Intelligent Information Systems

Intelligent Information Systems

John Fulcher (University of Wollongong, Australia)
DOI: 10.4018/978-1-60566-026-4.ch333
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Abstract

Information Systems (IS), not surprisingly, process information (data + meaning) on behalf of and for the benefit of human users. Information Systems comprise the basic building blocks shown in Figure 1, and as such can be likened to the familiar Von Neumann computer architecture model that has dominated computing since the mid 20th Century. In practice, IS encompass not just computer system hardware (including networking) and software (including DataBases), but also the people within an organization (Stair & Reynolds, 1999). Information Systems are ubiquitous in today’s world–the so-called “Digital Age”–and are tailor-made to suit the needs of many different industries. The following are some representative application domains: • Management Information Systems (MIS) • Business IS • Transaction processing systems (& by extension, eCommerce) • Marketing/Sales/Inventory IS (especially via the Internet) • Postal/courier/transport/fleet/logistics IS • Geographical Information System (GIS)/Global Positioning Satellite (GPS) systems • Health/Medical/Nursing IS The roles performed by IS have changed over the past few decades. More specifically, whereas IS focussed on data processing during the 1950s and 1960s, management reporting in the 1960s and 1970s, decision support during the 1970s and 1980s, strategies and end user support during the 1980s and 1990s, these days (the early years of the 21st Century) they focus more on global Internetworking (O’Brien, 1997). Accordingly, we nowadays find extensive use of IS in e-business, decision support, and business integration (Malaga, 2005). Let us take a closer look at one of these–Decision Support Systems. A DSS consists of (i) a (Graphical) User Interface, (ii) a Model Management System, and (iii) a Data Management System (comprising not only Data/Knowledge Bases but also Data Warehouses, as well as perhaps incorporating some Data Mining functionality). The DSS GUI typically displays output by way of text, graphs, charts and the like, enabling users to visualize recommendations/advice produced by the DSS. The Model Management System enables users to conduct simulations, perform sensitivity analysis, explore “what-if” scenarios (in a more extensive manner than what we are familiar with in spreadsheets), and so forth.
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Introduction (Information System Types & Functions)

Information Systems (IS), not surprisingly, process information (data + meaning) on behalf of and for the benefit of human users. Information Systems comprise the basic building blocks shown in Figure 1, and as such can be likened to the familiar Von Neumann computer architecture model that has dominated computing since the mid 20th Century. In practice, IS encompass not just computer system hardware (including networking) and software (including DataBases), but also the people within an organization (Stair & Reynolds, 1999).

Figure 1.

Generic information system

Information Systems are ubiquitous in today’s world–the so-called “Digital Age”–and are tailor-made to suit the needs of many different industries. The following are some representative application domains:

  • Management Information Systems (MIS)

  • Business IS

  • Transaction processing systems (& by extension, eCommerce)

  • Marketing/Sales/Inventory IS (especially via the Internet)

  • Postal/courier/transport/fleet/logistics IS

  • Geographical Information System (GIS)/Global Positioning Satellite (GPS) systems

  • Health/Medical/Nursing IS

The roles performed by IS have changed over the past few decades. More specifically, whereas IS focussed on data processing during the 1950s and 1960s, management reporting in the 1960s and 1970s, decision support during the 1970s and 1980s, strategies and end user support during the 1980s and 1990s, these days (the early years of the 21st Century) they focus more on global Internetworking (O’Brien, 1997). Accordingly, we nowadays find extensive use of IS in e-business, decision support, and business integration (Malaga, 2005). Let us take a closer look at one of these–Decision Support Systems. A DSS consists of (i) a (Graphical) User Interface, (ii) a Model Management System, and (iii) a Data Management System (comprising not only Data/Knowledge Bases but also Data Warehouses, as well as perhaps incorporating some Data Mining functionality). The DSS GUI typically displays output by way of text, graphs, charts and the like, enabling users to visualize recommendations/advice produced by the DSS. The Model Management System enables users to conduct simulations, perform sensitivity analysis, explore “what-if” scenarios (in a more extensive manner than what we are familiar with in spreadsheets), and so forth.

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Background

Whenever we encounter “intelligence” in relation to IS, it is usually in the context of (i) the decision making process itself, (ii) intelligent organizations, (iii) software agents, or (iv) the incorporation of Artificial Intelligence (AI) techniques.

For instance, Filos (2006) characterizes a “smart/intelligent” organization as being networked in the following three dimensions: (a) Information & Communications Technology (ICT), (b) organizational, and (c) knowledge (it is interesting to note the link here between (c) and the discussion which follows in this article). Furthermore, in the “Digital Age,” the latter necessarily incorporates uncertainty and unpredictability. In this regard, Kelly and Allison (1999) demonstrate how the following concepts from Complexity Theory can be applied to improve business:

  • 1.

    Nonlinear dynamics

  • 2.

    Open systems

  • 3.

    Feedback loops

  • 4.

    Fractal structures

  • 5.

    Evolutionary theory

  • 6.

    Group self-organization

Key Terms in this Chapter

Artificial Intelligence (AI): The field of study devoted to building machines which exhibit “intelligence,” as commonly understood in relation to humans.

Computational Intelligence (CI): Incorporates ANN, Fuzzy and Evolutionary approaches, and more especially hybrids of these (some authors extend this definition to include intelligent agents, stochastic reasoning and other techniques).

Intelligent System: Used in this article primarily to mean (a) biologically-inspired “soft computing” techniques which can be incorporated into an information system (IS) in order to improve performance, and secondarily (b) in the sense of intelligence gathering, in order to render an IS more secure.

DNA Computing: The implementation of classical computing algorithms by way of chemical reactions within a test tube (so called “wet computing”).

Evolutionary Algorithms (EAs): An iterative procedure which involve the “mating” of suitable parents from a population of solutions to a problem of interest, in the hope that more suitable “offspring” (i.e., solutions) will evolve over time.

Artificial Neural Networks (ANNs): Simplified models of the human brain (biological neural network) which are particularly adept at pattern recognition or classification.

Backpropagation (BP) Algorithm: Used to train Multilayer Perceptrons (supervised, feedforward neural networks); BP adjusts the weights connecting neurons in the various layers according to the error (or difference) between actual and desired outputs generated in response to presentation of input-output training pattern pairs (exemplars).

Soft Computing: An older term for Computational Intelligence (see above).

Swarm Intelligence (SI): Refers to a class of algorithms inspired by the collective behaviour of insect swarms, ant colonies, the flocking behaviour of some bird species, or the herding behaviour of some mammals, such that the behaviour of the whole can be considered as exhibiting a rudimentary form of “intelligence.”

Immune-Based Computing: Uses “antigens” to discriminate between “self” and “nonself,” and to affect self-repair within a system.

Intelligence: Difficult to define exactly but incorporates awareness of (and ability to interact with and adapt to) ones environment, as well as an ability to learn from experience (thereby increasing our knowledge).

Expert (or Knowledge-Based) System (ES/KBS): Comprise a (Graphical) User Interface, an Inference Engine and a Knowledge Base. The GUI accepts user queries (inputs), and presents the results to these queries (outputs) in a comprehensible manner, usually together with some justification (rules) or confidence level.

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