Authors:
Haifa Elsidani Elariss
;
Souheil Khaddaj
and
Darrel Greenhill
Affiliation:
Kingston University London, United Kingdom
Keyword(s):
Query Optimization, Query Melting, Proximity Analysis, Dynamic Complex Queries, Large-scale GIS Servers, Visual Query Languages and Mobile GIS.
Related
Ontology
Subjects/Areas/Topics:
Databases and Information Systems Integration
;
e-Business
;
Enterprise Information Systems
;
Geographical Information Systems
;
Human-Computer Interaction
;
Middleware Integration
;
Middleware Platforms
;
Mobile Databases
;
Technology Platforms
Abstract:
Recently, non-expert mobile-user applications have been developed to query Geographic Information Systems (GIS) particularly Location Based Services where users ask questions related to their position whether they are moving (dynamic) or not (static). A new Iconic Visual Query Language (IVQL) has been developed to handle proximity analysis queries that find k-nearest-neighbours and objects within a buffer area. Each operator in IVQL queries corresponds to an execution plan to be evaluated by the GIS server. Since commonalities exist between the execution plans, the same operations are executed many times leading to slow results. Hence, the need arises to develop a multi-user dynamic complex query optimizer that handles commonalities and processes the queries faster especially with the large-scale of mobile-users. We present a new query processor, a generic optimization framework for GIS and a middleware, which employs the new Query Melting paradigm (QM) that is based on the sharing p
aradigm and push-down optimization strategy. QM is implemented through a new Melting-Ruler strategy that works at the low-level, melts repetitions in plans to share spatial areas, temporal intervals, objects, intermediate results, maps, user locations, and functions, then re-orders them to get time-cost effective results, and is illustrated using a sample tourist GIS system.
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