bluuebunny/crossref_metadata_2025 huggingface dataset:
IT Evaluation Practices in Electronic Customer Relationship Management (eCRM)
Authors: Lin Chad
Published: May 2011

Organizations are becoming increasingly aware of the need to scrutinize their bottom-line financial returns of business automation initiatives. To achieve this, organizations have to become more customer-centric. According to Karakostas, Karadaras and Papathanassiou (2005), a 5% increase in customer retention can result in an 18% reduction in operating costs. Therefore, the need to build and maintain customer relationship has become a priority for organizations. However, according to a KPMG survey, only a small percentage of companies were able to obtain even basic customer information despite the fact that 89% of companies consider customer information to be extremely important to the success of their business (McKeen and Smith, 2003). As a result, many organizations are adopting electronic customer relationship management (eCRM) applications in order to gather, organize, understand, anticipate, and respond to the constant evolution of customers’ requirements and demands. Indeed, eCRM is forecasted to become increasingly important as businesses seek to deliver their services and information as well as to provide transactional facilities via online and wireless platforms, in additional to the more traditional means of communication channels (e.g., call centers and customer service) (Tan, Yen and Fang, 2002). The market worldwide for eCRM applications is predicted to grow from US $3.4 billion in 2000 to US $10.5 billion in 2005 (EPS, 2001). Yet, despite the huge investment and widespread agreement that eCRM has direct and indirect impact on customer satisfaction, loyalty, sales, and profit, it has been found that 70% of eCRM solutions that have been implemented by businesses fail (Feinberg, Kadam, Hokama and Kim, 2002). Moreover, studies carried out by Gartner, Forrester, AMR Research, and the Yankee Group claim that most of CRM implementations did not return the expected ROI (Foley, 2002). This is because management tends to be myopic when considering their IT (information technology) decisions, primarily because they are unable to evaluate (specifically the indirect benefits and costs) eCRM applications (Ernst and Young, 1999). To address this issue, this paper sets out to investigate the current evaluation practices by Australian organizations implementing eCRM. The other objective is to identify the key issues faced by managers to justify and measure their eCRM. Hopefully, the finding can help business organizations to better manage their eCRM investment and its contribution to improving their long term profitability.
Knowledge Combination vs. Meta-Learning
Authors: Bruha Ivan
Published: May 2011

Research in intelligent information systems investigates the possibilities of enhancing their over-all performance, particularly their prediction accuracy and time complexity. One such discipline, data mining (DM), processes usually very large databases in a profound and robust way (Fayyad et al., 1996). DM points to the overall process of determining a useful knowledge from databases, that is, extracting high-level knowledge from low-level data in the context of large databases. This article discusses two newer directions in this field, namely knowledge combination and meta-learning (Vilalta & Drissi, 2002). There exist approaches to combine various paradigms into one robust (hybrid, multistrategy) system which utilizes the advantages of each subsystem and tries to eliminate their drawbacks. There is a general belief that integrating results obtained from multiple lower-level decision-making systems, each usually (but not required) based on a different paradigm, produce better performance. Such multi-level knowledgebased systems are usually referred to as knowledge integration systems. One subset of these systems is called knowledge combination (Fan et al., 1996). We focus on a common topology of the knowledge combination strategy with base learners and base classifiers (Bruha, 2004). Meta-learning investigates how learning systems may improve their performance through experience in order to become flexible. Its goal is to search dynamically for the best learning strategy. We define the fundamental characteristics of the meta-learning such as bias, and hypothesis space. Section 2 surveys the various directions in algorithms and topologies utilized in knowledge combination and meta-learning. Section 3 represents the main focus of this article: description of knowledge combination techniques, meta-learning, and a particular application including the corresponding flow charts. The last section presents the future trends in these topics.
Knowledge Discovery from Genomics Microarrays
Authors: Yu Lei
Published: May 2011

The advent of genomic microarray technology enables simultaneously measuring the expressions of thousands of genes in massive experiments, and hence provides scientists, for the first time, the opportunity of observing complex relationships between various genes in a genome. In order to extract biologically meaningful insights from a plethora of data generated from microarray experiments, knowledge discovery techniques, which discover patterns, statistical or predictive models, and relationships among massive data, have been widely applied in microarray data analysis. For example, clustering can be applied to identify groups of genes that are regulated in a similar manner under a number of experimental conditions or groups of samples that show similar expression patterns across a number of genes (Jiang, Tang, & Zhang, 2004). Classification can be performed to characterize the cellular difference between different samples, such as between normal and cancer cells or between cancer cells with different responses to treatment, and can potentially be used to predict the classes of samples based on their gene expression patterns (Statnikov, Aliferis, Tsamardinos, Hardin, & Levy, 2005). Feature selection or gene selection can help identify among thousands of genes a small fraction of genes that are relevant for discriminating between different sample types, and may potentially lead to the identification of a few biologically relevant “marker” genes for subsequent biological validation (Saeys, Inza, & Larranaga, 2007). This article provides a brief introduction to the field of knowledge discovery and its applications in discovering useful knowledge from genomic microarray data. It describes common knowledge discovery tasks for genomic microarray data, presents representative methods for each task, and identifies emerging challenges and trends in knowledge discovery from genomic microarray data.
Knowledge Management in E-Government
Authors: Carstens Deborah S. , Bean LuAnn , Barlow Judith
Published: May 2011

Over the past decade, government has created innovative and complex systems connecting people to information by focusing on Knowledge Management (KM) practices. KM, described as the comprehensive management of an organization’s expertise through collecting, categorizing and disseminating knowledge, leads to knowledge discovery through techniques such as data mining. These developments have transformed traditional access to public services into e-government. Ever increasing demand to access and information has also brought about e-government policy development challenges for integrative KM practices in public services (Riege & Lindsay, 2006). In particular, the size and complexity of governmental structures and the vast data stores have become problematic (Koh, Ryan, & Prybutok, 2005). Because government uses, collects, processes, and disseminates sensitive information containing personal, financial and medical data, it is very easy for organizations to reprocess the information and disseminate it (Hewett & Whitaker, 2002). Ebrahim and Irani (2005) state that the benefits gained by data mining and KM practices are erased when information is not viewed as confidential but instead as a commodity to be bought and sold. Therefore, e-government must uphold a higher standard of ethics in KM practices through continued development of codes of conduct and governance policies for data that build citizen trust and ensure success of e-government services and transactions (Verschoor, 2000). An excellent framework to effectively preserve this trust is a balanced scorecard (BSC), which was first introduced by Kaplan and Norton (1992, 1996a, 1996b). The framework serves to continuously improve the KM process when modified for e-government. Therefore, this chapter describes technological and organizational challenges faced by e-government in KM and retrieval and presents the BSC framework to overcome these challenges.
A Language/Action Based Approach to Information Modelling
Authors: Johannesson Paul
Published: May 2011

There are several different views of the role of information systems. Two of the most important are the data view and the communicative view. According to the data view, the primary purpose of an information system is to provide a model of a domain, thereby enabling people to obtain information about reality by studying the model. In this respect, an information system works as a repository of data that reflects the structure and behaviour of an enterprise, and the system provides data that can be used for decisions about the enterprise. In contrast, the communicative view states that the major role of an information system is to support communication within and between organisations by structuring and coordinating the actions performed by organisational agents. The system is seen as a medium through which people can perform social actions, such as stating facts, making promises, and giving orders. The data and communicative views of information systems are mirrored by two different views of organisations: the functional view and the constructional view (Dietz, 2003a). The functional view focuses on the functions of an organisation with respect to its environment, in particular, the resources that the organisation consumes and produces. A model of an organisation from a functional perspective is a black-box model, as it shows the interactions with the environment but not the internal mechanisms. The constructional view, on the other hand, focuses on how behaviour and function are brought about by the operations and structure of an organisation. A model of an organisation from a constructional perspective is a white-box model as it shows the inner workings of the organisation. In information systems design, the starting point has often been based on the data view and the functional view, though frequently augmented by concepts like reasoning and monitoring. However, these views easily lead to a computerand technology-biased management of the communication taking place in an organisation, and they benefit from being complemented by the communicative and constructional views. A promising theoretical foundation for these views is the language/action approach, which is based on theories from linguistics and the philosophy of language. In the language/action approach, business actions are modelled on the notions of speech acts and discourses, which provide a basis for distinguishing between different communication phases, such as preparation, negotiation, and acceptance. The purpose of this chapter is to outline how the language/action approach can be used as a basis for the information modelling of communicative aspects in organisations.
Lip Extraction for Lipreading and Speaker Authentication
Authors: Wang Shilin , Liew Alan Wee-Chung
Published: May 2011

In recent years, there is a growing interest in using visual information for automatic lipreading (Kaynak, Zhi, Cheok, Sengupta, Jian, & Chung, 2004) and visual speaker authentication (Mok, Lau, Leung, Wang, & Yan, 2004). It has been shown that visual cues, such as lip shape and lip movement, would greatly improve the performance of these systems. Various techniques have been proposed in the past decades to extract speech/speaker relevant information from lip image sequences. One approach is to extract the lip contour from lip image sequences. This generally involves lip region segmentation and lip contour modeling (Liew, Leung, & Lau, 2002; Wang, Lau, Leung, & ALiew, 2004), and the performance of the visual speech recognition and visual speaker authentication systems depends much on the accuracy and efficiency of these two procedures. Lip region segmentation aims to label the pixels in the lip image into lip and non-lip. The accuracy and robustness of the lip segmentation process is of vital importance for subsequent lip extraction. However, large variations caused by different speakers, lighting condition, or make-ups make the task difficult. The low color contrast between lip and facial skin, and the presence of facial hair, further complicate the problem. Given a correctly segmented lip region, the lip extraction process then involves fitting a lip model to the lip region. A good lip model should be compact, that is, with a small number of parameters, and should adequately represent most valid lip shapes while rejecting most invalid shapes. As most lip extraction techniques involve iterative model fitting, the efficiency of the optimization process is another important issue.
A Literacy Integral Definition
Authors: Espinoza Matheus Norelkys , Pérez Reyes MariCarmen
Published: May 2011

Due to the lack of a unique definition of literacy and the need for redefining this conception in a context characterized by the changes generated by the inclusion of new technologies in all aspects of the society, this explicative research article is oriented toward proposing a definition of literacy from an integral conception which is based on three main kinds of literacy: functional, informational and ethical. This integral conception must orient the basic contents in the school curricula in all current educational models, mainly at the university level. We consider that knowledge is unique; it should not be divided into pieces. Therefore, it is necessary to integrate the new technologies, from this new paradigm, in the contents of the school curricula. The present article compiles some general considerations about literacy, proposes a new definition of literacy from an integral conception, as well as each one of its components.
Managing IS Security and Privacy
Authors: Katos Vasilios
Published: May 2011

The concept of privacy has received attention for over a century now and its definition?let alone, understanding?has been profoundly challenging. This is primarily attributed to the “incompatible” and rich set of characteristics privacy comprises. As Brunk (2002) states very sharply, “Privacy is a matter of intellectual and philosophical thought and retains few tangible characteristics, making it resistant to simple explanation.” Perhaps the first scholarly work on privacy was that of Warren and Brandeis (1980), who introduced the highly abstractive yet popular definition of privacy as the “right to be left alone.” As privacy was recognized as a right, it primarily existed within a legal context. Legislation for protecting one’s privacy exists in many countries and in some cases at a constitutional level (see for example the Fourth Amendment of the U.S. Constitution). It was soon realized in the information revolution era that privacy and information are somewhat coupled. More precisely, emerging privacy concepts and metrics relate to the intentional or unintentional information flows. However, when it comes to studying, using, and investing in information, security appeared to have a higher priority over privacy. Security and privacy seemingly operate under different agendas; privacy is about protecting one’s actions in terms of offering anonymity, whereas security includes the notion of accountability which implies that anonymity is waived. Still, security is a vital component of an information system, as it is well needed in order to protect privacy. This contradictory relation between security and privacy has caused a considerable amount of debate, political and technical, resulting in a plethora of position and research papers. Accepting that there may be no optimum solution to the problem of striking a balance between security and privacy, this article presents a recently developed methodology that could support policy decision making on a strategic level, thus allowing planners to macro-manage security and privacy.
Measurement Issues in Decision Support Systems
Authors: Holstein William K. , Crnkovic Jakov
Published: May 2011

The past decade has seen tremendous progress in systems for information support–flexible and adaptable systems to support decision makers and to accommodate individual needs and preferences. These model- or data-driven or hybrid decision support systems (DSS), now often called business intelligence (BI) systems, incorporate diverse data drawn from many different internal and external sources. Increasingly, these sources include sophisticated enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, data warehouses and other enterprise-wide systems that contain vast amounts of data and permit relatively easy access to that data by a wide variety of users at many different levels of the organization. Decision support, DSS and BI have entered our lexicon and are now common topics of discussion and development in large, and even in medium-sized, enterprises. Now that DSS is well established, attention is turning to measurement and the metrics that populate such systems.
Metrics for the Evaluation of Test-Delivery Systems
Authors: Valenti Salvatore
Published: May 2011

Most solutions to the problem of delivering course content supporting both student learning and assessment nowadays imply the use of computers, thanks to the continuous advances of information technology. According to Bull (1999), using computers to perform assessment is more contentious than using them to deliver content and to support student learning. In many papers, the terms computer-assisted assessment (CAA) and computer-based assessment (CBA) are often used interchangeably and somewhat inconsistently. The former refers to the use of computers in assessment. The term encompasses the uses of computers to deliver, mark, and analyze assignments or examinations. It also includes the collation and analysis of data gathered from optical mark readers. The latter (that will be used in this paper) addresses the use of computers for the entire process, including assessment delivery and feedback provision (Charman & Elmes, 1998). A typical CBA system is composed of the following. • Test-Management System (TMS) - that is, a tool providing the instructor with an easy-to-use interface, the ability to create questions and to assemble them into tests, and the possibility of grading the tests and making some statistical evaluations of the results • Test-Delivery System (TDS) - that is, a tool for the delivery of tests to the students. The tool may be used to deliver tests using paper and pencil, or a stand-alone computer on a LAN (local area network) or over the Web. The TDS may be augmented with a Web enabler used to deliver the tests over the Internet. In many cases, producers distribute two different versions of the same TDS: one to deliver tests either on single computers or on a LAN and the other to deliver tests over the WWW (World Wide Web). This is the policy adopted, for instance, by Cogent Computing Co. (2004) with CQuest LAN and CQuest Net. The TMS and TDS modules may be integrated in a single application as, for instance, Perception developed by Question Mark Computing (2004), or may be delivered as separate applications as it occurs for MicroTest and MicroGrade developed by Chariot Software Group (2004).
Mobile Commerce and the Evolving Wireless Technologies
Authors: Lei Pouwan , Wang Jia Jia
Published: May 2011

The mobile phone industry has experienced an explosive growth in recent years. The emerging markets such as China, India, and Brazil contribute this growth. In China, the number of mobile subscribers has already surpassed the number of fixed landline phone subscribers. In Korea and Japan, there is an explosion of mobile and wireless services. The United States are joining too and there were 207.9 million subscribers in 2005 (CTIA, 2006). Mobile e-commerce (m-commerce) makes business mobility a reality; mobile users could access the Internet at any time, from anywhere with handheld devices or laptop. A 3G enabled smart phone enables you to access a wide range of services anywhere and anytime. For example, you can send and receive e-mail, make cinema and restaurant reservations and pay for them, check real train time, look at digital maps, download music and games, and also browse the Internet. Mobile and wireless services are ranging from mobile communication networks to wireless local area networks. The service provided by mobile communication systems has achieved huge success as mobile and wireless communication technologies are converging at fast speed. We will study mobile and wireless communication in relation to mobile phones. Hence, m-commerce is defined as electronic commerce carried out in handheld devices such as smart phone through mobile and wireless communication network.
Mobile Telecommunications and M-Commerce Applications
Authors: Tan Clarence N.W. , Teo Tiok-Woo
Published: May 2011

This article presents an overview of prevailing trends and developments shaping mobile commerce (m-commerce) and the wireless economy. A review of wireless telecommunications infrastructure attempts to demystify the evolving technology landscape. Mobile Internet deployment and adoption demographics are highlighted, as are innovative wireless applications and current m-commerce issues.
Modeling for E-Learning Systems
Authors: Rentroia-Bonito Maria Alexandra , Pires Jorge Joaquim Armando
Published: May 2011

Computer-based instruction is touted as an effective tool to support knowledge dissemination within predefined learning environments. Indeed, many see it as a way to overcome geographical or social barriers to knowledge transmission and educational institutions. However, its domain of application has traditionally been restricted to basic skills and educational contexts. Recently, dynamic and complex business environments shaped by technological changes and the downsizing trend of the ’90s placed new constraints on the underlying assumptions (Fuglseth, 2003). Organizations are now pushing for skill flexibility, demanding specialized knowledge and requiring faster learning curves from employees. Many advocate Internet-based education materials as one way to meet those challenges (Bernardes & O’Donoghue, 2003; Karoulis et al., 2004; Storey et al., 2002; Strazzo & Wentling, 2001). However, this raises important questions concerning both effectiveness and efficiency of such tools and materials. Indeed, developing interactive multimedia-based courseware remains pretty much a black art, consuming enormous resources. So far, there is a lack of established models to predict the performance and evaluate how adequately courseware can meet user needs. In fact, developing courseware should take into account the target constituency requirements, organizational context, and the stated educational or training goals. Developing the wrong training materials can lead to costly investments in creating and maintaining content to match the increasing expectations on e-learning. Perhaps this can explain the recent rash of failed e-learning projects—current results do not measure up to business and individual expectations yet. A better understanding of the many factors affecting e-learning performance would allow individuals and organizations to achieve the expected benefits. In so doing, development teams need methods, techniques, and tools to evaluate in advance which features are needed to achieve higher outcomes, namely, performance and satisfaction. Thus, the need to develop predictive models to improve learning effectiveness is in order. This overview includes four sections. “Background” presents a proposed e-learning theoretical framework to guide our analysis based upon the reviewed literature. “Key Issues” section describes main issues arising from the proposed elearning conceptual framework. “Future Trends” describes our vision on how to approach e-learning initiatives and future trends. Finally, we present a general conclusion.
Multimedia Content Adaptation
Authors: Knight David , Angelides Marios C.
Published: May 2011

The previous decade has witnessed a wealth of advancements and trends in the field of communications and subsequently, multimedia access. Four main developments from the last few years have opened up the prospect for ubiquitous multimedia consumption: wireless communications and mobility, standardised multimedia content, interactive versus passive consumption and the Internet and the World Wide Web. While individual and isolated developments have produced modest boosts to this existing state of affairs, their combination and cross-fertilisation have resulted in today’s complex but exciting landscape. In particular, we are beginning to see delivery of all types of data for all types of users in all types of conditions (Pereira & Burnett, 2003). Compression, transport, and multimedia description are examples of individual technologies that are improving all the time. However, the lack of interoperable solutions across these spaces is holding back the deployment of advanced multimedia packaging and distribution applications. To enable transparent access to multimedia content, it is essential to have available not only the description of the content but also a description of its format and of the usage environment in order that content adaptation may be performed to provide the end-user with the best content experience for the content requested with the conditions available (Vetro, 2003). In the following sections, we will look at the background of multimedia content adaptation, why do we require it and why are present solutions not adequate. We then go onto the main focus of the article, which describes the main themes of modern multimedia content adaptation, such as present day work that defines the area and overviews and descriptions of techniques used. We then look at what this research will lead to in the future and what we can expect in years to come. Finally, we conclude this article by reviewing what has been discussed.
Neural Networks for Automobile Insurance Pricing
Authors: Yeo Ai Cheo
Published: May 2011

In highly competitive industries, customer retention has received much attention. Customer retention is an important issue, as loyal customers tend to produce greater cash flow and profits, are less sensitive to price, bring along new customers and do not require any acquisition or start-up costs.
Online Student and Instructor Characteristics
Authors: Kilburn Michelle , Henckell Martha , Starrett David
Published: May 2011

As technological advances become mainstream in higher education, many universities have begun delving into online learning as an effective means of course delivery. Transitioning from the Industrial Age to the Digital Age of learning has forced some evaluators to rethink standards of success and the idea of productivity and learning (Leonard, 1999). Understanding the positive attributes of students and instructors in the online environment will contribute to the understanding of how we can enhance the learning experience for the student and the teaching experience for the instructor. This article will also assist students and instructors in understanding the differences that may be experienced in the online environment vs. the face-to-face environment and provide the opportunity to consider whether online learning or teaching is a “good fit” for them. Understanding why students or instructors might choose the online environment will also assist administrators in developing successful, quality online programs that enrich the experiences for both students and instructors.
Organization of Home Video
Authors: Zhang Yu-Jin
Published: May 2011

With the progress of electronic equipments and computer technology for taking motion pictures and processing huge data, an increasing number of people now own and use camcorders to make home videos that capture their experiences and document their lives. Home video has no time limits and no restriction in content (Lienhart, 2000), so these videos easily add up to many hours of material. However, the organization and edition of the large amount of information contained in home videos present technical challenges due to the lack of efficient tools. Though a number of prototype systems for content-based video analysis and retrieval have been constructed, for example, as shown in (Wactlar, 1996; Chang, 1998), the development of tools and systems specialized for addressing home video, that is for extracting, representing, organizing, browsing, querying and retrieving video, is just on a preliminary stage (Huang, 2005; Wu, 2005). Several tasks are needed to confront to make the organization of home video possible and feasible. Home video has certain particular characteristics. The organization of home video should be based on the understanding of video structures, and by taking advantages of this structure. Home video are completed and stored straight in compressed domain. In order to save both time and space, techniques that manipulate home videos directly in compressed domain should be considered (Wang, 2003). Some typical techniques working on compressed domain could be found in (Taskiran, 2004). Home video are made by shot after shot without storyline, these shots may or may not have immediate relationship. To group shots, the visual features should be extracted from every shot (Gatica-Perez, 2003) Facing these tasks and difficulties, a novel technique is described in this article. It is based on the analysis of characteristics of home video, on the detection of motion attention regions in compressed domain, on the time weighting based on camera motion, and on a novel two-layer shot clustering approach and organization strategy. Experiments made on two home videos from MPEG-7 data set provide encouraging results.
An Overview of Software Engineering Process and Its Improvement
Authors: April Alain , Laporte Claude
Published: May 2011

The software engineering process is concerned with the definition, implementation, measurement, change, and improvement of software processes. This short article presents software engineering process knowledge along the lines of the software engineering body of knowledge (International Organization for Standardization & International Electrotechnical Commission [ISO/IEC], 2005b). The objective of the software engineering process is to implement new or better processes in current software engineering practice.
Pattern-Oriented Use Case Modeling
Authors: Kamthan Pankaj
Published: May 2011

The majority of the present software systems, such as those that run on automatic banking machines (ABMs), on mobile devices, and on the Web, are interactive in nature. Therefore, it is critical to precisely understand, identify, and document the services that an interactive software system will provide from the viewpoint of its potential users. A large and important class of models that these services encapsulate is use cases (Jacobson, Christerson, Jonsson, & Övergaard, 1992). In the last few years, use cases have become indispensable as means for behavioral modeling of interactive software systems. They play a crucial role in various software development activities, including estimating development cost (Anda, 2003), eliciting behavioral requirements, and defining test cases. It is well known that addressing quality early is crucial to avoid the propagation of problems to later artifacts (Moody, 2005). With the increasing deployment of use cases as early artifacts in software process environments, the question of how these models should be developed so as to attain high quality arises. In response, this article focuses on the use case modeling process (the act of constructing use case models) and, based on the notion of patterns (Appleton, 1997), proposes a systematic approach towards the development of use case models. The rest of the article is organized as follows. The background and related work necessary for the discussion that follows is outlined. This is followed by the presentation of a pattern-oriented use case modeling process for systematically addressing the semiotic quality of use case models in a feasible manner. Next, challenges and directions for future research are outlined, and finally, concluding remarks are given.
Peer-to-Peer Computing
Authors: Pereira Manuela
Published: May 2011

The term peer-to-peer (P2P) was originally used to refer to network protocols where all the nodes had the same role and there were no nodes with specific responsibilities to act as the administrators or supervisors of a network (Ye, Makedon, & Ford, 2004). However, with the evolution of Internet as the dominant architecture for applications, contents, and services, applications and services have gradually migrated from the client-server paradigm to the edge services paradigm and now to the P2P computing paradigm. Therefore, nowadays, the term P2P refers to a class of systems and applications that use distributed resources to perform some function in a decentralized manner, where every participating node can act as both a client and a server (Ye et al., 2004). This article provides an overview of P2P computing, being focused on the types of multimedia distribution services and cooperation models in P2P systems. These models are classified regarding the functionality, the degree of decentralization, and the degree of structure of the information system.
Process-Based Data Mining
Authors: Hirji Karim K.
Published: May 2011

In contrast to the Industrial Revolution, the Digital Revolution is happening much more quickly. For example, in 1946, the world’s first programmable computer, the Electronic Numerical Integrator and Computer (ENIAC), stood 10 feet tall, stretched 150 feet wide, cost millions of dollars, and could execute up to 5,000 operations per second. Twenty- five years later, Intel packed 12 times ENIAC’s processing power into a 12–square-millimeter chip. Today’s personal computers with Pentium processors perform in excess of 400 million instructions per second. Database systems, a subfield of computer science, has also met with notable accelerated advances. A major strength of database systems is their ability to store volumes of complex, hierarchical, heterogeneous, and time-variant data and to provide rapid access to information while correctly capturing and reflecting database updates. Together with the advances in database systems, our relationship with data has evolved from the prerelational and relational period to the data-warehouse period. Today, we are in the knowledge-discovery and data-mining (KDDM) period where the emphasis is not so much on identifying ways to store data or on consolidating and aggregating data to provide a single, unified perspective. Rather, the emphasis of KDDM is on sifting through large volumes of historical data for new and valuable information that will lead to competitive advantage. The evolution to KDDM is natural since our capabilities to produce, collect, and store information have grown exponentially. Debit cards, electronic banking, e-commerce transactions, the widespread introduction of bar codes for commercial products, and advances in both mobile technology and remote sensing data-capture devices have all contributed to the mountains of data stored in business, government, and academic databases. Traditional analytical techniques, especially standard query and reporting and online analytical processing, are ineffective in situations involving large amounts of data and where the exact nature of information one wishes to extract is uncertain. Data mining has thus emerged as a class of analytical techniques that go beyond statistics and that aim at examining large quantities of data; data mining is clearly relevant for the current KDDM period. According to Hirji (2001), data mining is the analysis and nontrivial extraction of data from databases for the purpose of discovering new and valuable information, in the form of patterns and rules, from relationships between data elements. Data mining is receiving widespread attention in the academic and public press literature (Berry & Linoff, 2000; Fayyad, Piatetsky-Shapiro, & Smyth, 1996; Kohavi, Rothleder, & Simoudis, 2002; Newton, Kendziorski, Richmond, & Blattner, 2001; Venter, Adams, & Myers, 2001; Zhang, Wang, Ravindranathan, & Miles, 2002), and case studies and anecdotal evidence to date suggest that organizations are increasingly investigating the potential of data-mining technology to deliver competitive advantage.
PROLOG
Authors: Garrett Bernie
Published: May 2011

Prolog is a logic based programming language, and was developed in the early 1970s and is a practical programming language particularly useful for knowledge representation and artificial intelligence (AI) applications. Prolog is different from many common computer languages in that it is not a procedural language (such as Basic, C, or Java). It is an interpreted logic based declarative language and as such has no loops, jumps, type declarations or arrays, and no fixed control constructs. In the past this has led to the impression that Prolog is a restricted language, useful only for highly specialized programming tasks by enthusiasts (Callear, 1994; Krzysztof, 1997). However, this is not the case and modern versions of Prolog are well equipped and versatile, and can be used for any programming task. The latest generations of the language (e.g., Visual Prolog) can also be integrated into more common object oriented languages.
Prolonging the Aging of Software Systems
Authors: Constantinides Constantinos , Arnaoudova Venera
Published: May 2011

The evolution of programming paradigms and languages allows us to manage the increasing complexity of systems. Furthermore, we have introduced (and demanded) increasingly complex requirements because various paradigms provide mechanisms to support their implementation. As a result, complex requirements constitute a driving factor for the evolution of languages which in turn can support system complexity. In this circular relationship, the maintenance phase of the software life cycle becomes increasingly important and factors which affect maintenance become vital. In this chapter we review the notions of software aging and discuss activities undertaken during maintenance. We also discuss challenges and trends for the development of well-maintained systems as well as for aiding in the maintenance of legacy systems.
Quality Assurance Issues for Online Universities
Authors: Grasso Floriana , Leng Paul
Published: May 2011

Online delivery of degree-level programmes is an attractive option, especially for working professionals and others who are unable to contemplate full-time residential university attendance. If such programmes are to be accepted, however, it is essential that they attain the same standards and quality as conventionally delivered degrees. The key challenge is to find ways to ensure that the qualities that make university education attractive are preserved in the context of a new and quite different model of delivery. Many systems have been developed to support online learning (see, e.g., Anderson & Kanuka, 1997; Davies, 1998; Persico & Manca, 2000; Suthers & Jones, 1997; Yaskin & Everhart, 2002). These systems may or may not mimic conventional lecture-room teaching, but will necessarily involve major differences in the ways in which teaching and student support are organised. Furthermore, the Internet lends itself naturally to an internationalisation of education delivery, but this also poses challenges for universities that have developed their structures within the framework of national education systems. To address these issues, it may be desirable for the university to work in partnership with other agencies, for example to provide local support services for students. This too, however, may introduce new problems of quality control and management. We will discuss here what structures are required to ensure the quality of the education provided and the standards of the degrees offered in this context.
Security and Reliability of RFID Technology in Supply Chain Management
Authors: Modrák Vladimír , Knuth Peter
Published: May 2011

RFID (radio frequency identification) technology can be expressed in the most universal manner as wireless identi- fication technology, which does not need the line-of-sight to be read or written. It offers enhancement of identification technologies like barcode technology. Optical barcode technology was developed in 1948 by Silver and Woodland at Drexel Institute of Technology and first commercially used in 1966 (Adams, 2002). Barcode technology stores data in the widths and spacings of printed parallel lines, or in patterns of dots, concentric circles, and hidden within images. The most extended is UPC code which was invented in 1973 and since then became everyday part of our life. Other commonly used types of barcodes are Code 128, Code 93 (Groover, 1980) and DataMatrix 2D barcode. At this time, mostly the barcodes are keeping inventory and shipments moving. RFID and barcode technology complement each other because both of them are beneficial in different situations and can be used together in many applications. RFID technology has several advantages for managing and collecting object’s data or tracking it as it moves through the supply chain (SC). Two of them are related to the increased abilities of security and reliability of the identification systems. These two properties of identification technologies are equally important for their use in supply chain management (SCM). The purpose of this chapter is to highlight selected areas of this technology that may be critical specific aspects of further RFID development and applications. We have also discussed about differences between RFID and barcode technologies especially in terms of their use in SCM and concluded this article with expectations of further development of this still progressive technology.
Signal Processing Techniques for Audio and Speech Applications
Authors: Perez-Meana Hector , Nakano-Miyatake Mariko
Published: May 2011

Since the apparition of the first standalone digital signal processor (DSP) in 1980, the development of very-largescale integration (VLSI) technology has allowed an impressive improvement on the performance of signal processing devices. This fact has made it possible to implement more efficient systems for storage, transmission, enhancement, protection, and reproduction of speech and audio signals. Some of these successful applications, shown in Table 1, have contributed to improving the performance of communications, storage, and medical systems, as well as security and copyright protection.
Social Learning Aspects of Knowledge Management
Authors: Ali Irena , Warne Leoni , Pascoe Celina
Published: May 2011

There are probably as many variations of knowledge management definitions as there are practitioners and researchers in the discipline. Complete consensus in such a group would be a surprising finding. This is because the two words are loaded with pre-existing meanings that do not always sit comfortably in juxtaposition, so what it means to “manage knowledge” is difficult to ascertain, and hence comes to mean different things to different people. We do know however, that knowledge exists in the minds of individuals and is generated and shaped through interaction with others. In an organizational setting, knowledge management must, at the very least, be about how knowledge is acquired, constructed, transferred, and otherwise shared with other members of the organization, in a way that seeks to achieve the organization’s objectives. Put another way, knowledge management seeks to harness the power of individuals by supporting them with information technologies and other tools, with the broad aim of enhancing the learning capability of individuals, groups, and in turn, organizations (Ali, Warne, Bopping, Hart, & Pascoe, 2004). Social learning, in this context, is defined as learning occurring in or by a cultural cluster or organizational group or team and includes procedures for transmitting knowledge and practices across different work situations, settings, and time. However, the application of technology must be guided by the needs of the organization and its workers. As Davenport (2005, p.162) states, “While I don’t question the importance of technology in organizations today, it’s only one source of knowledge and learning for knowledge workers.”
Socio-Cognitive Model of Trust
Authors: Falcone Rino , Castelfranchi Cristiano
Published: May 2011

Humans have learned to cooperate in many ways and in many environments, on different tasks, and for achieving different and several goals. Collaboration and cooperation in their more general sense (and, in particular, negotiation, exchange, help, delegation, adoption, and so on) are important characteristics - or better, the most foundational aspects - of human societies (Tuomela, 1995). In the evolution of cooperative models, a fundamental role has been played by diverse constructs of various kinds (purely interactional, technical-legal, organizational, sociocognitive, etc.), opportunely introduced (or spontaneously emerged) to support decision making in collaborative situations. The new scenarios we are destined to meet in the third millennium transfigure the old frame of reference, in that we have to consider new channels and infrastructures (i.e., the Internet), new artificial entities for cooperating with artificial or software agents, and new modalities of interaction (suggested/imposed by both the new channels and the new entities). In fact, it is changing the identification of the potential partners, the perception of the other agents, the space-temporal context in which interaction happen, the nature of the interaction traces, the kind and role of the authorities and guarantees, etc. For coping with these scenarios, it will be necessary to update the traditional supporting decision-making constructs. This effort will be necessary especially to develop the new cyber-societies in such a way as not to miss some of the important cooperative characteristics that are so relevant in human societies.
Sociological Insights in Structuring Australian Distance Education
Authors: Ragusa Angela T.
Published: May 2011

Sociology is well-known for analyzing institutions and social change (Holmes, Hughes, & Julian, 2007). Yet, a dearth of sociological research explores technology and distance education (DE) despite imperatives to include cultural issues (Jorgensen, 2002; Lum, 2006). Meta-analysis shows social studies scholars fail to prioritize technological research (Marri, 2007). Sociologists have examined Webbased instruction and anxiety levels (Gundy, Morton, Liu, & Kline, 2006), flaming (Lee, 2005) and the relationship between learning environment, pedagogy, social roles, relations (Jaffee, 2003) and unintended benefits of traditional classrooms using DE (Edwards, Cordray, & Dorbolo, 2000). This qualitative exploratory research looks at asynchronous forum (AF) and DE student experiences in Australia. Using social constructivism, learning is seen as praxis, or doing (Vygotsky, 1986) in contrast with ancient traditionalists’ tabula rasa/“blank slate” understanding of learners waiting to be filled with knowledge (Palloff & Pratt, 2001). Case studies show how culture and learning environments affect virtual communication (VC) when all communication, student-teacher and student-student, is technologically mediated. Experiences from four 2005-2006 cohorts show social structure affects student perceptions’ of learning, satisfaction and agency.