Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Components of DSS – Data Management Subsystem – Model Management Subsystem – User Interface (Dialog) Subsystem – Knowledge-based Management Subsys-tem – User Components of DSS DSS Components Data Management Subsystem • • • • DSS database DBMS Data directory Query facility Data Management Subsystem The DSS Database Internal Data come mainly from the organization’s Transaction Processing System (TPS) Private Data can include guidelines used by some decision makers assessments of specific data and/or situations External Data includes industry data market research data census data regional employment data government regulations tax rate schedules national economic data 4 Data Management Subsystem Data Organization • Data for DSS can be – entered directly into models – extracted directly from larger databases e.g. Data Warehouse • Can include multimedia objects • OODBs in XML used in m-commerce 5 Data Management Subsystem Data Extraction (ETL) • The process of – capturing data from several sources – synthesizing, summarizing – determining which of them are relevant – and organizing them • resulting in their effective integration 6 Data Management Subsystem Database Management System • A database is created, accessed and updated by a DBMS – Software for establishing, updating, and querying e.g. managing a database • record navigation • data relationships • report generation 7 Data Management Subsystem Query Facility • The (database) mechanism that – accepts requests for data – accesses – manipulates – and queries data • Includes a query language – e.g. SQL 8 Data Management Subsystem Data Directory • A catalog of all the data in a database or all the models in a model base • Contains – data definitions – data source – data meaning • Supports addition and deletion of new entries 9 Data Management Subsystem Key DB & DBMS Issues • Data quality – “Garbage in/garbage out" (GIGO) – Managers feel they do not get the data they need – 54% satisfied – Poor quality data leads to poor quality information • waste • lost opportunities • unhappy customers 10 Data Management Subsystem Key DB & DBMS Issues • Data integration – For DSS to work, data must be integrated from disparate sources – “Creating a single version of the truth” • Scalability – Volume of data increases dramatically • e.g. from 2001 – 2003, size of largest TPS DB increase two-fold (11 – 20 terabytes) – Needs new storage and search technologies 11 Data Management Subsystem Key DB & DBMS Issues • Data security – data must be protected from unauthorized access through security measures – tools to monitor database activities – audit trail 12 10 Key Ingredients of Data (Information) Quality Management 1. 2. 3. 4. 5. Data quality is a business problem, not only a systems problem Focus on information about customers and suppliers, not just data Focus on all components of data: definition, content, and presentation Implement data/information quality management processes, not just software to handle them Measure data accuracy as well as validity 10 Key Ingredients of Data (Information) Quality Management 6. 7. 8. 9. 10. Measure real costs (not just the percentage) of poor quality data/information Emphasize process improvement/preventive maintenance, not just data cleansing Improve processes (and hence data quality) at the source Educate managers about the impacts of poor data quality and how to improve it Actively transform the culture to one that values data quality DSS Components Model Management Subsystem • • • • • Model base MBMS Modeling language Model directory Model execution, integration, and command processor Note: MBMS – model base management systems – software for establishing, updating, combining A DSS model base DSS Components Model Management Subsystem • The four (4) functions Model creation, using programming languages, DSS tools and/or subroutines, and other building blocks 2. Generation of new routines and reports 3. Model updating and changing 4. Model data manipulation 1. Model Management Subsystem Categories of Models • Strategic Models – 5- 10 years planning – Models that represent problems for the strategic level – E.g Southwest Airlines – used its system to create accurate financial forecasts to identify strategic opportunities. Can plan large, expensive equipment needed in future – i.e. executive level of management • developing corporate objectives • forecasting sales target • Tactical Models 1 month – 4 years – Models that represent problems for the tactical level – i.e. mid-level management – allocates and controls resources • labour requirement planning • sales promotion planning 17 Model Management Subsystem Categories of Models Operational Model Models that represent problems for the operational level of management Supports day-to-day working activities manufacturing targets e-commerce transaction acceptance approval of personal loans Analytical Models – perform analysis of data – Mathematical models into which data are loaded for analysis Statistical models management science models data mining algorithms Financial models Integrated with other models, e.g. strategic planning model 18 Model Management Subsystem Model Directory • Similar to database directory • A catalog of all models and other software in the model base – model definitions – functions – availability and capability 19 Model Management Subsystem Model Execution, Integration • Model execution – the process of controlling the actual running of the model • Model integration – involves combining the operations of several models when needed e.G use a DSS that contains six integrated models: planning and scheduling models, forecasting models 20 Model Management Subsystem Model Command Processor • A model command processor – accepts and interpret modeling instructions from the user interface component – and route them to • the MBMS • model execution or integration functions 21 Model Management Subsystem Some DSS Questions • Which models should be used for what situations? – Cannot be done by MBMS • What method should be used to solve a problem in a specific model class? – highly dependent on the knowledge component 22 DSS Components User Interface (Dialog) Subsystem • Interface – Application interface – User Interface • Graphical User Interface (GUI) • DSS User Interface – Portal – Graphical icons • Dashboard – Color coding • Interfacing with PDAs, cell phones, etc. DSS Components Knowledgebase Management System • Incorporation of intelligence and expertise • Knowledge components: – – – – – – Expert systems, Knowledge management systems, Neural networks, Intelligent agents, Fuzzy logic, Case-based reasoning systems, and so on • Often used to better manage the other DSS components DSS User • One faced with a decision that an MSS is designed to support – Manager, decision maker, problem solver, … • The users differ greatly from each other – Different organizational positions they occupy; cognitive preferences/abilities; the ways of arriving at a decision (i.e., decision styles) • User = Individual versus Group • Managers versus Staff Specialists [staff assistants, expert tool users, business (system) analysts, facilitators (in a GSS)] DSS Components Future/current DSS Developments • Hardware enhancements – Smaller, faster, cheaper, … • Software/hardware advancements – data warehousing, data mining, OLAP, Web technologies, integration and dissemination technologies (XML, Web services, SOA, grid computing, cloud computing, …) • Integration of AI -> smart systems DSS Hardware • Typically, MSS run on standard hardware • Can be composed of mainframe computers with legacy DBMS, workstations, personal computers, or client/server systems • Nowadays, usually implemented as a distributed/integrated, loosely-coupled Web-based systems • Can be acquired from – A single vendor – Many vendors (best-of-breed) End of the Chapter • Questions / Comments… All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall