ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ - ΤΜΗΜΑ ΜΗΧΑΝΙΚΩΝ Η/Υ & ΠΛΗΡΟΦΟΡΙΚΗΣ SITE MAP | ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ - ΤΜΗΜΑ ΜΗΧΑΝΙΚΩΝ Η/Υ & ΠΛΗΡΟΦΟΡΙΚΗΣ ΕΛΛΗΝΙΚΑ | ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ - ΤΜΗΜΑ ΜΗΧΑΝΙΚΩΝ Η/Υ & ΠΛΗΡΟΦΟΡΙΚΗΣ ENGLISH
Πέμπτη, 02/06/2016   -   Ομιλίες
Σεμινάριο Τμήματος με τίτλο: "Spatial Search in Large RDF Knowledge Bases", N. Mamoulis

Στο πλαίσιο της διοργάνωσης των σεμιναρίων του τμήματος, θα πραγματοποιηθεί την Πέμπτη 02/06/2016 και ώρα 12:00 στην αίθουσα Σεμιναρίων του Τμήματος Μηχανικών Η/Υ και Πληροφορικής, ομιλία με τίτλο "Spatial Search in Large RDF Knowledge Bases". Ομιλητής θα είναι ο κ. Νικόλαος Μαμουλής, Αναπληρωτής Καθηγητής του Τμήματος Μηχανικών Η/Υ & Πληροφορικής, Πανεπιστημίου Ιωαννίνων.

ΠΕΡΙΛΗΨΗ
In this talk, I will present our recent work on indexing spatial information in RDF knowledge bases in order to facilitate efficient spatial search. The RDF data model has recently been extended to support representation and querying of spatial information (i.e., locations and geometries) associated with RDF entities. Still, there are limited efforts towards extending RDF stores to efficiently support spatial search. Our first contribution in this direction is an effective encoding scheme for entities having spatial locations, paired with the introduction of on-the-fly spatial filters and spatial join algorithms, and several optimizations that minimize the overhead of geometry and dictionary accesses. We implemented the proposed techniques as an extension to the open-source RDF-3X engine and we experimentally evaluated them using real RDF knowledge bases. The results show that our system offers robust performance for spatial queries, while introducing little overhead to the original query engine. Our second contribution is the introduction of spatial RDF keyword queries, which enable users to express their search needs without having to know structured query languages, such as SPARQL and the RDF schema. The user only inputs a set of keywords and the goal is to find small RDF subgraphs which contain all keywords, centered at a node which is spatially located near the user. We design a basic method for the processing of such queries. To further accelerate retrieval, two pruning approaches and a data pre-processing technique are proposed. Our empirical studies on two real datasets demonstrate the effectiveness and efficiency of our approaches.
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