Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Compressed data cubes for OLAP aggregate query approximation on continuous dimensions
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
ACM Transactions on Information Systems (TOIS)
Conceptual on-line analytical processing
Information organization and databases
Journal of Intelligent Information Systems
Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
Compressed data cube for approximate OLAP query processing
Journal of Computer Science and Technology
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
A Triadic Approach to Formal Concept Analysis
ICCS '95 Proceedings of the Third International Conference on Conceptual Structures: Applications, Implementation and Theory
Using Loglinear Models to Compress Datacube
WAIM '00 Proceedings of the First International Conference on Web-Age Information Management
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Approximate query processing using wavelets
The VLDB Journal — The International Journal on Very Large Data Bases
Dynamic sample selection for approximate query processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Incremental maintenance of quotient cube based on Galois lattice
Journal of Computer Science and Technology
Index Selection for Databases: A Hardness Study and a Principled Heuristic Solution
IEEE Transactions on Knowledge and Data Engineering
A new OLAP aggregation based on the AHC technique
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Using Datacube Aggregates for Approximate Querying and Deviation Detection
IEEE Transactions on Knowledge and Data Engineering
Mining frequent closed cubes in 3D datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
TRIAS--An Algorithm for Mining Iceberg Tri-Lattices
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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
A probabilistic model for data cube compression and query approximation
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Mining a new fault-tolerant pattern type as an alternative to formal concept discovery
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
Convex cube: towards a unified structure for multidimensional databases
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Review: Formal concept analysis in knowledge processing: A survey on applications
Expert Systems with Applications: An International Journal
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Formal concept analysis (FCA) has been successfully used in several Computer Science fields such as databases, software engineering, and information retrieval, and in many domains like medicine, psychology, linguistics and ecology. In data warehouses, users exploit data hypercubes (i.e., multi-way tables) mainly through online analytical processing (OLAP) techniques to extract useful information from data for decision support purposes. Many topics have attracted researchers in the area of data warehousing: data warehouse design and multidimensional modeling, efficient cube materialization (pre-computation), physical data organization, query optimization and approximation, discovery-driven data exploration as well as cube compression and mining. Recently, there has been an increasing interest to apply or adapt data mining approaches and advanced statistical analysis techniques for extracting knowledge (e.g., outliers, clusters, rules, closed n-sets) from multidimensional data. Such approaches or techniques cover (but are not limited to) FCA, cluster analysis, principal component analysis, log-linear modeling, and non-negative multi-way array factorization. Since data cubes are generally large and highly dimensional, and since cells contain consolidated (e.g., mean value), multidimensional and temporal data, such facts lead to challenging research issues in mining data cubes. In this presentation, we will give an overview of related work and show how FCA theory (with possible extensions) can be used to extract valuable and actionable knowledge from data warehouses.