managing data resources chapter seven. softwareinformation systems for management2 hierarchy of data...
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Software Information Systems for Management
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Hierarchy of Data
• Bit• Byte• Field• Record• File• Database• Database management system
Software Information Systems for Management
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Traditional Data Environment
• Files for each application/department/function• Duplicated/redundant data/files• Inability to link data/files• Program-data dependence (change one, you
must change the other)
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Problems with Traditional Data Environment
Data redundancy leads to• Lack of data integrity• Program-data dependence• Lack of flexibility (no ad hoc reports/different
views• Poor security (access)• Lack of data sharing & availability
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DBMS Approach
• Database: Collection of one or more files containing data organized to serve multiple applications by minimizing redundant data.
• Database management system controls organization of & access to data and database files by acting as interface between the data & application programs and as an environment for developing and using databases.
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Views/Schemas
• Logical view: How end users perceive the data is organized– Schema: The view of all the data– Subschema: A partial view of the data accessible to
an end user (e.g., “view only” a subset of screens/data)
• Physical view: How the data are actually organized on physical storage media
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Components of a DBMS #1
• Data definition language (DDL)– Formal language associated with DBMS– Used by both end users & programmers to
manipulate data
• Data manipulation language– Commands to modify/extract data & to develop apps– Structured Query Language (SQL)– Can use various languages in addition to/ instead of
SQL
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Components of a DBMS #2
• Data dictionary
–Defines each data element (# bytes, text/numeric, etc., format, range, access, use, ownership, physical representation)
–Used for communication between developers & users and for standardization of data/databases/ programs
–Some data dictionaries are active; changes automatically change related databases/programs
NOTE: Any properly developed information system should have a data dictionary.
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Database Models (Types) #1
– Hierarchical• Upside-down tree-like structure• Root (top most data element) is the key field• Each child record can have only one parent record (1:M relationships); parents can have many children• Pointers for expressing relationships• Hard to change & limited retrieval capabilities• “Legacy” systems
– Network• Similar to hierarchical but M:M relationships between
records• Complex and hard to change
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Database Models (Types) #2
• Relational– 2-dimensional tables (relations)– Physically appear similar to files (but are not)– Row/record/tuple– Column/field/attribute/data element– Ability to link relations on-the-fly
• Select creates a subset of all records that meet specified criteria
• Join combines tables into a single new table• Project creates a subset of columns in a table, resulting in
new tables/views
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SQL
• Principal data manipulation language for relational DBMS
• Versions that can run on almost any OS & computer (mainframe, PC, etc.)
• Easy to learn & use– Select lists desired columns from desired table(s)– From identifies tables/views from which to select
columns– Where are conditions for selecting specific records &
for joining multiple tables
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SQL Example
SELECT Part:Part_Number, Supplier:Supplier_Number, Supplier:Supplier_Name, Supplier:Supplier_Address
FROM Part, Supplier
WHERE Part:Supplier_Number=Supplier:Supplier_Number AND Part_Number=137 OR Part_Number=152
Note: No line returns in any of the commands
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Object-Oriented Databases (OODBMS)
• Store data & procedures that act on the data as objects that can be automatically retrieved & shared
• Objects can contain multimedia• Object-relational databases: Relational
databases that can store both traditional data & object-oriented data that store graphics & multimedia
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Designing Databases
• Entity-relationship diagram (E-R diagram)– Documents database by showing relationships
among entities in database
• Normalization– Creates small, stable data structures (tables) from
complex groups of data– Example: Student data: Normalization results in
several DB tables of student data: Name/address, Courses taken, Funds/received/distributed, etc.
– See Figures 7-14 & 7-15
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Distributed Databases
• Stored in more than one physical location• Reduce vulnerability• Increase responsiveness• Can run on cheaper computer systems• Weakness: Vulnerability of telecommunications• Sometimes, locals can depart from acceptable DB
practices– Partitioned: Each remote processor has its own necessary data– Duplicated: Duplicated database (reconciled periodically)
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Data Administration
• Information policy: Planning & rules governing DB operations & information use
• Data planning: Enterprise analysis• Maintenance of data dictionaries• Data quality standards• Database Administration
– Technical– Operational– May include personnel, purchasing, etc. for DB
function
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DB Trends
• Multidimensional Data Analysis– Online analytical processing (OLAP)
• Ability to “slice and dice” data interactively• Multiple perspectives• Matrices or cubes
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Data Warehouses & Datamining
• Data warehouse– Consolidates & stores current & historical data
extracted from various operational systems– Meta-data (summaries of transactional data)– Reporting & query tools, including OLAP & data
mining
• Data mart: Subset of data warehouse• Datamining: Analysis of data in data
warehouses to find patterns/rules to aid in decision making
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Databases & the Web
• Hypermedia database– Organize data in network of nodes linked in user-
specified patterns/relationships– Text, graphics, sound, video, programs
• Linking internal DBs to the Web– Middleware is interface between DB & browser– Application server uses middleware to interface
between DB & browser– Common gateway interface (CGI) written in a
language interfaces between DB, app server, & browser
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Ford & Firestone: Tire Disaster
• Late September, 2001: Firestone recalled another 3.5 million Wilderness tires
• How does this crisis represent an information management problem?
• Why did management of these two companies (and government officials) not see the trends in the data?
• What would you suggest should have been done in terms of database management & queries?
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Next Classes:
• October 17: Midterm– Bring a pencil for Scantron sheets– Bring a pen if you prefer to answer short answer
questions in pen rather than pencil– Room 343: 6-8:00 p.m.– Bring your UM ID; it will be checked
• October 22– Chapter 8: Telecommunications & Networks– Case Study: Monitoring Employees on Networks
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