A compact representation for trips over networks built on self-indexes. (November 2018)

• Record Type: Journal Article
• Authors: Brisaboa, Nieves R.; Fariña, Antonio; Galaktionov, Daniil; Rodriguez, M. Andrea
• Is Part Of: Information systems. Volume 78(2018)
• Page Start: 1
• Page End: 22
• Publication Date: 2018-11
• Subjects: Trips on networks -- Counting queries -- Self-index -- Compression; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2018.06.010
• Languages: English
• ISSNs: 0306-4379

Abstract

Highlights: We provide a representation for trips over networks and answer counting-based queries. We adapt a Compressed Suffix Array to deal with the spatial component of trips. We use a wavelet matrix or a Hu–tucker-shaped Wavelet Tree for the temporal component. Experiments show space needs until a 50% when compared with a plain representation. Experiments show counting-based query-times typically within 1–1000 µs. Abstract: Representing the movements of objects (trips) over a network in a compact way while retaining the capability of exploiting such data effectively is an important challenge of real applications. We present a new Compact Trip Representation ( CTR ) that handles the spatio-temporal data associated with users' trips over transportation networks. Depending on the network and types of queries, nodes in the network can represent intersections, stops, or even street segments. CTR represents separately sequences of nodes and the time instants when users traverse these nodes. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array (CSA), which provides both a compact representation and interesting indexing capabilities. The temporal component is self-indexed with either a Hu–Tucker-shaped Wavelet-Tree or a Wavelet Matrix that solve range-interval queries efficiently. We show how CTR can solve relevant counting-based spatial, temporal, and spatio-temporal queries over large sets of trips. Experimental results show the space requirements (around 50–70% of the space needed by a compact non-indexed baseline) and query efficiency (most queries are solved in the range of 1–1000 µs) of CTR .