techniques are introduced, and they are shown to be significantly faster than previous methods when facing path queries using the descendant axis and wild-cards. The first is conceptually simple and combines inverted lists, selectivity estimation,(More)
The F&B-index is used to speed up pattern matching in tree and graph data, and is based on the maximum F&B-bisimulation, which can be computed in loglinear time for graphs. It has been shown that the maximum F-bisimulation can be computed in linear time for DAGs. We build on this result, and introduce a linear algorithm for computing the maximum(More)
More and more data is accumulated inside social networks. Keyword search provides a simple interface for exploring this content. However, a lot of the content is private, and a search system must enforce the privacy settings of the social network. In this paper, we present a workload-aware keyword search system with access control based on a social network.(More)
Bitmap indexes are widely used in Decision Support Systems (DSSs) to improve query performance. In this paper, we evaluate the use of compressed inverted indexes with adapted query processing strategies from Information Retrieval as an alternative. In a thorough experimental evaluation on both synthetic data and data from the Star Schema Benchmark, we show(More)
This report evaluates the performance of uncom-pressed and compressed substring indexes on build time, space usage and search performance. It is shown how the structures react to increasing data size, alphabet size and repetitiveness in the data. The main contribution is the strong relationship shown between time performance and locality in the data(More)
The F&B-index is used to speed up pattern matching in tree and graph data, and is based on the maximum F&B-bisimulation, which can be computed in loglinear time for graphs. It has been shown that the maximum F-bisimulation can be computed in linear time for DAGs. We build on this result, and introduce a linear algorithm for computing the maximum(More)