Multi-Dimensional Analysis of Data Streams Using Stream Cubes

Large volumes of dynamic stream data pose great challenges to its analysis. Besides its dynamic and transient behavior, stream data has another important characteristic: multi-dimensionality. Much of stream data resides at a multidimensional space and at rather low level of abstraction, whereas most analysts are interested in relatively high-level dynamic changes in some combination of dimensions. To discover high-level dynamic and evolving characteristics, one may need to perform multi-level, multi-dimensional on-line analytical processing (OLAP) of stream data. Such necessity calls for the investigation of new architectures that may facilitate on-line analytical processing of multi-dimensional stream data.

[1]  Sunita Sarawagi,et al.  Intelligent Rollups in Multidimensional OLAP Data , 2001, VLDB.

[2]  Jian Pei,et al.  Mining Multi-Dimensional Constrained Gradients in Data Cubes , 2001, VLDB.

[3]  Jeffrey F. Naughton,et al.  On the Computation of Multidimensional Aggregates , 1996, VLDB.

[4]  Philip S. Yu,et al.  On demand classification of data streams , 2004, KDD.

[5]  Jiawei Han,et al.  MM-Cubing: computing Iceberg cubes by factorizing the lattice space , 2004, Proceedings. 16th International Conference on Scientific and Statistical Database Management, 2004..

[6]  Jiawei Han,et al.  Star-Cubing: Computing Iceberg Cubes by Top-Down and Bottom-Up Integration , 2003, Very Large Data Bases Conference.

[7]  RamakrishnanRaghu,et al.  Bottom-up computation of sparse and Iceberg CUBE , 1999 .

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

[9]  Nimrod Megiddo,et al.  Discovery-Driven Exploration of OLAP Data Cubes , 1998, EDBT.

[10]  Leonid Khachiyan,et al.  Cubegrades: Generalizing Association Rules , 2002, Data Mining and Knowledge Discovery.

[11]  Sanjeev Khanna,et al.  Space-efficient online computation of quantile summaries , 2001, SIGMOD '01.

[12]  Jennifer Widom,et al.  Continuous queries over data streams , 2001, SGMD.

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

[14]  Rajeev Motwani,et al.  Approximate Frequency Counts over Data Streams , 2012, VLDB.

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

[16]  Divesh Srivastava,et al.  On computing correlated aggregates over continual data streams , 2001, SIGMOD '01.

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

[18]  Philip S. Yu,et al.  A Framework for Clustering Evolving Data Streams , 2003, VLDB.

[19]  Jennifer Widom,et al.  Models and issues in data stream systems , 2002, PODS.

[20]  Jiawei Han,et al.  MAIDS: mining alarming incidents from data streams , 2004, SIGMOD '04.

[21]  Philip S. Yu,et al.  Mining Frequent Patterns in Data Streams at Multiple Time Granularities , 2002 .

[22]  Sudipto Guha,et al.  Clustering Data Streams , 2000, FOCS.

[23]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

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

[25]  Philip S. Yu,et al.  A Framework for Projected Clustering of High Dimensional Data Streams , 2004, VLDB.

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

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

[28]  Geoff Hulten,et al.  Mining time-changing data streams , 2001, KDD '01.

[29]  Jiawei Han,et al.  High-Dimensional OLAP: A Minimal Cubing Approach , 2004, VLDB.

[30]  S. Muthukrishnan,et al.  Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries , 2001, VLDB.

[31]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[32]  Jeffrey D. Ullman,et al.  Implementing data cubes efficiently , 1996, SIGMOD '96.