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Exam 2 Review

Exam 2 Review. June 9, 2014. Info. Systems in Organizations Decision Making. IS & Hierarchical Organizational structure. 3. Administrative Information Systems. Transaction Processing Systems (TPS)

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Exam 2 Review

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  1. Exam 2 Review June 9, 2014

  2. Info. Systems in OrganizationsDecision Making

  3. IS & Hierarchical Organizational structure . 3

  4. Administrative Information Systems Transaction Processing Systems (TPS) Basic business system that serves the operational level (including analysts) in organizations Capture & process data generated during day-to-day activities Office Automation Systems (OAS) Systems designed to help office workers in doing their job. Decision Support Systems (DSS) Systems designed to support middle managers and business professionals during the decision-making process Executive Information Systems (EIS) or Executive Support Systems (ESS) Specialized DSS that help senior level executives make decisions. GDSS: computer-based systems that facilitate solving of unstructured problems by set of decision makers 4

  5. Organization & IS: another view Types of Information Systems:- Transaction Processing Systems- Office Automation Systems- Knowledge Worker Systems- Management Information Systems- Decision Support Systems- Executive Information Systems Top ManagementMiddle ManagementLower ManagementOperational workers Officeworkers Officeworkers Questions Officeworkers Officeworkers Knowledgeworkers Q: What kind of IS are designed to provide help for decision makers?

  6. Decision Making process Simon’s decision-making process model Intelligence Design Choice (Implementation) Herbert Simon (1955), A Behavioral Model of Rational Choice, Quarterly Journal of Economics, vol. 69, 99–188 Newell, A., and Simon, H. A. (1972). Human problem solving Englewood Cliffs, Prentice-Hall, New Jersey. 6

  7. Intelligence Phase Data source • Scan the environment for a problem. • Determine if decision-maker can solve the problem. • Within their scope of influence? • Fully define the problem by gathering more information about the problem. Scan Environment forproblem to be solvedor decision to be made Organizational IS Problem ? No END Yes Problem within scope of influence? No END Yes Gather more informationabout the problem Internal & External data

  8. Design Phase • Develop a model of the problem. • Determine type of model. • Verify model. • Develop and analyze potential solutions. Develop a model ofproblem to be solved Verify that the model is accurate Develop potentialsolutions

  9. Choice Phase • Select the solution to implement. • More detailed analysis of selected solutions might be needed. • Verify initial conditions. • Analyze proposed solution against real-world constraints. Questions

  10. DSS structure Systems designed to help middle managers make decisions Major components Data management subsystem Internal and external data sources Analysis subsystem Typically mathematical in nature User interface How the people interact with the DSS Data visualization is the key Text Graphs Charts UserInterface Analysis - Sensitivity Analysis- What-if Analysis - Goal-seeking Analysis • Data-driven tools -> Data mining -> OLAP* Data Management - Transactional Data- Data warehouse- Business partners data- Economic data 10 * OLAP: OnLine Analytical Processing

  11. DSS Analysis Tools Simulation is used to examine proposed solutions and their impact Sensitivity analysis Determine how changes in one part of the model influence other parts of the model What-if analysis Manipulate variables to see what would happen in given scenarios Goal-seeking analysis Work backward from desired outcome 11 Determine monthly payment given various interest rates. Works backward from a given monthly payment to determine various loans that would give that payment.

  12. Executive Information Systems • Specialized DSS that supports senior level executives within the organization • Most EISs offer the following capabilities: • Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information • Drill-down – enables users to get details, and details of details, of information • Slice-and-dice – looks at information from different perspectives • Digital dashboards are common features 12

  13. Artificial Intelligence (AI) systems Common categories of AI systems: Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems Neural Network– attempts to emulate the way the human brain works Analyses large quantities of info to establish patterns and characteristics in situations where logic or rules are unknown Uses Fuzzy logic – a mathematical method of handling imprecise or subjective information Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users 13

  14. Expert Systems Artificial Intelligence systems that codify human expertise in a computer system Main goal is to transfer knowledge from one person to another Wide range of subject areas Medical diagnosis Computer purchasing Knowledge engineer elicits the expertise from the expert and encodes it in the expert system 14

  15. Expert Systems Components Knowledge base: database of the expertise, often in IF THEN rules. Inference engine: derives recommendations from knowledge base and problem-specific data User interface: controls the dialog between the user and the system Explanation system: Explain the how and why of recommendations Example of rules IFfamily is albatross ANDcolor is white THENbird is laysan albatross. IFfamily is albatross ANDcolor is dark THENbird is black footed albatross User Domain Expert UserInterface Expertise System Engineer InferenceEngine Knowledge Engineer Encoded expertise Knowledgebase ExplanationSystem - Knowledge engineer codify the human expert’s expertise into the systems’ knowledge base.- System engineer is the IT professional who develop the user interface, the inference engine, and the explanation system. 15

  16. Database & Data Warehouse

  17. Basic Concepts of Database systems Table Two-dimensional structure composed of rows and columns Field Like a column in a spreadsheet Field name Like a column name in a spreadsheet Examples: AccountID, Customer, Type, Balance Field values Actual data for the field Record Set of fields that describe an entity (a person, an account, etc.) Primary key A field, or group of fields, that uniquely identifies a record Accounts table Each table has: • Fields • Records • 1 Primary key 17

  18. Basic Concepts in Data Management A Primary key could be a single field like in these tables Primary key • Primary key could be a composite key, i.e. multiple fields 18

  19. Traditional File Systems System of files that store groups of records used by a particular software application Simple but with a cost Inability to share data Inadequate security Difficulties in maintenance and expansion Allows data duplication (e.g. redundancy) Application 1 Application 2 Program 1 Program 2 Program 2 Program 1 File 1 File 1 File 1 File 1 File 2 File 2 File 2 File 2 File 3 File 3 File 3 File 3 19

  20. Traditional File System Anomalies Insertion anomaly Data needs to be entered more than once if located in multiple file systems Modification anomaly Redundant data in separate file systems Inconsistent data in your system Deletion anomaly Failure to simultaneously delete all copies of redundant data Deletion of critical data 20

  21. Database Advantages • Database advantages from a business perspective include • Ease of data insertion • Example: can insert a new address once; and the address is updated in all forms, reports, etc. • Increased flexibility • Handling changes quickly and easily • Increased scalability and performance • Scalability: how the DB can adapt to increased demand • Reduced information redundancy & inconsistency • Increased information integrity (quality) • Can’t delete a record if related info is used in other container • Increased information security

  22. Types of DBMSs Desktop Designed to run on desktop computers Used by individuals or small businesses Requires little or no formal training Does not have all the capabilities of larger DBMSs Examples: Microsoft Access, FileMaker Desktop Server / Enterprise Handheld 22

  23. Types of DBMSs(Cont.) Server / Enterprise Designed for managing larger and complex databases by large organizations Typically operate in a client/server setup Either centralized or distributed Centralized – all data on one server Easy to maintain Prone to run slowly when many simultaneous users No access if the one server goes down Distributed – each location has part of the database Very complex database administration Usually faster than centralized If one server crashes, others can still continue to operate. Examples: Oracle Enterprise, DB2, Microsoft SQL Server 23

  24. Types of DBMSs (Cont.) Handheld Designed to run on handheld devices Less complex and have less capabilities than Desktop or Server DBMSs Example: Oracle Database Lite, IBM’s DB2 Everywhere. 24

  25. DBMS Functions Create database structure (tables, relationships, schema, etc.) Transform data into information (reports, ..) Provide user with different logical views of actual database content Provide security: password authentication, access control DBMSs control who can add, view, change, or delete data in the database Logical views Physical view IDName02 Linda IDNameAmt01 John 23.0002 Linda 3.00 IDNameAmt01 John 23.0002 Linda 3.0003 Paul 53.00 NameAmt Paul 53.00 25

  26. DBMS Functions (cont.) Allowing multi-user access with control Control concurrency of access to data Prevent one user from accessing data that has not been completely updated When selling tickets online, Ticketmaster allows you to hold a ticket for only 2 minutes to make your purchase decision, then the ticket is released to sell to someone else – that is concurrency control 26

  27. Database Models Database model = a representation of the relationship between structures (e.g. tables) in a database Common database models Flat file model Relational model (the most common, today) Object-oriented database model 27

  28. Flat File Database model • Stores data in basic table structures • No relationship between tables • Used on PDAs for address book 28

  29. Relational Database Model Multiple two-dimensional tables related by common fields Uses controlled redundancy to create fields that provide linkage relationships between tables in the database These fields are called foreign keys – the secret to a relational database A foreign key is a field, or group of fields, in one table that is the primary key of another table Handles One-to-Many and One-to-One relationships 29

  30. Object-Oriented Database model Needed for multimedia applications that manage images, voice, videos, graphics, etc. Used in conjunction with Object-oriented programming languages Slower compared to relational DBMS for processing large volume of transactions Hybrid object-relational Databases are emerging 30

  31. Data Warehouse 31 • A logical collection of information gathered from many different operational databases • Supports business analysis activities and decision-making tasks • The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision-making purposes

  32. Data Warehouse Fundamentals Many organizations need internal, external, current, and historical data Data Warehouse are designed to, typically, store and manage data from operational transaction systems, Web site transactions, external sources, etc. 32

  33. Multidimensional Analysis 33 • Data mining – the process of analyzing data to extract information not offered by the raw data alone • Data-mining tools use a variety of techniques (fuzzy-logic, neural networks, intelligent agents) in order to • find patterns and relationships in large volumes of data • and infer rules that predict future behavior and guide decision making • Other analytical tools: query tools, statistical tools, etc. used to • Analyze data, determine relationships, and test hypotheses about the data

  34. Data Warehouse Fundamentals • Extraction, transformation, and loading (ETL)– a process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.

  35. Information Cleansing or Scrubbing 35 • Organizations must maintain high-quality data in the data warehouse • Information cleansing or scrubbing • a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information • first, occurs during ETL. Then, when the data is in the Data Warehouse using Information cleansing or scrubbing tools.

  36. Data Mart Subset of data warehouses that is highly focused and isolated for a specific population of users Example: Marketing data mart, Sales data mart, etc. 36

  37. Database vs. Data Warehouse Date 2Qtr 1Qtr sum 3Qtr 4Qtr TV Product U.S.A PC VCR sum Canada Country Mexico sum All, All, All Total annual sales of TV in U.S.A. 37 • Databases contain information in a series of two-dimensional tables • In a Data Warehouse and data mart, information is multidimensional, it contains layers of columns and rows

  38. Networking & Telecom

  39. Why Networking ? Resource sharing Sharing hardware (printers, processors, etc.) Sharing software (programs, data files) High reliability Can set automatic backup of programs and data at different locations Fault tolerance (if one server is down, others can provide service. If a disk fails, data available through mirror or RAID-3 disks) Possible cost savings Communication tool Internal email service Remote Access service 39

  40. Computer Network An interconnection of computers and computing equipment using either wires or wireless transmission media over small or large geographical distances. Once connected to the network, the computer (or another device) becomes a network node DEF GHI “Connect to GHI” ABC JKL MNO 40

  41. Network scope Local area network (LAN): computer network where the nodes are all in close proximity spanning a room, building, or campus Metropolitan area network (MAN): network that serves an area of 3 to 30 miles - approximately the area of a typical city. Wide area network (WAN): a large network that encompasses parts of states, multiple states, countries, and the world 41

  42. Transmission Media Physical media Transmission media used to physically connect nodes to the network Transmits electrical or optical signals Could be copper wire or fiber optic cable Physical Wireless 42

  43. Transmission Media (Continued) Twisted Pair • Fiber optic • Thin glass fibers surrounded by coating • Uses laser or light for data transmission • Very fast (10+ Gbps, 100 miles without any repeater) • Very secure Photo receptor (LED or LD) Photo diode (LED or LD) Destination Source 43 Fiber optic cable

  44. Wireless transmission media • Infrared light • Has many of the same characteristics as visible light • Travels in straight lines • Cannot penetrate solid objects • Radio waves • Travel in straight lines • Can penetrate through nonmetallic objects • Can travel long distances

  45. Wireless Media issues • Use electromagnetic waves or electromagnetic radiation for data transmission • Propagation through space, and indirectly, through solid objects • Many problems: Thick objects can block the direct path. So, Receiver will be in a Shadow zone where it cannot well receive. Radio waves tend to bounce off objects. Receiver can receive 2 or more signals. Electromagnetic Interference (EMI) from Other stations, Microwave ovens, etc Shadow Zone Insecure: Easier to “intercept” messages Multipath Interference Comm. Tower Laptop + Much more attenuation: Inverse Square law

  46. Computing Equipment Network interface card (NIC): Device that provides a computer with unique address Converts data into signal for transmission Hub / Switch: Central collection point for transmission media that interconnect computers Modem Converts digital data into analog signal and back again Router special hardware that determines optimal routing path for data packets Usually used to connect a LAN to a WAN Bridge Forwards messages between LANs 46

  47. Hub operation (Except sending station) Hubs split available bandwidth among computers, i.e. with a 100 Mbps hub, the network speed will be 100 Mbps / n (where n is the number of computers)Active hubs include repeater capabilities for regenerating signals.Passive hubs don't regenerate signals. Limited to a 30meter distance apart from computers.

  48. Switch operation Switching table MAC Address Port A1-44-D55-1F-AA-4C 1 (Station A) B2-CD-13-5B-E4-65 2 (Station B) C3-2D-55-3B-A9-4F 5 (Station C) ; ; Switches send out a single port: destination port.Most switches can efficiently handle simultaneous transmissionsSwitches provide a full bandwidth to all connected computers.

  49. Network Software Network operating system Used on servers Used for managing network resources Examples: Novell NetWare, Windows Server 2008 Workstation operating system Used on client PCs Used to manage local resources & access network resources Network monitoring software Packet sniffers – allow seeing data as it moves over network Keystroke monitors – allow seeing what users are typing 49

  50. Protocols An agreed upon set of rules that govern communication in a network All computers on a network must use same protocol for effective communication Example of protocols: Ethernet (for communication in a LAN) Token Ring (for communication in a LAN) TCP/IP suite (for communication in a LAN and the Internet) Computer 1 Computer 2 Rules for Task 1 Rules for Task 1 Rules for Task 2 Rules for Task 2 Rules for Task 3 Rules for Task 3 Rules for Task 4 Rules for Task 4 Rules for Task 5 Rules for Task 5

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