Exploit cache for optimizing source selection in the Web of Data

Francesco De Fino

Exploit cache for optimizing source selection in the Web of Data
Francesco De Fino
Short Bio:
Francesco is a second year PhD student in Computer Science (supervisors: Barbara Catania, Giovanna Guerrini). He is graduated in Computer Science, his main research interest concerns query processing optimization for graph queries. Currently, he is working on approximate caching for optimizing source selection in Linked Data environment.
The traditional Web is evolving into the Web of Data, which gathers huge collections of structured data over distributed, heterogeneous data sources. Live queries are needed to get current information out of this global data space. In live query processing, source selection deserves special attention, because it allows us to identify the sources that most likely contain relevant content for a given query. A promising approach for source selection is to rely on some index structure containing knowledge about the source content and select the most relevant sources before the query processing starts. Selecting relevant sources for complex queries in a reasonable time is not always an easy task. One possible approach is to exploit information about similar requests executed in the past and request approximation for optimizing the source selection process. In this talk, we overview the approach we are currently investigating for addressing this issue. The approach we propose relies on two main ideas: (i) the definition of a smart cache approach tailored to source selection; (ii) the exploitation of query relaxation for further improving query processing performance.
Thursday, May 17th, 2018 - 14.30
DIBRIS Conference Room - Via Dodecaneso 35, Genova (III floor)


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