Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Building the Data Warehouse
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Modeling Multidimensional Databases
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
MM-Cubing: Computing Iceberg Cubes by Factorizing the Lattice Space
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Distributed and Parallel Databases
Star-cubing: computing iceberg cubes by top-down and bottom-up integration
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
DS-Cuber: an integrated OLAP environment for data streams
Proceedings of the 18th ACM conference on Information and knowledge management
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This paper proposes a dynamic data cube for applying a data cube to a data stream environment. The dynamic data cube specifies user-interesting areas with the support ratio of attribute value, and manages the attribute groups dynamically by grouping and dividing methods. With these methods, the memory usage and processing time are reduced. It also efficiently shows and emphasizes user-interesting areas by increasing the granularity for attributes that have higher support. We also propose an exception detecting method to quickly identify exception by using the reversed way of a multi-stage cluster sampling method. We perform experiments to verify how efficiently the dynamic data cube works in limited memory space.