Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Finding interesting summaries in genspace graphs
Finding interesting summaries in genspace graphs
Dynamic view selection for OLAP
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
PMC: select materialized cells in data cubes
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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Summary mining aims to find interesting summaries for a data set and to use data mining techniques to improve the functionality of Online Analytical Processing (OLAP) systems. In this paper, we propose an interactive summary mining approach, called GenSpace summary mining, to find interesting summaries based on user expectations. In the mining process, to record the user's evolving knowledge, the system needs to update and propagate new expectations. In this paper, we propose a linear method for consistently and efficiently propagating user expectations in a GenSpace graph. For a GenSpace graph where uninteresting nodes can be marked by the user before the mining process, we propose a greedy algorithm to determine the propagation paths in a GenSpace subgraph that reduces the time cost subject to a fixed amount of space.