Machine Learning
On parallel processing of aggregate and scalar functions in object-relational DBMS
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
MOCHA: a self-extensible database middleware system for distributed data sources
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Cache investment: integrating query optimization and distributed data placement
ACM Transactions on Database Systems (TODS)
Business applications of data mining
Communications of the ACM - Evolving data mining into solutions for insights
Probabilistic Estimation-Based Data Mining for Discovering Insurance Risks
IEEE Intelligent Systems
Computational and data Grids in large-scale science and engineering
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Distributed data mining on the grid
Future Generation Computer Systems - Grid computing: Towards a new computing infrastructure
Embedded predictive modeling in a parallel relational database
Proceedings of the 2006 ACM symposium on Applied computing
International Journal of Hybrid Intelligent Systems
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
A probabilistic estimation framework for predictive modeling analytics
IBM Systems Journal
XG: a data-driven computation grid for enterprise-scale mining
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Grid-enabling data mining applications with DataMiningGrid: An architectural perspective
Future Generation Computer Systems
APHID: An architecture for private, high-performance integrated data mining
Future Generation Computer Systems
Global peer-to-peer classification in mobile ad-hoc networks: a requirements analysis
CONTEXT'11 Proceedings of the 7th international and interdisciplinary conference on Modeling and using context
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
We describe a grid-based approach for enterprise-scale data mining, which is based on leveraging parallel database technology for data storage, and on-demand compute servers for parallelism in the statistical computations. This approach is targeted towards the use of data mining in highly-automated vertical business applications, where the data is stored on one or more relational database systems, and an independent set of high-performance compute servers or a network of low-cost, commodity processors is used to improve the application performance and overall workload management. The goal of this paper is to describe an algorithmic decomposition of data mining kernels between the data storage and compute grids, which makes it possible to exploit the parallelism on the respective grids in a simple way, while minimizing the data transfer between these grids. This approach is compatible with existing standards for data mining task specification and results reporting, so that larger applications using these data mining algorithms do not have to be modified to benefit from this grid-based approach.