An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
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
Searching for dependencies at multiple abstraction levels
ACM Transactions on Database Systems (TODS)
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
Composition of Mining Contexts for Efficient Extraction of Association Rules
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Decision Tables: Scalable Classification Exploring RDBMS Capabilities
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Extracting semantics from data cubes using cube transversals and closures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Pushing Convertible Constraints in Frequent Itemset Mining
Data Mining and Knowledge Discovery
Mining Constrained Gradients in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining border descriptions of emerging patterns from dataset pairs
Knowledge and Information Systems
C-Cubing: Efficient Computation of Closed Cubes by Aggregation-Based Checking
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
CURE for cubes: cubing using a ROLAP engine
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubing Approach
IEEE Transactions on Knowledge and Data Engineering
Efficient approaches for materialized views selection in a data warehouse
Information Sciences: an International Journal
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
ROLAP implementations of the data cube
ACM Computing Surveys (CSUR)
Supporting the data cube lifecycle: the power of ROLAP
The VLDB Journal — The International Journal on Very Large Data Bases
Upper Borders for Emerging Cubes
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Emerging Cubes: Borders, size estimations and lossless reductions
Information Systems
Reduced representations of Emerging Cubes for OLAP database mining
International Journal of Business Intelligence and Data Mining
An efficient method for maintaining data cubes incrementally
Information Sciences: an International Journal
Information Sciences: an International Journal
Aggregation functions: Construction methods, conjunctive, disjunctive and mixed classes
Information Sciences: an International Journal
Flexible online association rule mining based on multidimensional pattern relations
Information Sciences: an International Journal
Emerging cubes for trends analysis in OLAP databases
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Searching semantic data warehouses: models, issues, architectures
Proceedings of the 2nd International Workshop on Semantic Search over the Web
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Data cubes capture general trends aggregated from multidimensional data from a categorical relation. When provided with two relations, interesting knowledge can be exhibited by comparing the two underlying data cubes. Trend reversals or particular phenomena irrelevant in one data cube may indeed clearly appear in the other data cube. In order to capture such trend reversals, we have proposed the concept of Emerging Cube. In this article, we emphasize on two new approaches for computing Emerging Cubes. Both are devised to be integrated within standard Olap systems, since they do not require any additional nor complex data structures. Our first approach is based on Sql. We propose three queries with different aims. The most efficient query uses a particular data structure merging the two input relations to achieve a single data cube computation. This query works fine even when voluminous data are processed. Our second approach is algorithmic and aims to improve efficiency and scalability while preserving integration capability. The E-Idea algorithm works a'laBuc and takes the specific features of Emerging Cubes into account. E-Idea is automaton-based and adapts its behavior to the current execution context. Our proposals are validated by various experiments where we measure query response time. Comparative experiments show that E-Idea's response time is proportional to the size of the Emerging Cube. Experiments also demonstrate that extracting Emerging Cubes can be computed in practice, in a time compatible with user expectations.