bluuebunny/crossref_metadata_2025 huggingface dataset:
Agent Technology
Authors: Meyer J.J. Ch.
Published: May 2011

Agent technology is a rapidly growing subdiscipline of computer science on the borderline of artificial intelligence and software engineering that studies the construction of intelligent systems. It is centered around the concept of an (intelligent/rational/autonomous) agent. An agent is a software entity that displays some degree of autonomy; it performs actions in its environment on behalf of its user but in a relatively independent way, taking initiatives to perform actions on its own by deliberating its options to achieve its goal(s). The field of agent technology emerged out of philosophical considerations about how to reason about courses of action, and human action, in particular. In analytical philosophy there is an area occupied with so-called practical reasoning, in which one studies so-called practical syllogisms, that constitute patterns of inference regarding actions. By way of an example, a practical syllogism may have the following form (Audi, 1999, p. 728): Would that I exercise. Jogging is exercise. Therefore, I shall go jogging. Although this has the form of a deductive syllogism in the familiar Aristotelian tradition of “theoretical reasoning,” on closer inspection it appears that this syllogism does not express a purely logical deduction. (The conclusion does not follow logically from the premises.) It rather constitutes a representation of a decision of the agent (going to jog), where this decision is based on mental attitudes of the agent, namely, his/her beliefs (“jogging is exercise”) and his/her desires or goals (“would that I exercise”). So, practical reasoning is “reasoning directed toward action—the process of figuring out what to do,” as Wooldridge (2000, p. 21) puts it. The process of reasoning about what to do next on the basis of mental states such as beliefs and desires is called deliberation (see Figure 1). The philosopher Michael Bratman has argued that humans (and more generally, resource-bounded agents) also use the notion of an intention when deliberating their next action (Bratman, 1987). An intention is a desire that the agent is committed to and will try to fulfill till it believes it has achieved it or has some other rational reason to abandon it. Thus, we could say that agents, given their beliefs and desires, choose some desire as their intention, and “go for it.” This philosophical theory has been formalized through several studies, in particular the work of Cohen and Levesque (1990); Rao and Georgeff (1991); and Van der Hoek, Van Linder, and Meyer (1998), and has led to the so-called Belief- Desire-Intention (BDI) model of intelligent or rational agents (Rao & Georgeff, 1991). Since the beginning of the 1990s researchers have turned to the problem of realizing artificial agents. We will return to this hereafter.
Alignment with Sound Relationships and SLA Support
Authors: Leonard AC
Published: May 2011

International data corportation surveyed 283 top executives across three vertical industries: finance, manufacturing, and retail/wholesale. They found "a strong correlation between the effectiveness of the IT department (IS organization) and the relationship between the CIO and the CEO. "We suspect taht this relationship, if it is close, permits the CIO to develop the IT department (IS organization) into a service that delivers competitive advantage for the company, thus enhancing the careers of every IT professional in the organization." In other words, "a certain amount of mutual esteem will help IT (IS) function as a business partner." In terms of alignment, sound relationships between IT and the business become even more important. Boar (1994) states that aligning with anything other than the customer leads to momentary success. For the IT function to achieve a state of alignment with the business, it must align with the businses scope, and through that business scope enable all business functions and processes to serve the customers in a superior manner.
Application of Cognitive Map in Knowledge Management
Authors: Reza Montazemi Ali , Esfahanipour Akbar
Published: May 2011

to capture perceived relationships of attributes related to ill-structured decision problems that decision makers have to face. This article provides an overview of the application of cognitive maps (CMs) in the design and development of intelligent information systems. Here, CM is used as a set of techniques to identify subjective beliefs and to portray those beliefs externally as follows: • Causal mapping is used to investigate the cognition of decision-makers. A causal map represents a set of causal relationships (i.e., cause and effect relationships) among constructs within a system. For example, Figure 1 shows that better sanitation facilities, causing an initial improvement in health, led to an increase in the city’s population. This growth led to more garbage, more bacteria, and therefore more disease. Causal map aids: 1) in identification of irrelevant data, 2) to evaluate the factors that affect a given class of decisions, and 3) enhances the overall understanding of a decision maker’s environment, particularly when it is ill-structured. • Semantic mapping, also known as idea mapping, is used to explore an idea without the constraints of a superimposed structure. A semantic map visually organizes related concepts around a main concept with tree-like branches. Figure 2 depicts different types of transportation, organized in three categories: land, water, and air. This technique facilitates communication between end-users and system analysts in support of information requirements analysis. • Concept mapping is a useful tool for organizing and representing concepts (events or objects) and their interrelationships in a particular domain. Each concept is designated with a label. The relationship between two concepts in a concept map is referred to as a proposition; propositions connect concepts to form a meaningful statement. Relationships between concepts are associative. For example, in Figure 3, two concepts of “plants” and “flowers” are associated via “may have” that form the proposition of “plants may have flowers.” Describing complex structures with simple propositions improve quality of conceptual modeling in the development of information systems.
Application of Fuzzy Logic to Fraud Detection
Authors: Lenard Mary Jane , Alam Pervaiz
Published: May 2011

In light of recent reporting of the failures of some of the major publicly-held companies in the U.S. (e.g., Enron & WorldCom), it has become increasingly important that management, auditors, analysts, and regulators be able to assess and identify fraudulent financial reporting. The Enron and WorldCom failures illustrate that financial reporting fraud could have disastrous consequences both for stockholders and employees. These recent failures have not only adversely affected the U.S. accounting profession but have also raised serious questions about the credibility of financial statements. KPMG (2003) reports seven broad categories of fraud experienced by U.S. businesses and governments: employee fraud (60%), consumer fraud (32%), third-party fraud (25%), computer crime (18%), misconduct (15%), medical/insurance fraud (12%), and financial reporting fraud (7%). Even though it occurred with least frequency, the average cost of financial reporting fraud was the highest, at $257 million, followed by the cost of medical/insurance fraud (average cost of $33.7 million). Statistical methods, expert reasoning, and data mining may be used to achieve the objective of identifying financial reporting fraud. One way that a company can justify its fi- nancial health is by developing a database of financial and non-financial variables to evaluate the risk of fraud. These variables may help determine if the company has reached a stress level susceptible to fraud, or the variables may identify fraud indicators. There are a number of methods of analysis that may be used in fraud determination. Fuzzy logic is one method of analyzing financial and non-financial statement data. When applied to fraud detection, a fuzzy logic program clusters the information into various fraud risk categories. The clusters identify variables that are used as input in a statistical model. Expert reasoning is then applied to interpret the responses to questions about financial and non-financial conditions that may indicate fraud. The responses provide information for variables that can be developed continuously over the life of the company. This article summarizes the specifics of fraud detection modeling and presents the features and critical issues of fuzzy logic when applied for that purpose.
Applying a Teaching Strategy to Create a Collaborative Educational Mode
Authors: Moncallo Nidia J. , Herrero Pilar , Joyanes Luis
Published: May 2011

The evolution of ICT and the influence over educational areas has been very significant in recent years, changing conception of learning environments, communications and interactions forms, and educational material. Researchers, like Buzon and Barragán (2004), have expressed the need to create new learning (online)-environments that allow teaching and learning without the time and space restrictions of residential courses, and ensures continual (virtual) communication between students and professors, or the need to find new material courses, learning strategies that allow the efficient use of new systems and educational resources emerged from technical advances (Wai-Chung and Li, 2007; Weert, 2006). On the other hand, among the conclusions reached at the Second Virtual Congress, “Education through Internet and Internet in Education” (2004), was the need for all technological research to take into account the pedagogical, economic, and social aspects, so that a coherent integration between technology and education can be achieved. Nevertheless, it is still difficult to incorporate the use of tools like chat, electronic mail, text editors, and forums, in other activities that involve no more than the simple exchange of information; this limits their potential and benefits. According to Friendals and Pauls (2005), the majority of Professors still depend on well-established, primitive teaching aids, like, for example, chalk and board. Their analysis revealed that the need for teaching aids in classrooms, which include educational integrative mini-applications, should be one of teaching’s main priorities. Based on these criteria, that is, the need to effectively incorporate ICTs to make changes in the educational field, this research has focused in submitting a proposal of a collaborative teaching strategy, empirical education collaborative teaching strategy, shortening EE-Col, like first link to develop later on, a collaborative educational model. EE–CoL’s validation will enable to lay down the basis for the design of an exclusive model in distributed environments where the generated learning elements are interoperable and reusable, using shared and coordinated resources.
Barriers to Successful Knowledge Management
Authors: Richter Alexander , Derballa Volker
Published: May 2011

Knowledge and Knowledge Management (KM) are gaining more and more attention in theory and practice. This development can be observed by an increasing number of publications since the 1990s, addressing the question of how knowledge in organizations can be organized and managed (Davenport & Prusak, 1998; Nonaka & Takeuchi, 1995). It is argued that knowledge is becoming the pre-eminent source of competitive advantage compared to the traditional factors of production, labour, capital and land. This theoretical discourse is accompanied in practice by an increasing number of KM initiatives. In many cases however, the results of those KM implementation projects have not lived up to the high expectations associated with them. Reasons for that are manifold. In this article, we will present the results of an extensive analysis of KM literature identifying the major barriers to KM. Those barriers represent current challenges during any holistic KM implementation that includes knowledge management systems (KMS).
Bibliomining for Library Decision-Making
Authors: Nicholson Scott , Stanton Jeffrey
Published: May 2011

Most people think of a library as the little brick building in the heart of their community or the big brick building in the center of a campus. These notions greatly oversimplify the world of libraries, however. Most large commercial organizations have dedicated in-house library operations, as do schools, non-governmental organizations, as well as local, state, and federal governments. With the increasing use of the Internet and the World Wide Web, digital libraries have burgeoned, and these serve a huge variety of different user audiences. With this expanded view of libraries, two key insights arise. First, libraries are typically embedded within larger institutions. Corporate libraries serve their corporations, academic libraries serve their universities, and public libraries serve taxpaying communities who elect overseeing representatives. Second, libraries play a pivotal role within their institutions as repositories and providers of information resources. In the provider role, libraries represent in microcosm the intellectual and learning activities of the people who comprise the institution. This fact provides the basis for the strategic importance of library data mining: By ascertaining what users are seeking, bibliomining can reveal insights that have meaning in the context of the library’s host institution. Use of data mining to examine library data might be aptly termed bibliomining. With widespread adoption of computerized catalogs and search facilities over the past quarter century, library and information scientists have often used bibliometric methods (e.g., the discovery of patterns in authorship and citation within a field) to explore patterns in bibliographic information. During the same period, various researchers have developed and tested data mining techniques—advanced statistical and visualization methods to locate non-trivial patterns in large data sets. Bibliomining refers to the use of these bibliometric and data mining techniques to explore the enormous quantities of data generated by the typical automated library.
Biometric Authentication
Authors: Mahier Julien , Pasquet Marc , Rosenberger Christophe , Cuozzo Félix
Published: May 2011

For ages, humans recognized themselves according to different characteristics (appearance, behavior…). Biometrics is a well known technique to identify an individual or verify its identity; as, for example, fingerprints have been used for more than 100 years to identify one criminal. With computers, this analysis can be realized very quickly and with a higher reliability. Biometrics has many applications: site monitoring (Bird, Masoud, Papanikolopoulos, & Isaacs, 2005), e-commerce (Jain & Pankanti, 2006)… The main benefits of biometrics are to provide better security and to facilitate the authentication process for a user. For example, it can be easy to obtain the password of a user, but it is more difficult to look like the user if a face recognition system is used for the user verification. Biometrics can also provide many advantages for particular applications. Indeed, biometric authentication can be realized in a contactless way that could be important for cultural aspects or reasons of hygiene. For all these motivations, biometrics is an emergent technology that could be more present in our daily life. The goal of this chapter is to make an overview of biometrics. We focus on the authentication process, whose goal is to verify the identity of an individual. Ideal biometric information must have multiple properties: • Universality: all individuals must be characterized by this information; • Uniqueness: this information must as dissimilar as possible for two different persons; • Permanency: it should be present during the whole life of an individual; • Collectability: it can be measured (in a easy way); • Acceptability: it concerns the possibility of a real use by users. The plan of this chapter is given below. The background part presents the different biometric modalities studied in the research labs and used in real conditions. The main thrust of this chapter is an analysis of the benefits and limitations of biometric authentication. We present also the general architecture of a biometric system. Future trends stress the different research topics that should be treated to improve the biometric authentication. It concerns the combination of different biometric systems and their performance evaluation. We conclude by resuming the main aspects of this domain.
Biometric Technologies
Authors: Du Yingzi ("Eliza")
Published: May 2011

Biometrics is an emerging technology for automatic human identification and verification using unique biological traits (Woodward, Orlans, & Higgins, 2002). These traits include face, fingerprints, iris, voice, hand geometry, handwriting, retina, and veins. For example, fingerprint recognition analyzes ridge ends, bifurcation, or dots of finger tips; voice recognition analyzes speech signal characteristics; iris recognition analyzes the pits, striations, filaments, rings, dark spots, and freckles of eyes; and face recognition analyzes facial parameters (Du et al., 2004). It is based on “something you are” rather than “something you have” (Du, 2005). Compared to the traditional identification and verification ways, such as user name/password, and paper IDs, biometrics is more convenient to use, reduces fraud, and is more secure (Reid, 2004).
Business Processes and Knowledge Management
Authors: Edwards John S.
Published: May 2011

Knowledge has been a subject of interest and inquiry for thousands of years since at least the time of the ancient Greeks, and no doubt even before that. “What is knowledge” continues to be an important topic of discussion in philosophy. More recently, interest in managing knowledge has grown in step with the perception that increasingly we live in a knowledge-based economy. Drucker (1969) is usually credited as being the first to popularize the knowledge-based economy concept by linking the importance of knowledge with rapid technological change in Drucker (1969). Karl Wiig coined the term knowledge management (hereafter KM) for a NATO seminar in 1986, and its popularity took off following the publication of Nonaka and Takeuchi’s book “The Knowledge Creating Company” (Nonaka & Takeuchi, 1995). Knowledge creation is in fact just one of many activities involved in KM. Others include sharing, retaining, refining, and using knowledge. There are many such lists of activities (Holsapple & Joshi, 2000; Probst, Raub, & Romhardt, 1999; Skyrme, 1999; Wiig, De Hoog, & Van der Spek, 1997). Both academic and practical interest in KM has continued to increase throughout the last decade. In this article, first the different types of knowledge are outlined, then comes a discussion of various routes by which knowledge management can be implemented, advocating a process-based route. An explanation follows of how people, processes, and technology need to fit together for effective KM, and some examples of this route in use are given. Finally, there is a look towards the future.
Classification of Semantic Web Technologies
Authors: Pereira Rui G. , Freire Mário M.
Published: May 2011

Semantic Web is the name of the next generation World Wide Web, that has been recently proposed by Tim Berners-Lee and the World Wide Web Consortium (W3C)1. In this new Web architecture, information and Web services will be easily understandable and usable by both humans and computers. The objective is not to make computers understand the human language, but to define a universal model for the expression of the information and a set of inference rules that machines can easily use in order to process and relate the information as if they really understood it (Berners-Lee, 1998). Though, as the current Web provided sharing of documents among previously incompatible computers, the Semantic Web intends to go beyond, allowing stovepipe systems, hardwired computers, and other devices to share contents embedded in different documents. The most known architecture for Semantic Web is based on a stack of related technologies, each one being a whole research area by itself (Berners-Lee, Hendler, & Lassila. 2001; Pereira & Freire, 2005). Accomplishment of the Semantic Web is considered a great challenge, not only due to the complexity of implementation but also because of the vast applicability in several areas. In spite of this, Semantic Web is still one of the most promising research areas among those which aim to define a new architecture for the Web. Semantic Web goes far beyond previous information retrieval and knowledge representation projects, presenting a non-centralized way to represent and contextualize real-world concepts, unambiguously, for several areas of knowledge. Semantic Web-enabled machines will handle information at our communication level. It is clear that the ability to interpret reality is still very primitive, however, Semantic Web points a way towards machine interaction and learning (Pereira et al., 2005). Semantic Web will integrate, interact with, and bring benefits to most human activities. Its full potential will go beyond the Web to real-world machines, providing increased interaction between machines and with humans—smarter phones, radios, and other electronic devices. Semantic Web will bring a different kind of approach in the understanding of reality by the machines and will constitute a mark in the evolution of human knowledge (Pereira et al., 2005).
Clustering Algorithms for Data Streams
Authors: Makris Christos , Tsirakis Nikos
Published: May 2011

The World Wide Web has rapidly become the dominant Internet tool which has overwhelmed us with a combination of rich hypertext information, multimedia data and various resources of dynamic information. This evolution in conjunction with the immense amount of available information imposes the need of new computational methods and techniques in order to provide, in a systematical way, useful information among billions of Web pages. In other words, this situation poses great challenges for providing knowledge from Web-based information. The area of data mining has arisen over the last decade to address this type of issues. There are many methods, techniques and algorithms that accomplish different tasks in this area. All these efforts examine the data and try to find a model that fits to their characteristics in order to examine them. Data can be either typical information from files, databases and so forth, or with the form of a stream. Streams constitute a data model where information is an undifferentiated, byte-by-byte flow that passes over the time. The area of algorithms for processing data streams and associated applications has become an emerging area of interest, especially when all this is done over the Web. Generally, there are many data mining functions (Tan, Steinbach, & Kumar, 2006) that can be applied in data streams. Among them one can discriminate clustering, which belongs to the descriptive data mining models. Clustering is a useful and ubiquitous tool in data analysis.
A Comparison of Data Modeling in UML and ORM
Authors: Halpin Terry
Published: May 2011

The Unified Modeling Language (UML) was adopted by the Object Management Group (OMG) in 1997 as a language for object-oriented (OO) analysis and design. After several minor revisions, a major overhaul resulted in UML version 2.0 (OMG, 2003), and the language is still being refined. Although suitable for object-oriented code design, UML is less suitable for information analysis, since its graphical language provides only weak support for the kinds of business rules found in data-intensive applications, and its textual Object Constraint Language (OCL) is too technical for most business people to understand. Moreover, UML’s graphical language does not lend itself readily to verbalization and multiple instantiation for validating data models with domain experts. These problems can be remedied by using a fact-oriented approach for information analysis, where communication takes place in simple sentences, each sentence type can easily be populated with multiple instances, and attributes are avoided in the base model. At design time, a fact-oriented model can be used to derive a UML class model or a logical database model. Object Role Modeling (ORM), the main exemplar of the fact-oriented approach, originated in Europe in the mid-1970s (Falkenberg, 1976), and has been extensively revised and extended since, along with commercial tool support (e.g., Halpin, Evans, Hallock, & MacLean, 2003). Recently, a major upgrade to the methodology resulted in ORM 2, a second-generation ORM (Halpin 2005). Neumont ORM Architect (NORMA), an open source tool accessible online at http://sourceforge.net/projects/orm, is under development to provide deep support for ORM 2 (Curland & Halpin, 2007). This article provides a concise comparison of the data modeling features within UML and ORM. The next section provides background on both approaches. The following section summarizes the main structural differences between the two approaches, and outlines some benefits of ORM’s factoriented approach. A simple example is then used to highlight the need to supplement UML’s class modeling notation with additional constraints, especially those underpinning natural identification schemes. Future trends are then briefly outlined, and the conclusion motivates the use of both approaches in concert to provide a richer data modeling experience, and provides references for further reading.
Critical Success Factors for E-Health
Authors: Wickramasinghe Nilmini , Schaffer Jonathan L.
Published: May 2011

Within the umbrella of e-commerce, one area, e-health, has yet to reach its full potential in many developed countries, let alone developing countries. Each country is positioned differently and has varying potential and preparedness regarding embracing e-commerce technologies generally and e-health in particular. Given the macrolevel nature of many issues pertaining to the development of e-health (Alvarez, 2002), in order to be more effective in their e-health initiatives, it is important for countries to assess their potential, identify their relative strengths and weaknesses, and thereby develop strategies and policies to address these issues to effectively formulate and implement appropriate e-health initiatives. To do this effectively, it is valuable to have an integrative framework that enables the assessment of a country’s e-health preparedness. This article serves to develop such a framework that can be applied to various countries throughout the globe, and from this generate an e-health preparedness grid. In so doing, we hope to facilitate better understanding of e-health initiatives and thus maximize their power.
Design Patterns from Theory to Practice
Authors: Dong Jing , Peng Tu , Sun Yongtao , Tang Longji , Zhao Yajing
Published: May 2011

Design patterns (Gamma, Helm, Johnson, & Vlissides, 1995) extract good solutions to standard problems in a particular context. Modern software industry has widely adopted design patterns to reuse best practices and improve the quality of software systems. Each design pattern describes a generic piece of design that can be instantiated in different applications. Multiple design patterns can be integrated to solve different design problems. To precisely and unambiguously describe a design pattern, formal specification methods are used. Each design pattern presents extensible design that can evolve after the pattern is applied. While design patterns have been applied in many large systems, pattern-related information is generally not available in source code or even the design model of a software system. Recovering pattern-related information and visualizing it in design diagrams can help to understand the original design decisions and tradeoffs. In this article, we concentrate on the issues related to design pattern instantiation, integration, formalization, evolution, visualization, and discovery. We also discuss the research work addressing these issues.
From Digital Divides to Digital Inequalities
Authors: Amoretti Francesco , Casula Clementina
Published: May 2011

Concerns about inequalities deriving from the penetration of new information and communication technologies (ICTs) have only recently become a widely debated issue in industrial societies. Until the 1980s the diffusion of ICT was mainly considered a matter of technological innovation regarding selected fields and limited territorial areas (such as the military and academic centers in the U.S.). Gradually, scholars started to point to the rise of an information society based on the production of information as the crucial resource to manage coordination and control of increasingly interconnected organizational systems (Masuda, 1981; Beniger, 1986; Toffler, 1990). The expression offered an alternative to the otherwise negative definitions used by scholars since the 1970s to identify changes occurring in Western democratic societies (‘post-capitalism’, ‘post-industrialism’, ‘post-materialism’, etc.) (Touraine, 1969; Bell, 1973). The debate over the information society, enthusiastically greeted by some authors (Negroponte, 1995) and critically observed by others (Castells, 1996, 2001; May, 2002; Mattelart, 2003), witnessed since the mid-1990s widespread success in public and political debates (Thomas, 1996). In front of the fast and capillary diffusion of ICTs virtually to all sectors of private and public life, most Western countries’ governments and international organizations have inserted within their policy agendas a reference to the unavoidability, if not desirability, of a radical shift to the new information age. The rhetoric accompanying those discourses often presents the expansion of the ICT sector?and especially the Internet?as offering citizens returns at both the individual and collective level, in the form of greater access to goods and services, increased levels of social and civic participation, and wider economic and working opportunities for all. Presented as a crucial means to participate in the new global information society, ICTs become recognized as a resource that should be fairly distributed among citizens, albeit on the basis of different arguments (ranging from social equity to economic efficiency or global development concerns), often leading to opposite conclusions on the scope for redistributive interventions (Strover, 2003; Selwyn, 2007).
Digital Identity in Current Networks
Authors: Sandrasegaran Kumbesan , Huang Xiaoan
Published: May 2011

The daily activities of humans and business are increasingly depending on the usage of (digital) identity for interaction with other parties and for accessing resources. Current networks use a number of digital-identity management schemes. For example, in the public switched telephone network (PSTN), a telephone number is simply used as the digital identity of a user. Most of the digital-identity management schemes are effective only within their networks and have limited support for interoperability. In the hybrid network environment of next-generation networks (NGNs), new digital-identity management models are expected to be proposed for digitalidentity management. The rest of the chapter is focused on the introduction of digital identity, the digital-identity schemes used in current telecommunication networks, and the future trends.
Distributed Construction through Participatory Design
Authors: Zaphiris Panayiotis , Laghos Andrew , Zacharia Giorgos
Published: May 2011

This article presents an empirical study of an online learning community that collaborates with the course design team under the Participatory Design methodology. The different phases of this methodology were implemented using a four-stage participatory design process (Zaphiris & Zacharia, 2001): 1) building bridges with the intended users, 2) mapping user needs and suggestions to the system, 3) developing a prototype, and 4) integrating feedback and continuing the iteration. We took advantage of the online and distributed nature of the student community to asynchronously design, implement, and study the course. We carried out the participatory design methodology by following the Distributed Constructionism pedagogical theory. During the different phases of the design process, we measured the student participation and the changes in their behavior when new design elements were introduced. We conclude that the most important element of this course was our discussion board, which helped us to promote student collaboration and the identification of the key community users who can participate productively in Participation Design activities. There are three main sections to this article. After defining the key terminology, our Participatory Design approach is presented and its linkage to the Distributed Constructionism pedagogical theory specified. The article ends with ideas for future research and a set of conclusions.
The Effect of Sound Relationships on SLA's
Authors: Leonard A.C.
Published: May 2011

End users have expectations regarding services and support, and the quality thereof, provided by the supplier. They compare their expectations to the received service to assess the service quality (Coye, 2004). In order to ensure that the service supplied by the service provider meets the expectations of end users, a successful service level agreement (SLA) is required. Quality SLA’s clearly define, amongst many other elements, the commitments and responsibilities of the IT service provider and end users within the service delivery processes (Larson, 1998). One method of measuring the success of SLA’s is by using service metrics with regard to the availability, reliability, serviceability, response, and user satisfaction of the SLA (Larson, 1998). Therefore, the success of the SLA depends on a clear, common understanding of the services and service quality between the service provider and end users. Furthermore commitment, trust, and cooperation between all parties is necessary to achieve success with SLA’s (Hiles, 1994). However, in this paper it is argued that all these soft issues can only form a basis when sound relationships are established and maintained between the IT service provider and end users (Leonard, 2002). This paper aims to determine how the establishment of a sound IT-end user relationship can add value to the SLA for both the IT service provider and the end users, and increase the success of SLA’s.
E-Government and Digital Divide in Developing Countries
Authors: Averweg Udo Richard
Published: May 2011

The transition of the global economy from an industrial focus to one based on knowledge and information presents numerous opportunities and challenges to countries, especially those in the developing world (Cape IT Initiative, 2003). The government sector (and especially the local government sector) needs to embrace information and communication technologies (ICTs) that enable it to operate more efficiently and communicate better with its citizens. ICTs encompass all technologies that facilitate the processing and transfer of information and communication services (United Nations, 2002). Many factors affect how local governments (i.e., municipalities) in developing countries access ICTs. In order to bridge the digital divide?which separates the technology ‘haves’ from the technology ‘have nots’?it is necessary to gauge where citizens are in terms of ICT adoption, that is, their e-readiness. E-readiness can be defined in terms of availability of ICT infrastructure, the accessibility of ICT to the general citizen population, and the effect of the legal and regulatory framework on ICT use in, for example, an e-government strategy. eThekwini Municipality (2003), in the city of Durban in the developing country of South Africa, sees the e-government strategy and its Web site at http://www.durban.gov.za as important management tools for improved citizen service delivery and communication. The objective of this article is to report, as an example, on the survey of ICT and information needs of a selected metropolitan municipal area (eThekwini Municipality in South Africa). Such a report maybe useful to other municipalities in developing countries for their egovernment strategies. This article is organized as follows. The background to e-government and the digital divide are discussed. eThekwini Municipality in South Africa is then described. The research goals are outlined, the research method and data gathering are discussed, the survey results and discussion are given, and future trends for implementing an e-government strategy in municipalities in developing countries are suggested. Finally, a conclusion is given.
E-Technology Challenges to Information Privacy
Authors: Szewczak Edward J.
Published: May 2011

The collection of personal information by electronic technology (e-technology) and the possibility of misuse of that information are primary reasons why people limit their use of the Internet and are even limiting the success of e-commerce (Szewczak, 2004). Various uses of e-technology that collect and/or disseminate personal information include corporate and government databases, e-mail, wireless communications, clickstream tracking, and PC software. The main challenge to personal information privacy is the surreptitious monitoring of user behavior on the Internet without the user’s consent and the possible misuse of the collected information resulting in financial and personal harm to the user. Our focus is primarily on Internet use in the United States of America, though clearly e-technology is global in nature and poses challenges and issues for societies around the world.
Ethics of New Technologies
Authors: Gilbert Joe
Published: May 2011

Information processing has been done through telling stories, drawing on cave walls, writing on parchment, printing books, talking on telephones, sending messages via telegraphs, broadcasting on radio and television, processing data in computers, and now by instantaneous network dissemination. Since the mid-1990’s, personal computers have been the instrument of choice for sending and receiving information, and for processing much of it. The technology is the latest in a long series, but social issues involved have not really changed. Issues of content (is it true? obscene?), ownership (whose picture/text/idea? whose parchment/telephone system/ computer?), and impact (anti-government, anti-social, harmful to children) appear today just as they did hundreds or thousands of years ago.
Evaluating UML Using a Generic Quality Framework
Authors: Krogstie John
Published: May 2011

According to Booch, Rumbaugh, and Jacobson (2005), developing a model for an industrial strength software system before its construction is increasingly regarded as a necessary activity in information systems development. The use of object-oriented modeling in analysis and design started to become popular in the late 80s, producing a large number of different languages and approaches. Over the last 10 years, UML (OMG, 2006a) has taken a leading position in this area. In this chapter, we give an overview assessment of UML using a generic evaluation framework. We will first present the evaluation framework. We will then evaluate the language quality of UML before pointing to the future direction and potential of UML.
Extensions to UML Using Stereotypes
Authors: Riesco Daniel , Daniele Marcela , Romero Daniel , Montejano German
Published: May 2011

The Unified Modeling Language (UML) allows to visualize, to specify, to build and to document the devices of a system that involves a great quantity of software. It provides a standard form for writing the models of a system, covering so much of the conceptual aspects (such as processes of the business and functions of the system) as the concrete ones (such as the classes written in a specific programming language, schemas of databases and software components). In 1997, UML 1.1 was approved by the OMG becoming the standard notation for the analysis and the design oriented to objects. UML is the first language of modelling in which a metamodel in its own notation has been published. It is a strict subset called Core. It is a self-referential metamodel. It is a very expressive language that covers all of the necessary views to develop and to deploy systems. UML is a language that provides three extension mechanisms (Booch, Rumbaugh, & Jacobson, 1999): stereotypes, tag values, and constrains. The stereotypes allow to create new types of elements of model based on the elements that form the metamodel UML extending the semantics of the same one, the tag values are an extension of the properties of an element of UML, allowing to add new information to the specification of the same one, and the constrains are an extension of the semantics of UML that allow to add new rules or to modify the existent ones. The organization of this overview is given in the following way: first, we present the stereotypes according to the standard of OMG; second, we expose the analysis of works that extend UML using stereotypes in diverse real domains; third, we make an analysis of the stereotypes of UML; and we finish giving a general conclusion where we focus ourselves in the distinction of the works according to their inclusion or not of the created stereotypes in the metamodel of UML.
Geography and Public Health
Authors: Lipton Robert , Gorman D. M. , Wieczorek William F. , Banerjee Aniruddha , Gruenewald Paul
Published: May 2011

From John Snow’s pioneering work on cholera in the 19th century until the present day, placing illness and disease within the context of a geographic framework has been an integral, if understated, part of the practice of public health. Indeed, geographical/spatial methods are an increasingly important tool in understanding public health issues. Spatial analysis addresses a seemingly obvious yet relatively misunderstood aspect of public health, namely, studying the dynamics of people in places. As advances in computer technology increase almost exponentially, computer intensive spatial methods (including mapping) have become an appealing way to understand the manner in which the individual relates to larger frameworks that compose the human community and the physical nature of human environments (streets with intersections, dense vs. sparse neighborhoods, high or low densities of liquor stores or restaurants, etc.). Spatial methods are extremely data intensive, often pulling together information from disparate sources that have been collected for other purposes such as research, business practice, governmental policy, and law enforcement. Although initially more demanding in regard to data manipulation compared to typical population level methods, the ability to compile and compare data in a spatial framework provides information about human populations that lies beyond typical survey or census research. We will discuss general methods of spatial analysis and mapping that will help to elucidate when and how spatial analysis might be used in a public health setting. This discussion will include a method for transforming arbitrary administrative units, such as zip codes, into a more useable uniform grid structure. In addition, a practical research example will be discussed focusing on the relationship between alcohol and violence. A relatively new Bayesian spatial method will be part of this example.
Impediments for Knowledge Sharing in Professional Service Firms
Authors: Disterer Georg
Published: May 2011

Professional service firms (PSFs), where professionals (consultants, lawyers, accountants, tax advisors, etc.) work, are interested in knowledge management because their businesses are heavily dependent on the knowledge of their employees. A core asset is their ability to solve complex problems through creative and innovative solutions, and the basis for this is their employees’ knowledge. The “product” that PSFs offer their clients is knowledge (Kay, 2002; Ofek & Sarvary, 2001; Chait, 1999). Sharing knowledge between colleagues improves the economical benefits a firm can realize from the knowledge of employees. This is especially true for PSFs (Huang, 1998; Quinn, Anderson, & Finkelstein, 1996), where broad ranges of knowledge must be kept to provide intellectual services, and real-life experiences with certain questions and situations are an important asset. The organizations and its members are spread over various offices across the country or the world. The necessity for sharing grows because the network of professionals in most cases can offer significantly better professional advice than any individual. “We sell knowledge… the most valuable thing we can offer is the collective, institutional knowledge of our firm” (Roger Siboni, KPMG executive, in Alavi, 1997, p. 1). Working together openly without holding back or protecting vital pieces of knowledge will result in more productivity and innovation than could be reached individually.
Improving Public Sector Service Delivery through Knowledge Sharing
Authors: Wright Gillian H. , Taylor W. Andrew
Published: May 2011

Since the publication of the first knowledge management article in Harvard Business Review (Nonaka, 1991), the world has witnessed a revolution in management practice. While the origins of knowledge management extend further back in history (see Prusak, 2001; Wiig, 1997), it is certainly true that in the last decade the creation, sharing and application of knowledge are increasingly seen as a source of competitive advantage. However, knowledge management is largely a private sector innovation at the present time, although gradually moving towards the public service sector (Bate & Robert, 2002; Hartley & Allison, 2002). The implementation of knowledge management places an emphasis on organizational factors such as learning capability, culture and leadership as well as renewed focus on the importance of information quality (Alavi & Leidner, 2001). The ability to manage the sharing of information (and hence knowledge) effectively remains one of the most important but still least understood activities in modern organizations, no less so in public services.