CONTEMPORARY DECISION SUPPORT AND KNOWLEDGE MANAGEMENT TECHNOLOGIES

Size: px
Start display at page:

Download "CONTEMPORARY DECISION SUPPORT AND KNOWLEDGE MANAGEMENT TECHNOLOGIES"

Transcription

1 I International Symposium Engineering Management And Competitiveness 2011 (EMC2011) June 24-25, 2011, Zrenjanin, Serbia CONTEMPORARY DECISION SUPPORT AND KNOWLEDGE MANAGEMENT TECHNOLOGIES Slavoljub Milovanovic * University of Nis, The Faculty of Economics in Nis Trg Kralja Aleksandra 11, Nis, Serbia ABSTRACT In latest decade of last century, turbulent technological changes occured in all fields. Dynamic technological changes influence on the type of information systems which are primary used for decision support and knowledge management. Traditional decision support systems were mainly based on models, while contemporary decision support systems are based on multidimensional analysis of huge data amount and trasformation of data to knowledge. Data is organized in data warehouses and processed by data mining tools. Datawarehousing and data mining are two most important knowledge management technologies which are used for decision support, so the paper talks about the technologies. Key words: decision support systems, knowledge management, data mining, data warehouses INTRODUCTION Decision Support Systems (DSSs) are similar and complementary to standard management information systems (MISs) and represent superstructure of the standard information systems (ISs). Characteristics of DSSs are defined in relation to classic data processing and management information systems. Therefore, DSSs are different from classic data processing and MISs because they require symbiosis between a user and the system to achieve planed business efficiency. While MISs give information for structured decision making (operational and tactical decisions), DSSs support semistructured and unstructured decision making (strategic level of management). DSSs are often defined as interactive information systems that support solving semistructured and unstructured class of problems in decision making process. In order to support semistructured and unstructured decisions, DSSs use sophisticated analytic models and tools and user-friendly software. Thus, DSSs provide to users flexible set of tools and abilities for analysis of important data. Contemporary DSSs are driven by data in contrast to old classic DSSs that were mainly driven by models. Therefore, contemporary DSSs are based on datawarehousing and data mining technologies for elicitation knowledge from data. Many organizations today install data warehouses and sophisticated tools for data analysis to better use information stored in their transaction information systems. Also, the organizations use ability of connecting transaction databases to World Wide Web and analysing data from web. (Turban et al., 2004) Characteristics and functions of contemporary technologies for decision support and knowledge management are analysed in two sections. In first section of the paper, architecture of contemporary decision support systems is presented and components of the architecture are analysed. In second section, tools for multidimensional analytical data processing and data mining are explained. 303

2 ARCHITECTURE OF CONTEMPORARY DECISION SUPPORT SYSTEMS Classic DSSs driven by models were used in eighties and nineties years of last century and were based on small sets of data. These were primarily autonomous systems isolated from main ISs of organizations and they have used some types of models to achieve what if and the other kind of analyses. Such systems was usually developed by end users without centalised control of IT (Information Technology) department. Analyitical capacity of the systems were based on rigor models combined with good user interface facilitating use of the models. IBM was developed Capacity Optimization Planning System that is typical example of DSS driven by model. Contemporary improvements in computing and databases technology have broaden definition of classic DSSs and included analysis of great amount of data from transaction ISs (TPSs Transaction Processing Systems) and data from web. DSSs driven by data analysing great amount of data from transaction ISs and other sources have emerged. The systems support decision making enabling users to extract useful information that previously was hidden in great transaction databases. Data from TPSs is colected and organised in special data bases called data warehouses. Data warehouses are essentially different from transaction databases because contain aggregative data extracted from transaction databases. Data Warehouses (DWs) provide several ad hoc and standardized query tools, analytical tools and graphical reporting capabilities including OLAP (On-line Analytical Processing) and DM (data mining) tools. DM software tools discover patterns and relations in great data warehouses and derive rules based on them. The rules can be used for forcasting future behaviour and decision making. DWs enable decision makers data access without impact on performance of operational, transaction ISs and databases. In addition, many organizations use web technology to facilitate access to DWs. (Wrembel & Koncilia, 2007) Figure 1 illustrates DW concept. Catalog of information provides to users information on data available in DW. DW must be carefully designed by business and information specialists in order to be right information for critical business decisions obtained. An organization perhaps should change its business processes to use information from DW efficiently. An organization can build DW as central database serving needs of whole organization or to create smaller, decentalised DWs called datamarts. Datamarts (DMs) are subsets of a DW. Summarized and high focused parts of whole organization data are stored in separate databases for specific population of users and these parts are DMs. For example, organization can develop datamarts of marketing and sales for efficient customer relations management. DMs are usually focused to one area and line of business, so it can be constructed faster and with lower costs than DW of whole organization. However, if an organization creates too many datamarts, complexity, costs and management problems will emerge. Organizations more and more develop DSSs driven by data in order to analyse data on customers collected from their web sites and data from other ISs, as well. DMs help organizations to engage in one-to-one marketing where personalized and individualized messages can be created on base of individual customer preferences. The systems can achieve complex analyses of patterns or trends in data to discover more details on some occurrence, when it is needed. Figure 2 illustrates architecture of contemporary DSSs. As we can see, DSSs have folowing components: 1. database that is used for generation of queries and analysis; 2. software system with models, data mining and other analytical tools and 3. user interface. 1. Data base. DSS database is set of current and historical data from many applications. DSS database can be small database stored on PC containing subset of whole organization data. The internal data is combined with external data. Alternatively, DSS database can be massive DW that is continually updated from data generated in transaction ISs and web sites. Data in DSS databases 304

3 is extracts or copies of transaction databases, so use of DSS do not impact on function of operational ISs. Figure 1: Components of DW 2. Software system. DSS software system is made of software tools that are used for data analysis. The system contains OLAP tools, data mining tools or collection of mathematical and statistical models which can be easily accessed by users. A model is abstract representation illustrating components or relations in some phenomenon. Model can be phisical model (such as aircraft model), mathematical model (such as equation) or verbal model (such as description of order making procedure). Every DSS is built for specific set of goals and has various collections of models which are available in dependence of the goals. Figure 2: Architecture of contemporary DSSs 305

4 Perhaps most usuall models are the models in form of library. There are libraries of statistical models. Library of models for specific functions (for example, financial models, risk analysis models etc.) can also be created. Libraries of statistical models usually contain full range of most used statistical functions, including average, deviation, median and dispersion graphics. This software enables forecast of future results by analysis of data series. Statistic modeling software can be used for establishing relations, such as relating of product sale with attributes of customers like age, revenue and other attributes. Optimization models usually use linear programming and determine optimal allocation of resources for maximize or minimize specific variables, such as costs or time. Classic use of optimization models is determination of right mix of products for specific market to maximize profit. Forecasting models are often used for forecast, prognosis of sale. User of the type of models gets range of historical data for projecting future conditions of business and sale which can result from the conditions. A decision maker can change the future conditions in model (for example, increasing of material costs or entry of new competitor on market with low prices) to determine how the new conditions can influence on sale. Organizations usually use the models to forecast actions of competitors. Most used models are sensitivity analysis models that ask what if questions in order to determine impact of changes in one and many factors on results. What if analysis taking into account known and assumed conditions allows users to change specific values for testing results in order to better predict outcomes of real changes if they occure. What will happen, if we increase price for 5% or increase promotion budget for $10000? What will happen if we keep the same price and budget? Desktop software for table calculations (spreadsheet software like Microsoft Excel), are often used in answer on the questions. There is software analysing sensitivity backward and use principle of goal seeking. For example: If I want to sale million units of product next year, how much I must reduce price of the product? 3. User interface. DSS user interface enables that user easy interacts with DSS software tools. Graphical, easy to use and flexible user inteface supports dialog between user and DSS. DSS users can be managers or employees without patience for learning complex tools, so the interface must be relatively intuitive. Today, many DSSs are built with interfaces based on web technology because of easy to use web interface, interactivity and potential for personalization and customization. Development of effective DSS requires high level of users participation and use of prototype as a method of system development. Accordingly, a DSSs that fully meet users needs can be developed. DATA MINING TOOLS AND MULTIDIMENSIONAL ANALYSIS OF DATA Managers sometime need to analyse data on such manner that is not possible by traditional database. Traditional queries to databases can answer on questions such as: How many units of some product was delivered in november 2008? However, managers need multidimensional analysis supporting more complex requirements for information, such as: Compare amount of the product sale in a region with quarter plan of sale in last two years? With data mining tools and technics for data analysis, users get better information for decision making. (Han & Kamber, 2006) We can take example of organization selling four products (bear, vine, brandy and juices) on east, west and central region of country. Management of the organization wants to know amount of sale by product for every region and to compare the amount with planed sale. This analysis require multidimensional view on data that can be provided on two way: 1. use of specialized multidimensional databases (data warehouses) and 2. use of tools that create multidimensional views on twodimensional relational databases. 306

5 Multidimensional analysis enable users to view same data on different ways by using many dimensions. Every aspect of information (product, price, costs, region or time period) represents one dimension. For example, manager responsible for sale of juices can use multidimensional analysis to determine how much of juices was sold on east region in june and to compare the amount of sale with sale in previous mounth and in june of previous year or with planed amount of sale. Data mining analysis of data is more oriented to explore and discover of new knowledge. Data mining tools usually have elements of artificiel intelligence (Coppin, 2004), such as neural networks, fuzzy logic, genetic algorithms, technics based on rules and other intelligent technics. Thus data mining tools are categorized in technologies for knowledge management and classic DSSs become knowledge-based DSSs. (Gottschalk, 2007) Data mining analysis provides insight into data which can not be obtained by other technics. The insights can be obtained by finding hiden patterns and relations in great databases and elicitating inferences on rules related to it. The patterns and rules than can be used in decision making and predicting effects of the decisons. Types of information that can be provided from data mining analyses incude associations, sequences, classifications, clusters and forecasts. (Wrembel & Koncilia, 2007) Associations are appearances related to an event. For example, study of sample of purchases in supermarket can discover that when potato chips is bought, cola drink is sold in 65% of all cases, but when there is promotion, cola is sold in 85% of all cases. In case of sequences, events are chronologically related. For example, we can determine if house is bought, then new refrigerator will be bought in period of two weeks, in 65% of all cases, and cooker will be bought in period of one mounth after purchase of house, in 45% of all cases. Classification recognises patterns describing some group to which an item belong by examining of existing items that are classified and by determination of rule set. For example, companies are worried due to loss of loyal customers. Therefore, they create model supporting managers to predict who are these customers and to develop special campaign for keeping the customers. Clustering is similar as classification, with difference that none of groups is not defined yet. Data mining tools discover various clusters in data, such as similar groups of bank card users. Data mining tools include forecasts which can be done on various ways. In forecasts, series of existing values are used for predicting other values. There are a lot of examples how some organizations use data mining tools for decision support and improvement of business. ShopKoStores company uses DM tools to find correlation between position of items on store shelves and habits in parchases of customers. Aim is to increase sale by arrangement of products so that are appropriate to customers. Second example is Notdstrem company using DM for analysis of data that is generated by visitors of its web site. Results of the analysis then are used for adjustment of advertising and content of advertisments to individual attributes and needs of customers and for improvement of on-line services for customers. (Han & Kamber, 2006; Lin et al., 2005) CONCLUSION Decision Support Systems are similar and complementary to standard management information systems and mainly support unstructured and semistructured processes of decision making. Components of the systems are: databases, software system with models and tools and user interface. Contemporary DSSs use special databases called data warehouses that are made by 307

6 extracting of data from transaction databases and putting data in appropriate form for decision making. The data is subject of multidimensional analysis with support of dataminig technology that uses various types of models (statistical, mathematical, financial etc.). Many examples (some of them are explained in the paper) show that great business benefits can be achieved by support of the two technologies. Business benefits follow from enhanced making of unstructured and semistructured decisions that are multidimensional in nature. REFERENCES Coppin, B. (2004). Artificial Intelligence Illuminated, Jones and Bartlett Publishers, Inc. Sudbury. Gottschalk, P. (2007). Knowledge Management Systems - Value Shop Creation, Idea Group Publishing, Hershey. Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann Publishers, San Francisco. Lin, T.Y., Ohsuga, S., Liau, C.J., Hu, X. & Tsumoto, S. (2005). Foundations of Data Mining and Knowledge Discovery, Springer-Verlag, Berlin Heidelberg. Turban, E., King, D., Lee, J., Warkentin, M. & Chung, H.M. (2004). Electronic Commerce: A Managerial Perspective, Prentice Hall, Pearson Education Inc., New Jersey. Wrembel, R. & Koncilia, C. (2007). Data Warehouses and OLAP: Concepts, Architectures and Solutions, Idea Group Publishing, Hershey. 308

5.5 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall. Figure 5-2

5.5 Copyright 2011 Pearson Education, Inc. publishing as Prentice Hall. Figure 5-2 Class Announcements TIM 50 - Business Information Systems Lecture 15 Database Assignment 2 posted Due Tuesday 5/26 UC Santa Cruz May 19, 2015 Database: Collection of related files containing records on

More information

Chapter Managing Knowledge in the Digital Firm

Chapter Managing Knowledge in the Digital Firm Chapter Managing Knowledge in the Digital Firm Essay Questions: 1. What is knowledge management? Briefly outline the knowledge management chain. 2. Identify the three major types of knowledge management

More information

TIM 50 - Business Information Systems

TIM 50 - Business Information Systems TIM 50 - Business Information Systems Lecture 15 UC Santa Cruz March 1, 2015 The Database Approach to Data Management Database: Collection of related files containing records on people, places, or things.

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Chapter 5 Foundations of Business Intelligence: Databases and Information Management 5.1 Copyright 2011 Pearson Education, Inc. Student Learning Objectives How does a relational database organize data,

More information

DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM.

DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM. DATA MINING TECHNOLOGY Georgiana Marin 1 Abstract In terms of data processing, classical statistical models are restrictive; it requires hypotheses, the knowledge and experience of specialists, equations,

More information

Data Mining Solutions for the Business Environment

Data Mining Solutions for the Business Environment Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Problem: HP s numerous systems unable to deliver the information needed for a complete picture of business operations, lack of

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

Course 103402 MIS. Foundations of Business Intelligence

Course 103402 MIS. Foundations of Business Intelligence Oman College of Management and Technology Course 103402 MIS Topic 5 Foundations of Business Intelligence CS/MIS Department Organizing Data in a Traditional File Environment File organization concepts Database:

More information

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data INFO 1500 Introduction to IT Fundamentals 5. Database Systems and Managing Data Resources Learning Objectives 1. Describe how the problems of managing data resources in a traditional file environment are

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Content Problems of managing data resources in a traditional file environment Capabilities and value of a database management

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Chapter 6 Foundations of Business Intelligence: Databases and Information Management 6.1 2010 by Prentice Hall LEARNING OBJECTIVES Describe how the problems of managing data resources in a traditional

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

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

Chapter 6 FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT Learning Objectives

Chapter 6 FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT Learning Objectives Chapter 6 FOUNDATIONS OF BUSINESS INTELLIGENCE: DATABASES AND INFORMATION MANAGEMENT Learning Objectives Describe how the problems of managing data resources in a traditional file environment are solved

More information

Chapter 6. Foundations of Business Intelligence: Databases and Information Management

Chapter 6. Foundations of Business Intelligence: Databases and Information Management Chapter 6 Foundations of Business Intelligence: Databases and Information Management VIDEO CASES Case 1a: City of Dubuque Uses Cloud Computing and Sensors to Build a Smarter, Sustainable City Case 1b:

More information

Chapter 6 - Enhancing Business Intelligence Using Information Systems

Chapter 6 - Enhancing Business Intelligence Using Information Systems Chapter 6 - Enhancing Business Intelligence Using Information Systems Managers need high-quality and timely information to support decision making Copyright 2014 Pearson Education, Inc. 1 Chapter 6 Learning

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

Data Mart/Warehouse: Progress and Vision

Data Mart/Warehouse: Progress and Vision Data Mart/Warehouse: Progress and Vision Institutional Research and Planning University Information Systems What is data warehousing? A data warehouse: is a single place that contains complete, accurate

More information

Chapter 6 8/12/2015. Foundations of Business Intelligence: Databases and Information Management. Problem:

Chapter 6 8/12/2015. Foundations of Business Intelligence: Databases and Information Management. Problem: Foundations of Business Intelligence: Databases and Information Management VIDEO CASES Chapter 6 Case 1a: City of Dubuque Uses Cloud Computing and Sensors to Build a Smarter, Sustainable City Case 1b:

More information

OLAP. Business Intelligence OLAP definition & application Multidimensional data representation

OLAP. Business Intelligence OLAP definition & application Multidimensional data representation OLAP Business Intelligence OLAP definition & application Multidimensional data representation 1 Business Intelligence Accompanying the growth in data warehousing is an ever-increasing demand by users for

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

Data Warehousing and Data Mining in Business Applications

Data Warehousing and Data Mining in Business Applications 133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business

More information

Towards applying Data Mining Techniques for Talent Mangement

Towards applying Data Mining Techniques for Talent Mangement 2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011) (2011) IACSIT Press, Singapore Towards applying Data Mining Techniques for Talent Mangement Hamidah Jantan 1,

More information

Decision Support and Business Intelligence Systems. Chapter 1: Decision Support Systems and Business Intelligence

Decision Support and Business Intelligence Systems. Chapter 1: Decision Support Systems and Business Intelligence Decision Support and Business Intelligence Systems Chapter 1: Decision Support Systems and Business Intelligence Types of DSS Two major types: Model-oriented DSS Data-oriented DSS Evolution of DSS into

More information

Customer Classification And Prediction Based On Data Mining Technique

Customer Classification And Prediction Based On Data Mining Technique Customer Classification And Prediction Based On Data Mining Technique Ms. Neethu Baby 1, Mrs. Priyanka L.T 2 1 M.E CSE, Sri Shakthi Institute of Engineering and Technology, Coimbatore 2 Assistant Professor

More information

Data mining and official statistics

Data mining and official statistics Quinta Conferenza Nazionale di Statistica Data mining and official statistics Gilbert Saporta président de la Société française de statistique 5@ S Roma 15, 16, 17 novembre 2000 Palazzo dei Congressi Piazzale

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Wienand Omta Fabiano Dalpiaz 1 drs. ing. Wienand Omta Learning Objectives Describe how the problems of managing data resources

More information

Hybrid Support Systems: a Business Intelligence Approach

Hybrid Support Systems: a Business Intelligence Approach Journal of Applied Business Information Systems, 2(2), 2011 57 Journal of Applied Business Information Systems http://www.jabis.ro Hybrid Support Systems: a Business Intelligence Approach Claudiu Brandas

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

DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support

DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support DMDSS: Data Mining Based Decision Support System to Integrate Data Mining and Decision Support Rok Rupnik, Matjaž Kukar, Marko Bajec, Marjan Krisper University of Ljubljana, Faculty of Computer and Information

More information

Web Data Mining: A Case Study. Abstract. Introduction

Web Data Mining: A Case Study. Abstract. Introduction Web Data Mining: A Case Study Samia Jones Galveston College, Galveston, TX 77550 Omprakash K. Gupta Prairie View A&M, Prairie View, TX 77446 okgupta@pvamu.edu Abstract With an enormous amount of data stored

More information

ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies

ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online

More information

Databases and Information Management

Databases and Information Management Databases and Information Management Reading: Laudon & Laudon chapter 5 Additional Reading: Brien & Marakas chapter 3-4 COMP 5131 1 Outline Database Approach to Data Management Database Management Systems

More information

Chapter 11. Managing Knowledge

Chapter 11. Managing Knowledge Chapter 11 Managing Knowledge VIDEO CASES Video Case 1: How IBM s Watson Became a Jeopardy Champion. Video Case 2: Tour: Alfresco: Open Source Document Management System Video Case 3: L'Oréal: Knowledge

More information

Improving Decision Making and Managing Knowledge

Improving Decision Making and Managing Knowledge Improving Decision Making and Managing Knowledge Decision Making and Information Systems Information Requirements of Key Decision-Making Groups in a Firm Senior managers, middle managers, operational managers,

More information

Subject Description Form

Subject Description Form Subject Description Form Subject Code Subject Title COMP417 Data Warehousing and Data Mining Techniques in Business and Commerce Credit Value 3 Level 4 Pre-requisite / Co-requisite/ Exclusion Objectives

More information

Business Intelligence and Decision Support Systems

Business Intelligence and Decision Support Systems Chapter 12 Business Intelligence and Decision Support Systems Information Technology For Management 7 th Edition Turban & Volonino Based on lecture slides by L. Beaubien, Providence College John Wiley

More information

Data Mining for Knowledge Management in Technology Enhanced Learning

Data Mining for Knowledge Management in Technology Enhanced Learning Proceedings of the 6th WSEAS International Conference on Applications of Electrical Engineering, Istanbul, Turkey, May 27-29, 2007 115 Data Mining for Knowledge Management in Technology Enhanced Learning

More information

Information Systems and Technologies in Organizations

Information Systems and Technologies in Organizations Information Systems and Technologies in Organizations Information System One that collects, processes, stores, analyzes, and disseminates information for a specific purpose Is school register an information

More information

Knowledge Discovery and Data. Data Mining vs. OLAP

Knowledge Discovery and Data. Data Mining vs. OLAP Knowledge Discovery and Data Mining Data Mining vs. OLAP Sajjad Haider Spring 2010 1 Acknowledgement All the material in this presentation is taken from the Internet. A simple search of Data Mining vs.

More information

Enhancing Decision Making

Enhancing Decision Making Enhancing Decision Making Content Describe the different types of decisions and how the decision-making process works. Explain how information systems support the activities of managers and management

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

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA

OLAP and OLTP. AMIT KUMAR BINDAL Associate Professor M M U MULLANA OLAP and OLTP AMIT KUMAR BINDAL Associate Professor Databases Databases are developed on the IDEA that DATA is one of the critical materials of the Information Age Information, which is created by data,

More information

Database Marketing, Business Intelligence and Knowledge Discovery

Database Marketing, Business Intelligence and Knowledge Discovery Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski

More information

DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM

DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM M. Mayilvaganan 1, S. Aparna 2 1 Associate

More information

Technology in Action. Alan Evans Kendall Martin Mary Anne Poatsy. Eleventh Edition. Copyright 2015 Pearson Education, Inc.

Technology in Action. Alan Evans Kendall Martin Mary Anne Poatsy. Eleventh Edition. Copyright 2015 Pearson Education, Inc. Copyright 2015 Pearson Education, Inc. Technology in Action Alan Evans Kendall Martin Mary Anne Poatsy Eleventh Edition Copyright 2015 Pearson Education, Inc. Technology in Action Chapter 9 Behind the

More information

INTERACTIVE DECISION SUPPORT SYSTEM BASED ON ANALYSIS AND SYNTHESIS OF DATA - DATA WAREHOUSE

INTERACTIVE DECISION SUPPORT SYSTEM BASED ON ANALYSIS AND SYNTHESIS OF DATA - DATA WAREHOUSE INTERACTIVE DECISION SUPPORT SYSTEM BASED ON ANALYSIS AND SYNTHESIS OF DATA - DATA WAREHOUSE Prof. Georgeta Şoavă Ph. D University of Craiova Faculty of Economics and Business Administration, Craiova,

More information

Data Mining. Shahram Hassas Math 382 Professor: Shapiro

Data Mining. Shahram Hassas Math 382 Professor: Shapiro Data Mining Shahram Hassas Math 382 Professor: Shapiro Agenda Introduction Major Elements Steps/ Processes Examples Tools used for data mining Advantages and Disadvantages What is Data Mining? Described

More information

14. Data Warehousing & Data Mining

14. Data Warehousing & Data Mining 14. Data Warehousing & Data Mining Data Warehousing Concepts Decision support is key for companies wanting to turn their organizational data into an information asset Data Warehouse "A subject-oriented,

More information

A Brief Tutorial on Database Queries, Data Mining, and OLAP

A Brief Tutorial on Database Queries, Data Mining, and OLAP A Brief Tutorial on Database Queries, Data Mining, and OLAP Lutz Hamel Department of Computer Science and Statistics University of Rhode Island Tyler Hall Kingston, RI 02881 Tel: (401) 480-9499 Fax: (401)

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Chapter 5 Foundations of Business Intelligence: Databases and Information Management 5.1 See Markers-ORDER-DB Logically Related Tables Relational Approach: Physically Related Tables: The Relationship Screen

More information

Customer Analytics. Turn Big Data into Big Value

Customer Analytics. Turn Big Data into Big Value Turn Big Data into Big Value All Your Data Integrated in Just One Place BIRT Analytics lets you capture the value of Big Data that speeds right by most enterprises. It analyzes massive volumes of data

More information

Introduction to Management Information Systems

Introduction to Management Information Systems IntroductiontoManagementInformationSystems Summary 1. Explain why information systems are so essential in business today. Information systems are a foundation for conducting business today. In many industries,

More information

COURSE SYLLABUS. Enterprise Information Systems and Business Intelligence

COURSE SYLLABUS. Enterprise Information Systems and Business Intelligence MASTER PROGRAMS Autumn Semester 2008/2009 COURSE SYLLABUS Enterprise Information Systems and Business Intelligence Instructor: Malov Andrew, Master of Computer Sciences, Assistant,aomalov@mail.ru Organization

More information

Data Mining for Successful Healthcare Organizations

Data Mining for Successful Healthcare Organizations Data Mining for Successful Healthcare Organizations For successful healthcare organizations, it is important to empower the management and staff with data warehousing-based critical thinking and knowledge

More information

Prediction of Heart Disease Using Naïve Bayes Algorithm

Prediction of Heart Disease Using Naïve Bayes Algorithm Prediction of Heart Disease Using Naïve Bayes Algorithm R.Karthiyayini 1, S.Chithaara 2 Assistant Professor, Department of computer Applications, Anna University, BIT campus, Tiruchirapalli, Tamilnadu,

More information

ECLT 5810 E-Commerce Data Mining Techniques - Introduction. Prof. Wai Lam

ECLT 5810 E-Commerce Data Mining Techniques - Introduction. Prof. Wai Lam ECLT 5810 E-Commerce Data Mining Techniques - Introduction Prof. Wai Lam Data Opportunities Business infrastructure have improved the ability to collect data Virtually every aspect of business is now open

More information

HGH BI Solutions. Business Intelligence & Integration. Equipping Your Organization for Effective Decision Making

HGH BI Solutions. Business Intelligence & Integration. Equipping Your Organization for Effective Decision Making HGH BI Solutions Business Intelligence & Integration Equipping Your Organization for Effective Decision Making Peter Kranenburg RI MCP HGH Business Consultancy B.V. Agenda BI building blocks - components

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

one Introduction chapter OVERVIEW CHAPTER

one Introduction chapter OVERVIEW CHAPTER one Introduction CHAPTER chapter OVERVIEW 1.1 Introduction to Decision Support Systems 1.2 Defining a Decision Support System 1.3 Decision Support Systems Applications 1.4 Textbook Overview 1.5 Summary

More information

Sunnie Chung. Cleveland State University

Sunnie Chung. Cleveland State University Sunnie Chung Cleveland State University Data Scientist Big Data Processing Data Mining 2 INTERSECT of Computer Scientists and Statisticians with Knowledge of Data Mining AND Big data Processing Skills:

More information

A New Approach for Evaluation of Data Mining Techniques

A New Approach for Evaluation of Data Mining Techniques 181 A New Approach for Evaluation of Data Mining s Moawia Elfaki Yahia 1, Murtada El-mukashfi El-taher 2 1 College of Computer Science and IT King Faisal University Saudi Arabia, Alhasa 31982 2 Faculty

More information

Application of Business Intelligence in Transportation for a Transportation Service Provider

Application of Business Intelligence in Transportation for a Transportation Service Provider Application of Business Intelligence in Transportation for a Transportation Service Provider Mohamed Sheriff Business Analyst Satyam Computer Services Ltd Email: mohameda_sheriff@satyam.com, mail2sheriff@sify.com

More information

Cis330. Mostafa Z. Ali

Cis330. Mostafa Z. Ali Fall 2009 Lecture 1 Cis330 Decision Support Systems and Business Intelligence Mostafa Z. Ali mzali@just.edu.jo Lecture 2: Slide 1 Changing Business Environments and Computerized Decision Support The business

More information

TRENDS IN THE DEVELOPMENT OF BUSINESS INTELLIGENCE SYSTEMS

TRENDS IN THE DEVELOPMENT OF BUSINESS INTELLIGENCE SYSTEMS 9 8 TRENDS IN THE DEVELOPMENT OF BUSINESS INTELLIGENCE SYSTEMS Assist. Prof. Latinka Todoranova Econ Lit C 810 Information technology is a highly dynamic field of research. As part of it, business intelligence

More information

Business Intelligence

Business Intelligence Business Intelligence Data Mining and Data Warehousing Dominik Ślęzak slezak@infobright.com www.infobright.com Research Interests Data Warehouses, Knowledge Discovery, Rough Sets Machine Intelligence,

More information

Analyzing Polls and News Headlines Using Business Intelligence Techniques

Analyzing Polls and News Headlines Using Business Intelligence Techniques Analyzing Polls and News Headlines Using Business Intelligence Techniques Eleni Fanara, Gerasimos Marketos, Nikos Pelekis and Yannis Theodoridis Department of Informatics, University of Piraeus, 80 Karaoli-Dimitriou

More information

Customer Analysis - Customer analysis is done by analyzing the customer's buying preferences, buying time, budget cycles, etc.

Customer Analysis - Customer analysis is done by analyzing the customer's buying preferences, buying time, budget cycles, etc. Data Warehouses Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical

More information

Data Mining + Business Intelligence. Integration, Design and Implementation

Data Mining + Business Intelligence. Integration, Design and Implementation Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution

More information

Data Warehouse Architecture Overview

Data Warehouse Architecture Overview Data Warehousing 01 Data Warehouse Architecture Overview DW 2014/2015 Notice! Author " João Moura Pires (jmp@di.fct.unl.pt)! This material can be freely used for personal or academic purposes without any

More information

DSS based on Data Warehouse

DSS based on Data Warehouse DSS based on Data Warehouse C_13 / 6.01.2015 Decision support system is a complex system engineering. At the same time, research DW composition, DW structure and DSS Architecture based on DW, puts forward

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

Class 2. Learning Objectives

Class 2. Learning Objectives Class 2 BUSINESS INTELLIGENCE Learning Objectives Describe the business intelligence (BI) methodology and concepts and relate them to DSS Understand the major issues in implementing computerized support

More information

Business Analytics C_12 / 16.12.2014

Business Analytics C_12 / 16.12.2014 C_12 / 16.12.2014 Business Analytics Analytics = science of analysis analysis of data: methods and software tools. Business analytics = applications and techniques for gathering, storing, analyzing and

More information

A Knowledge Management Framework Using Business Intelligence Solutions

A Knowledge Management Framework Using Business Intelligence Solutions www.ijcsi.org 102 A Knowledge Management Framework Using Business Intelligence Solutions Marwa Gadu 1 and Prof. Dr. Nashaat El-Khameesy 2 1 Computer and Information Systems Department, Sadat Academy For

More information

Master of Science in Health Information Technology Degree Curriculum

Master of Science in Health Information Technology Degree Curriculum Master of Science in Health Information Technology Degree Curriculum Core courses: 8 courses Total Credit from Core Courses = 24 Core Courses Course Name HRS Pre-Req Choose MIS 525 or CIS 564: 1 MIS 525

More information

IT and CRM A basic CRM model Data source & gathering system Database system Data warehouse Information delivery system Information users

IT and CRM A basic CRM model Data source & gathering system Database system Data warehouse Information delivery system Information users 1 IT and CRM A basic CRM model Data source & gathering Database Data warehouse Information delivery Information users 2 IT and CRM Markets have always recognized the importance of gathering detailed data

More information

Journal of Information Technology Impact

Journal of Information Technology Impact Journal of Information Technology Impact Vol. 5, No. 3, pp. 129-138, 2005 Using a Priori Algorithm for Supporting an e-commerce System Mohammad Nazir Ahmad Sharif 1 Ng Moon Ching 2 Aryati Bakri 3 Nor Hidayati

More information

PREDICTIVE DATA MINING ON WEB-BASED E-COMMERCE STORE

PREDICTIVE DATA MINING ON WEB-BASED E-COMMERCE STORE PREDICTIVE DATA MINING ON WEB-BASED E-COMMERCE STORE Jidi Zhao, Tianjin University of Commerce, zhaojidi@263.net Huizhang Shen, Tianjin University of Commerce, hzshen@public.tpt.edu.cn Duo Liu, Tianjin

More information

Database Marketing simplified through Data Mining

Database Marketing simplified through Data Mining Database Marketing simplified through Data Mining Author*: Dr. Ing. Arnfried Ossen, Head of the Data Mining/Marketing Analysis Competence Center, Private Banking Division, Deutsche Bank, Frankfurt, Germany

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

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

CHAPTER SIX DATA. Business Intelligence. 2011 The McGraw-Hill Companies, All Rights Reserved

CHAPTER SIX DATA. Business Intelligence. 2011 The McGraw-Hill Companies, All Rights Reserved CHAPTER SIX DATA Business Intelligence 2011 The McGraw-Hill Companies, All Rights Reserved 2 CHAPTER OVERVIEW SECTION 6.1 Data, Information, Databases The Business Benefits of High-Quality Information

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

Introduction. A. Bellaachia Page: 1

Introduction. A. Bellaachia Page: 1 Introduction 1. Objectives... 3 2. What is Data Mining?... 4 3. Knowledge Discovery Process... 5 4. KD Process Example... 7 5. Typical Data Mining Architecture... 8 6. Database vs. Data Mining... 9 7.

More information

Data Mining Algorithms and Techniques Research in CRM Systems

Data Mining Algorithms and Techniques Research in CRM Systems Data Mining Algorithms and Techniques Research in CRM Systems ADELA TUDOR, ADELA BARA, IULIANA BOTHA The Bucharest Academy of Economic Studies Bucharest ROMANIA {Adela_Lungu}@yahoo.com {Bara.Adela, Iuliana.Botha}@ie.ase.ro

More information

1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing

1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing 1. OLAP is an acronym for a. Online Analytical Processing b. Online Analysis Process c. Online Arithmetic Processing d. Object Linking and Processing 2. What is a Data warehouse a. A database application

More information

Increasing the Efficiency of Customer Relationship Management Process using Data Mining Techniques

Increasing the Efficiency of Customer Relationship Management Process using Data Mining Techniques Increasing the Efficiency of Customer Relationship Management Process using Data Mining Techniques P.V.D PRASAD Lead Functional Consultant, JMR InfoTech, Sigma Soft Tech Park Whitefield, Bangalore 560066

More information

IMPROVING PRODUCTIVITY USING STANDARD MATHEMATICAL PROGRAMMING SOFTWARE

IMPROVING PRODUCTIVITY USING STANDARD MATHEMATICAL PROGRAMMING SOFTWARE IMPROVING PRODUCTIVITY USING STANDARD MATHEMATICAL PROGRAMMING SOFTWARE $QWRQýLåPDQ 1, Samo Cerc 2, Andrej Pajenk 3 1 University of Maribor, Fakulty of Organizational Sciences.UDQM.LGULþHYDD(PDLODQWRQFL]PDQ#IRYXQLPEVL

More information

Data mining in the e-learning domain

Data mining in the e-learning domain Data mining in the e-learning domain The author is Education Liaison Officer for e-learning, Knowsley Council and University of Liverpool, Wigan, UK. Keywords Higher education, Classification, Data encapsulation,

More information

S.Thiripura Sundari*, Dr.A.Padmapriya**

S.Thiripura Sundari*, Dr.A.Padmapriya** Structure Of Customer Relationship Management Systems In Data Mining S.Thiripura Sundari*, Dr.A.Padmapriya** *(Department of Computer Science and Engineering, Alagappa University, Karaikudi-630 003 **

More information

Data Mining: Motivations and Concepts

Data Mining: Motivations and Concepts POLYTECHNIC UNIVERSITY Department of Computer Science / Finance and Risk Engineering Data Mining: Motivations and Concepts K. Ming Leung Abstract: We discuss here the need, the goals, and the primary tasks

More information

An Overview of Database management System, Data warehousing and Data Mining

An Overview of Database management System, Data warehousing and Data Mining An Overview of Database management System, Data warehousing and Data Mining Ramandeep Kaur 1, Amanpreet Kaur 2, Sarabjeet Kaur 3, Amandeep Kaur 4, Ranbir Kaur 5 Assistant Prof., Deptt. Of Computer Science,

More information

IMPROVING THE QUALITY OF THE DECISION MAKING BY USING BUSINESS INTELLIGENCE SOLUTIONS

IMPROVING THE QUALITY OF THE DECISION MAKING BY USING BUSINESS INTELLIGENCE SOLUTIONS IMPROVING THE QUALITY OF THE DECISION MAKING BY USING BUSINESS INTELLIGENCE SOLUTIONS Maria Dan Ştefan Academy of Economic Studies, Faculty of Accounting and Management Information Systems, Uverturii Street,

More information

Distance Learning and Examining Systems

Distance Learning and Examining Systems Lodz University of Technology Distance Learning and Examining Systems - Theory and Applications edited by Sławomir Wiak Konrad Szumigaj HUMAN CAPITAL - THE BEST INVESTMENT The project is part-financed

More information

Mario Guarracino. Data warehousing

Mario Guarracino. Data warehousing Data warehousing Introduction Since the mid-nineties, it became clear that the databases for analysis and business intelligence need to be separate from operational. In this lecture we will review the

More information

INFO1400. 1. What are business processes? How are they related to information systems?

INFO1400. 1. What are business processes? How are they related to information systems? Chapter 2 INFO1400 Review Questions 1. What are business processes? How are they related to information systems? Define business processes and describe the role they play in organizations. A business process

More information

THE INTELLIGENT BUSINESS INTELLIGENCE SOLUTIONS

THE INTELLIGENT BUSINESS INTELLIGENCE SOLUTIONS THE INTELLIGENT BUSINESS INTELLIGENCE SOLUTIONS ADRIAN COJOCARIU, CRISTINA OFELIA STANCIU TIBISCUS UNIVERSITY OF TIMIŞOARA, FACULTY OF ECONOMIC SCIENCE, DALIEI STR, 1/A, TIMIŞOARA, 300558, ROMANIA ofelia.stanciu@gmail.com,

More information