Integrating OLAP and Ranking: The Ranking-Cube Methodology

OLAP (on-line analytical processing) and ranking are currently separate technologies in the database systems. OLAP emphasizes on efficient multidimensional data analysis and ranking is good for effective data exploration in massive data. In this paper, we discuss the problem of integrating OLAP and ranking, such that ranking serves as a function block for data analysis and exploration in the OLAP environment. Towards this goal, we present the ranking cube: a semi off-line materialization and semi online computation model. This paper discusses the framework, the implementation issues and the possible extensions of the ranking cube.

[1]  Yuan-Chi Chang,et al.  The onion technique: indexing for linear optimization queries , 2000, SIGMOD 2000.

[2]  Divesh Srivastava,et al.  Ranked join indices , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[3]  Yixin Chen,et al.  Multi-Dimensional Regression Analysis of Time-Series Data Streams , 2002, VLDB.

[4]  Christian Böhm,et al.  Determining the Convex Hull in Large Multidimensional Databases , 2001, DaWaK.

[5]  Walid G. Aref,et al.  Rank-aware query optimization , 2004, SIGMOD '04.

[6]  A. Guttmma,et al.  R-trees: a dynamic index structure for spatial searching , 1984 .

[7]  Donald Kossmann,et al.  The Skyline operator , 2001, Proceedings 17th International Conference on Data Engineering.

[8]  Luis Gravano,et al.  Evaluating Top-k Selection Queries , 1999, VLDB.

[9]  Vagelis Hristidis,et al.  PREFER: a system for the efficient execution of multi-parametric ranked queries , 2001, SIGMOD '01.

[10]  Patrick Valduriez,et al.  Join indices , 1987, TODS.

[11]  Moni Naor,et al.  Optimal aggregation algorithms for middleware , 2001, PODS '01.

[12]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[13]  John R. Smith,et al.  The onion technique: indexing for linear optimization queries , 2000, SIGMOD '00.

[14]  Kevin Chen-Chuan Chang,et al.  RankSQL: query algebra and optimization for relational top-k queries , 2005, SIGMOD '05.

[15]  Jiawei Han,et al.  Answering top-k queries with multi-dimensional selections: the ranking cube approach , 2006, VLDB.

[16]  Yi Lin,et al.  Prediction Cubes , 2005, VLDB.