TopSig: Topology Preserving Document Signatures

Performance comparisons between File Signatures and Inverted Files for text retrieval have previously shown several significant shortcomings of file signatures relative to inverted files. The inverted file approach underpins most state-of-the-art search engine algorithms, such as Language and Probabilistic models. It has been widely accepted that traditional file signatures are inferior alternatives to inverted files. This paper describes TopSig, a new approach to the construction of file signatures. Many advances in semantic hashing and dimensionality reduction have been made in recent times, but these were not so far linked to general purpose, signature file based, search engines. This paper introduces a different signature file approach that builds upon and extends these recent advances. We are able to demonstrate significant improvements in the performance of signature file based indexing and retrieval, performance that is comparable to that of state of the art inverted file based systems, including Language models and BM25. These findings suggest that file signatures offer a viable alternative to inverted files in suitable settings and from the theoretical perspective it positions the file signatures model in the class of Vector Space retrieval models.Comment: 12 pages, 8 figures, CIKM 201

Efficient Spatial Keyword Search in Trajectory Databases

An increasing amount of trajectory data is being annotated with text descriptions to better capture the semantics associated with locations. The fusion of spatial locations and text descriptions in trajectories engenders a new type of top-$k$ queries that take into account both aspects. Each trajectory in consideration consists of a sequence of geo-spatial locations associated with text descriptions. Given a user location $\lambda$ and a keyword set $\psi$, a top-$k$ query returns $k$ trajectories whose text descriptions cover the keywords $\psi$ and that have the shortest match distance. To the best of our knowledge, previous research on querying trajectory databases has focused on trajectory data without any text description, and no existing work has studied such kind of top-$k$ queries on trajectories. This paper proposes one novel method for efficiently computing top-$k$ trajectories. The method is developed based on a new hybrid index, cell-keyword conscious B$^+$-tree, denoted by \cellbtree, which enables us to exploit both text relevance and location proximity to facilitate efficient and effective query processing. The results of our extensive empirical studies with an implementation of the proposed algorithms on BerkeleyDB demonstrate that our proposed methods are capable of achieving excellent performance and good scalability.Comment: 12 page