Project Participants

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Project Participants"

Transcription

1 Annual Report for Period:10/ /2007 Submitted on: 08/15/2007 Principal Investigator: Yang, Li. Award ID: Organization: Western Michigan Univ Title: Projection and Interactive Exploration of Large Relational Data Senior Personnel Name: Yang, Li Project Participants Post-doc Graduate Student Name: Sanver, Mustafa Mustafa Sanver is a Ph.D. student who has been working on both data visualization and database components. Name: Zhao, Dongfang Dongfang Zhao worked on the data embedding component and was supported in the academic year. Name: Hua, Danyang Danyang Hua works on the database component and has been supported since Undergraduate Student Technician, Programmer Other Participant Research Experience for Undergraduates Organizational Partners Other Collaborators or Contacts Activities and Findings Research and Education Activities: Page 1 of 5

2 This research project consists of three technical components: data visualization, database support, and data embedding. Please refer to the Publications section for related references. In the data visualization component, we have been developing a visualization tool [Yang & Sanver TVCG'07] that takes multi-resolution aggregated data as data input. Two interactive visualization techniques, density-based parallel coordinates and footprint splatting with grand tour, have been extended to support the rendering of data aggregated at multiple resolutions. The tool supports overview-and-drill-down of large relational data and allows users to interactively specify subsets of data for further visualization, possibly at more detailed resolutions. The visualization tool, with further development, can be used by industry and other agencies for scalable interactive data visualization and exploration. We have also developed techniques for pruning and visualizing frequent itemsets and many-to-many association rules [Yang TKDE'03]. Future work in this component includes usability study and better GUI design. In the database component, we have studied multi-resolution data aggregation as a common representation of data between database and visualization tools [Yang & Sanver TVCG'07]. Data aggregated at multiple resolutions are piggybacked onto internal nodes of a k-d-b tree. The k-d-b tree structure is extended to improve query performance and node fan-outs while keeping data aggregation information. We have conducted experiments on both synthetic and real world data sets. Performances of data access and index maintenance have been tested. Future work includes study of better indexing mechanism and data mining techniques using multi-resolution data aggregation as input. In the data embedding component, four methods (k-mst [Yang ICPR'04], min-k-st [Yang TPAMI'05], k-ec [Yang PRL'05], and k-vc [Yang SIGKDD'05, Yang TPAMI'06]) were proposed to build connected neighborhood graphs for robust and reliable dimensionality reduction. A new locally isometric embedding method LMDS [Yang ICPR'06, Yang TPAMI'07] is discovered. Incremental methods [Zhao & Yang ICPR'06, Zhao & Yang TPAMI'07] have been developed for neighborhood graph construction and projection of large data and data streams. Future work includes systemization and evaluation of existing data embedding methods. Assessment of Project's Status: Most activities of this project, as defined in the research and education plan in the original proposal in 01/2004 and revised in 07/2004, have been completed. Compared with the original research objectives, the following list highlights some of the major accomplishments: 1. To support interactive exploration of large relational data, we have studied multi-resolution data aggregation and used high dimensional partition-based tree index to piggyback the aggregated data as an intermediate representation of large relational data for interaction visualization. 2. Two visualization techniques, footprint splatting with grand tour and parallel coordinates, have been extended to visualize multidimensional aggregated data. 3. A client-server visualization tool has been developed to demonstrate the feasibility and effectiveness of this approach in multiresolution visualization of large relational data. Multiple visualization clients can get data from a data server using TCP/IP connections. The feature-rich visualization tool supports many graphical user interactions, including overview-and-drill-down by allowing users to interactively specify subsets of data for further visualization. Software design allows easy integration of new data visualization techniques into the tool. 4. Four methods were proposed to build connected neighborhood graphs for data embedding. A locally isometric embedding method LMDS is proposed. Incremental methods have been developed for projection of large data sets and data streams. The following summarizes our ongoing work. These are what we expect to accomplish during the No-Cost Extension period: 1. Better GUI and query interface design; documentation for end users and developers; 2. Conducting user study; 3. Performance experiments on large data sets up to a few terabytes; 4. Data clustering and other data mining algorithms using multi-resolution data aggregation as data input; 5. Ongoing data embedding research. Findings: Page 2 of 5

3 Visual exploration of large relational data poses fundamental challenges to both data visualization and database management systems. A major finding of this project is the density-based methodology to interactively explore large relational data sets. It uses multi-resolution data aggregation as a common representation of data between relational databases and visualization tools. Data aggregated at multiple resolutions are stored in internal nodes of a partition-based high dimensional tree index. Such a piggyback ride of aggregated data supports the overview-and-drill-down data access pattern for interactive data exploration. It has build-in support for visual interaction and data scalability. Existing visualization techniques are extended to support this data representation. In addition, the proposed multi-resolution data representation has potential applications to accelerate data aggregation queries and OLAP queries. It can be used as data input for efficient mining of large data sets. It also provides support for privacy preservation where permissions can be granted to users based on data resolutions. New techniques and algorithms in these areas are parts of our ongoing research. We have developed a set of algorithms and methods for nonlinear data embedding and dimensionality reduction. This research calls for new ideas from differential geometry and may have fundamental impacts on multivariate data mining and data processing. This is an important part of our ongoing work. Training and Development: Three Ph.D. students (Mustafa Sanver, Dongfang Zhao, and Danyang Hua) are supported by this grant. M.S. students doing thesis work have also greatly benefited from the research supported by this grant. Parts of this research are used in two courses (CS Advanced DBMS and CS Knowledge Discovery and Data Mining) taught at the Department of Computer Science, Western Michigan University ( ). Outreach Activities: We have established collaborations with the Department of Business Information Systems, College of Business and the Department of Educational Leadership, Research and Technology, College of Education. Through such collaborations, we expect to have access to real world data sets and applications and to conduct user study with participation from students with diversified backgrounds. Journal Publications Li Yang, "Distance-preserving projection of high dimensional data for nonlinear dimensionality reduction", IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 1243, vol. 26, (2004). Published, /TPAMI Li Yang, "Pruning and visualizing generalized association rules in parallel coordinates", IEEE Transactions on Knowledge and Data Engineering, p. 60, vol. 17, (2005). Published, /TKDE Li Yang, "Building k-edge-connected neighborhood graphs for distance-based data projection", Pattern Recognition Letters, p. 2, vol. 26, (2005). Published, /j.patrec Li Yang, "Building k edge-disjoint spanning trees of minimum total length for isometric data embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 16, vol. 27, (2005). Published, /TPAMI Li Yang, "Data embedding techniques and applications", Proceedings of the 2nd International Workshop on Computer Vision meets Databases (CVDB'2005), Baltimore, MD, June 2005, p. 29, vol., (2005). Published, / Li Yang, "Building connected neighborhood graphs for isometric data embedding", Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2005), Chicago, IL, August 2005, p. 722, vol., (2005). Published, / Li Yang, "Building k-connected neighborhood graphs for isometric data embedding", IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 827, vol. 28, (2006). Published, /TPAMI Li Yang, "Alignment of overlapping locally scaled patches for multidimensional scaling and dimensionality reduction", IEEE Transactions on Pattern Analysis and Machine Intelligence, p., vol., (2007). Accepted, /TPAMI Page 3 of 5

4 Li Yang, " k-edge connected neighborhood graph for geodesic distance estimation and nonlinear data projection", Proceedings of the 17th International Conference on Pattern Recognition (ICPR'04), Cambridge, UK, August 2004, p. 196, vol. 1, (2004). Published, /ICPR Li Yang, "Sammon's nonlinear mapping using geodesic distances", Proceedings of the 17th International Conference on Pattern Recognition (ICPR'04), Cambridge, UK, August 2004, p. 303, vol. 2, (2004). Published, /ICPR Dongfang Zhao, Li Yang, "Incremental construction of neighborhood graphs for nonlinear dimensionality reduction", Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), Hong Kong, China, August 2006, p. 177, vol. 3, (2006). Published, /ICPR Li Yang, "Building connected neighborhood graphs for locally linear embedding", Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), Hong Kong, China, August 2006, p. 194, vol. 4, (2006). Published, /ICPR Li Yang, "Locally multidimensional scaling for nonlinear dimensionality reduction", Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), Hong Kong, China, August 2006, p. 202, vol. 4, (2006). Published, /ICPR Dongfang Zhao, Li Yang, "Incremental isometric embedding of high dimensional data using connected neighborhood graphs", IEEE Transactions on Pattern Analysis and Machine Intelligence, p., vol., (2007). Submitted, Li Yang, Mustafa Sanver, "Multiresolution data aggregation for visual exploration of large relational data", IEEE Transactions on Visualization and Computer Graphics, p., vol., (2007). Submitted, Books or Other One-time Publications Li Yang, "Data projection techniques and their application in sensor array data processing", (2005). Book chapter, Published Editor(s): Mehmed Kantardzic, Jozef Zurada Collection: Next Generation of Data Mining Applications Bibliography: pages 57-77, Wiley-IEEE Press Li Yang, Tosiyasu L. Kunii, "Visual database", (2007). Book chapter, Submitted Editor(s): Benjamin Wah, Jeffrey Tsai Collection: Wiley Encyclopedia of Computer Science and Engineering Bibliography: John Wiley & Sons Inc Web/Internet Site URL(s): Description: A dedicated web site will be setup once we finish the development of the first release of the software tool. Other Specific Products Contributions Contributions within Discipline: We have devised multi-resolution data aggregation and have used high dimensional partition-based tree index to piggyback the data aggregated at multiple resolutions as an intermediate representation of large relational data for interactive visualization. Two visualization techniques, footprint splatting with grand tour and parallel coordinates, are extended to visualize the multi-resolution Page 4 of 5

5 aggregated data. A client/server visualization tool is developed to demonstrate the feasibility and effectiveness of this approach. We have developed an approach to visualize generalized association rules in parallel coordinates. In data embedding, a set of algorithms and methods are developed for building connected neighborhood graphs and for locally isometric data embedding. Incremental methods are developed to project large data sets and data streams. Contributions to Other Disciplines: The proposed multi-resolution data representation has potential applications in: (1) optimization of traditional database queries such as data aggregation queries and OLAP queries; (2) efficient mining of large data sets; (3) privacy-preserving data mining. Contributions to Human Resource Development: Three Ph.D. students (Mustafa Sanver, Dongfang Zhao, and Danyang Hua) are supported by this grant. The PI and the students have gained great research experience in working on this project. M.S. students doing thesis work have also greatly benefited from the research supported by this grant. Contributions to Resources for Research and Education: Parts of this research are used in two courses (CS Advanced DBMS and CS Knowledge Discovery and Data Mining) taught at the Department of Computer Science, Western Michigan University ( ). Students in these courses have benefited from the results of this research. Contributions Beyond Science and Engineering: Special Requirements Special reporting requirements: None Change in Objectives or Scope: None Unobligated funds: less than 20 percent of current funds Animal, Human Subjects, Biohazards: None Categories for which nothing is reported: Organizational Partners Any Product Contributions: To Any Beyond Science and Engineering Page 5 of 5

Professional Organization Checklist for the Computer Science Curriculum Updates. Association of Computing Machinery Computing Curricula 2008

Professional Organization Checklist for the Computer Science Curriculum Updates. Association of Computing Machinery Computing Curricula 2008 Professional Organization Checklist for the Computer Science Curriculum Updates Association of Computing Machinery Computing Curricula 2008 The curriculum guidelines can be found in Appendix C of the report

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION Exploration is a process of discovery. In the database exploration process, an analyst executes a sequence of transformations over a collection of data structures to discover useful

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:

More information

Professional Organization Checklist for the Computer Information Systems Curriculum

Professional Organization Checklist for the Computer Information Systems Curriculum Professional Organization Checklist f the Computer Infmation Systems Curriculum Association of Computing Machinery and Association of Infmation Systems IS 2002 Model Curriculum and Guidelines f Undergraduate

More information

Depth and Excluded Courses

Depth and Excluded Courses Depth and Excluded Courses Depth Courses for Communication, Control, and Signal Processing EECE 5576 Wireless Communication Systems 4 SH EECE 5580 Classical Control Systems 4 SH EECE 5610 Digital Control

More information

The Department of Electrical and Computer Engineering (ECE) offers the following graduate degree programs:

The Department of Electrical and Computer Engineering (ECE) offers the following graduate degree programs: Note that these pages are extracted from the full Graduate Catalog, please refer to it for complete details. College of 1 ELECTRICAL AND COMPUTER ENGINEERING www.ece.neu.edu SHEILA S. HEMAMI, PHD Professor

More information

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA POLITECNICO DI MILANO GRADUATE SCHOOL OF BUSINESS BABD INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA Courses Description A JOINT PROGRAM WITH POLITECNICO DI MILANO SCHOOL OF MANAGEMENT PRE-COURSES

More information

A Review of Data Mining Techniques

A Review of Data Mining Techniques Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,

More information

Data Mining: Opportunities and Challenges

Data Mining: Opportunities and Challenges Data Mining: Opportunities and Challenges Xindong Wu University of Vermont, USA; Hefei University of Technology, China ( 合 肥 工 业 大 学 计 算 机 应 用 长 江 学 者 讲 座 教 授 ) 1 Deduction Induction: My Research Background

More information

Information Management course

Information Management course Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)

More information

Grid Density Clustering Algorithm

Grid Density Clustering Algorithm Grid Density Clustering Algorithm Amandeep Kaur Mann 1, Navneet Kaur 2, Scholar, M.Tech (CSE), RIMT, Mandi Gobindgarh, Punjab, India 1 Assistant Professor (CSE), RIMT, Mandi Gobindgarh, Punjab, India 2

More information

Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier

Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier Data Mining: Concepts and Techniques Jiawei Han Micheline Kamber Simon Fräser University К MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF Elsevier Contents Foreword Preface xix vii Chapter I Introduction I I.

More information

James B. Fenwick, Jr., Program Director and Associate Professor Ph.D., The University of Delaware FenwickJB@appstate.edu

James B. Fenwick, Jr., Program Director and Associate Professor Ph.D., The University of Delaware FenwickJB@appstate.edu 118 Master of Science in Computer Science Department of Computer Science College of Arts and Sciences James T. Wilkes, Chair and Professor Ph.D., Duke University WilkesJT@appstate.edu http://www.cs.appstate.edu/

More information

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP

OLAP and Data Mining. Data Warehousing and End-User Access Tools. Introducing OLAP. Introducing OLAP Data Warehousing and End-User Access Tools OLAP and Data Mining Accompanying growth in data warehouses is increasing demands for more powerful access tools providing advanced analytical capabilities. Key

More information

Xianrui Meng. MCS 138, 111 Cummington Mall Department of Computer Science Boston, MA 02215 +1 (857) 540 0460 xmeng@cs.bu.edu www.xianruimeng.

Xianrui Meng. MCS 138, 111 Cummington Mall Department of Computer Science Boston, MA 02215 +1 (857) 540 0460 xmeng@cs.bu.edu www.xianruimeng. Xianrui Meng MCS 138, 111 Cummington Mall Boston, MA 02215 +1 (857) 540 0460 xmeng@cs.bu.edu www.xianruimeng.org RESEARCH INTERESTS In my research, I investigate practical privacy-preserving solutions

More information

Knowledge Discovery from Data Bases Proposal for a MAP-I UC

Knowledge Discovery from Data Bases Proposal for a MAP-I UC Knowledge Discovery from Data Bases Proposal for a MAP-I UC P. Brazdil 1, João Gama 1, P. Azevedo 2 1 Universidade do Porto; 2 Universidade do Minho; 1 Knowledge Discovery from Data Bases We are deluged

More information

COMPUTER ENGINEERING GRADUTE PROGRAM FOR MASTER S DEGREE (With Thesis)

COMPUTER ENGINEERING GRADUTE PROGRAM FOR MASTER S DEGREE (With Thesis) COMPUTER ENGINEERING GRADUTE PROGRAM FOR MASTER S DEGREE (With Thesis) PREPARATORY PROGRAM* COME 27 Advanced Object Oriented Programming 5 COME 21 Data Structures and Algorithms COME 22 COME 1 COME 1 COME

More information

FOUNDATIONS OF A CROSS- DISCIPLINARY PEDAGOGY FOR BIG DATA

FOUNDATIONS OF A CROSS- DISCIPLINARY PEDAGOGY FOR BIG DATA FOUNDATIONS OF A CROSSDISCIPLINARY PEDAGOGY FOR BIG DATA Joshua Eckroth Stetson University DeLand, Florida 3867402519 jeckroth@stetson.edu ABSTRACT The increasing awareness of big data is transforming

More information

Machine Learning Department, School of Computer Science, Carnegie Mellon University, PA

Machine Learning Department, School of Computer Science, Carnegie Mellon University, PA Pengtao Xie Carnegie Mellon University Machine Learning Department School of Computer Science 5000 Forbes Ave Pittsburgh, PA 15213 Tel: (412) 916-9798 Email: pengtaox@cs.cmu.edu Web: http://www.cs.cmu.edu/

More information

Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association

More information

Accelerated Undergraduate/Graduate (BS/MS) Dual Degree Program in Computer Science

Accelerated Undergraduate/Graduate (BS/MS) Dual Degree Program in Computer Science Accelerated Undergraduate/Graduate (BS/MS) Dual Degree Program in The BS degree in requires 126 semester hours and the MS degree in Computer Science requires 30 semester hours. Undergraduate majors who

More information

Fluency With Information Technology CSE100/IMT100

Fluency With Information Technology CSE100/IMT100 Fluency With Information Technology CSE100/IMT100 ),7 Larry Snyder & Mel Oyler, Instructors Ariel Kemp, Isaac Kunen, Gerome Miklau & Sean Squires, Teaching Assistants University of Washington, Autumn 1999

More information

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

More information

Methodology for Emulating Self Organizing Maps for Visualization of Large Datasets

Methodology for Emulating Self Organizing Maps for Visualization of Large Datasets Methodology for Emulating Self Organizing Maps for Visualization of Large Datasets Macario O. Cordel II and Arnulfo P. Azcarraga College of Computer Studies *Corresponding Author: macario.cordel@dlsu.edu.ph

More information

International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April-2014 442 ISSN 2229-5518

International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April-2014 442 ISSN 2229-5518 International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April-2014 442 Over viewing issues of data mining with highlights of data warehousing Rushabh H. Baldaniya, Prof H.J.Baldaniya,

More information

UNDERGRADUATE DEGREE PROGRAMME IN COMPUTER SCIENCE ENGINEERING SCHOOL OF COMPUTER SCIENCE ENGINEERING, ALBACETE

UNDERGRADUATE DEGREE PROGRAMME IN COMPUTER SCIENCE ENGINEERING SCHOOL OF COMPUTER SCIENCE ENGINEERING, ALBACETE UNDERGRADUATE DEGREE PROGRAMME IN COMPUTER SCIENCE ENGINEERING SCHOOL OF COMPUTER SCIENCE ENGINEERING, ALBACETE SCHOOL OF COMPUTER SCIENCE, CIUDAD REAL Core Subjects (CS) Compulsory Subjects (CPS) Optional

More information

MINING THE DATA FROM DISTRIBUTED DATABASE USING AN IMPROVED MINING ALGORITHM

MINING THE DATA FROM DISTRIBUTED DATABASE USING AN IMPROVED MINING ALGORITHM MINING THE DATA FROM DISTRIBUTED DATABASE USING AN IMPROVED MINING ALGORITHM J. Arokia Renjit Asst. Professor/ CSE Department, Jeppiaar Engineering College, Chennai, TamilNadu,India 600119. Dr.K.L.Shunmuganathan

More information

CITY UNIVERSITY OF HONG KONG 香 港 城 市 大 學. Self-Organizing Map: Visualization and Data Handling 自 組 織 神 經 網 絡 : 可 視 化 和 數 據 處 理

CITY UNIVERSITY OF HONG KONG 香 港 城 市 大 學. Self-Organizing Map: Visualization and Data Handling 自 組 織 神 經 網 絡 : 可 視 化 和 數 據 處 理 CITY UNIVERSITY OF HONG KONG 香 港 城 市 大 學 Self-Organizing Map: Visualization and Data Handling 自 組 織 神 經 網 絡 : 可 視 化 和 數 據 處 理 Submitted to Department of Electronic Engineering 電 子 工 程 學 系 in Partial Fulfillment

More information

Chapter 5. Warehousing, Data Acquisition, Data. Visualization

Chapter 5. Warehousing, Data Acquisition, Data. Visualization Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization 5-1 Learning Objectives

More information

Curriculum of the research and teaching activities. Matteo Golfarelli

Curriculum of the research and teaching activities. Matteo Golfarelli Curriculum of the research and teaching activities Matteo Golfarelli The curriculum is organized in the following sections I Curriculum Vitae... page 1 II Teaching activity... page 2 II.A. University courses...

More information

Information Systems. Administered by the Department of Mathematical and Computing Sciences within the College of Arts and Sciences.

Information Systems. Administered by the Department of Mathematical and Computing Sciences within the College of Arts and Sciences. Information Systems Dr. Haesun Lee Professor Dr. Haesun Lee is a Professor of Computer Science. She received her Ph.D. degree from Illinois Institute of Technology, Chicago, Illinois (1997). Her primary

More information

A THREE-TIERED WEB BASED EXPLORATION AND REPORTING TOOL FOR DATA MINING

A THREE-TIERED WEB BASED EXPLORATION AND REPORTING TOOL FOR DATA MINING A THREE-TIERED WEB BASED EXPLORATION AND REPORTING TOOL FOR DATA MINING Ahmet Selman BOZKIR Hacettepe University Computer Engineering Department, Ankara, Turkey selman@cs.hacettepe.edu.tr Ebru Akcapinar

More information

The Applied and Computational Mathematics (ACM) Program at The Johns Hopkins University (JHU) is

The Applied and Computational Mathematics (ACM) Program at The Johns Hopkins University (JHU) is The Applied and Computational Mathematics Program at The Johns Hopkins University James C. Spall The Applied and Computational Mathematics Program emphasizes mathematical and computational techniques of

More information

Search and Data Mining: Techniques. Applications Anya Yarygina Boris Novikov

Search and Data Mining: Techniques. Applications Anya Yarygina Boris Novikov Search and Data Mining: Techniques Applications Anya Yarygina Boris Novikov Introduction Data mining applications Data mining system products and research prototypes Additional themes on data mining Social

More information

An Introduction to Data Mining

An Introduction to Data Mining An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail

More information

An Overview of Knowledge Discovery Database and Data mining Techniques

An Overview of Knowledge Discovery Database and Data mining Techniques An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,

More information

Ezgi Dinçerden. Marmara University, Istanbul, Turkey

Ezgi Dinçerden. Marmara University, Istanbul, Turkey Economics World, Mar.-Apr. 2016, Vol. 4, No. 2, 60-65 doi: 10.17265/2328-7144/2016.02.002 D DAVID PUBLISHING The Effects of Business Intelligence on Strategic Management of Enterprises Ezgi Dinçerden Marmara

More information

Eastern Washington University Department of Computer Science. Questionnaire for Prospective Masters in Computer Science Students

Eastern Washington University Department of Computer Science. Questionnaire for Prospective Masters in Computer Science Students Eastern Washington University Department of Computer Science Questionnaire for Prospective Masters in Computer Science Students I. Personal Information Name: Last First M.I. Mailing Address: Permanent

More information

Accelerating BIRCH for Clustering Large Scale Streaming Data Using CUDA Dynamic Parallelism

Accelerating BIRCH for Clustering Large Scale Streaming Data Using CUDA Dynamic Parallelism Accelerating BIRCH for Clustering Large Scale Streaming Data Using CUDA Dynamic Parallelism Jianqiang Dong, Fei Wang and Bo Yuan Intelligent Computing Lab, Division of Informatics Graduate School at Shenzhen,

More information

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing

More information

A Clustering Model for Mining Evolving Web User Patterns in Data Stream Environment

A Clustering Model for Mining Evolving Web User Patterns in Data Stream Environment A Clustering Model for Mining Evolving Web User Patterns in Data Stream Environment Edmond H. Wu,MichaelK.Ng, Andy M. Yip,andTonyF.Chan Department of Mathematics, The University of Hong Kong Pokfulam Road,

More information

South Dakota Board of Regents New Graduate Degree Program. South Dakota State University Proposed Graduate Program: M.S. in Computer Science Degree:

South Dakota Board of Regents New Graduate Degree Program. South Dakota State University Proposed Graduate Program: M.S. in Computer Science Degree: Attachment I South Dakota Board of Regents New Graduate Degree Program University: South Dakota State University Proposed Graduate Program: M.S. in Computer Science Degree: M.S. Existing or New Degree(s):

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014 RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

More information

Daniel J. Adabi. Workshop presentation by Lukas Probst

Daniel J. Adabi. Workshop presentation by Lukas Probst Daniel J. Adabi Workshop presentation by Lukas Probst 3 characteristics of a cloud computing environment: 1. Compute power is elastic, but only if workload is parallelizable 2. Data is stored at an untrusted

More information

Visualization of large data sets using MDS combined with LVQ.

Visualization of large data sets using MDS combined with LVQ. Visualization of large data sets using MDS combined with LVQ. Antoine Naud and Włodzisław Duch Department of Informatics, Nicholas Copernicus University, Grudziądzka 5, 87-100 Toruń, Poland. www.phys.uni.torun.pl/kmk

More information

Computer Science. General Education Students must complete the requirements shown in the General Education Requirements section of this catalog.

Computer Science. General Education Students must complete the requirements shown in the General Education Requirements section of this catalog. Computer Science Dr. Ilhyun Lee Professor Dr. Ilhyun Lee is a Professor of Computer Science. He received his Ph.D. degree from Illinois Institute of Technology, Chicago, Illinois (1996). He was selected

More information

Static Data Mining Algorithm with Progressive Approach for Mining Knowledge

Static Data Mining Algorithm with Progressive Approach for Mining Knowledge Global Journal of Business Management and Information Technology. Volume 1, Number 2 (2011), pp. 85-93 Research India Publications http://www.ripublication.com Static Data Mining Algorithm with Progressive

More information

Computer Science Electives and Clusters

Computer Science Electives and Clusters Course Number CSCI- Computer Science Electives and Clusters Computer Science electives belong to one or more groupings called clusters. Undergraduate students with the proper prerequisites are permitted

More information

Example application (1) Telecommunication. Lecture 1: Data Mining Overview and Process. Example application (2) Health

Example application (1) Telecommunication. Lecture 1: Data Mining Overview and Process. Example application (2) Health Lecture 1: Data Mining Overview and Process What is data mining? Example applications Definitions Multi disciplinary Techniques Major challenges The data mining process History of data mining Data mining

More information

Application of Data Warehouse and Data Mining. in Construction Management

Application of Data Warehouse and Data Mining. in Construction Management Application of Data Warehouse and Data Mining in Construction Management Jianping ZHANG 1 (zhangjp@tsinghua.edu.cn) Tianyi MA 1 (matianyi97@mails.tsinghua.edu.cn) Qiping SHEN 2 (bsqpshen@inet.polyu.edu.hk)

More information

COURSE RECOMMENDER SYSTEM IN E-LEARNING

COURSE RECOMMENDER SYSTEM IN E-LEARNING International Journal of Computer Science and Communication Vol. 3, No. 1, January-June 2012, pp. 159-164 COURSE RECOMMENDER SYSTEM IN E-LEARNING Sunita B Aher 1, Lobo L.M.R.J. 2 1 M.E. (CSE)-II, Walchand

More information

Graduate Co-op Students Information Manual. Department of Computer Science. Faculty of Science. University of Regina

Graduate Co-op Students Information Manual. Department of Computer Science. Faculty of Science. University of Regina Graduate Co-op Students Information Manual Department of Computer Science Faculty of Science University of Regina 2014 1 Table of Contents 1. Department Description..3 2. Program Requirements and Procedures

More information

Jagir Singh, Greeshma, P Singh University of Northern Virginia. Abstract

Jagir Singh, Greeshma, P Singh University of Northern Virginia. Abstract 224 Business Intelligence Journal July DATA WAREHOUSING Ofori Boateng, PhD Professor, University of Northern Virginia BMGT531 1900- SU 2011 Business Intelligence Project Jagir Singh, Greeshma, P Singh

More information

An Order-Invariant Time Series Distance Measure [Position on Recent Developments in Time Series Analysis]

An Order-Invariant Time Series Distance Measure [Position on Recent Developments in Time Series Analysis] An Order-Invariant Time Series Distance Measure [Position on Recent Developments in Time Series Analysis] Stephan Spiegel and Sahin Albayrak DAI-Lab, Technische Universität Berlin, Ernst-Reuter-Platz 7,

More information

131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10

131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10 1/10 131-1 Adding New Level in KDD to Make the Web Usage Mining More Efficient Mohammad Ala a AL_Hamami PHD Student, Lecturer m_ah_1@yahoocom Soukaena Hassan Hashem PHD Student, Lecturer soukaena_hassan@yahoocom

More information

Future trends in data mining

Future trends in data mining in data mining 1/16 Good reviews: Baker, R. and Yacef, K. The state of educational data mining in 2009: A review and future visions.journal of Educational (2009) 1:3-17 Hans-Peter Kriegel, Karsten M. Borgwardt,

More information

REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc])

REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc]) 305 REGULATIONS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE (MSc[CompSc]) (See also General Regulations) Any publication based on work approved for a higher degree should contain a reference

More information

Preparing Data Sets for the Data Mining Analysis using the Most Efficient Horizontal Aggregation Method in SQL

Preparing Data Sets for the Data Mining Analysis using the Most Efficient Horizontal Aggregation Method in SQL Preparing Data Sets for the Data Mining Analysis using the Most Efficient Horizontal Aggregation Method in SQL Jasna S MTech Student TKM College of engineering Kollam Manu J Pillai Assistant Professor

More information

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015 An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

More information

Eastern Washington University Department of Computer Science. Questionnaire for Prospective Masters in Computer Science Students

Eastern Washington University Department of Computer Science. Questionnaire for Prospective Masters in Computer Science Students Eastern Washington University Department of Computer Science Questionnaire for Prospective Masters in Computer Science Students I. Personal Information Name: Last First M.I. Mailing Address: Permanent

More information

XIAOBAI (BOB) LI ACADEMIC EXPERIENCE RESEARCH HIGHLIGHTS TEACHING HIGHLIGHTS

XIAOBAI (BOB) LI ACADEMIC EXPERIENCE RESEARCH HIGHLIGHTS TEACHING HIGHLIGHTS XIAOBAI (BOB) LI Department of Operations & Information Systems Manning School of Business One University Ave., Lowell, MA 01854 Phone: 978-934-2707 Email: xiaobai_li@uml.edu ACADEMIC EXPERIENCE 2011-present

More information

Clustering Big Data. Anil K. Jain. (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012

Clustering Big Data. Anil K. Jain. (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012 Clustering Big Data Anil K. Jain (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012 Outline Big Data How to extract information? Data clustering

More information

Search Result Optimization using Annotators

Search Result Optimization using Annotators Search Result Optimization using Annotators Vishal A. Kamble 1, Amit B. Chougule 2 1 Department of Computer Science and Engineering, D Y Patil College of engineering, Kolhapur, Maharashtra, India 2 Professor,

More information

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON Overview * Introduction * Multiple faces of Big Data * Challenges of Big Data * Cloud Computing

More information

Large-Scale Data Sets Clustering Based on MapReduce and Hadoop

Large-Scale Data Sets Clustering Based on MapReduce and Hadoop Journal of Computational Information Systems 7: 16 (2011) 5956-5963 Available at http://www.jofcis.com Large-Scale Data Sets Clustering Based on MapReduce and Hadoop Ping ZHOU, Jingsheng LEI, Wenjun YE

More information

Adina Crainiceanu. Ph.D. in Computer Science, Cornell University, Ithaca, NY May 2006 Thesis Title: Answering Complex Queries in Peer-to-Peer Systems

Adina Crainiceanu. Ph.D. in Computer Science, Cornell University, Ithaca, NY May 2006 Thesis Title: Answering Complex Queries in Peer-to-Peer Systems Adina Crainiceanu Associate Professor Department of Computer Science United States Naval Academy 572M Holloway Road, Stop 9F Annapolis, MD 21402 http://www.usna.edu/users/cs/adina Email: adina@usna.edu

More information

Page 1 of 5. (Modules, Subjects) SENG DSYS PSYS KMS ADB INS IAT

Page 1 of 5. (Modules, Subjects) SENG DSYS PSYS KMS ADB INS IAT Page 1 of 5 A. Advanced Mathematics for CS A1. Line and surface integrals 2 2 A2. Scalar and vector potentials 2 2 A3. Orthogonal curvilinear coordinates 2 2 A4. Partial differential equations 2 2 4 A5.

More information

PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS.

PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS. PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS Project Project Title Area of Abstract No Specialization 1. Software

More information

Understanding Web personalization with Web Usage Mining and its Application: Recommender System

Understanding Web personalization with Web Usage Mining and its Application: Recommender System Understanding Web personalization with Web Usage Mining and its Application: Recommender System Manoj Swami 1, Prof. Manasi Kulkarni 2 1 M.Tech (Computer-NIMS), VJTI, Mumbai. 2 Department of Computer Technology,

More information

Oracle9i Data Warehouse Review. Robert F. Edwards Dulcian, Inc.

Oracle9i Data Warehouse Review. Robert F. Edwards Dulcian, Inc. Oracle9i Data Warehouse Review Robert F. Edwards Dulcian, Inc. Agenda Oracle9i Server OLAP Server Analytical SQL Data Mining ETL Warehouse Builder 3i Oracle 9i Server Overview 9i Server = Data Warehouse

More information

The Prophecy-Prototype of Prediction modeling tool

The Prophecy-Prototype of Prediction modeling tool The Prophecy-Prototype of Prediction modeling tool Ms. Ashwini Dalvi 1, Ms. Dhvni K.Shah 2, Ms. Rujul B.Desai 3, Ms. Shraddha M.Vora 4, Mr. Vaibhav G.Tailor 5 Department of Information Technology, Mumbai

More information

CITRIS Founding Corporate Members Meeting

CITRIS Founding Corporate Members Meeting Massive Data Exploration Virtual Reality and Visualization Oliver Staadt, Bernd Hamann CIPIC and CS Department, UC Davis CITRIS Founding Corporate Members Meeting Thursday, February 27, 2003 University

More information

An Enhanced Clustering Algorithm to Analyze Spatial Data

An Enhanced Clustering Algorithm to Analyze Spatial Data International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Volume-2, Issue-7, July 2014 An Enhanced Clustering Algorithm to Analyze Spatial Data Dr. Mahesh Kumar, Mr. Sachin Yadav

More information

The Scientific Data Mining Process

The Scientific Data Mining Process Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In

More information

Data Mining Analytics for Business Intelligence and Decision Support

Data Mining Analytics for Business Intelligence and Decision Support Data Mining Analytics for Business Intelligence and Decision Support Chid Apte, T.J. Watson Research Center, IBM Research Division Knowledge Discovery and Data Mining (KDD) techniques are used for analyzing

More information

BIG DATA What it is and how to use?

BIG DATA What it is and how to use? BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14

More information

Statistics for BIG data

Statistics for BIG data Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before

More information

Information and Decision Sciences (IDS)

Information and Decision Sciences (IDS) University of Illinois at Chicago 1 Information and Decision Sciences (IDS) Courses IDS 400. Advanced Business Programming Using Java. 0-4 Visual extended business language capabilities, including creating

More information

Visualization Techniques in Data Mining

Visualization Techniques in Data Mining Tecniche di Apprendimento Automatico per Applicazioni di Data Mining Visualization Techniques in Data Mining Prof. Pier Luca Lanzi Laurea in Ingegneria Informatica Politecnico di Milano Polo di Milano

More information

DATA MINING CONCEPTS AND TECHNIQUES. Marek Maurizio E-commerce, winter 2011

DATA MINING CONCEPTS AND TECHNIQUES. Marek Maurizio E-commerce, winter 2011 DATA MINING CONCEPTS AND TECHNIQUES Marek Maurizio E-commerce, winter 2011 INTRODUCTION Overview of data mining Emphasis is placed on basic data mining concepts Techniques for uncovering interesting data

More information

MS and PhD Degree Requirements

MS and PhD Degree Requirements MS and PhD Degree Requirements Department of Electrical and Computer Engineering September 1, 2014 General Information on ECE Graduate Courses This document is prepared to assist ECE graduate students

More information

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition

More information

Overview. Background. Data Mining Analytics for Business Intelligence and Decision Support

Overview. Background. Data Mining Analytics for Business Intelligence and Decision Support Mining Analytics for Business Intelligence and Decision Support Chid Apte, PhD Manager, Abstraction Research Group IBM TJ Watson Research Center apte@us.ibm.com http://www.research.ibm.com/dar Overview

More information

Text Mining: The state of the art and the challenges

Text Mining: The state of the art and the challenges Text Mining: The state of the art and the challenges Ah-Hwee Tan Kent Ridge Digital Labs 21 Heng Mui Keng Terrace Singapore 119613 Email: ahhwee@krdl.org.sg Abstract Text mining, also known as text data

More information

A COGNITIVE APPROACH IN PATTERN ANALYSIS TOOLS AND TECHNIQUES USING WEB USAGE MINING

A COGNITIVE APPROACH IN PATTERN ANALYSIS TOOLS AND TECHNIQUES USING WEB USAGE MINING A COGNITIVE APPROACH IN PATTERN ANALYSIS TOOLS AND TECHNIQUES USING WEB USAGE MINING M.Gnanavel 1 & Dr.E.R.Naganathan 2 1. Research Scholar, SCSVMV University, Kanchipuram,Tamil Nadu,India. 2. Professor

More information

Course Descriptions - Computer Science and Software Engineering

Course Descriptions - Computer Science and Software Engineering One of the nation's top undergraduate engineering, science, and mathematics colleges Course Descriptions - Computer Science and Software Engineering Professors Anderson, Boutell, Chenoweth, Chidanandan,

More information

MEng, BSc Applied Computer Science

MEng, BSc Applied Computer Science School of Computing FACULTY OF ENGINEERING MEng, BSc Applied Computer Science Year 1 COMP1212 Computer Processor Effective programming depends on understanding not only how to give a machine instructions

More information

Alex Leykin. Visual people tracking and group activity recognition. Visual attention, behavior and body language analysis with non-intrusive sensors.

Alex Leykin. Visual people tracking and group activity recognition. Visual attention, behavior and body language analysis with non-intrusive sensors. Alex Leykin Tel: 812-219-6672 oleykin@indiana.edu http://cgi.cs.indiana.edu/~oleykin Education 2007 PhD in Computer Science, Indiana University 2002 MS in Computer Science, Indiana University 2000 MS in

More information

A Web-based Interactive Data Visualization System for Outlier Subspace Analysis

A Web-based Interactive Data Visualization System for Outlier Subspace Analysis A Web-based Interactive Data Visualization System for Outlier Subspace Analysis Dong Liu, Qigang Gao Computer Science Dalhousie University Halifax, NS, B3H 1W5 Canada dongl@cs.dal.ca qggao@cs.dal.ca Hai

More information

ANALYTICS CENTER LEARNING PROGRAM

ANALYTICS CENTER LEARNING PROGRAM Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals

More information

ANALYSIS OF WEBSITE USAGE WITH USER DETAILS USING DATA MINING PATTERN RECOGNITION

ANALYSIS OF WEBSITE USAGE WITH USER DETAILS USING DATA MINING PATTERN RECOGNITION ANALYSIS OF WEBSITE USAGE WITH USER DETAILS USING DATA MINING PATTERN RECOGNITION K.Vinodkumar 1, Kathiresan.V 2, Divya.K 3 1 MPhil scholar, RVS College of Arts and Science, Coimbatore, India. 2 HOD, Dr.SNS

More information

Knowledge Discovery from patents using KMX Text Analytics

Knowledge Discovery from patents using KMX Text Analytics Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers

More information

COURSE TITLE COURSE DESCRIPTION

COURSE TITLE COURSE DESCRIPTION COURSE TITLE COURSE DESCRIPTION CS-00X COMPUTING EXIT INTERVIEW All graduating students are required to meet with their department chairperson/program director to finalize requirements for degree completion.

More information

Resume of Hanan H. Elazhary

Resume of Hanan H. Elazhary Resume of Hanan H. Elazhary Home Phone: 35853017, 35853986 Cell Phone: 0112302019 E-mail: hanan@eri.sci.eg, hananelazhary@hotmail.com Nationality: Egyptian Gender: Female EDUCATION Ph.D. in Computer Science

More information

Praseeda Manoj Department of Computer Science Muscat College, Sultanate of Oman

Praseeda Manoj Department of Computer Science Muscat College, Sultanate of Oman International Journal of Electronics and Computer Science Engineering 290 Available Online at www.ijecse.org ISSN- 2277-1956 Analysis of Grid Based Distributed Data Mining System for Service Oriented Frameworks

More information

ORACLE OLAP. Oracle OLAP is embedded in the Oracle Database kernel and runs in the same database process

ORACLE OLAP. Oracle OLAP is embedded in the Oracle Database kernel and runs in the same database process ORACLE OLAP KEY FEATURES AND BENEFITS FAST ANSWERS TO TOUGH QUESTIONS EASILY KEY FEATURES & BENEFITS World class analytic engine Superior query performance Simple SQL access to advanced analytics Enhanced

More information

Data Mining System, Functionalities and Applications: A Radical Review

Data Mining System, Functionalities and Applications: A Radical Review Data Mining System, Functionalities and Applications: A Radical Review Dr. Poonam Chaudhary System Programmer, Kurukshetra University, Kurukshetra Abstract: Data Mining is the process of locating potentially

More information

INTERACTIVE MANIPULATION, VISUALIZATION AND ANALYSIS OF LARGE SETS OF MULTIDIMENSIONAL TIME SERIES IN HEALTH INFORMATICS

INTERACTIVE MANIPULATION, VISUALIZATION AND ANALYSIS OF LARGE SETS OF MULTIDIMENSIONAL TIME SERIES IN HEALTH INFORMATICS Proceedings of the 3 rd INFORMS Workshop on Data Mining and Health Informatics (DM-HI 2008) J. Li, D. Aleman, R. Sikora, eds. INTERACTIVE MANIPULATION, VISUALIZATION AND ANALYSIS OF LARGE SETS OF MULTIDIMENSIONAL

More information