KNOWLEDGE GRIDS Akshat Mishra GRID SEMINAR WINTER 2008 Feb 2008.

Slides:



Advertisements
Similar presentations
1 From Grids to Service-Oriented Knowledge Utilities research challenges Thierry Priol.
Advertisements

Designing Services for Grid-based Knowledge Discovery A. Congiusta, A. Pugliese, Domenico Talia, P. Trunfio DEIS University of Calabria ITALY
C. Grimme, A. Papaspyrou Scheduling in C3-Grid AstroGrid-D Workshop Project: C3-Grid Collaborative Climate Community Data and Processing Grid Scheduling.
University of Illinois Visualizing Text Loretta Auvil UIUC February 25, 2011.
SEVENPRO – STREP KEG seminar, Prague, 8/November/2007 © SEVENPRO Consortium SEVENPRO – Semantic Virtual Engineering Environment for Product.
From Relational to Semantics A Methodology Arka Mukherjee, Ph.D. Founder / CTO Global IDs David Schaengold Director,
Institut für Softwarewissenschaft - Universität WienP.Brezany 1 Toward Knowledge Discovery in Databases Attached to Grids Peter Brezany Institute for Software.
Mike Smorul Saurabh Channan Digital Preservation and Archiving at the Institute for Advanced Computer Studies University of Maryland, College Park.
CS 290C: Formal Models for Web Software Lecture 6: Model Driven Development for Web Software with WebML Instructor: Tevfik Bultan.
SESSION 9 THE INTERNET AND THE NEW INFORMATION NEW INFORMATIONTECHNOLOGYINFRASTRUCTURE.
Automatic Data Ramon Lawrence University of Manitoba
Web-based Portal for Discovery, Retrieval and Visualization of Earth Science Datasets in Grid Environment Zhenping (Jane) Liu.
WORKFLOWS IN CLOUD COMPUTING. CLOUD COMPUTING  Delivering applications or services in on-demand environment  Hundreds of thousands of users / applications.
University of ViennaP. Brezany 1 Knowledge Discovery in Grid Datasets – Goals, Design Concepts and the Architecture Peter Brezany University of Vienna.
CLOUD COMPUTING. A general term for anything that involves delivering hosted services over the Internet. And Cloud is referred to the hardware and software.
Architectural Design.
LÊ QU Ố C HUY ID: QLU OUTLINE  What is data mining ?  Major issues in data mining 2.
Cluj Napoca, 28 August IEEE International Conference on Intelligent Computer Communication and Processing Digital Libraries Workshop Towards.
Module 3: Business Information Systems
Špindlerův Mlýn, Czech Republic, SOFSEM Semantically-aided Data-aware Service Workflow Composition Ondrej Habala, Marek Paralič,
Module 3: Business Information Systems Chapter 11: Knowledge Management.
1.Knowledge management 2.Online analytical processing 3. 4.Supply chain management 5.Data mining Which of the following is not a major application.
Katanosh Morovat.   This concept is a formal approach for identifying the rules that encapsulate the structure, constraint, and control of the operation.
Supporting Research with Weblogs: A Study on Web-based Research Support Systems JingTao Yao Department of Computer Science, University or Regina CANADA.
Cloud Computing 1. Outline  Introduction  Evolution  Cloud architecture  Map reduce operation  Platform 2.
Authors: Jiann-Liang Chenz, Szu-Lin Wuy,Yang-Fang Li, Pei-Jia Yang,Yanuarius Teofilus Larosa th International Wireless Communications and Mobile.
Database System Concepts and Architecture
Intelligent Grid Solutions GridMiner A Framework for Knowledge Discovery on the Grid – from a Vision to Design and Implementation Peter.
Agents on the Semantic Web – a roadmap to the future An arial view from feet.
Chapter 9 Moving to Design
DOMENICO TALIA (joint work with M. Cannataro, A. Congiusta, P. Trunfio) DEIS University of Calabria ITALY Grid-Based Data Mining and.
What is Cyberinfrastructure? Russ Hobby, Internet2 Clemson University CI Days 20 May 2008.
© DATAMAT S.p.A. – Giuseppe Avellino, Stefano Beco, Barbara Cantalupo, Andrea Cavallini A Semantic Workflow Authoring Tool for Programming Grids.
The Grid System Design Liu Xiangrui Beijing Institute of Technology.
1 The ESSnet project GEOSTAT 1A/B Vilni Verner Holst Bloch Coordinator of ESSnet project GEOSTAT 1A Statistics Norway Representing Census data in a European.
Issues in (Financial) High Performance Computing John Darlington Director Imperial College Internet Centre Fast Financial Algorithms and Computing 4th.
Ihr Logo Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang.
Resource Brokering in the PROGRESS Project Juliusz Pukacki Grid Resource Management Workshop, October 2003.
Service - Oriented Middleware for Distributed Data Mining on the Grid ,劉妘鑏 Antonio C., Domenico T., and Paolo T. Journal of Parallel and Distributed.
Towards Using Grid Services for Mining Fuzzy Association Rules Mihai Gabroveanu, Ion Iancu, Mirel Cosulschi, Nicolae Constantinescu Faculty of Mathematics.
GRID Overview Internet2 Member Meeting Spring 2003 Sandra Redman Information Technology and Systems Center and Information Technology Research Center National.
ISERVOGrid Architecture Working Group Brisbane Australia June Geoffrey Fox Community Grids Lab Indiana University
OWL Representing Information Using the Web Ontology Language.
An approach for Framework Construction and Instantiation Using Pattern Languages Rosana Teresinha Vaccare Braga Paulo Cesar Masiero ICMC-USP: Institute.
Chapter 4 Decision Support System & Artificial Intelligence.
©2012 LIESMARS Wuhan University Building Integrated Cyberinfrastructure for GIScience through Geospatial Service Web Jianya Gong, Tong Zhang, Huayi Wu.
7. Grid Computing Systems and Resource Management
Cyberinfrastructure Overview Russ Hobby, Internet2 ECSU CI Days 4 January 2008.
1 Copyright © Oracle Corporation, All rights reserved. Business Intelligence and Data Warehousing.
Developing GRID Applications GRACE Project
ETICS An Environment for Distributed Software Development in Aerospace Applications SpaceTransfer09 Hannover Messe, April 2009.
PLATFORM TO EASE THE DEPLOYMENT AND IMPROVE THE AVAILABILITY OF TRENCADIS INFRASTRUCTURE IberGrid 2013 Miguel Caballer GRyCAP – I3M - UPV.
September 2003, 7 th EDG Conference, Heidelberg – Roberta Faggian, CERN/IT CERN – European Organization for Nuclear Research The GRACE Project GRid enabled.
Business Intelligence Overview. What is Business Intelligence? Business Intelligence is the processes, technologies, and tools that help us change data.
ACGT Architecture and Grid Infrastructure Juliusz Pukacki ‏ EGEE Conference Budapest, 4 October 2007.
Information Systems and Technologies in Organizations.
Discovering Computers 2010: Living in a Digital World Chapter 14
Management Support Systems: An Overview by Dr. S. Sridhar,Ph. D
ArcGIS Online Ming-Chun Lee.
A. Rama Bharathi Regd. No: 08931F0040 III M.C.A
University of Technology
MANAGING KNOWLEDGE FOR THE DIGITAL FIRM
Geospatial and Problem Specific Semantics Danielle Forsyth, CEO and Co-Founder Thetus Corporation 20 June, 2006.
Enterprise Program Management Office
KNOWLEDGE MANAGEMENT (KM) Session # 34
Chapter 1: The Database Environment
The Database Environment
Improving Decision Making and Managing Knowledge
L. Glimcher, R. Jin, G. Agrawal Presented by: Leo Glimcher
Presentation transcript:

KNOWLEDGE GRIDS Akshat Mishra GRID SEMINAR WINTER 2008 Feb 2008

KNOWLEDGE GRIDS A multinational financial organization has data repositories in various locations allover the world The company performs data mining operation such as clustering classification and other knowledge discovery procedures. It uses data ming tools for knowlege discovery but faces problem doing co-ordinated work with all its centres.

DISTRIBUTED MINING Parallel platforms were used. Implementing this task on geographically distributed sites was a challenge Grid computing could be a possible solution How could it be done?

Evolution of Data Grids. Data Grids store large data sets and are moved with the same ease like files. Data Grids evolved into knowledge grid to aid distibuted high performance computation. Along with the basic infrastructure of a grid, provided tools for knowledge discovery. Knowledge Grids are distributed paralled software architecture supporting knowledge discovery

Fig ARCHITECTURE OF KNOWLEDGE GRID Fig. 1. KNOWLEDGE GRID architecture Distributed Data Mining on Grids:Services Tools and Applications

ARCHITECTURE OF KNOWLEDGE GRID Two main services of Core-K Grid layer Knowledge discovery services Extends the globus monitoring service,manages metadata The metadata include tools and resources to be mined. Mining Tools and algorithms are also mined Distributed execution plans

ARCHITECTURE OF KNOWLEDGE GRID RAEMS(Resoe allocation and exection management services)‏ To find suitable mapping between execution plan and available resources The goal is to satisfy application requirements The layer can also use the facility of GRAM

ARCHITECTURE OF KNOWLEDGE GRID HIGHER LEVEL K GRID LAYER DAS(Data Access Service)‏ DAS is reponsible for searching,selecting,extracting,transforming and deleivering data to be mined It is based on user requirement TAAS(Tools and algorithms access service)‏ TAAS is responsible for downloading algos & tools

ARCHITECTURE OF KNOWLEDGE GRID EPMS(Execution plan management Service)‏ An execution plan is represented by graph Graph describes data flow. It is performed by a semi automatic tool The tool takes data and programs selected by the user and generates an abstract exectution plan Finally RPS analyzes the result pattern

Design proces of Data Mining fig2 Fig. 2. Design process of a data mining computation Distributed Data Mining on Grids: Services Tools and Applications

VEGA SOFTWARE MODULES Vega Software Modules Distributed Data Mining on Grids: Services Tools and Applications

GRAPHICAL REPRESENTATION Visual Interface of Vega Distributed Data Mining on Grids: Services Tools and Applications FIGURE 3 :Dis

AN EXAMPLE WORKSPACE 1 DISTRIBUTED DATA MINING ON GRIDS:SERVICES TOOLS AND APPLICATIONS

Another defintion of Knowledge Grid According to H.Zhuge knowledge grid is an intelligent sustainable internet application that enables people or virtual roles to effectively capture,publish,share and manage explicit knowledge resources It also provides on demand services to support innovation cooperative teamwork,problem solving and decision making.

WHY KNOWLEDGE GRID Evolving of Internet meant that the number of pages kept on increasing Info in the form blogs, forums etc are exploding on the net The large explode of informaiton meant there is need to have a information service that spans over an administrative domain.

Evolving concept An emerging technology and evolving concept Google ---->SEARCH ENGINE YAHOO----->INDEXING INFORMATION ON THE BASIS OF CATEGORY Semantic web --->provides services along with information services

References Distributed Data Mining on Grids:Services Tools and applicatios, IEEE TRANSACTIONS ON Systems,MAN and Cybernetics,Vol 34,No6,December 2004 The knowledge grid,By Mario Cannataro and Domenico Talia A knowledge grid model and and platform for global knowledge sharing By H.Zughe Grid Portals By Fugang Wang es/GridPortal.ppt es/GridPortal.ppt