Joining interval data in relational databases

The increasing use of temporal and spatial data in present-day relational systems necessitates an efficient support of joins on interval-valued attributes. Standard join algorithms do not support those data types adequately, whereas special approaches for interval joins usually require an augmentation of the internal access methods which is not supported by existing relational systems. To overcome these problems we introduce new join algorithms for interval data. Based on the Relational Interval Tree, these algorithms can easily be implemented on top of any relational database system while providing excellent performance on joining intervals. As experimental results on an Oracle9i server show, the new techniques outperform existing relational methods for joining intervals significantly.

[1]  Marcel Kornacker,et al.  High-Performance Extensible Indexing , 1999, VLDB.

[2]  Yun Wang,et al.  High Level Indexing of User-Defined Types , 1999, VLDB.

[3]  Sridhar Ramaswamy,et al.  A Unified Approach for Indexed and Non-Indexed Spatial Joins , 2000, EDBT.

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

[5]  Chuan-Heng Ang,et al.  The Interval B-Tree , 1995, Inf. Process. Lett..

[6]  R. Elmasri,et al.  Efficient temporal join processing using time index , 1996, Proceedings of 8th International Conference on Scientific and Statistical Data Base Management.

[7]  Sushil Jajodia,et al.  Temporal Databases: Theory, Design, and Implementation , 1993 .

[8]  Peter J. Stuckey,et al.  Improving Temporal Joins Using Histograms , 2000, DEXA.

[9]  Theodore Johnson,et al.  Selection Predicate Indexing for Active Databases Using Interval Skip Lists , 1996, Inf. Syst..

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

[11]  Samuel DeFazio,et al.  Extensible indexing: a framework for integrating domain-specific indexing schemes into Oracle8i , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[12]  Sridhar Ramaswamy,et al.  Scalable Sweeping-Based Spatial Join , 1998, VLDB.

[13]  Sridhar Ramaswamy.  Efficient Indexing for Constraint and Temporal Databases , 1997, ICDT.

[14]  Beng Chin Ooi,et al.  The TP-Index: a dynamic and efficient indexing mechanism for temporal databases , 1994, Proceedings of 1994 IEEE 10th International Conference on Data Engineering.

[15]  Christian S. Jensen,et al.  Developing a DataBlade for a new index , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[16]  Ming-Ling Lo,et al.  Spatial hash-joins , 1996, SIGMOD '96.

[17]  Christos Faloutsos,et al.  Fractals for secondary key retrieval , 1989, PODS.

[18]  David J. DeWitt,et al.  Partition based spatial-merge join , 1996, SIGMOD '96.

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

[20]  Christian S. Jensen,et al.  Efficient evaluation of the valid-time natural join , 1994, Proceedings of 1994 IEEE 10th International Conference on Data Engineering.

[21]  Farshad Fotouhi,et al.  Efficient Processing of Time-Joins in Temporal Data Bases , 1993, DASFAA.

[22]  Hans-Peter Kriegel,et al.  Object-Relational Spatial Indexing , 2005, Spatial Databases.

[23]  David J. DeWitt,et al.  On supporting containment queries in relational database management systems , 2001, SIGMOD '01.

[24]  Yannis Manolopoulos.  Spatial Databases: Technologies, Techniques and Trends , 2005, Spatial Databases.

[25]  Ramez Elmasri,et al.  The Time Index: An Access Structure for Temporal Data , 1990, VLDB.

[26]  Dieter Pfoser,et al.  Incremental join of time-oriented data , 1999, Proceedings. Eleventh International Conference on Scientific and Statistical Database Management.

[27]  Bernhard Seeger,et al.  Query Processing Techniques for Multiversion Access Methods , 1996, VLDB.

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

[29]  H. Edelsbrunner.  A new approach to rectangle intersections part I , 1983 .

[30]  Richard R. Muntz,et al.  Stream Processing: Temporal Query Processing and Optimization , 1993, Temporal Databases.

[31]  Christian Böhm,et al.  XZ-Ordering: A Space-Filling Curve for Objects with Spatial Extension , 1999, SSD.

[32]  Elke A. Rundensteiner,et al.  Spatial Joins Using R-trees: Breadth-First Traversal with Global Optimizations , 1997, VLDB.

[33]  Michael Stonebraker,et al.  Inclusion of new types in relational data base systems , 1986, 1986 IEEE Second International Conference on Data Engineering.

[34]  Christian S. Jensen,et al.  Join operations in temporal databases , 2005, The VLDB Journal.

[35]  Beng Chin Ooi,et al.  On Spatially Partitioned Temporal Join , 1994, VLDB.

[36]  Z. Meral Özsoyoglu,et al.  Indexing Valid Time Intervals , 1998, DEXA.

[37]  Donghui Zhang,et al.  Index based processing of semi-restrictive temporal joins , 2002, Proceedings Ninth International Symposium on Temporal Representation and Reasoning.

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

[39]  H. Gunadhi,et al.  Query processing algorithms for temporal intersection joins , 1991, [1991] Proceedings. Seventh International Conference on Data Engineering.