Agents that reduce work and information overload
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Modern Information Retrieval
A Foundation for Multi-dimensional Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Computing appropriate representations for multidimensional data
Data & Knowledge Engineering - Special issue: Advances in OLAP
Bridging the gap between OLAP and SQL
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A personalization framework for OLAP queries
Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
OLAP preferences: a research agenda
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
A framework for recommending OLAP queries
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
The Data Warehouse Lifecycle Toolkit
The Data Warehouse Lifecycle Toolkit
A preference-based recommender system
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
A framework for OLAP content personalization
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
A clustering based approach for skyline diversity
Expert Systems with Applications: An International Journal
Combining objects with rules to represent aggregation knowledge in data warehouse and OLAP systems
Data & Knowledge Engineering
Semantics and usage statistics for multi-dimensional query expansion
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Multi-dimensional navigation modeling using BI analysis graphs
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
Query Recommendations for OLAP Discovery-Driven Analysis
International Journal of Data Warehousing and Mining
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This paper presents a framework for integrating OLAP and recommendations. We focus on the anticipatory recommendation process that assists the user during his OLAP analysis by proposing to him the forthcoming analysis step. We present a context-aware preference model that matches decision-makers intuition, and we discuss a preference-based approach for generating personalized recommendations.