Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Scalable parallel data mining for association rules
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
Data mining: concepts and techniques
Data mining: concepts and techniques
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
| Hi-index | 0.00 |
In this paper, we propose a hardware-enhanced mining framework to cope with many challenging data mining tasks in a data stream environment. In this framework, hardware enhancements are implemented in commercial Field Programmable Gate Array (FPGA) devices, which have been growing rapidly in terms of density and speed. By exploiting the parallelism in hardware, many data mining primitive subtasks can be executed with high throughput, thus increasing the performance of the overall data mining tasks. Simple operations like counting, which take a major portion of conventional mining execution time, can in fact be executed on the hardware enhancements very efficiently. Subtask modules that are used repetitively can also be replaced with the equivalent hardware enhancements. Specifically, we realize an Apriori-like algorithm with our proposed hardware-enhanced mining framework to mine frequent temporal patterns from data streams. The frequent counts of 1-itemsets and 2-itemsets are obtained after one pass of scanning the datasets with our hardware implementation. It is empirically shown that the hardware enhancements provide the scalability by mapping the high complexity operations such as subset itemsets counting to the hardware. Our approach achieve considerably higher throughput than traditional database architectures with pure software implementation. With fast increase in applications of mobile devices where power consumption is a concern and complicated software executions are prohibited, it is envisioned that hardware enhanced mining is an important direction to explore.