An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Approximating multi-dimensional aggregate range queries over real attributes
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Extending Practical Pre-Aggregation in On-Line Analytical Processing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Optimization of Run-time Management of Data Intensive Web-sites
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Extracting Large-Scale Knowledge Bases from the Web
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Data-Driven, One-To-One Web Site Generation for Data-Intensive Applications
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
What can Hierarchies do for Data Warehouses?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A Distributed OLAP Infrastructure for E-Commerce
COOPIS '99 Proceedings of the Fourth IECIS International Conference on Cooperative Information Systems
A Data-Warehouse/OLAP Framework for Scalable Telecommunication Tandem Traffic Analysis
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
User Defined Partitioning - Group Data Based on Computation Model
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
OLAP over continuous domains via density-based hierarchical clustering
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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Collecting and mining web lag records (WLRs) from e-commerce web sites has become increasingly important for targeted marketing, promotions, and traffic analysis. In this paper, we describe a scalable data werehousing and OLAP-based engine for analyzing WLRs. We have to address several scalability and performance challenges in developing such a framework. Because an active web site may generate hundreds of millions of WLRs daily, we have to deal with huge data volumes and data flow rates. To support fine-grained analysis, e.g., individual users' access profiles, we end up with huge, sparse data cubes defined over very large-sized dimensions (there may be hunderds of thousands of visitors to the site and tens of thousands of pages). While OLAP servers store sparse cubes quite efficiently, rolling up a very large cube can take prohibitively long. We have applied several non-traditional approaches to deal with this problem, which allow us to speed up WLR analysis by 3 orders of magnitude. Our framework support multilevel and multidimensional pattern extraction, analysis and feature ranking, and in addition to the typical OLAP operations, supports data mining operations such as extended multilevel and multidimensional association rules.