Truls Amundsen Bjørklund

Search in social networks with access control

More and more important data is accumulated inside social networks. Limiting the flow of private information across a social network is very important, and most social networks provide sophisticated privacy settings to control this flow. Creating such extensive access control knobs makes the search for content a hard problem since each user sees a unique subset of all the data. In this work, we take a first step at integrating access control based on a social network in a search system. We describe a set of solutions to the problem, including what indexes to construct and how to filter out inaccessible results. An experimental analysis illustrates the tradeoffs of the various strategies, and we point out a set of interesting future research directions in this area.

Fast optimal twig joins

In XML search systems twig queries specify predicates on node values and on the structural relationships between nodes, and a key operation is to join individual query node matches into full twig matches. Linear time twig join algorithms exist, but many non-optimal algorithms with better average-case performance have been introduced recently. These use somewhat simpler data structures that are faster in practice, but have exponential worst-case time complexity. In this paper we explore and extend the solution space spanned by previous approaches. We introduce new data structures and improved strategies for filtering out useless data nodes, yielding combinations that are both worst-case optimal and faster in practice. An experimental study shows that our best algorithm outperforms previous approaches by an average factor of three on common benchmarks. On queries with at least one unselective leaf node, our algorithm can be an order of magnitude faster, and it is never more than 20% slower on any tested benchmark query.

Workload-aware indexing for keyword search in social networks

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. We make two technical contributions: (1) HeapUnion, a novel union operator that improves processing of search queries with access control by up to a factor of two compared to the best previous solution; and (2) highly accurate cost models that vary in sophistication and accuracy; these cost models provide input to an optimization algorithm that selects the most efficient organization of access control meta-data for a given workload. Our experimental results with real and synthetic data show that our approach outperforms previous work by up to a factor of three.

Inverted indexes vs. bitmap indexes in decision support systems

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 that inverted indexes are more compact than bitmap indexes in almost all cases. This compactness combined with efficient query processing strategies results in inverted indexes outperforming bitmap indexes for most queries, often significantly.

XLeaf: Twig Evaluation with Skipping Loop Joins and Virtual Nodes

XML indexing and search has become an important topic, and twig joins are key building blocks in XML search systems. This paper describes a novel approach using a nested loop twig join algorithm, which combines several existing techniques to speed up evaluation of XML queries. We combine structural summaries, path indexing and prefix path partitioning to reduce the amount of data read by the join. This effect is amplified by only reading data for leaf query nodes, and inferring data for internal nodes from the structural summary. Skipping is used to speed up merges where query leaves have differing selectivity. Multiple access methods are implemented as materialized views instead of succinct secondary indexes for better locality. This redundancy is made affordable in terms of space by using compression in a back-end with columnar storage. We have implemented an experimental prototype, which shows a speedup of two orders of magnitude on XPath queries with value predicates, when compared to existing open source and commercial systems using a subset of the techniques. Space usage is also improved.

Linear computation of the maximum simultaneous forward and backward bisimulation for node-labeled trees

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 F&B-bisimulation for tree data. It first computes the maximum F-bisimulation, and then refines this to a maximal B-bisimulation. We prove that the result equals the maximum F&B-bisimulation.