Processing (Multiple) Spatio-temporal Range Queries in Multicore Settings

Research in Moving Objects Databases (MOD) has addressed various aspects of storing and querying trajectories of moving objects: from modelling, through linguistic constructs and formalisms/ algebras, to indexing structures and efficient processing of different querycategories have been subjects to a large body of works. Given the architectural trends of multicore CPUs becoming a commonplace, in this work we focus on efficient processing of spatio-temporal range queries in such settings. We postulate that coupling the semantics of the problem domain into the query processing algorithms in a manner that is aware of the multicore features, can yield performance improvements that surpass the gains obtained by relying solely on the compiler-generated threads parallelization. Towards that end, we present and evaluate heuristics for processing variants spatio-temporal range queries in multicore settings by partitioning the load (i.e., data set) and assigning partial tasks to the individual cores. Our experiments demonstrate that 5-fold speed-ups can be achieved, when compared to the (semi) naive approach which relies on the compiler to generate the multicore-compatible code.

[1]  Kunle Olukotun,et al.  The case for a single-chip multiprocessor , 1996, ASPLOS VII.

[2]  Klaus H. Hinrichs,et al.  Managing uncertainty in moving objects databases , 2004, TODS.

[3]  Hanan Samet,et al.  Maintenance of K-nn and spatial join queries on continuously moving points , 2006, TODS.

[4]  Nectaria Tryfona,et al.  Spatio-Temporal Databases: The CHOROCHRONOS Approach , 2003 .

[5]  Sunil Prabhakar,et al.  Evaluating probabilistic queries over imprecise data , 2003, SIGMOD '03.

[6]  Ralf Hartmut Güting,et al.  Algorithms for Moving Objects Databases , 2003, Comput. J..

[7]  Ling Liu,et al.  Quality-aware dstributed data delivery for continuous query services , 2006, SIGMOD Conference.

[8]  Dimitrios Gunopulos,et al.  Efficient Indexing of Spatiotemporal Objects , 2002, EDBT.

[9]  Ivan Stojmenovic,et al.  Bisections and Ham-Sandwich Cuts of Convex Polygons and Polyhedra , 1991, Inf. Process. Lett..

[10]  Marianne Winslett,et al.  Scientific and Statistical Database Management, 21st International Conference, SSDBM 2009, New Orleans, LA, USA, June 2-4, 2009, Proceedings , 2009, SSDBM.

[11]  Mukesh K. Mohania,et al.  Advances in Databases: Concepts, Systems and Applications , 2007 .

[12]  Jörg Sander,et al.  A framework for spatio-temporal query processing over wireless sensor networks , 2004, DMSN '04.

[13]  Kyriakos Mouratidis,et al.  Continuous nearest neighbor monitoring in road networks , 2006, VLDB.

[14]  Cédric du Mouza,et al.  Multiscale classification of moving objects trajectories , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..

[15]  Ling Liu,et al.  MobiEyes: A Distributed Location Monitoring Service Using Moving Location Queries , 2006, IEEE Transactions on Mobile Computing.

[16]  Shashi Shekhar,et al.  Spatio-temporal Network Databases and Routing Algorithms: A Summary of Results , 2007, SSTD.

[17]  Jianwen Su,et al.  QUESTO: A Query Language for Uncertain and Exact Spatio-temporal Objects , 2008, ADBIS.

[18]  Christian S. Jensen,et al.  Nearest and reverse nearest neighbor queries for moving objects , 2006, The VLDB Journal.

[19]  Timothy M. Chan,et al.  Dynamic ham-sandwich cuts in the plane , 2009, Comput. Geom..

[20]  Ralf Hartmut Güting,et al.  Modeling and querying moving objects in networks , 2006, The VLDB Journal.

[21]  Farnoush Banaei Kashani,et al.  Towards modeling the traffic data on road networks , 2009, IWCTS '09.

[22]  Philippe Rigaux,et al.  Multiscale classification of moving objects trajectories , 2004 .

[23]  Dan Lin,et al.  Optimizing Moving Queries over Moving Object Data Streams , 2007, DASFAA.

[24]  Andrew U. Frank,et al.  Spatio-Temporal Databases , 2003, Lecture Notes in Computer Science.

[25]  Walid G. Aref,et al.  SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams , 2008, The VLDB Journal.

[26]  Maurice Herlihy,et al.  The art of multiprocessor programming , 2020, PODC '06.

[27]  Hui Ding,et al.  Efficient Maintenance of Continuous Queries for Trajectories , 2008, GeoInformatica.

[28]  Klaus H. Ecker,et al.  Handbook on Parallel and Distributed Processing , 2000, International Handbooks on Information Systems.

[29]  Ralf Hartmut Güting,et al.  Moving Objects Databases , 2005 .

[30]  George Kollios,et al.  Spatio-temporal data services in a shared-nothing environment , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..

[31]  Kunle Olukotun,et al.  Niagara: a 32-way multithreaded Sparc processor , 2005, IEEE Micro.

[32]  Luiz André Barroso,et al.  Piranha: a scalable architecture based on single-chip multiprocessing , 2000, Proceedings of 27th International Symposium on Computer Architecture (IEEE Cat. No.RS00201).

[33]  Jochen Schiller,et al.  Location Based Services , 2004 .

[34]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[35]  Christian S. Jensen,et al.  Indexing the past, present, and anticipated future positions of moving objects , 2006, TODS.

[36]  Jimeng Sun,et al.  The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries , 2003, VLDB.

[37]  Joseph O'Rourke,et al.  Computational Geometry in C. , 1995 .

[38]  J. O´Rourke,et al.  Computational Geometry in C: Arrangements , 1998 .

[39]  Cyrus Shahabi,et al.  Robust Time-Referenced Segmentation of Moving Object Trajectories , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[40]  Chengyang Zhang,et al.  Advances in Spatial and Temporal Databases , 2015, Lecture Notes in Computer Science.

[41]  David J. DeWitt,et al.  Hybrid-Range Partitioning Strategy: A New Declustering Strategy for Multiprocessor Database Machines , 1990, VLDB.

[42]  Klemens Böhm,et al.  Deriving Spatio-temporal Query Results in Sensor Networks , 2010, SSDBM.

[43]  Thomas C. Shermer.  A Linear Algorithm for Bisecting a Polygon , 1992, Inf. Process. Lett..

[44]  Bart Kuijpers,et al.  Trajectory databases: Data models, uncertainty and complete query languages , 2007, J. Comput. Syst. Sci..

[45]  Matthias Jarke,et al.  Advances in Database Technology — EDBT 2002 , 2002, Lecture Notes in Computer Science.