Paper Info
Title
A Services Oriented Framework for Next Generation Data Analysis Centers
Abstract
Over the past decade, advances in computational and sensor technology have enabled us to dynamically collect vast amounts of data from observations, health screening tests, simulations, and experiments at an ever-increasing pace. Knowledge discovery and data mining is an iterative process concerned with deriving interesting, non-obvious, and useful patterns and models from such large volumes of data. Although inexpensive storage is conducive to maintaining said data, accessing and managing it for knowledge discovery and data mining becomes a performance issue when datasets are large, dynamic, and distributed. In this work, we present our vision of a software framework consisting of middleware services to support interactive data mining over dynamic data at data analysis centers built on top of heterogeneous clusters. The design of a sampling service for dynamic data, together with initial performance results, are also presented.
Year
DOI
Venue
2005
10.1109/IPDPS.2005.66
IPDPS
Keywords
Field
DocType
ever-increasing pace,data mining,health screening test,performance issue,knowledge discovery,large volume,dynamic data,interactive data mining,data analysis center,services oriented framework,next generation data analysis,initial performance result,iterative process,sensor technology,knowledge management,software framework,sampling methods,testing,middleware,databases,data analysis,computational modeling,data access
Middleware,Data science,Data stream mining,Pace,Iterative and incremental development,Computer science,Dynamic data,Sampling (statistics),Knowledge extraction,Software framework,Database
Conference
ISBN
Citations 
PageRank 
0-7695-2312-9
1
0.39
References 
Authors
31
7
Name
Order
Citations
PageRank
H. Wang18415.66
Amol Ghoting264633.02
G. Buehrer351.18
Shirish Tatikonda464029.87
Srinivasan Parthasarathy54666375.76
Tahsin M. Kurç61423149.77
Joel H. Saltz74046569.91