XG: A Data-Driven Computation Grid for Enterprise-Scale Mining

In this paper we introduce a novel architecture for data processing, based on a functional fusion between a data and a computation layer. We show how such an architecture can be leveraged to offer significant speedups for data processing jobs such as data analysis and mining over large data sets. One novel contribution of our solution is its data-driven approach. The computation infrastructure is controlled from within the data layer. Grid compute job submission events are based within the query processor on the DBMS side and in effect controlled by the data processing job to be performed. This allows the early deployment of on-the-fly data aggregation techniques, minimizing the amount of data to be transfered to/from compute nodes and is in stark contrast to existing Grid solutions that interact with data layers as external (mainly) “storage” components. By integrating scheduling intelligence in the data layer itself we show that it is possible to provide a close to optimal solu...

• Type: Conference
• Year: 2005
• Where: DEXA
• Authors: Radu Sion, Ramesh Natarajan, Inderpal Narang, Wen-Syan Li, Thomas Phan
• Keywords: Data Layer, Data Processing, Data Processing Job, DEXA 2005