Chapter 4Chapter 4Operations and TransactionsOperations and Transactions
The StrategicManagement of
InformationSystems
Transaction Processing Transaction Processing SystemSystem
Input Output
Process
Two Levels of PlanningTwo Levels of Planning Systems Planning
– Gives Managers, Users, and Information Systems Personnel Projects
– Establishes what should be done– Sets a budget for the total cost of these projects
Systems Project Planning– Setting a plan for the development of each
specific systems project
Systems Professional SkillsSystems Professional Skills Systems Planning
– Form project team after proposed systems project is cleared for development
Systems Analysis– Business Systems Analysts knowledgeable in business
General Systems Design– Business Systems Analysts
Systems Evaluation and Selection– Business Systems Analysts
Detailed Systems Design– Wide Range of Systems and Technical Designers
Systems Implementation– Systems analysts, programmers, and special technicians
Effective Leadership StyleEffective Leadership Style Autocratic Style
– Crisis-Style Management– Used to Correct Major Problem, such as Schedule
Slippage Democratic Style
– Team-oriented Leadership– Gives each team member the freedom to achieve goals
which he/she helped set Laissez-Faire Style
– Highly-motivated, Highly-Skilled Team Members– People who work best alone
Project Management SkillsProject Management Skills Planning
– States what should be done– Estimates how long it will take– Estimates what it will cost
Leading– Adapts to dynamics of enterprise and deals with setbacks– Guides and induces people to perform at maximum abilities
Controlling– Monitors Progress Reports and Documented Deliverables– Compares Plans with Actuals
Organizing– Staffs a Systems Project Team– Brings together users, managers, and team members
Project ManagementProject Management
Gantt Chart Pert Chart
Gantt ChartGantt Chart
Compares Planned Performance against actual performance to determine whether the project is ahead of, behind, or on schedule
Schedule a complete systems project by phases
PERT ChartPERT Chart Four Steps
– Identify Tasks– Determine Proper Sequence of Tasks– Estimate the Time Required to Perform
each Task– Prepare Time-Scaled Chart of Tasks and
Events to Determine the Critical Path
PERT ChartPERT Chart Estimate, Schedule, and Control a network of
interdependent tasks Shown by arrows, nodes, or circles Determine minimum time needed to complete a
project, phase, or task Critical Path
– Minimum time needed to complete a project or phase
Program, Evaluation and Renew Technique– Total of the most time-consuming chain of events
CASECASE
Computer-Aided Systems and Software Engineering
Increase Productivity of Systems Professionals
Improve the Quality of Systems Produced
Improve Software Maintenance Issue
CASECASE
Includes:– workstations– central repository– numerous modeling tools– project management– Systems Development Life Cycle Support– Prototyping Applications– Software Design Features
Central RepositoryCentral Repository Models Derived from Modeling Tools Project Management Elements Documented Deliverables Screen Prototypes and Report Designs Software Code from Automatic Code Generator Module and Object Libraries of Reusable Code Reverse Engineering, Reengineering, and
Restructuring Features
Software MaintenanceSoftware Maintenance Reverse Engineering
– Extract original design from spaghetti-like, undocumented code to make maintenance change request
– Abstract meaningful design specifications that can be used by maintenance programmers to perform maintenance tasks
Reengineering– Examination and changing of a system to reconstitute it in
form and functionality– Reimplementation
Restructuring– Restructures code into standard control constructs
sequence, selection, repetition
Business Rules For Data Basic selection of what data elements are of
interest, what are their characteristics (data type and acceptable range - also called syntactic structure)
How they are related to, or dependent on, each other in a business sense (key, foreign key and referential constraint rule - also called the semantic structure)
Data Integrity Rules
Data RationalizationData Rationalization
Identification of data synonyms and homonyms across multiple and disparate data sources and the creation of a map that points back to their original sources.
Data Access GatewayData Access Gateway
A system that sits between end users (usually in PC networks) and a legacy database, that accepts data read requests (expressed as SQL statements), converts the requests to legacy access method instructions, and then provides the resulting data to the users. The data flow is one-way read-only.
Structured Analysis Identifies
the functions or activities which are to be handled by the system
the external entities which interact with the system
the logical data stores, and the data flows among all the the above Data flow diagrams (DFD) are used to
diagrammatically describe the elements.
Conversion into Normalized Record Types
For every data flow which either enters or emanates from a data store (in the leaf level DFDs), the integral data elements are identified
For every data store, a list of the data elements which are entering and emanating are drawn up
The dependencies among all the data elements are analyzed, and the normalization rules are applied in steps so that at every step a given relation is split into more “simple” relations
– Every relation has a key which consists of one or more data elements
– Every non-key data element functionally depends on that entire key and not on part of it
– No non-key data element depends on any other non-key data element in the relation (there are no transitive dependencies)
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Conversion into Normalized Record Types
Part Description for ModelPart Description for Modelfor General Motorsfor General Motors
“Part #123 that is supplied by GM was assembled on bus 456 on May 28, 1996” is decomposed into the following elementary sentences:
a). A part... is supplied by a manufacturer...
b). A part... was assembled on a bus...
c). The assembly [part*bus] was performed on a date...
Manufacturer (name)
Supplier of
Supplied of
Part (p#)
Part Distribution Modelfor General Motors
Relationship TypesRelationship Typesa). One-to-one (1:1): means that an occurrence if
one OT uniquely determines an occurrence of other OT - and vice-versa
b). One-to-many (1:n): means that an occurrence of one OT determines an occurrence of the other OT - but not vice-versa
c). Many-to-many (n:m):means that an occurrence of one OT can be related to many occurrences of other OT - and vice-versa
Bus (License #) Part
(p#)Supplier
Manu-facturer (name)
Date of Assembly
Date (Calc. date)
Assembly Distribution Model
Normalization ModelNormalization Model The SA/Normalization method is based on the use of
decomposition rules, which enable one to decompose tables/relations. – Database design starts with flat tables/relations, each of which is
created out of a data stores in the DFDs and then decomposed into the normal form relations. No conceptual schema of the enterprise is created to express the semantics of its information structure.
The SA/IA method is based on the use of grouping rules which map simple relationships in the binary-relationship data model onto normal form relationships. – The relational model and the normalization method have been criticized
for being too detailed to use at the initial design stage, and for lacking a semantic structure for making unambiguous choices in modeling the enterprise.
– The IA method incorporates a semantic model of the enterprise which captures its essential semantic features from which the normal form relations are derived.
Basic selection of what data elements are of interest, what are their characteristics (data type and acceptable range - also called syntactic structure)
How they are related to, or dependent on, each other in a business sense (key, foreign key and referential constraint rule - also called the semantic structure)
Data Integrity Rules
Business Rules For Data
Data RationalizationData Rationalization
Identification of data synonyms and homonyms across multiple and disparate data sources and the creation of a map that points back to their original sources.
Data Access GatewayData Access Gateway sits between end users (usually in PC
networks) and a legacy database accepts data read requests (expressed as SQL
statements) converts the requests to legacy access method
instructions provides the resulting data to the users data flow is one-way read-only.
Data DesignData Design Define all the entities to be dealt with and the relationships
between them Transform the conceptual design into logical design wherein all
the views are combined and all the resulting data elements are defined and the data structure is syntactically and semantically determined
Normalize this logical design for mathematically minimized redundancy and maximized integrity
Transform this logical design to a physical design where the underlying RDBMS, hardware, and use patterns are taken into account
Develop the SQL DDL code specific to each RDBMS vendor’s product is generated
Data WarehouseData Warehouse
An intermediate, read-only store (usually based in a purchased RDBMS product) and the programs that manage it.
Contains recent and summarized data extracted from across some or all of the legacy data systems
Presents a subject-based view
De-NormalizationDe-Normalization
The process of selectively – combining two or more normalized tables into
one, or – decomposing one normalized table into two or
more
Entity Relationship Diagrams Entity Relationship Diagrams (ERDs)(ERDs)
A method of documenting and visualizing a conceptual data model.
Functional DependencyFunctional Dependency
Mathematical term for the key relationship (using rational terminology) between data elements. A data element (attribute) that is functionally dependent on another data element (the key) will always exist in a relation (table) such that a unique value for the key will always “determine” or “locate” or “define a unique value of” the dependent.
MetadataMetadata Data about data that is generally extracted from an existing system or
created for a new system and stored in a design repository for developers to use in maintaining or extending the system during its lifecycle
Metadata refers to the table, attribute, and key definitions contained in the catalog of a relational database. It can also mean the business rules for data designed for a new design, or the business rules for data thought to be enforced in a legacy system (semantic data structure, sometimes called meta-data, or meta2 data).
The actual syntactic and semantic data structure (not just what the documentation might say), including a complete synonym and homonym map, plus the business rules for data that are actually being enforced in the legacy system.
NormalizationNormalization
The process based on the business rules for data– a set of data elements (attributes) are arranged
in a mathematically minimum set of tables (relations), within which all the attributes are dependent on a primary key attribute (the key).
Relational ModelRelational Model
The Relational Model for data design is the foundation of the relational database and the industry that produces the “engines” that run them.
It puts data design (and data modeling) on a formal, mathematical footing.
Advantages of Data QueryAdvantages of Data Query “slice and dice” dynamic query support standard high-level access language (SQL) minimum data redundancy self-protecting data integrity
– no insert, delete and update anomalies
GM Parts ExampleGM Parts Example“Part #123 that is supplied by GM was
assembled on bus 456 on May 28, 1996” is decomposed into the following elementary sentences:
a). A part... is supplied by a manufacturer...
b). A part... was assembled on a bus...
c). The assembly [part*bus] was performed on a date...
Manufacturer (name)
Supplier of
Supplied of
Part (p#)
GM Parts ExampleGM Parts Example
Relationship TypesRelationship Typesa). One-to-one (1:1): means that an occurrence
if one OT uniquely determines an occurrence of other OT - and vice-versa
b). One-to-many (1:n): means that an occurrence of one OT determines an occurrence of the other OT - but not vice-versa
c). Many-to-many (n:m):means that an occurrence of one OT can be related to many occurrences of other OT - and vice-versa
Bus (License #) Part
(p#)Supplier
Manu-facturer (name)
Date of Assembly
Date (Calc. date)
GM Parts Assembly ExampleGM Parts Assembly Example