Finding patterns in large star schemas at the right aggregation level

Authors:
Andreia Silva;Cláudia Antunes
Affiliations:
Department of Computer Science and Engineering, Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal;Department of Computer Science and Engineering, Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal
Venue:
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Year:
2012

Citing 8
Cited 0

Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling

The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
Frequent Itemset Counting Across Multiple Tables

PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Mining Association Rules in Multiple Relations

ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Mining Association Rules from Stars

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams

DS '09 Proceedings of the 12th International Conference on Discovery Science
Methods for mining frequent items in data streams: an overview

Knowledge and Information Systems
Pattern mining on stars with FP-growth

MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

There are many stand-alone algorithms to mine different types of patterns in traditional databases. However, to effectively and efficiently mine databases with more complex and large data tables is still a growing challenge in data mining. The nature of data streams makes streaming techniques a promising way to handle large amounts of data, since their main ideas are to avoid multiple scans and optimize memory usage. In this paper we propose in detail an algorithm for finding frequent patterns in large databases following a star schema, based on streaming techniques. It is able to mine traditional star schemas, as well as stars with degenerate dimensions. It is able to aggregate the rows in the fact table that relate to the same business fact, and therefore find patterns at the right business level. Experimental results show that the algorithm is accurate and performs better than the traditional approach.