bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Warehouse Back-End Tools
Authors: Simitsis Alkis , Theodoratos Dimitri
Published: May 2011

The back-end tools of a data warehouse are pieces of software responsible for the extraction of data from several sources, their cleansing, customization, and insertion into a data warehouse. They are known under the general term extraction, transformation and loading (ETL) tools. In all the phases of an ETL process (extraction and exportation, transformation and cleaning, and loading), individual issues arise and, along with the problems and constraints that concern the overall ETL process, make its lifecycle a very complex task.
Discovering Ranking Functions for Information Retrieval
Authors: Fan Weiguo , Pathak Praveen
Published: May 2011

The field of information retrieval deals with finding relevant documents from a large document collection or the World Wide Web in response to a user’s query seeking relevant information. Ranking functions play a very important role in the retrieval performance of such retrieval systems and search engines. A single ranking function does not perform well across different user queries, and document collections. Hence it is necessary to “discover” a ranking function for a particular context. Adaptive algorithms like genetic programming (GP) are well suited for such discovery.
Discretization for Continuous Attributes
Authors: Muhlenbach Fabrice , Rakotomalala Ricco
Published: May 2011

In the data-mining field, many learning methods — such as association rules, Bayesian networks, and induction rules (Grzymala-Busse & Stefanowski, 2001) — can handle only discrete attributes. Therefore, before the machine-learning process, it is necessary to re-encode each continuous attribute in a discrete attribute constituted by a set of intervals. For example, the age attribute can be transformed in two discrete values representing two intervals: less than 18 (a minor) and 18 or greater. This process, known as discretization, is an essential task of the data preprocessing not only because some learning methods do not handle continuous attributes, but also for other important reasons. The data transformed in a set of intervals are more cognitively relevant for a human interpretation (Liu, Hussain, Tan, & Dash, 2002); the computation process goes faster with a reduced level of data, particularly when some attributes are suppressed from the representation space of the learning problem if it is impossible to find a relevant cut (Mittal & Cheong, 2002); the discretization can provide nonlinear relations — for example, the infants and the elderly people are more sensitive to illness. This relation between age and illness is then not linear — which is why many authors propose to discretize the data even if the learning method can handle continuous attributes (Frank & Witten, 1999). Lastly, discretization can harmonize the nature of the data if it is heterogeneous — for example, in text categorization, the attributes are a mix of numerical values and occurrence terms (Macskassy, Hirsh, Banerjee, & Dayanik, 2001).
Explanation-Oriented Data Mining
Authors: Yao Yiyu , Zhao Yan
Published: May 2011

Data mining concerns theories, methodologies, and, in particular, computer systems for knowledge extraction or mining from large amounts of data (Han & Kamber, 2000). The extensive studies on data mining have led to many theories, methodologies, efficient algorithms and tools for the discovery of different kinds of knowledge from different types of data. In spite of their differences, they share the same goal, namely, to discover new and useful knowledge, in order to gain a better understanding of nature.
Financial Ratio Selection for Distress Classification
Authors: Galvao Roberto Kawakami Harrop , Becerra Victor M. , Abou-Seada Magda
Published: May 2011

Prediction of corporate financial distress is a subject that has attracted the interest of many researchers in finance. The development of prediction models for financial distress started with the seminal work by Altman (1968), who used discriminant analysis. Such a technique is aimed at classifying a firm as bankrupt or nonbankrupt on the basis of the joint information conveyed by several financial ratios.
High Frequency Patterns in Data Mining
Authors: Lin Tsau Young
Published: May 2011

The principal focus is to examine the foundation of association (rule) mining (AM) via granular computing (GrC). The main results is: The set of all high frequency patterns can be found by sloving linear inequalities within a polynomial time.
Incorporating the People Perspective into Data mining
Authors: Wickramasinghe Nilmini
Published: May 2011

Today’s economy is increasingly based on knowledge and information (Davenport & Grover, 2001). Knowledge is now recognized as the driver of productivity and economic growth, leading to a new focus on the roles of information technology and learning in economic performance. Organizations trying to survive and prosper in such an economy are turning their focus to strategies, processes, tools, and technologies that can facilitate the creation of knowledge. A vital and well-respected technique in knowledge creation is data mining, which enables critical knowledge to be gained from the analysis of large amounts of data and information. Traditional data mining and the KDD process (knowledge discovery in data bases) tends to view the knowledge product as a homogeneous product. Knowledge, however, is a multifaceted construct, drawing upon various philosophical perspectives including Lockean/Leibnitzian and Hegelian/Kantian, exhibiting subjective and objective aspects, as well as having tacit and explicit forms (Nonaka, 1994; Alavi & Leidner, 2001; Schultze & Leidner, 2002; Wickramasinghe et al., 2003). The thesis of this article is that taking a broader perspective of the resultant knowledge product from the KDD process, namely by incorporating a people-based perspective into the traditional KDD process, not only provides a more complete and macro perspective on knowledge creation but also a more balanced approach, which in turn serves to enhance the knowledge base of an organization and facilitates the realization of effective knowledge. The implications for data mining are clearly far-reaching and are certain to help organizations more effectively realize the full potential of their knowledge assets, improve the likelihood of using/reusing the created knowledge, and thereby enables them to be well positioned in today’s knowledgeable economy.
Learning Bayesian Networks
Authors: Ramoni Marco F. , Sebastiani Paola
Published: May 2011

Born at the intersection of artificial intelligence, statistics, and probability, Bayesian networks (Pearl, 1988) are a representation formalism at the cutting edge of knowledge discovery and data mining (Heckerman, 1997). Bayesian networks belong to a more general class of models called probabilistic graphical models (Whittaker, 1990; Lauritzen, 1996) that arise from the combination of graph theory and probability theory, and their success rests on their ability to handle complex probabilistic models by decomposing them into smaller, amenable components. A probabilistic graphical model is defined by a graph, where nodes represent stochastic variables and arcs represent dependencies among such variables. These arcs are annotated by probability distribution shaping the interaction between the linked variables. A probabilistic graphical model is called a Bayesian network, when the graph connecting its variables is a directed acyclic graph (DAG). This graph represents conditional independence assumptions that are used to factorize the joint probability distribution of the network variables, thus making the process of learning from a large database amenable to computations. A Bayesian network induced from data can be used to investigate distant relationships between variables, as well as making prediction and explanation, by computing the conditional probability distribution of one variable, given the values of some others.
Locally Adaptive Techniques for Pattern Classification
Authors: Domeniconi Carlotta , Gunopulos Dimitrios
Published: May 2011

Pattern classification is a very general concept with numerous applications ranging from science, engineering, target marketing, medical diagnosis, and electronic commerce to weather forecast based on satellite imagery. A typical application of pattern classification is mass mailing for marketing. For example, credit card companies often mail solicitations to consumers. Naturally, they would like to target those consumers who are most likely to respond. Often, demographic information is available for those who have responded previously to such solicitations, and this information may be used in order to target the most likely respondents. Another application is electronic commerce of the new economy. E-commerce provides a rich environment to advance the state of the art in classification, because it demands effective means for text classification in order to make rapid product and market recommendations.
Logical Analysis of Data
Authors: Boros Endre , Hammer Peter L. , Ibaraki Toshihide
Published: May 2011

The logical analysis of data (LAD) is a methodology aimed at extracting or discovering knowledge from data in logical form. The first paper in this area was published as Crama, Hammer, & Ibaraki (1988) and precedes most of the data mining papers appearing in the 1990s. Its primary target is a set of binary data belonging to two classes for which a Boolean function that classifies the data into two classes is built. In other words, the extracted knowledge is embodied as a Boolean function, which then will be used to classify unknown data. As Boolean functions that classify the given data into two classes are not unique, there are various methodologies investigated in LAD to obtain compact and meaningful functions. As will be mentioned later, numerical and categorical data also can be handled, and more than two classes can be represented by combining more than one Boolean function.
Methods for Choosing Clusters in Phylogenetic Trees
Authors: Burr Tom
Published: May 2011

One data mining activity is cluster analysis, of which there are several types. One type deserving special attention is clustering that arises due to evolutionary relationships among organisms. Genetic data is often used to infer evolutionary relations among a collection of species, viruses, bacterial, or other taxonomic units (taxa). A phylogenetic tree (Figure 1, top) is a visual representation of either the true or the estimated branching order of the taxa, depending on the context. Because the taxa often cluster in agreement with auxiliary information, such as geographic or temporal isolation, a common activity associated with tree estimation is to infer the number of clusters and cluster memberships, which is also a common goal in most applications of cluster analysis. However, tree estimation is unique because of the types of data used and the use of probabilistic evolutionary models which lead to computationally demanding optimization problems. Furthermore, novel methods to choose the number of clusters and cluster memberships have been developed and will be described here. The methods include a unique application of model-based clustering, a maximum likelihood plus bootstrap method, and a Bayesian method based on obtaining samples from the posterior probability distribution on the space of possible branching orders.
Mining Historical XML
Authors: Zhao Qiankun , Bhowmick Sourav Saha
Published: May 2011

Nowadays the Web poses itself as the largest data repository ever available in the history of humankind (Reis et al., 2004). However, the availability of huge amount of Web data does not imply that users can get whatever they want more easily. On the contrary, the massive amount of data on the Web has overwhelmed their abilities to find the desired information. It has been claimed that 99% of the data reachable on the Web is useless to 99% of the users (Han & Kamber, 2000, pp. 436). That is, an individual may be interested in only a tiny fragment of the Web data. However, the huge and diverse properties of Web data do imply that Web data provides a rich and unprecedented data mining source.
Modeling Web-Based Data in a Data Warehouse
Authors: Peter Hadrian , Greenidge Charles
Published: May 2011

Good database design generates effective operational databases through which we can track customers, sales, inventories, and other variables of interest. The main reason for generating, storing, and managing good data is to enhance the decision-making process. The tool used during this process is the decision support system (DSS). The information requirements of the DSS have become so complex, that it is difficult for it to extract all the necessary information from the data structures typically found in operational databases. For this reason, a new storage facility called a data warehouse has been developed. Data in the data warehouse have characteristics that are quite distinct from those in the operational database (Rob & Coronel, 2002).
Text Mining Methods for Hierarchical Document Indexing
Authors: Kim Han-Joon
Published: May 2011

We have recently seen a tremendous growth in the volume of online text documents from networked resources such as the Internet, digital libraries, and company-wide intranets. One of the most common and successful methods of organizing such huge amounts of documents is to hierarchically categorize documents according to topic (Agrawal, Bayardo, & Srikant, 2000; Kim & Lee, 2003). The documents indexed according to a hierarchical structure (termed ‘topic hierarchy’ or ‘taxonomy’) are kept in internal categories as well as in leaf categories, in the sense that documents at a lower category have increasing specificity. Through the use of a topic hierarchy, users can quickly navigate to any portion of a document collection without being overwhelmed by a large document space. As is evident from the popularity of Web directories such as Yahoo (http://www.yahoo.com/) and Open Directory Project (http://dmoz.org/), topic hierarchies have increased in importance as a tool for organizing or browsing a large volume of electronic text documents.
Using Dempster-Shafer Theory in Data Mining
Authors: Beynon Malcolm J.
Published: May 2011

The origins of Dempster-Shafer theory (DST) go back to the work by Dempster (1967) who developed a system of upper and lower probabilities. Following this, his student Shafer (1976), in his book “A Mathematical Theory of Evidence” added to Dempster’s work, including a more thorough explanation of belief functions. In summary, it is a methodology for evidential reasoning, manipulating uncertainty and capable of representing partial knowledge (Haenni & Lehmann, 2002; Kulasekere, Premaratne, Dewasurendra, Shyu, & Bauer, 2004; Scotney & McClean, 2003).
Utilizing Fuzzy Decision Trees in Decision Making
Authors: Beynon Malcolm J.
Published: May 2011

The seminal work of Zadeh (1965), fuzzy set theory (FST) has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (and prediction), (see Breiman 2001).
Video Data Mining
Authors: Oh Jung Hwan , Lee Jeong Kyu , Hwang Sae
Published: May 2011

Data mining, which is defined as the process of extracting previously unknown knowledge and detecting interesting patterns from a massive set of data, has been an active research area. As a result, several commercial products and research prototypes are available nowadays. However, most of these studies have focused on corporate data — typically in an alpha-numeric database, and relatively less work has been pursued for the mining of multimedia data (Zaïane, Han, & Zhu, 2000). Digital multimedia differs from previous forms of combined media in that the bits representing texts, images, audios, and videos can be treated as data by computer programs (Simoff, Djeraba, & Zaïane, 2002). One facet of these diverse data in terms of underlying models and formats is that they are synchronized and integrated hence, can be treated as integrated data records. The collection of such integral data records constitutes a multimedia data set. The challenge of extracting meaningful patterns from such data sets has lead to research and development in the area of multimedia data mining. This is a challenging field due to the non-structured nature of multimedia data. Such ubiquitous data is required in many applications such as financial, medical, advertising and Command, Control, Communications and Intelligence (C3I) (Thuraisingham, Clifton, Maurer, & Ceruti, 2001). Multimedia databases are widespread and multimedia data sets are extremely large. There are tools for managing and searching within such collections, but the need for tools to extract hidden and useful knowledge embedded within multimedia data is becoming critical for many decision-making applications.
World Wide Web Personalization
Authors: Nasraoui Olfa
Published: May 2011

The Web information age has brought a dramatic increase in the sheer amount of information (Web content), in the access to this information (Web usage), and in the intricate complexities governing the relationships within this information (Web structure). Hence, not surprisingly, information overload when searching and browsing the World Wide Web (WWW) has become the plague du jour. One of the most promising and potent remedies against this plague comes in the form of personalization. Personalization aims to customize the interactions on a Web site, depending on the user’s explicit and/or implicit interests and desires.
Applying Database Techniques to the Semantic Web
Authors: del Mar Roldán-García Maria , Navas-Delgado Ismael , Aldana-Montes José F.
Published: May 2011

Information on the Web has grown very quickly. The semantics of this information are becoming explicit and the Semantic Web (Berners-Lee, Hendler, & Lassila, 2001) is emerging. Ontologies provide a formal representation of the real world by defining concepts and relationships between them. In order to provide semantics to Web resources, instances of such concepts and relationships are used to annotate them. These annotations on the resources, which are based on ontologies, are the foundation of the Semantic Web. Because of the Web’s size we have to deal with large amounts of knowledge. All this information must be represented and managed efficiently to guarantee the feasibility of the Semantic Web. Querying and reasoning over instances of ontologies will make the Semantic Web useful.
Data Quality Assessment
Authors: Kulikowski Juliusz L.
Published: May 2011

For many years the idea that for high information processing systems effectiveness, high quality of data is not less important than the systems’ technological perfection was not widely understood and accepted. The way to understanding the complexity of the data quality notion was also long, as will be shown in this paper. However, progress in modern information processing systems development is not possible without improvement of data quality assessment and control methods. Data quality is closely connected both with data form and value of information carried by the data. High-quality data can be understood as data having an appropriate form and containing valuable information. Therefore, at least two aspects of data are reflected in this notion: (1) technical facility of data processing and (2) usefulness of information supplied by the data in education, science, decision making, etc.
Geometric Quality in Geographic Information
Authors: Zelasco José Francisco , Porta Gaspar , Ausinaga José Luis Fernandez
Published: May 2011

A typical way to build surface numerical models or Digital Elevation Models (DEMs) for Geographical Information Systems (GIS) is by processing the stereo images obtained from, for example, aerial photography or SPOT satellite data. These GIS can perform many computations involving their geographic databases. The quality control of a geographic database, and in particular the topological and geometric integrity, are, therefore, important topics (Guptill & Morrison, 1995; Harvey, 1997; Laurini & Milleret-Raffort, 1993; Ubeda & Servigne, 1996). The geometric quality control of the stored DEM is what we are concerned with here. “Quality” means the geometric precision measured in terms of the difference between a DEM and a reference DEM (R-DEM). We assume the R-DEM is a faithful model of the actual surface. Its point density may be greater than the DEM point density.
High Quality Conceptual Schemes
Authors: Marjomaa Esko
Published: May 2011

The questions of quality may be divided into four distinct classes, namely, ontological, epistemological, value-theoretical, and pragmatic. However, there are plenty of important problems to the solutions which have bearings on the different classes. Some of the problems are very tricky, and we shall explore two of them: (1) How does the basic ontology affect the form and content of the resulting conceptual model? and (2) What is the status of formalization in pragmatics? There are good reasons to believe that the answers to these two questions may also answer the other ones.
The Information Quality of Databases
Authors: Chengalur-Smith InduShobha , Neely M. Pamela , Tribunella Thomas
Published: May 2011

A database is only as good as the data in it. Transaction-processing systems and decision-support systems provide data for strategic planning and operations. Thus, it is important to not only recognize the quality of information in databases, but also to deal with it. Research in information quality has addressed the issues of defining, measuring and improving quality in databases; commercial tools have been created to automate the process of cleaning and correcting data; and new laws have been created that deal with the fallout of poor information quality. These issues will be discussed in the next sections.
Integrative Document and Content Management Systems' Architecture
Authors: Asprey Len , Green Rolf , Middleton Michael
Published: May 2011

This paper discusses the benefits of managing digital content (business documents and Web content) within the context of an integrative information systems architecture. This architecture incorporates database management, document and Web content management, integrated scanning/imaging, workflow, and data warehousing technologies.
Knowledge Mining
Authors: Raisinghani Mahesh S.
Published: May 2011

Numerous conferences and several articles in scholarly and business journals have tried to get a handle on knowledge. The growth of knowledge consulting organizations signals a growing conviction that knowing about knowledge is critical to business success. Multiple factors have led to the current knowledge boom. The perception and the reality of a new global competitiveness are one driving force. Rapid change and increasing competition for the dollars, marks, and yen of increasingly sophisticated consumers have led firms to seek a sustainable advantage that distinguishes them in their business environments.
Query Processing in Spatial Databases
Authors: Corral Antonio , Vassilakopoulos Michael
Published: May 2011

Spatial data management has been an active area of intensive research for more than two decades. In order to support spatial objects in a database system, several important issues must be taken into account such as spatial data models, indexing mechanisms, and efficient query processing. A spatial database system (SDBS) is a database system that offers spatial data types in its data model and query language and supports spatial data types in its implementation, providing at least spatial indexing and efficient spatial query processing (Güting, 1994).
Raster Databases
Authors: Baumann Peter
Published: May 2011

Spatio-temporal data play an important role in science, both as observed natural phenomena like temperature curves or satellite imagery and as artificially generated data such as simulation results or statistical data derived from spatio-temporal phenomena. As a coarse but common classification, spatio-temporal data can be grouped into discretized and conceptually continuous data (Figure 1). The first category allows points, lines, areas, and bodies to have any coordinate value, while the latter category has all data values sitting at the crosspoints of equidistant grids. When dealing with maps, these categories are called vector and raster data, respectively, while in Computational Fluid Dynamics (CFD), for example, the terms general mesh and regular mesh are in use.
Real-Time Databases
Authors: Buchmann Alejandro
Published: May 2011

A variety of applications in the areas of control, navigation, mobile systems, telecommunications, and simulation depend on the timely and predictable delivery of data. Aircraft and spacecraft control are characterized by small main-memory databases with extremely short mean-latency requirements. Onboard navigation systems tend to combine a small portion of dynamic data with large amounts of static data, e.g., maps and landmark information. The same is true for many simulation environments, e.g., flight simulators or virtual test benches, where some parts of the system are simulated and some parts are physical components. In telecommunications the notion of real time is interpreted mostly statistically, i.e., a predetermined percentage of transactions must meet their deadline. Demanding requirements are imposed by mobile networks with extremely high transaction rates and short response times. A requirements analysis of real-time database systems can be found in Buchmann and Liebig (2001); Locke (1997); Purimetla, Sivasankaran, Ramamritham, and Stankovic (1995); and Raatikainen (1997).
Semistructured Data and its Conceptual Models
Authors: Mani Murali , Badia Antonio
Published: May 2011

Semistructured data is data with no predefined schema, or a very flexible schema. Because such data does not fit well in traditional databases, new data models have been developed to deal with it. XML is the most well known of these new data models.
Temporal Databases
Authors: Raisinghani Mahesh S. , Klassen Chris
Published: May 2011

Information has emerged as an agent of integration and the enabler of new competitiveness for today’s enterprise in the global marketplace. The degree of change in the paradigm for storage of data in databases is examined to determine whether it can support the accelerated response time required for information systems and technology. This paper discusses the key concepts for understanding temporal databases, including major data types and its principal purpose. Moreover, once the temporal extension is added to existing ANSI and ISO SQL standards, it will enable users to take advantage of new temporal features in the major database products.
Assessing Digital Video Data Similarity
Authors: Farag Waleed E.
Published: May 2011

Multimedia applications are rapidly spread at an ever-increasing rate, introducing a number of challenging problems at the hands of the research community. The most significant and influential problem among them is the effective access to stored data. In spite of the popularity of the keyword-based search technique in alphanumeric databases, it is inadequate for use with multimedia data due to their unstructured nature. On the other hand, a number of content-based access techniques have been developed in the context of image and video indexing and retrieval (Deb, 2004). The basic idea of content-based retrieval is to access multimedia data by their contents, for example, using one of the visual content features.
Biometric Technologies
Authors: Vatsa Mayank , Singh Richa , Gupta P. , Kaushik A. K.
Published: May 2011

Identity verification in computer systems is done based on measures like keys, cards, passwords, PIN and so forth. Unfortunately, these may often be forgotten, disclosed or changed. A reliable and accurate identification/verification technique may be designed using biometric technologies, which are further based on the special characteristics of the person such as face, iris, fingerprint, signature and so forth. This technique of identification is preferred over traditional passwords and PIN-based techniques for various reasons: • The person to be identified is required to be physically present at the time of identification. • Identification based on biometric techniques obviates the need to remember a password or carry a token.
Biometrics Security
Authors: Fleming Stewart T.
Published: May 2011

Information security is concerned with the assurance of confidentiality, integrity, and availability of information in all forms. There are many tools and techniques that can support the management of information security and systems based on biometrics that have evolved to support some aspects of information security. Biometric systems support the facets of identification/authorization, authentication and non-repudiation in information security.
Face for Interface
Authors: Pantic Maja
Published: May 2011

The human face is involved in an impressive variety of different activities. It houses the majority of our sensory apparatus—eyes, ears, mouth, and nose—allowing the bearer to see, hear, taste, and smell. Apart from these biological functions, the human face provides a number of signals essential for interpersonal communication in our social life. The face houses the speech production apparatus and is used to identify other members of the species; it regulates conversation by gazing or nodding and interprets what has been said by lip reading. It is our direct and naturally preeminent means of communicating and understanding somebody’s affective state and intentions on the basis of the shown facial expression (Lewis & Haviland-Jones, 2000). Personality, attractiveness, age, and gender also can be seen from someone’s face. Thus, the face is a multi-signal sender/receiver capable of tremendous flexibility and specificity. In general, the face conveys information via four kinds of signals listed in Table 1.
Fiber-to-the-Home Technologies and Standards
Authors: Kelic Andjelka
Published: May 2011

Fiber-to-the-home (FTTH) refers to the provisioning of narrowband and broadband services to the residential customer over an optical cable rather than traditional copper wiring. Early trials in the United States, England, and France to provide telephone and broadcast video service to residential customers occurred in the mid- to late 1980s, however, widespread deployment did not follow from these trials (Esty, 1987; Rowbotham, 1989; Shumate, 1989; Veyres & Mauro, 1988). Studies conducted at the time suggested that consumer demand for video and telephone service was not sufficient to warrant the funds necessary for wide-scale deployment of the systems (Bergen, 1986; Sirbu & Reed, 1988).
Global Navigation Satellite Systems
Authors: Olla Phillip
Published: May 2011

There is a need to determine precise ground locations for use in a variety of innovative and emerging applications such as earth observation, mobile-phone technology, and rescue applications. Location information is pertinent to a large number of remote sensing applications, some of which support strategic tasks such as disaster management, earth monitoring, protecting the environment, management of natural resources, and food production. With the availability of high-resolution images, some applications will require a location precision down to 1 m (Kline, 2004). The global navigation satellite systems (GNSSs) provide signals that can serve this purpose; these signals can be incorporated into a large range of innovative applications with immense benefits for the users (Hollansworth, 1999). Satellite navigation is achieved by using a global network of satellites that transmit radio signals from approximately 11,000 miles in high earth orbit. The technology is accurate enough to pinpoint locations anywhere in the world, 24 hours a day. Positions are provided in latitude, longitude, and altitude. This article provides an overview of the GNSSs in operation along with their uses.
Going Virtual
Authors: Baralou Evangelia , Shepherd Jill
Published: May 2011

Virtuality is a socially constructed reality mediated by electronic media (Morse, 1998). Characterized by the dimension of time-space distantiation (Giddens, 1991), virtuality has an impact on the nature and dynamics of knowledge creation (Thompson, 1995). The relentless advancement of Information and Communication Technology (ICT) in terms both of new technology and the convergence of technology (e.g., multimedia) is making virtual networking the norm rather than the exception. Socially, virtual communities are more dispersed, have different power dynamics, are less hierarchical, tend to be shaped around special interests, and are open to multiple interpretations, when compared to face-to-face equivalents. To successfully manage virtual communities these differences need firstly to be understood, secondly the understanding related to varying organizational aims and thirdly, the contextualised understanding needs to be translated into appropriate managerial implications.
Integrated Platform for Networked and User-Oriented Virtual Clothing
Authors: Volino Pascal , Di Giacomo Thomas , Dellas Fabien , Magnenat-Thalmann Nadia
Published: May 2011

Fashionizer is an integrated framework that fits the needs of the garment industry of virtual garment design and prototyping, concentrating on simulation and visualization features.
Measuring the Potential for IT Convergence at Macro Level
Authors: Pagani Margherita
Published: May 2011

Convergence describes a process change in industry structures that combines markets through technological and economic dimensions to meet merging consumer needs.
Mobile Agents
Authors: Karoui Kamel
Published: May 2011

The concept of mobile agent is not new; it comes from the idea of OS process migration firstly presented by Xerox in the 1980’s. The term mobile agent was introduced by White & Miller (1994), which supported the mobility as a new feature in their programming language called Telescript.
Modelling eCRM Systems with the Unified Modelling Language
Authors: Gurau Calin
Published: May 2011

Electronic commerce requires the redefinition of the firm’s relationships with partners, suppliers, and customers. The goal of effective Customer Relationship Management (CRM) practice is to increase the firm’s customer equity, which is defined by the quality, quantity, and duration of customer relationships (Fjermestad & Romano, 2003). The proliferation of electronic devices in the business environment has determined the companies to implement electronic customer relationship management (eCRM) systems, which are using advanced technology to enhance customer relationship management practices.
Multimedia Information Retrieval at a Crossroad
Authors: Li Qing , Yang Jun , Zhuang Yueting
Published: May 2011

In the late 1990s, the availability of powerful computing capability, large storage devices, high-speed networking and especially the advent of the Internet, led to a phenomenal growth of digital multimedia content in terms of size, diversity and impact. As suggested by its name, “multimedia” is a name given to a collection of multiple types of data, which include not only “traditional multimedia” such as images and videos, but also emerging media such as 3D graphics (like VRML objects) and Web animations (like Flash animations). Furthermore, multimedia techniques have been penetrating into a growing number of applications, ranging from document-editing software to digital libraries and many Web applications. For example, most people who have used Microsoft Word have tried to insert pictures and diagrams into their documents, and they have the experience of watching online video clips, such as movie trailers. In other words, multimedia data have been in every corner of the digital world. With the huge volume of multimedia data, finding and accessing the multimedia documents that satisfy people’s needs in an accurate and efficient manner became a non-trivial problem. This problem is defined as multimedia information retrieval.
Multimedia Proxy Cache Architectures
Authors: Kacimi Mouna , Chbeir Richard , Yetongnon Kokou
Published: May 2011

The Web has become a significant source of various types of data, which require large volumes of disk space and new indexing and retrieval methods. To reduce network load and improve user response delays, various traditional proxy-caching schemes have been proposed (Abonamah, Al-Rawi, & Minhaz, 2003; Armon & Levy, 2003; Chankhunthod, Danzig, Neerdaels, Schwartz, & Worrell, 1996; Chu, Rao, & Zhang, 2000; Fan, Cao, Almeida, & Broder, 2000; Francis, Jamin, Jin, Jin, Raz, Shavitt, & Zhang, 2001; Paul & Fei, 2001; Povey & Harrison, 1997; Squid Web Proxy Cache, 2004; Wang, Sen, Adler, & Towsley, 2002). A proxy is a server that sits between the client and the real server. It intercepts all queries sent to the real server to see if it can fulfill them itself. If not, it forwards the query to the real server. A cache is a disk space used to store the documents loaded from the server for future use. A proxy cache is a proxy having a cache. The characteristics of traditional caching techniques are threefold. First, they regard each cached object as having no dividable data, which must be recovered and stored in their entirety. As multimedia objects like videos are usually too large to be cached in their entirety, the traditional caching architectures cannot be efficient for this kind of object. Second, they do not take into account the data size to manage the space storage. Third, they do not consider in their caching-system design the timing constraints that need moving objects.
Network-Based Information System Model for Research
Authors: Maris Jo-Mae B.
Published: May 2011

Cross-discipline research requires researchers to understand many concepts outside their own discipline. Computers are becoming pervasive throughout all disciplines, as evident by the December 2002 issue of Communications of the ACM featuring “Issues and Challenges in Ubiquitous Computing” (Lyytinen & Yoo, 2002). Researchers outside of computer network-related disciplines must account for the affects of network-based information systems on their research. This paper presents a model to aid researchers with the tasks of properly identifying the elements and affects of a network-based information system within their studies.
The Online Discussion and Student Success in Web-Based Education
Authors: Benrud Erik
Published: May 2011

This article examines the performance of students in a Web-based corporate finance course and how the technologies associated with communication on the Internet can enhance student learning. The article provides statistical evidence that documents that the online discussion board in a Web-based course can significantly enhance the learning process even in a quantitative course such as corporate finance. The results show that ex ante predictors of student performance that had been found useful in predicting student success in face-to-face classes also had significant predictive power for exam performance in the online course. However, these predictors did not have predictive power for participation in the online discussion. Yet, online participation and exam performance were highly correlated. This suggests that the use of the online discussion board technology by the students enhanced the performance of students who otherwise would not have performed as well without the discussion.
Personalized Web-Based Learning Services
Authors: Esmahi Larbi
Published: May 2011

Computers have a great potential as support tools for learning; they promise the possibility of affordable, individualized learning environments. In early teaching systems, the goal was to build a clever teacher able to communicate knowledge to the individual learner. Recent and emerging work focuses on the learner exploring, designing, constructing, making sense of, and using adaptive systems as tools. Hence, the new tendency is to give the learner greater responsibility and control over all aspects of the learning process. This need for flexibility, personalization, and control results from a shift in the perception of the learning process. In fact, new trends emerging in the education domain are significantly influencing e-learning (Kay, 2001) in the following ways: • The shift from studying in order to graduate, to studying in order to learn; most e-learners are working and have well-defined personal goals for enhancing their careers. • The shift from student to learner; this shift has resulted in a change in strategy and control so that the learning process is becoming more cooperative than competitive. • The shift from expertise in a domain to teaching beliefs; the classical teaching systems refer to domain and teaching expertise when dealing with the knowledge transfer process, but the new trend is based on the concept of belief. One teacher may have different beliefs from another, and the different actors in the system (students, peers, teachers), may have different beliefs about the domain and teaching methods. • The shift from a four-year program to graduate to lifelong learning; most e-learners have a long-term learning plan related to their career needs. • The shift to conceiving university departments as communities of scholars, but not necessarily in a single location. • The shift to mobile learning; most e-learners are working and have little spare time. Therefore, any computer-based learning must fit into their busy schedules (at work, at home, when traveling), since they require a personal and portable system.
Potentials of Information Technology in Building Virtual Communities
Authors: Ajiferuke Isola , Markus Alexander
Published: May 2011

In recent years, virtual communities have become the topic of countless books, journal articles and television shows, but what are they, and where did they come from? According to Preece, Maloney-Krichmar, and Abras (2003), the roots of virtual communities date back to as early as 1971 when e-mail first made its appearance on the Advanced Research Projects Agency Network (ARPANET), which was created by the United State’s Department of Defense. This network would lead to the development of dial-up bulletin board systems (BBSs) which would allow people to use their modems to connect to remote computers and participate in the exchange of e-mail and the first discussion boards. From these beginnings a host of multi user domains (MUDs) and multi-user object oriented domains (MOOs) would spring up all over the wired world. These multi-user environments would allow people to explore an imaginary space and would allow them to interact both with the electronic environment and other users. Additionally, listservs (or mailing lists) sprang up in 1986, and now, almost two decades later, they are still in use as the major method of communication among groups of people sharing common personal or professional interests (L-Soft, 2003). Since then the Internet has exploded due to the development of Web browsers as well as the development of communications technologies such as broadband, digital subscriber line (DSL), and satellite communications. Groups of people from as few as two and reaching to many thousands now communicate via email, chat, and online communities such as the Whole Earth ‘Lectronic Link (WELL) and such services as MSN, Friendster, America Online (AoL), Geocities, and Yahoo! Groups. Other examples of online communities are collaborative encyclopedias like Wikipedia. Web logs (Blogs) like Slashdot.com and LiveJournal allow users to create their own content and also to comment on the content of others. They also allow the users to create identities and to make virtual “friends” with other users. The definition of virtual community itself becomes as convoluted as the multitude of technologies that drives it. Are e-mail lists, message boards, and chat rooms online communities or are they virtual communities? Virtual communities might be persistent worlds as those found in popular online games (Everquest, 2004, Ultima Online, 2004) or virtual worlds (such as MUDs and MOOs) where the user is able to explore a simulated world or to take on a digital “physicality” in the form of an avatar. It becomes clear from the literature that the terms are still used interchangeably.
A Risk-Control Framework for E-Marketplace Participation
Authors: Ratnasingam Pauline
Published: May 2011

The increasing trend in the use of Internet-based e-marketplace applications has created tremendous opportunities for businesses to manage effective supply chain management. Forrester Research predicts that the growth and use of e-marketplaces may reach US $7 to $10 trillion by the year 2005 and may account for 53% of all online businesses by the year 2005. White and Daniel (2003) describe e-marketplaces as Web-based systems that enable automated transactions, trading, or collaboration between business partners. According to Bakos (1998), an electronic marketplace is an interorganizational system that allows participating buyers and sellers to exchange information about processes, products, and services. This study aims to examine risks in e-marketplaces. We identify four types of risks: economic, technological, implementation, and relational risks in seven e-marketplace firms from a cross-section of different industries. We then present the control measures in the responses that the seven firms enforced in order to reduce and manage their risks. The contribution of this study is the development of a risk-control framework based on the findings for e-marketplace participation.
Software Ad Hoc for E-Learning
Authors: Sánchez-Segura Maria-Isabel , de Amescua Antonio , García Luis , Esteban Luis A.
Published: May 2011

Although they are non-educational institutions, financial institutions have specific training needs. The greatest priority in employee training arises when the bank launches a new financial product or service. The difficulty, in such cases, lies in training the employees in all the regional branches so that they can offer good service to meet the clients’ demand for the product.
2G-4G Networks
Authors: Akhtar Shakil
Published: May 2011

The fourth-generation wireless mobile systems, commonly known as 4G, is expected to provide global roaming across different types of wireless and mobile networks; for instance, from satellite to mobile networks and to Wireless Local Area Networks (WLANs). 4G is an all IP-based mobile network using different radio access technologies and providing seamless roaming and connection via always the best available network (Zahariadis & Kazakos, 2003). The vision of 4G wireless/mobile systems will be the provision of broadband access, seamless global roaming and Internet/data/voice everywhere, utilizing for each the most “appropriate” always-best connected technology (Gustafsson & Jonsson, 2003). These systems are about integrating terminals, networks and applications to satisfy increasing user demands (Ibrahim, 2002; Lu & Berezdivin, 2002). 4G systems are expected to offer a speed of more than 100 Mbps in stationary mode and an average of 20 Mbps for mobile stations, reducing the download time of graphics and multimedia components by more than 10 times compared to currently available 2 Mbps on 3G systems.