MGS exam 2 guide - chapters 3,4,6 (XLM C HAAG) chapters 4,5 (salesforce) PDF

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chapters 3,4,6 (XLM C HAAG)
chapters 4,5 (salesforce)...

Description

Exam 2 -

Chapter 3, 4, 6, XLM C (HAAG) (majority of questions) Chapter 4, 5 (Salesforce)

Data Mining -

Used to find hidden patterns and unknown trends

DBMS Components -

Data Definition Language (DDL) – specifies content and structure of database and defines each data element (data type, length, properties) o o o o o o

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CREATE TABLE Customers (Cust_no varchar2(12), FName char(30), Lname char(30), DOB date, Address varchar2(50));

Data Manipulation Language (DML) – manipulates data records in a database o Select * from orders where customer_ID in (45, 16, 212) and order date > ‘20180220’; Data Dictionary – stores definitions of data elements, and data characteristics

Conceptual/Logical Design and Physical Design -

Conceptual / Logical Design – Abstract model of database from business perspective Physical Design – determines how the database is arranged, optimized and tuned on storage devices

Data Warehousing and Data Marts -

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Data Warehousing o Data Warehouse – a logical collection of information gathered from many different operational databases, used to create business intelligence that supports business analysis activities and decision making Data Marts

Data Mart – a subset of a data warehouse in which only a focused portion of the data warehouse information is kept Data Mining Tools o Data Mining – used to find hidden patterns and previously unknown trends o

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Expert System Components -

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Expert Systems o AI based information system that applies reasoning capabilities to solve very specific problems. These systems utilize expert knowledge and can replace an expert in the decision-making process o Good for diagnostic (what’s wrong) and prescriptive (what to do) problems Expert System Components o Knowledge base o Knowledge acquisition o Inference engine o User interface o Explanation module Expert Systems People o Domain Expert – provides the domain expertise in the form of problem-solving strategies o Knowledge Engineer – IT specialist who formulates the domain expertise into an expert system o Knowledge worker / user – (you) Expert Systems o An expert system can  Reduce errors  Improve customer service  Reduce costs o An expert system can’t  Use common sense  Automate all processes

DSS Components -

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Types of Decisions o Structured o Semi-structured o Unstructured o Recurring vs. ad-hoc Decision Support Systems (DSS) o Highly flexible and interactive IT system designed to support unstructured and semi-structured decision making DSS Components o Model Management – consists of both the DSS models and the DSS model management system

Data Management – performs the function of storing and maintaining the information that you want your DSS to use o User Interface Management – allows you to communicate with the DSS DSS Capabilities o Sensitivity Analysis- study of the effect that changes in one or more parts of a model have on other parts of the model o What-if Analysis – checks the impact of a change in the assumptions or other input data on the proposed solution o Goal-seeking Analysis – finds the value of the inputs necessary to achieve a desired level of output DSS Examples o General Accident Insurance: Customer buying patterns and fraud detection o Bank of America: Customer profiles o Frito-Lay: Price, advertising, promotion selection o Burlington Coat Factory: Store location and inventory mix o

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GIS -

Geographic Information Systems (GIS) o Computer system with software that can analyze and display data using digitized maps. Enables display and analysis of spatial information o Examples – location analysis, law enforcement, identifying efficient delivery routes

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Artificial Intelligence Systems (AI) o Branch of computer science that deals with ways of representing knowledge, using symbols rather than numbers, and heuristics (rules of thumb), rather than algorithms for processing information o Objectives  Make machines smarter / useful  Understand what intelligence is Commercial AI Systems o Expert systems o Neural networks o Genetic algorithms o Intelligent agents o Natural language technology o Speech understanding o Computer vision and scene recognition o Intelligent computer-aided instruction o Handwriting recognizers

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Neural Networks and Genetic Algorithms -

Artificial Neural Network (ANN) o Emulates a biological neural network

Receives information from other neurons or from external sources, transforms the information and passes it on to other neurons or as external outputs o Useful for pattern recognition, learning, and interpretation of incomplete inputs Neural Networks o Self-organizing neural network – finds patterns and relationships in vast amounts of data by itself o Back propagation neural network – a neural network trained Genetic Algorithms o AI based system that mimics the evolutionary, survival of the generate increasingly better solutions to a problem  Selection – survival of the fittest  Crossover – combining the portions of good outcomes in the hope of creating an even better outcome  Mutation – randomly trying combinations and evaluating the success (or failure) of the outcome o

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Characteristics of Intelligent Agents -

Intelligent Agents (IA) o Software that assists you, or acts on your behalf, in performing repetitive computer-related tasks o Four types of intelligent agents include  Buyer agents or shopping bots  User or personal agents  Monitoring and surveillance or predictive agents  Data-mining agents o Autonomy – act without you telling them every step to take o Adaptivity – discovering, learning, and taking action independently o Sociability – conferring with other agents

Phases of decision making Structured, Semi-structured and Unstructured decisions -

Structured o Clear right/wrong answers (2+2=4) Semi-structured o Unclear answers, some answers better than others Unstructured o Requires human judgement, somewhat correct answer

Know each Phase of the SDLC and what is done in each phase -

Systems Development Life Cycle (SDLC)

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SDLC – the development method used by most organizations today for large, complex systems Waterfall Approach – a sequence of steps in the SDLC with cycles returned to previous stops Systems Analysts – IS professionals who specialize in analyzing and designing information systems Programmers – IS professionals who modify existing computer programs or write new computer programs to satisfy user requirements Technical Specialists – experts in a certain type of technology, such as databases or telecommunications

Plan Define the system to be developed Set the project scope Develop the project plan including tasks, resources and timeframes Project Plan – defines the what, when and who questions of system development including all activities to be performed, the individuals, or resources, who will perform the activities, and the time required to complete each activity o Project Milestones – represent key dates for which you need a certain group of activities performed o Project Manager – an individual who is an expert in a project planning and management, defines and develops the project plan and tracks the plan to ensure all key project milestones are completed on time o o o o

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Analysis o Involves the examination of the business problem the organization plans to solve with information systems o Requires end users and IT specialists to work together to gather, understand, and document the business requirements for the proposed system. Joint Application Development (JAD) is often used to accomplish this o Requirements Definition Document – prioritizes the business requirements and places them in a formal comprehensive document. Requires a “sign off” by the knowledge workers o Strengths and weaknesses of existing system o Functions that the new systems must have to solve the business problem o User information requirements for the new systems

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Design Develop a technical blueprint of how the proposed system will work and define the technical architecture o Technical Design  System outputs, inputs, and user interfaces o

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Hardware, software, databases, telecommunications, personnel and procedures How these components are integrated

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Development o The translation of the design specifications into computer code which becomes the actual system o Also, build the technical architecture databases and programs

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Test Checks to see if the computer code will produce the expected and desired results under certain conditions o Syntax Errors – misspelled word or a misplaced comma o Logic Errors – permit the program to run, but result in incorrect output o Unit testing, system testing, acceptance testing, test plan

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Implementation o The process of converting from the old system to the new system o 4 strategies include o User documentation and training help to facilitate the conversion process

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Maintain o Monitor and support the new system to ensure it continues to meet the business goals o Help desk to support the system users o Provide an environment to support system changes  Debugging the program  Updating the system to accommodate changes in business conditions  Add new functionality to the system

System Implementation Strategies -

Implementation o The process of converting from the old system to the new system o 4 strategies include  Parallel implementation – most expensive, running new and old system at the same time (not relying on just new system when switching off)  Direct implementation – least expensive, most risk. Turn off old, turn on new (used if system isn’t THAT critical)  Pilot implementation – only implement system in 1 department / group  Phased implementation – slowly phasing out old and in new, step by step o User documentation and training help to facilitate the conversion process

Feasibility study, project scope, information requirements -

Feasibility Study – determines the probability of success of proposed system’s development project and assesses the projects

Technical feasibility Economic feasibility Behavioral feasibility Project Scope Document o A written definition of the project scope and is usually no longer than a paragraph o o o

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Insourcing, Self-sourcing, outsourcing -

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Insourcing o IT specialists within your organization will develop the system o 3 choices for building a system include  IT specialists within organization (insourcing)  Knowledge workers such as yourself (self-sourcing)  Another organization (outsourcing) Self-sourcing o Development of information systems by end users with little or no formal assistance from technical specialists o Allows users the specify their own business needs o Also called knowledge worker development, end user development or end user computing Self-sourcing Advantages o Gives users control over both the development of an application and the ongoing maintenance o No need to explain user requirements to IS analysts o Gives users control over the development budget o Increased possibility of greater user acceptance o Improves requirements determination o Increases knowledge worker participation and sense of ownership o Increases speed of systems development Self-sourcing Disadvantages o Can gloss over essential steps in development o Difficult to evaluate end-user development activities o Lack of documentation and external support leads to short-lived systems. Security may be breached o Inadequate knowledge worker expertise leads to inadequately developed systems o Lack of organizational focus creates privatized IT systems o Insufficient analysis of design alternatives leads to subpar IT systems Outsourcing o The delegation of specific work to a third party for a specified length of time, at a specific cost, and at a specified level of service Outsourcing o Focus on unique core competencies o Exploit the intellect of another organization

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Better predict future costs Acquire leading-edge technology Reduce costs Improve performance accountability Economies of scale

Outsourcing Disadvantages o Reduces technical know-how for future innovation o Reduces degree of control o Increases vulnerability of strategic information o Increases dependency on other organizations o Contract problems

Degrees of Organizational Change -

Automation – speeding up performance Rationalization of Procedures – streamlining of operating procedures Business Process Reengineering – radical design of business processes Paradigm Shift – radical reconceptualization

Chapter 3 HAAG (Databases and Data Warehouses) -

Data Hierarchy Traditional File Environment Databases (relational, hierarchical, network) Design and Normalization Data Warehousing

Data Hierarchy 1. Database 2. Table, File, Relation 3. Records, Rows, Tables 4. Fields, Columns, Attributes 5. Bytes 6. Bits Traditional File Environment Issues - Data Redundancy - Data Inconsistency - Data Isolation - Data Integrity - Security - Application / Data Dependence Database Approach Advantages - Minimal data redundancy - Data consistency - Integration of data - Sharing of data - Uniform security, privacy and integrity - Data independence DBMS Components - Data Definition Language (DDL) – specifies content and structure of database and defines each data element (data type, length, properties) o o o o o o

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CREATE TABLE Customers (Cust_no varchar2(12), FName char(30), Lname char(30), DOB date, Address varchar2(50));

Data Manipulation Language (DML) – manipulates data records in a database o Select * from orders where customer_ID in (45, 16, 212) and order date > ‘20180220’; Data Dictionary – stores definitions of data elements, and data characteristics

Relational Database - Represents data as 2 dimensional tables called relations - Relates data across tables based on common data element - Examples: DB2, Oracle, MS SQL Server, MySQL Hierarchical Database

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Organizes data in tree-like structure Supports 1-many parent-child relationships Prevalent in large legacy systems

Network Database - Depicts data logically as many-many relationships - Less flexible compared to RDBMS - Lack support for ad-hoc and English language-like queries Database Design - Conceptual / Logical Design – Abstract model of database from business perspective - Physical Design – determines how the database is arranged, optimized and tuned on storage devices Business Intelligence - Online Transaction Processing (OLTP) – the gathering of input information, processing that information, and updating existing information to reflect the gathered and processed information o Operational Databases – databases that support OLTP - Online Analytical Processing (OLAP) – the manipulation of information to support decision making Data Warehousing - Data Warehouse – a logical collection of information gathered from many different operational databases, used to create business intelligence that supports business analysis activities and decision making Data Marts - Data Mart – a subset of a data warehouse in which only a focused portion of the data warehouse information is kept Data Mining Tools - Data Mining – used to find hidden patterns and previously unknown trends Databases and the web \/ \/ \/

Chapter 4 HAAG (Decision Support and Artificial Intelligence) -

Types of Decisions

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Decision Support o Decision Support Systems (DSS) o Collaboration Systems o Geographic Information Systems (GIS) Artificial Intelligence (AI) o Expert Systems o Neural Networks o Genetic Algorithms o Intelligent Agents

Types of Decisions - Structured - Semi-structured - Unstructured - Recurring vs. ad-hoc Decision Support Systems (DSS) - Highly flexible and interactive IT system designed to support unstructured and semistructured decision making DSS Components - Model Management – consists of both the DSS models and the DSS model management system - Data Management – performs the function of storing and maintaining the information that you want your DSS to use - User Interface Management – allows you to communicate with the DSS DSS Capabilities - Sensitivity Analysis- study of the effect that changes in one or more parts of a model have on other parts of the model - What-if Analysis – checks the impact of a change in the assumptions or other input data on the proposed solution - Goal-seeking Analysis – finds the value of the inputs necessary to achieve a desired level of output DSS Examples - General Accident Insurance: Customer buying patterns and fraud detection - Bank of America: Customer profiles - Frito-Lay: Price, advertising, promotion selection - Burlington Coat Factory: Store location and inventory mix Collaboration Systems - Interactive computer-based system that facilitates the solution of semi structured and unstructured problems by a group of decision makers

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Tools include – electronic questionnaires, electronic brainstorming tools, idea organizers, questionnaire tools Collaboration System Benefits - Improved preplanning - Increased participation - Open, collaborative meeting atmosphere - Criticism-free idea generation - Evaluation objectivity - Idea organization and evaluation - Setting priorities and making decisions - Documentation of meetings - Access to external information Geographic Information Systems (GIS) - Computer system with software that can analyze and display data using digitized maps. Enables display and analysis of spatial information - Examples – location analysis, law enforcement, identifying efficient delivery routes Artificial Intelligence Systems (AI) - Branch of computer science that deals with ways of representing knowledge, using symbols rather than numbers, and heuristics (rules of thumb), rather than algorithms for processing information - Objectives o Make machines smarter / useful o Understand what intelligence is Commercial AI Systems - Expert systems - Neural networks - Genetic algorithms - Intelligent agents - Natural language technology - Speech understanding - Computer vision and scene recognition - Intelligent computer-aided instruction - Handwriting recognizers IBM Watson (computing system that won Jeopardy) - 90 IBM power 750 servers on 10 racks o Each server has 4 processors o Each processor has 8 cores - 16 Terabytes of RAM and 4 Terabytes of clustered storage - A single CPU machine would take 2 hours to answer a single question

Expert Systems - AI based information system that applies reasoning capabilities to solve very specific problems. These systems utilize expert knowledge and can replace an expert in the decision-making process - Good for diagnostic (what’s wrong) and prescriptive (what to do) problems Expert System Components - Knowledge base - Knowledge acquisition - Inference engine - User interface - Explanation module Expert Systems People - Domain Expert – provides the domain expertise in the form of problem-solving strategies - Knowledge Engineer – IT specialist who formulates the domain expertise into an expert system - Knowledge worker / user – (you) Expert Systems - An expert system can o Reduce errors o Improve customer service o Reduce costs - An expert system can’t o Use common sense o Automate all processes Artificial Neural Network (ANN) - Emulates a biological neural net...