Spatio-temporal join technique for disaster estimation in large-scale natural disaster

When a large-scale natural disaster occurs, it is necessary to collect damage information within about 10 minutes so that disaster-relief operations and wide-area support (depending on the the scale of the natural disaster) can be initiated. A high-performance method for "spatio-temporal join" which joins time-series grid data (such as results of simulations of natural disasters like tsunamis and fire spreading after a large-scale earthquake) and time-series point data representing people flows is proposed and applied to estimate damage situations following a natural disaster. The results of a performance evaluation of the method show that the response time for joining 100,000 point data and 250,000 grid data is about 50 seconds. They also show that it is possible to apply the proposed method to a real environment in which it is necessary to join one-million point data and hundreds of thousands of grid data within 10 minutes.

[1]  Yufei Tao,et al.  MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries , 2001, VLDB.

[2]  Walid G. Aref,et al.  Spatio-Temporal Access Methods: Part 2 (2003 - 2010) , 2010, IEEE Data Eng. Bull..

[3]  Hanan Samet,et al.  Speeding up construction of PMR quadtree-based spatial indexes , 2002, The VLDB Journal.

[4]  Hans-Peter Kriegel,et al.  The pyramid-technique: towards breaking the curse of dimensionality , 1998, SIGMOD '98.

[5]  Bernhard Seeger,et al.  Efficient temporal join processing using indices , 2002, Proceedings 18th International Conference on Data Engineering.

[6]  Beng Chin Ooi,et al.  iDistance: An adaptive B+-tree based indexing method for nearest neighbor search , 2005, TODS.

[7]  Norman W. Paton,et al.  An Experimental Performance Evaluation of Spatio‐Temporal Join Strategies , 2005, Trans. GIS.

[8]  Jin Huang,et al.  Towards a Painless Index for Spatial Objects , 2014, TODS.

[9]  E. Rundensteiner,et al.  BFRJ: Global Optimization of Spatial Joins Using R-trees , 1997 .

[10]  Hanan Samet,et al.  Spatial join techniques , 2007, TODS.

[11]  Simonas Saltenis Indexing the Positions of Continuously Moving Objects , 2017, Encyclopedia of GIS.

[12]  Oliver Günther,et al.  Efficient computation of spatial joins , 1993, Proceedings of IEEE 9th International Conference on Data Engineering.

[13]  Bernhard Seeger,et al.  An asymptotically optimal multiversion B-tree , 1996, The VLDB Journal.

[14]  Amit P. Sheth,et al.  Semantic (Web) Technology In Action: Ontology Driven Information Systems for Search, Integration and Analysis , 2003, IEEE Data Eng. Bull..

[15]  Yufei Tao,et al.  The Bdual-Tree: indexing moving objects by space filling curves in the dual space , 2008, The VLDB Journal.

[16]  H. V. Jagadish,et al.  Linear clustering of objects with multiple attributes , 1990, SIGMOD '90.

[17]  Masaru Kitsuregawa,et al.  Query Processing for Multi-Attribute Clustered Records , 1990, VLDB.

[18]  Hans-Peter Kriegel,et al.  Managing Intervals Efficiently in Object-Relational Databases , 2000, VLDB.

[19]  Margaret H. Dunham,et al.  Join processing in relational databases , 1992, CSUR.

[20]  Thomas Seidl,et al.  Joining interval data in relational databases , 2004, SIGMOD '04.

[21]  Xuan Song,et al.  Modeling and probabilistic reasoning of population evacuation during large-scale disaster , 2013, KDD.

[22]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[23]  Walid G. Aref,et al.  Spatio-Temporal Access Methods , 2003, IEEE Data Eng. Bull..

[24]  Beng Chin Ooi,et al.  ST2B-tree: a self-tunable spatio-temporal b+-tree index for moving objects , 2008, SIGMOD Conference.

[25]  Timos K. Sellis,et al.  Spatio-temporal indexing for large multimedia applications , 1996, Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems.

[26]  Jignesh M. Patel,et al.  Indexing Large Trajectory Data Sets With SETI , 2003, CIDR.

[27]  Beng Chin Ooi,et al.  Query and Update Efficient B+-Tree Based Indexing of Moving Objects , 2004, VLDB.

[28]  Young-Koo Lee,et al.  Spatial Join Processing Using Corner Transformation , 1999, IEEE Trans. Knowl. Data Eng..

[29]  Hideki Hayashi,et al.  Spatial search processing in embedded devices , 2009, GIS.

[30]  Masaru Kitsuregawa,et al.  Join strategies on KD-tree indexed relations , 1989, [1989] Proceedings. Fifth International Conference on Data Engineering.

[31]  Hans-Peter Kriegel,et al.  Efficient processing of spatial joins using R-trees , 1993, SIGMOD Conference.

[32]  Ken C. K. Lee,et al.  Approaching the Skyline in Z Order , 2007, VLDB.

[33]  Min-Jae Lee,et al.  A New Algorithm for Processing Joins Using the Multilevel Grid File , 1995, DASFAA.

[34]  E. F. CODD,et al.  A relational model of data for large shared data banks , 1970, CACM.

[35]  Beng Chin Ooi,et al.  Efficient indexing of the historical, present, and future positions of moving objects , 2005, MDM '05.

[36]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.