Finding the Typical Communication Black Hole in Big Data Environment

“Black hole” are widely spread in the mobile communication data, which will highly downgrade the mobile service quality. OLAP tools are extensively used for the decision-support application in the multidimensional data model, which just like the mobile communication case. As different dimensions of the mobile data are incomparable and, thus, can hardly generate one unique final value that satisfies all dimensions. We exploit the skyline operator as the postoperation while building data cubes, named as data cube of skyline. As the skyline of a cuboid is not derivable from another cuboid and the skyline operation is holistic, which makes this problem even challeging. In this paper, we propose a method in materializing the cube of skyline in the big communication data and proof its effectiveness and efficiency by extensive experiments.

[1]  Jian Pei,et al.  Efficient computation of Iceberg cubes with complex measures , 2001, SIGMOD '01.

[2]  Qing Liu,et al.  Towards multidimensional subspace skyline analysis , 2006, TODS.

[3]  Nikos Mamoulis,et al.  Scalable skyline computation using object-based space partitioning , 2009, SIGMOD Conference.

[4]  Alok N. Choudhary,et al.  A parallel scalable infrastructure for OLAP and data mining , 1999, Proceedings. IDEAS'99. International Database Engineering and Applications Symposium (Cat. No.PR00265).

[5]  Ilaria Bartolini,et al.  Efficient sort-based skyline evaluation , 2008, TODS.

[6]  Susanne E. Hambrusch,et al.  Parallelizing the Data Cube , 2001, Distributed and Parallel Databases.

[7]  Jian Pei,et al.  Catching the Best Views of Skyline: A Semantic Approach Based on Decisive Subspaces , 2005, VLDB.

[8]  Donald Kossmann,et al.  Shooting Stars in the Sky: An Online Algorithm for Skyline Queries , 2002, VLDB.

[9]  Hamid Pirahesh,et al.  Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals , 1996, Data Mining and Knowledge Discovery.

[10]  Jarek Gryz,et al.  Algorithms and analyses for maximal vector computation , 2007, The VLDB Journal.

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

[12]  Bernhard Seeger,et al.  Progressive skyline computation in database systems , 2005, TODS.

[13]  Andrew Rau-Chaplin,et al.  A cluster architecture for parallel data warehousing , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[14]  Raghu Ramakrishnan,et al.  Bottom-up computation of sparse and Iceberg CUBE , 1999, SIGMOD '99.

[15]  Beng Chin Ooi,et al.  Efficient Progressive Skyline Computation , 2001, VLDB.

[16]  Qing Liu,et al.  Efficient Computation of the Skyline Cube , 2005, VLDB.

[17]  Ying Chen,et al.  Parallel ROLAP Data Cube Construction on Shared-Nothing Multiprocessors , 2004, Distributed and Parallel Databases.

[18]  Jan Chomicki,et al.  Skyline with presorting , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[19]  Jeffrey F. Naughton,et al.  An array-based algorithm for simultaneous multidimensional aggregates , 1997, SIGMOD '97.