Midterm 3 Flashcards

1
Q

IT Infrastructure

A
  • Set of physical devices and software required to operate
    an enterprise
  • Set of firm-wide services including:
    – Computing platforms providing computing services
    – Physical facilities management services
    – I T management, education, and other services
  • “Service platform” perspective
    – More accurate view of value of investments
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2
Q

Evolution of IT Infrastructure

A
  • General-purpose mainframe and minicomputer era: 1959
    to present
  • Personal computer era: 1981 to present
  • Client/server era: 1983 to present
  • Enterprise computing era: 1992 to present
  • Cloud and mobile computing: 2000 to present
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3
Q

Drivers of Infrastructure
Evolution

A
  • Moore’s law and microprocessing power
    – Computing power doubles every 2 years
    – Nanotechnology
  • Law of Mass Digital Storage
    – The amount of data being stored each year doubles
  • Metcalfe’s Law and network economics
    – Value or power of a network grows exponentially as a
    function of the number of network members
  • Declining communication costs and the Internet
    – Exponential growth in size of the Internet
  • Standards and network effects
    – Technology standards
     Specifications that establish the compatibility of products
    and the ability to communicate in a network
     Unleash powerful economies of scale and result in price
    declines
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4
Q

Components of IT
Infrastructure

A
  1. Computer hardware platforms
  2. Operating system platforms
  3. Enterprise software applications
  4. Data management and storage
  5. Networking/telecommunications platforms
  6. Internet platforms
  7. Consulting system integration services
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5
Q

Computer Hardware Platforms

A
  • Client machines
    – Desktop PC s, laptops
    – Mobile computing: smartphones, tablets
    – Desktop chips vs. mobile chips
  • Servers
  • Mainframes
    – IBM mainframe
    – Digital workhorse for banking and telecommunications
    networks
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6
Q

Operating System Platforms

A
  • Corporate servers
    – Windows Server
    – Unix
    – Linux
  • Client level
    – Microsoft Windows
    – Android, iO S, Windows 10 (mobile/multitouch)
    – Google’s Chrome O S (cloud computing)
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7
Q

Data Management and Storage

A
  • Database software providers
    – IBM (DB2)
    – Oracle
    – Microsoft (SQL Server)
    – SAP Sybase (Adaptive Server Enterprise),
    – MySQL (Oracle)
    – Apache Hadoop
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8
Q

Telecomunications Platforms

A
  • Network operating systems
    – Windows Server, Linux, Unix
  • Network hardware providers
    – Cisco, Juniper Networks
  • Telecommunication services
    – Telecommunications, cable, telephone company
    charges for voice lines and Internet access
    – AT&T, Verizon
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9
Q

Internet Platforms

A
  • Hardware, software, management services to support company
    websites, intranets
    – Web-hosting services
    – Routers
    – Cabling or wireless equipment
  • Internet hardware server market
    – IBM, Dell, Oracle, HP
  • Web development tools/suites
    – Microsoft (Visual Studio and .NET), Oracle-Sun (Java),
    Adobe
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10
Q

Current Trends in
Computer Hardware Platforms

A
  • The mobile digital platform
  • Consumerization of IT and BYOD (bring your own device)
  • Quantum computing
    – Uses quantum physics to represent and operate on data
    – Dramatic increases in computing speed
  • Virtualization
    – Allows single physical resource to act as multiple resources, also enables multiple physical resources to appear as a single logical resource
    – Reduces hardware and power expenditures
    – Facilitates hardware centralization
  • Cloud computing
     Infrastructure as a service (IaaS)
     Software as a service (SaaS)
     Platform as a service (PaaS)
  • Edge computing
    – Servers at the edge of the network, near the source of
    the data
    – Reduces latency and network traffic
  • Green computing (Green IT )
    – Practices and technologies for manufacturing, using,
    disposing of computing and networking hardware
    – Reducing power consumption a high priority
    – Green data centers
  • High-performance, power-saving processors
    – Multicore processors
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11
Q

Current Computer Software
Platforms and Trends

A
  • Linux and opensource software
    – Produced by community of programmers
    – Linux
  • Software for the web: Java, HTML, and HTML5
    – Java Virtual Machine
    – Web browsers
    – HTML and HTML 5
    – Ruby and Python
  • Web services and service-oriented architecture
    – SOA : service-oriented architecture
     Set of self-contained services that communicate with one
    another to create a working software application
     Software developers reuse these services in other
    combinations to assemble other applications as needed
  • Software outsourcing and cloud services
  • Mashups and apps
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12
Q

Dealing with Platform and Infrastructure
Change

A
  • Scalability
    – Ability to expand to serve larger number of users
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13
Q

How should IT department be organized?

A

– Centralized
 Central IT department makes decisions
– Decentralized
 Business unit IT departments make own decisions

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14
Q

Total Cost of Ownership (TCO) Model

A
  • Analyzes direct and indirect costs
  • Hardware, software account for only about 20% of TCO
  • Other costs: Installation, training, support, maintenance,
    infrastructure, downtime, space, and energy
  • TCO can be reduced
    – Use of cloud services, greater centralization and standardization of hardware and software resources
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15
Q

Competitive Forces Model for IT
Infrastructure Investment

A
  • Market demand for firm’s services
  • Firm’s business strategy
  • Firm’s IT strategy, infrastructure, and cost
  • Information technology assessment
  • Competitor firm services
  • Competitor firm IT infrastructure investments
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16
Q

File Organization Terms and Concepts

A
  • Database: Group of related files
  • File: Group of records of same type
  • Record: Group of related fields
  • Field: Group of characters as word(s) or number(s)
  • Entity: Person, place, thing on which we store information
  • Attribute: Each characteristic, or quality, describing entity
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17
Q

Problems with the Traditional File
Environment

A
  • Files maintained separately by different departments
  • Data redundancy
  • Data inconsistency
  • Program-data dependence
  • Lack of flexibility
  • Poor security
  • Lack of data sharing and availability
18
Q

Database Management Systems

A

– Interfaces between applications and physical data files
– Separates logical and physical views of data
– Solves problems of traditional file environment
 Controls redundancy
 Eliminates inconsistency
 Uncouples programs and data
 Enables organization to centrally manage data and data
security

19
Q

Relational DBMS

A
  • Represent data as two-dimensional tables
  • Each table contains data on entity and attributes
  • Table: grid of columns and rows
    – Rows (tuples): Records for different entities
    – Fields (columns): Represents attribute for entity
    – Key field: Field used to uniquely identify each record
    – Primary key: Field in table used for key fields
    – Foreign key: Primary key used in second table as look-
    up field to identify records from original table
20
Q

Operations of a Relational DBMS

A
  • Three basic operations used to develop useful sets of data
    – SELECT
     Creates subset of data of all records that meet stated
    criteria
    – JOIN
     Combines relational tables to provide user with more
    information than available in individual tables
    – PROJECT
     Creates subset of columns in table, creating tables with
    only the information specified
21
Q

Capabilities of Database Management
Systems

A
  • Data definition
  • Data dictionary
  • Querying and reporting
    – Data manipulation language
     Structured Query Language (SQ L)
  • Many DBM S have report generation capabilities for
    creating polished reports (Microsoft Access)
22
Q

Designing Databases

A
  • Conceptual design vs. physical design
  • Normalization
    – Streamlining complex groupings of data to minimize redundant
    data elements and awkward many-to-many relationships
  • Referential integrity
    – Rules used by RDBM S to ensure relationships between tables
    remain consistent
  • Entity-relationship diagram
  • A correct data model is essential for a system serving the
    business well
23
Q

Non-relational databases: “No SQL”

A

– More flexible data model
– Data sets stored across distributed machines
– Easier to scale
– Handle large volumes of unstructured and structured data

24
Q
  • Distributed databases
A

Stored in multiple physical locations

25
Q
  • Blockchain
A

– Distributed ledgers in a peer-to-peer distributed database
– Maintains a growing list of records and transactions shared by all
– Encryption used to identify participants and transactions
– Used for financial transactions, supply chain, and medical records
– Foundation of Bitcoin, and other crypto currencies

26
Q

Big data

A

– Massive sets of unstructured/semi-structured data from
web traffic, social media, sensors

27
Q

The Challenge of Big Data

A
  • Volumes too great for typical DBM S
  • Can reveal more patterns, relationships and anomalies
  • Requires new tools and technologies to manage and analyze
28
Q

Business Intelligence Infrastructure

A

– Data warehouse
– Data mart
– Hadoop
– In-memory computing
– Analytical platforms

29
Q

Data warehouse

A

– Stores current and historical data from many core operational transaction
systems
– Consolidates and standardizes information for use across enterprise, but
data cannot be altered
– Provides analysis and reporting tool

30
Q

Data marts

A

– Subset of data warehouse
– Typically focus on single subject or line of business

31
Q

Hadoop

A

– Enables distributed parallel processing of big data across
inexpensive computers
– Used by Yahoo, NextBio

32
Q

In-memory computing

A

– Used in big data analysis
– Uses computers main memory (RA M) for data storage to
avoid delays in retrieving data from disk storage
– Can reduce hours/days of processing to seconds
– Requires optimized hardware

33
Q

Analytic platforms

A

– High-speed platforms using both relational and non-relational tools optimized for large datasets

34
Q

Analytical Tools

A

– Multidimensional data analysis (O LA P)
– Data mining
– Text mining
– Web mining

35
Q

Online Analytical Processing (O L A P)

A
  • Supports multidimensional data analysis
    – Viewing data using multiple dimensions
    – Each aspect of information is different dimension
  • O LA P enables rapid, online answers to ad hoc queries
36
Q

Data Mining

A
  • Finds hidden patterns, relationships in datasets
  • Infers rules to predict future behavior
  • Types of information obtainable from data mining:
    – Associations
    – Sequences
    – Classification
    – Clustering
    – Forecasting
37
Q

Text mining

A

– Extracts key elements from large unstructured text data sets
– Sentiment analysis software

38
Q

Web mining

A

– Discovery and analysis of useful patterns and information from web
– Web content mining
– Web structure mining
– Web usage mining

39
Q

Advantages of using the web for database

A

– Ease of use of browser software
– Web interface requires few or no changes to database
– Inexpensive to add web interface to system

40
Q

Data governance

A

– Encompasses policies and procedures through which
data can be managed as an organizational resource.
– Establishes rules for sharing, disseminating, acquiring,
standardizing, classifying and inventorying information

41
Q

Data Quality Assurance

A

Before new database is in place, a firm must
– Identify and correct faulty data
– Establish better routines for editing data once database
in operation