bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Data Mining
Authors: Weber Richard
Published: May 2011

Since the First KDD Workshop back in 1989 when “Knowledge Mining” was recognized as one of the top 5 topics in future database research (Piatetsky-Shapiro 1991), many scientists as well as users in industry and public organizations have considered data mining as highly relevant for their respective professional activities. We have witnessed the development of advanced data mining techniques as well as the successful implementation of knowledge discovery systems in many companies and organizations worldwide. Most of these implementations are static in the sense that they do not contemplate explicitly a changing environment. However, since most analyzed phenomena change over time, the respective systems should be adapted to the new environment in order to provide useful and reliable analyses. If we consider for example a system for credit card fraud detection, we may want to segment our customers, process stream data generated by their transactions, and finally classify them according to their fraud probability where fraud pattern change over time. If our segmentation should group together homogeneous customers using not only their current feature values but also their trajectories, things get even more difficult since we have to cluster vectors of functions instead of vectors of real values. An example for such a trajectory could be the development of our customers’ number of transactions over the past six months or so if such a development tells us more about their behavior than just a single value; e.g., the most recent number of transactions. It is in this kind of applications is where dynamic data mining comes into play! Since data mining is just one step of the iterative KDD (Knowledge Discovery in Databases) process (Han & Kamber, 2001), dynamic elements should be considered also during the other steps. The entire process consists basically of activities that are performed before doing data mining (such as: selection, pre-processing, transformation of data (Famili et al., 1997)), the actual data mining part, and subsequent steps (such as: interpretation, evaluation of results). In subsequent sections we will present the background regarding dynamic data mining by studying existing methodological approaches as well as already performed applications and even patents and tools. Then we will provide the main focus of this chapter by presenting dynamic approaches for each step of the KDD process. Some methodological aspects regarding dynamic data mining will be presented in more detail. After envisioning future trends regarding dynamic data mining we will conclude this chapter.
Feature Reduction for Support Vector Machines
Authors: Cheng Shouxian , Shih Frank Y.
Published: May 2011

The Support Vector Machine (SVM) (Cortes and Vapnik, 1995; Vapnik, 1995; Burges, 1998) is intended to generate an optimal separating hyperplane by minimizing the generalization error without the assumption of class probabilities such as Bayesian classifier. The decision hyperplane of SVM is determined by the most informative data instances, called Support Vectors (SVs). In practice, these SVMs are a subset of the entire training data. By now, SVMs have been successfully applied in many applications, such as face detection, handwritten digit recognition, text classification, and data mining. Osuna et al. (1997) applied SVMs for face detection. Heisele et al. (2004) achieved high face detection rate by using 2nd degree SVM. They applied hierarchical classification and feature reduction methods to speed up face detection using SVMs. Feature extraction and reduction are two primary issues in feature selection that is essential in pattern classification. Whether it is for storage, searching, or classification, the way the data are represented can significantly influence performances. Feature extraction is a process of extracting more effective representation of objects from raw data to achieve high classification rates. For image data, many kinds of features have been used, such as raw pixel values, Principle Component Analysis (PCA), Independent Component Analysis (ICA), wavelet features, Gabor features, and gradient values. Feature reduction is a process of selecting a subset of features with preservation or improvement of classification rates. In general, it intends to speed up the classification process by keeping the most important class-relevant features.
Measuring the Interestingness of News Articles
Authors: Pon Raymond K. , Cardenas Alfonso F. , Buttler David J.
Published: May 2011

An explosive growth of online news has taken place. Users are inundated with thousands of news articles, only some of which are interesting. A system to filter out uninteresting articles would aid users that need to read and analyze many articles daily, such as financial analysts and government officials. The most obvious approach for reducing the amount of information overload is to learn keywords of interest for a user (Carreira et al., 2004). Although filtering articles based on keywords removes many irrelevant articles, there are still many uninteresting articles that are highly relevant to keyword searches. A relevant article may not be interesting for various reasons, such as the article’s age or if it discusses an event that the user has already read about in other articles. Although it has been shown that collaborative filtering can aid in personalized recommendation systems (Wang et al., 2006), a large number of users is needed. In a limited user environment, such as a small group of analysts monitoring news events, collaborative filtering would be ineffective. The definition of what makes an article interesting – or its “interestingness” – varies from user to user and is continually evolving, calling for adaptable user personalization. Furthermore, due to the nature of news, most articles are uninteresting since many are similar or report events outside the scope of an individual’s concerns. There has been much work in news recommendation systems, but none have yet addressed the question of what makes an article interesting.
Modeling the KDD Process
Authors: Bhatnagar Vasudha , Gupta S. K.
Published: May 2011

Knowledge Discovery in Databases (KDD) is classically defined as the “nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in large databases” ( Fayyad, Piatetsky-Shapiro & Smyth, 1996a). The recently developed KDD technology is based on a well-defined, multi-step “KDD process” for discovering knowledge from large data repositories. The basic problem addressed by the KDD process is one of mapping lowlevel data (operational in nature and too voluminous) to a more abstract form (descriptive approximation or model of the process that generated the data) or a useful form (for example, a predictive model) (Fayyad, Piatetsky-Shapiro & Smyth, 1996b). The KDD process evolves with pro-active intervention of the domain experts, data mining analyst and the end-users. It is a ‘continuous’ process in the sense that the results of the process may fuel new motivations for further discoveries (Chapman et al., 2000). Modeling and planning of the KDD process has been recognized as a new research field (John, 2000). In this chapter we provide an introduction to the process of knowledge discovery in databases (KDD process), and present some models (conceptual as well as practical) to carry out the KDD endeavor.
Multidimensional Modeling of Complex Data
Authors: Boussaid Omar , Boukraa Doulkifli
Published: May 2011

While the classical databases aimed in data managing within enterprises, data warehouses help them to analyze data in order to drive their activities (Inmon, 2005). The data warehouses have proven their usefulness in the decision making process by presenting valuable data to the user and allowing him/her to analyze them online (Rafanelli, 2003). Current data warehouse and OLAP tools deal, for their most part, with numerical data which is structured usually using the relational model. Therefore, considerable amounts of unstructured or semi-structured data are left unexploited. We qualify such data as “complex data” because they originate in different sources; have multiple forms, and have complex relationships amongst them. Warehousing and exploiting such data raise many issues. In particular, modeling a complex data warehouse using the traditional star schema is no longer adequate because of many reasons (Boussaïd, Ben Messaoud, Choquet, & Anthoard, 2006; Ravat, Teste, Tournier, & Zurfluh, 2007b). First, the complex structure of data needs to be preserved rather than to be structured linearly as a set of attributes. Secondly, we need to preserve and exploit the relationships that exist between data when performing the analysis. Finally, a need may occur to operate new aggregation modes (Ben Messaoud, Boussaïd, & Loudcher, 2006; Ravat, Teste, Tournier, & Zurfluh, 2007a) that are based on textual rather than on numerical data. The design and modeling of decision support systems based on complex data is a very exciting scientific challenge (Pedersen & Jensen, 1999; Jones & Song, 2005; Luján-Mora, Trujillo, & Song; 2006). Particularly, modeling a complex data warehouse at the conceptual level then at a logical level are not straightforward activities. Little work has been done regarding these activities. At the conceptual level, most of the proposed models are object-oriented (Ravat et al, 2007a; Nassis, Rajugan, Dillon, & Rahayu 2004) and some of them make use of UML as a notation language. At the logical level, XML has been used in many models because of its adequacy for modeling both structured and semi structured data (Pokorný, 2001; Baril & Bellahsène, 2003; Boussaïd et al., 2006). In this chapter, we propose an approach of multidimensional modeling of complex data at both the conceptual and logical levels. Our conceptual model answers some modeling requirements that we believe not fulfilled by the current models. These modeling requirements are exemplified by the Digital Bibliography & Library Project case study (DBLP).
Online Analytical Processing Systems
Authors: Boon-Noi Tan Rebecca
Published: May 2011

Since its origin in the 1970’s research and development into databases systems has evolved from simple file storage and processing systems to complex relational databases systems, which have provided a remarkable contribution to the current trends or environments. Databases are now such an integral part of day-to-day life that often people are unaware of their use. For example, purchasing goods from the local supermarket is likely to involve access to a database. In order to retrieve the price of the item, the application program will access the product database. A database is a collection of related data and the database management system (DBMS) is software that manages and controls access to the database (Elmasri & Navathe, 2004).
Sampling Methods in Approximate Query Answering Systems
Authors: Das Gautam
Published: May 2011

In recent years, advances in data collection and management technologies have led to a proliferation of very large databases. These large data repositories typically are created in the hope that, through analysis such as data mining and decision support, they will yield new insights into the data and the real-world processes that created them. In practice, however, while the collection and storage of massive datasets has become relatively straightforward, effective data analysis has proven more difficult to achieve. One reason that data analysis successes have proven elusive is that most analysis queries, by their nature, require aggregation or summarization of large portions of the data being analyzed. For multi-gigabyte data repositories, this means that processing even a single analysis query involves accessing enormous amounts of data, leading to prohibitively expensive running times. This severely limits the feasibility of many types of analysis applications, especially those that depend on timeliness or interactivity. While keeping query response times short is very important in many data mining and decision support applications, exactness in query results is frequently less important. In many cases, ballpark estimates are adequate to provide the desired insights about the data, at least in preliminary phases of analysis. For example, knowing the marginal data distributions for each attribute up to 10% error often will be enough to identify top-selling products in a sales database or to determine the best attribute to use at the root of a decision tree.
Secure Computation for Privacy Preserving Data Mining
Authors: Lindell Yehuda
Published: May 2011

The increasing use of data mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy preserving data mining solutions achieve the somewhat paradoxical property of enabling a data mining algorithm to use data without ever actually “seeing” it. Thus, the benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals.
Segmentation of Time Series Data
Authors: Chundi Parvathi , Rosenkrantz Daniel J.
Published: May 2011

Time series data is usually generated by measuring and monitoring applications, and accounts for a large fraction of the data available for analysis purposes. A time series is typically a sequence of values that represent the state of a variable over time. Each value of the variable might be a simple value, or might have a composite structure, such as a vector of values. Time series data can be collected about natural phenomena, such as the amount of rainfall in a geographical region, or about a human activity, such as the number of shares of GoogleTM stock sold each day. Time series data is typically used for predicting future behavior from historical performance. However, a time series often needs further processing to discover the structure and properties of the recorded variable, thereby facilitating the understanding of past behavior and prediction of future behavior. Segmentation of a given time series is often used to compactly represent the time series (Gionis & Mannila, 2005), to reduce noise, and to serve as a high-level representation of the data (Das, Lin, Mannila, Renganathan & Smyth, 1998; Keogh & Kasetty, 2003). Data mining of a segmentation of a time series, rather than the original time series itself, has been used to facilitate discovering structure in the data, and finding various kinds of information, such as abrupt changes in the model underlying the time series (Duncan & Bryant, 1996; Keogh & Kasetty, 2003), event detection (Guralnik & Srivastava, 1999), etc. The rest of this chapter is organized as follows. The section on Background gives an overview of the time series segmentation problem and solutions. This section is followed by a Main Focus section where details of the tasks involved in segmenting a given time series and a few sample applications are discussed. Then, the Future Trends section presents some of the current research trends in time series segmentation and the Conclusion section concludes the chapter. Several important terms and their definitions are also included at the end of the chapter.
Sequential Pattern Mining
Authors: Masseglia Florent , Teisseire Maguelonne , Poncelet Pascal
Published: May 2011

Sequential pattern mining deals with data represented as sequences (a sequence contains sorted sets of items). Compared to the association rule problem, a study of such data provides “inter-transaction” analysis (Agrawal & Srikant, 1995). Applications for sequential pattern extraction are numerous and the problem definition has been slightly modified in different ways. Associated to elegant solutions, these problems can match with reallife timestamped data (when association rules fail) and provide useful results.
Statistical Metadata Modeling and Transformations
Authors: Vardaki Maria
Published: May 2011

The term metadata is frequently used in many different sciences. Statistical metadata generally used to denote “every piece of information required by a data user to properly understand and use statistical data.” Modern statistical information systems (SIS) use metadata in relational or complex object-oriented metadata models, making an extensive and active usage of metadata. Early phases of many software development projects emphasize the design of a conceptual data/metadata model. Such a design can be detailed into a logical data/metadata model. In later stages, this model may be translated into physical data/metadata model. Organisations aspects, user requirements and constraints created by existing data warehouse architecture lead to a conceptual architecture for metadata management, based on a common, semantically rich, object-oriented data/metadata model, integrating the main steps of data processing and covering all aspects of data warehousing (Pool et al, 2002). In this paper we examine data/metadata modeling according to the techniques and paradigms used for metadata schemas development. However, only the integration of a model into a SIS is not sufficient for automatic manipulation of related datasets and quality assurance, if not accompanied by certain operators/ transformations. Two types of transformations can be considered: (i) the ones used to alleviate breaks in the time series and (ii) a set of model-integrated operators for automating data/metadata management and minimizing human errors. This latter category is extensively discussed. Finally, we illustrate the applicability of our scientific framework in the area of Biomedical statistics.
A Survey of Feature Selection Techniques
Authors: Chizi Barak , Rokach Lior , Maimon Oded
Published: May 2011

Dimensionality (i.e., the number of data set attributes or groups of attributes) constitutes a serious obstacle to the efficiency of most data mining algorithms (Maimon and Last, 2000). The main reason for this is that data mining algorithms are computationally intensive. This obstacle is sometimes known as the “curse of dimensionality” (Bellman, 1961). The objective of Feature Selection is to identify features in the data-set as important, and discard any other feature as irrelevant and redundant information. Since Feature Selection reduces the dimensionality of the data, data mining algorithms can be operated faster and more effectively by using Feature Selection. In some cases, as a result of feature selection, the performance of the data mining method can be improved. The reason for that is mainly a more compact, easily interpreted representation of the target concept. The filter approach (Kohavi , 1995; Kohavi and John ,1996) operates independently of the data mining method employed subsequently -- undesirable features are filtered out of the data before learning begins. These algorithms use heuristics based on general characteristics of the data to evaluate the merit of feature subsets. A sub-category of filter methods that will be refer to as rankers, are methods that employ some criterion to score each feature and provide a ranking. From this ordering, several feature subsets can be chosen by manually setting There are three main approaches for feature selection: wrapper, filter and embedded. The wrapper approach (Kohavi, 1995; Kohavi and John,1996), uses an inducer as a black box along with a statistical re-sampling technique such as cross-validation to select the best feature subset according to some predictive measure. The embedded approach (see for instance Guyon and Elisseeff, 2003) is similar to the wrapper approach in the sense that the features are specifically selected for a certain inducer, but it selects the features in the process of learning.
Text Mining by Pseudo-Natural Language Understanding
Authors: Lu Ruqian
Published: May 2011

Text mining by pseudo natural language understanding (TM by PNLU for short) is a technique developed by the AST group of Chinese Academy of Sciences, as part of the project automatic knowledge acquisition by PNLU, which introduces a partial parse technique to avoid the difficulty of full NLU. It consists of three parts: PNL design, PNL parser implementation and PNLU based automatic knowledge acquisition. Its essence is twofold: a trade-off between information gain and feasibility of parsing, and a rational work division between human and computer.
Tree and Graph Mining
Authors: Katsaros Dimitrios
Published: May 2011

During the past decade, we have witnessed an explosive growth in our capabilities to both generate and collect data. Various data mining techniques have been proposed and widely employed to discover valid, novel and potentially useful patterns in these data. Data mining involves the discovery of patterns, associations, changes, anomalies, and statistically significant structures and events in huge collections of data. One of the key success stories of data mining research and practice has been the development of efficient algorithms for discovering frequent itemsets – both sequential (Srikant & Agrawal, 1996) and non-sequential (Agrawal & Srikant, 1994). Generally speaking, these algorithms can extract co-occurrences of items (taking or not taking into account the ordering of items) in an efficient manner. Although the use of sets (or sequences) has effectively modeled many application domains, like market basket analysis, medical records, a lot of applications have emerged whose data models do not fit in the traditional concept of a set (or sequence), but require the deployment of richer abstractions, like graphs or trees. Such graphs or trees arise naturally in a number of different application domains including network intrusion, semantic Web, behavioral modeling, VLSI reverse engineering, link analysis and chemical compound classification. Thus, the need to extract complex tree-like or graphlike patterns in massive data collections, for instance, in bioinformatics, semistructured or Web databases, became a necessity. The class of exploratory mining tasks, which deal with discovering patterns in massive databases representing complex interactions among entities, is called Frequent Structure Mining (FSM) (Zaki, 2002). In this article we will highlight some strategic application domains where FSM can help provide significant results and subsequently we will survey the most important algorithms that have been proposed for mining graph-like and tree-like substructures in massive data collections.
Advances of Radio Interface in WCDMA Systems
Authors: Wang Ju , Liu Jonathan C.L.
Published: May 2011

Recent years have witnessed the rapid progress in handheld devices. This has resulted in a growing number of mobile phones or PDAs that have a built-in camera to record still pictures or live videos. Encouraged by the success of second generation cellular wireless networks, researchers are now pushing the 3G standard to support a seamless integration of multimedia data services. One of the main products is WCDMA (Holma & Toskala, 2001), short for wideband code division multiple access. WCDMA networks have 80 million subscribers in 46 countries at the time of this writing. WCDMA can be viewed as a successor of the 2G CDMA system. In fact, many WCDMA technologies can be traced back to the 2G CDMA system. However, WCDMA air interface is specifically designed with envision to support real time multimedia services. To name some highlights, WCDMA: • Supports both packet-switched and circuitswitched data services. Mobile best-effort data services, such as Web surfing and file downloads, are available through packet service. • Has more bandwidth allocated for downlink and uplink than the 2G systems. It uses a 5 MHz wide radio signal and a chip rate of 3.84 mcps, which is about three times higher than CDMA2000. • Support a downlink data rate of 384 kbps for wide area coverage and up to 2 Mbps for hot-spot areas, which is sufficient for most existing packet-data applications. WCDMA Release 5 (Erricson, 2004) adopts HSDPA (High-speed downlink packet access), which increases peak data rates to 14 Mbps in the downlink. To achieve high data rate, WCDMA uses several new radio interface technologies, including (1) shared channel transmission, (2) higher-order modulation, (3) fast link adaptation, (4) fast scheduling, and (5) hybrid automatic-repeat-request (HARQ). These technologies have been successfully used in the downlink HSDPA, and will be used in upcoming improved uplink radio interface in the future. The rest of this article will explain the key components of the radio interface in WCDMA.
Architectures of the Interworking of 3G Cellular Networks and Wireless LANs
Authors: Ma Maode
Published: May 2011

Recent development on the wireless networks has indicated that IEEE 802.11.x standards based wireless LAN and third-generation cellular wireless networks such as CDMA2000 or UMTS (i.e., WCDMA) could be integrated together to offer ubiquitous Internet access to end users. The two technologies can offer functions that are complementary to each other. The 802.11. x standards based wireless LANs support data rates from 1 Mbps to 54 Mbps. However, by IEEE 802.11.x standard, one access point (AP) can only cover an area of a few thousand square meters. It is perfectly applied for enterprise networks and public hot-spots such as hotels and airports. On the contrary, wireless cellular networks built with 3G standards can only support peak data transmission rates from 64Kbps to nearly 2 Mbps with a much wider area. It is reasonable and feasible to combine these two technologies to make Internet access much easier and more convenient. The design of an interworking architecture to efficiently integrate 3G cellular wireless networks and 802.11.x standard based wireless LANs is a challenge. Its difficulty lies in the objective of the integration, which is to achieve the seamless interoperation between the two types of the wireless networks with certain QoS guarantee and other requirements kept simultaneously, from the perspectives of both the end-users and the operators. There are basically two proposals as the solutions to the architecture of the integration. One is the tight coupling. The other is the loose coupling. Although there is no final selection on whether the future integrated network would use either of these techniques or another one, much focus of the research is on the loose coupling due to its feasibility. To implement the integration based on the corresponding approach, there are a lot of issues needed to be addressed. They are the mobility management for vertical handoff, the QoS maintenance during the vertical handoff, and the schemes of authentication, authorization and the accounting (AAA). In this article, we will focus on the issue of interworking architecture. The rest of the text is organized as follows. The second section will present the general ideas on the architecture of the integration of 3G cellular networks with wireless LANs. The third section will present several proposals on the architectures of the integration. At last, the fourth section will conclude the article.
Consumer Attitudes toward RFID Usage
Authors: Boslau Madlen
Published: May 2011

The term RFID refers to radio frequency identification and describes transponders or tags that are attached to animate or inanimate objects and are automatically read by a network infrastructure or networked reading devices. Current solutions such as optical character recognition (OCR), bar codes, or smart card systems require manual data entry, scanning, or readout along the supply chain. These procedures are costly, timeconsuming, and inaccurate. RFID systems are seen as a potential solution to these constraints, by allowing non-line-of-sight reception of the coded data. Identification codes are stored on a tag that consists of a microchip and an attached antenna. Once the tag is within the reception area of a reader, the information is transmitted. A connected database is then able to decode the identification code and identify the object. Such network infrastructures should be able to capture, store, and deliver large amounts of data robustly and efficiently (Scharfeld, 2001). The applications of RFID in use today can be sorted into two groups of products: • The first group of products uses the RFID technology as a central feature. Examples are security and access control, vehicle immobilization systems, and highway toll passes (Inaba & Schuster, 2005). Future applications include rechargeable public transport tickets, implants holding critical medical data, or dog tags (Böhmer, Brück, & Rees, 2005). • The second group of products consists of those goods merely tagged with an RFID label instead of a bar code. Here, the tag simply substitutes the bar code as a carrier of product information for identification purposes. This seems sensible, as RFID tags display a number of characteristics that allow for faster, easier, more reliable, and superior identification. Once consumers are able to buy RFID tagged products, their attitude toward such tags is of central importance. Consumer acceptance of RFID tags may have severe consequences for all companies tagging their products with RFID.
Evolution of DSL Technologies Over Copper Cabling
Authors: Chochliouros Ioannis , Spiliopoulou Anastasia S. , Chochliouros Stergios P. , Chochliourou Elpida
Published: May 2011

A variety of digital technologies can be used for effective implementation of access networks in fast growing (global) markets. The relative challenge becomes of greater importance, due to the extended penetration and the wider (technical and business) adoption of the much-promising broadband perspective (Chochliouros & Spiliopoulou, 2005). Regarding the four different (and actually “primary”) media that can be used to reach several categories of end-users (namely, copper, fiber, air, and power lines), Figure 1 demonstrates possible alternatives that can currently be used in all related cases.
Human Factors Assessment of Multimedia Products and Systems
Authors: Kortum Philip
Published: May 2011

Human factors assessment is a set of methods that are employed in order to determine if a product, service, or system meets the needs of the end users. These needs are measured along the dimensions of effectiveness (can the user actually accomplish the task at hand?), efficiency (can the user accomplish the task with a minimum of effort?), and satisfaction (is the user satisfied with his or her interaction with the product?). Multimedia technology requires significantly more attention to human factors and usability because the mode interactions create a more complex operating environment for the end user. This complexity can make these systems difficult for consumers to learn and use, reducing both the satisfaction of the users and their willingness to purchase or use similar systems in the future. It is critically important to assess the usability of a product from the onset of the project. Although it is common to perform a summative human factors assessment of the product at the end of development, it is typically too late to do anything meaningful with the results at this point because of the cost of changing a complete or nearly complete design. It is most beneficial to engage in a full human factors assessment during the concept generation phases, so that fundamental limitations of human perception and cognition can be considered before designs have already been established. Human factors assessment should continue throughout the project lifecycle. Rigorous application of these methods helps insure that the resulting end product will have high user acceptance because of superior ease of use.
Hypervideo
Authors: Richter Kai
Published: May 2011

Hypervideo is the adaptation of the hypertext metaphor to video. By annotating and referencing video objects, diverse media, and pieces of information the video stream can be unlocked to the global web of knowledge. Hypervideo combines the advantages of hypermedia as a dynamic, associative, and extendable network of information that can be shared and searched by many users at the same time, and of video as a natural and intuitive media to convey complex dynamic processes. Therefore, hypervideo will be one of the most important media for learning on the Semantic Web. This article will first introduce the concepts of hypervideo. Then the history of hypervideo applications will be outlined, and current applications on the market will be presented. Design aspects will be discussed taking the example of one hypervideo application. Then, use cases for hypervideo will be presented. Finally, we will discuss possible future trends before coming to the conclusions.
Interacting Effectively in International Virtual Offices
Authors: St. Amant Kirk
Published: May 2011

Communication technologies are continually changing ideas of the “office.” One of the most interesting of these developments is the virtual office—a setting where individuals in different places use online media to collaborate on projects. Recent trends, furthermore, indicate knowledge workers will become increasingly involved in international virtual offices (IVOs) where they interact with coworkers in different countries. Such environments, however, can intensify problems related to cultural communication expectations. Employees must therefore understand how cultural factors can affect online discourse if they wish to work successfully in IVOs. This essay examines three IVO-related problem areas: contact, status, and language.
Interface-Based Differences in Online Decision Making
Authors: Mazursky David
Published: May 2011

In recent years the online Web site interface format was found to have significant effects on attitudes toward the store and people’s actual experiences when visiting Web sites. In attempting to gain a competitive advantage, online site managers are adopting state-of-the-art technologies, aiming to create a unique experience and to capture more of people’s attention during their navigation. Using 3D technology interface gives site designers the opportunity to create a total experience. They are generally used in two formats. The first, more common format, is adopted for displaying 3D objects within a 2D interface site, and characterizes today’s numerous Web site stores (Nantel, 2004). The second format, which is still rare, is for creation of virtual reality environments (Figure 1). Converting a 2D interface site to a 3D interface site requires both technological investment, and gaining user confidence in the new environment. Therefore, site managers must weigh the pros and cons of each method before performing the conversion. The current analysis compares 2D and 3D interfaces, pointing out the advantages and shortcomings of each format.
Local Loop Unbundling (LLU) Policies in the European Framework
Authors: Spiliopoulou Anastasia S. , Chochliouros Ioannis , Lalopoulos George K. , Chochliouros Stergios P.
Published: May 2011

Recent European policies have very early identified (European Commission, 1999) the immense challenge for the European Union (EU) to promote various liberalization and harmonization measures in the relevant electronic communications markets, especially by supporting a series of particular initiatives for competition, investment, innovation, the single market, and consumer benefits (Chochliouros & Spiliopoulou, 2003). In order to fully seize the growth of the digital, knowledge-based economy, it has been suggested that both businesses and citizens should have access to an inexpensive, world-class communications infrastructure and a wide range of modern services, all appropriate to support “broadband” evolution and a wider multimedia penetration. Moreover, all possible different means of access had to prevent from “info-exclusion,” while information technologies should be used to renew urban and regional development and to promote innovative technologies (Chochliouros & Spiliopoulou, 2005). To achieve all these expectations, an essential European policy was to “initiate” further competition in local access networks and support the “local loop unbundling” (LLU) perspective, in order to help bring about a considerable reduction in the costs (in terms of price, quality, and innovative services) of using the Internet and to promote high-speed and “always-on” access (Bourreau & Dogan, 2005; Commission of the European Communities, 2006b). The local loop mainly referred to the physical copper line circuit in the local access network connecting the customer’s premises to the operator’s local switch, concentrator, or any other equivalent facility. Traditionally, it takes the form of twisted metallic pairs of copper wires (one pair per ordinary telephone line). However, some other potential alternatives can also be taken into account: fiber optic cables are nowadays being increasingly deployed to connect various customers, while other technologies are also being rolled out in the local access network (such as wireless/satellite local loops, power-line networks, or even cable TV networks). Although technology’s evolution and market development are very rapid, the above alternatives—even in a combined use—cannot provide adequate guarantee to ensure sufficient and nationwide spreading for LLU in a quite reasonable time period (Philpot, 2006) and mainly to address the same customer population, if practically compared to the digital subscriber loop (DSL) option which is offered via the existing copper infrastructures.
Methods for Dependability and Security Analysis of Large Networks
Authors: Chochliouros Ioannis , Spiliopoulou Anastasia S. , Chochliouros Stergios P.
Published: May 2011

Dependability and security are rigorously related concepts that, however, differ for the specific proprieties they mainly concentrate on. In particular, in most commonly applied cases found in practical design techniques (Piedad & Hawkins, 2000), the dependability concept usually includes the security one, being a superset of it. In typical cases, security mainly comprises the following fundamental characteristics: confidentiality, integrity, and availability. Indeed, dependability mainly encompasses the following attributes (Avizienis, Laprie, Randell, & Landwehr, 2004): (1) availability: readiness for correct service; (2) reliability: continuity of correct service; (3) safety: absence of catastrophic consequences on the user(s) and the environment; (4) confidentiality: absence of unauthorized disclosure of information; (5) integrity: absence of improper system alterations; and (6) maintainability: ability to undergo modifications and repairs. The present work primarily intends to deal with formal methods, appropriate to perform both security and dependability analysis in modern networks. In general, security analysis of great networks takes the form of determining the exploitable vulnerabilities of a network, and intends to provide results or appropriate informative (or occasionally experimental) data about which network nodes can be compromised by exploiting chains of vulnerabilities, as well as specifying which fundamental security properties are altered (e.g., Confidentiality, Integrity, Availability). Therefore, such type of analysis is also referred as “network vulnerability analysis.” On the other hand, dependability analysis of networks typically intends to determine specific dependencies within the nodes (or the services offered) of the (appropriate) underlying network, so as to provide results about the consequences of (potential) faults (on services or hosts) and to find out which among these faults are able to cause unacceptable consequences, in terms of the basic dependability attributes. At this specific evaluation, it should be noted that it is possible to consider attacks (as well as attack consequences) as faults.
Multi-User Virtual Environments for Teaching and Learning
Authors: Dieterle Edward
Published: May 2011

In the late 1970s, Richard Bartle and Roy Trubshaw of the University of Essex developed the first MUD (multi-user dungeon/domain/dimension, depending on the source) to facilitate multiplayer role-playing games run over computer networks (Bartle, 1999; Dourish, 1998), allowing groups of individuals to build virtual realities collaboratively. Despite limited visual and social cues, immersion in text-based virtual environments have the capacity to support thriving virtual communities that demonstrate characteristics of traditional communities, such as love, hate, friendship, and betrayal (Rheingold, 1993). Advances in computational power and network connectivity have driven the evolution of MUDs, resulting in diverse human computer interfaces such as MOOs (object-oriented MUDs), multi-user virtual environments (MUVEs), and massively-multiplayer online role-playing games (MMORPGs), among others. The present article focuses primarily on MUVEs. Although MUVEs are commonplace to gamers (i.e., players of EverQuest, Doom, and Madden NFL), the affordances of this interface are rarely utilized for substantive teaching and learning. This article will discuss how MUVEs can be used to support the situated and distributed nature of cognition within an immersive, psychosocial context. After summarizing significant educational MUVEs, we present Harvard University’s River City MUVE (http://muve.gse.harvard.edu/rivercityproject) in depth as an illustrative case study.
Network Deployment for Social Benefits in Developing Countries
Authors: Rahman Hakikur
Published: May 2011

For many reasons, the establishment of technology is crucial to socio-economic development, as well as increasing democratization of a nation. The technology is pervasive in nature, but the cost benefit together with the national urgency for its introduction through various applications mostly depends on the grass roots awareness and utilization of computers (interconnected computers) for the common people. Hence, the transferability and applicability of e-applications (e-learning, e-commerce, e-governance, e-health, etc.) must assured to be applied at their consistent state and best obtained by the congenial atmosphere at all levels of the policy making. The essential issues are that developing countries, faced with enormous social and economic pressures, must start to confirm consistent economic growth and at the same time accelerate broad-based technology deployment. In this aspect, low-ranking developing countries had to utilize every benefit of it in order to compete with others for a dominant share of the global ICT contribution. In order to realize this vision more efficiently and cost effectively, and to integrate increased human participation within the technology, governments must be proactive in prioritizing limited resources by appropriate planning: build the foundation for a rational expansion of the ICT sector into higher value-added services. In addition, the drive to transform countries into “knowledge-based” societies will necessitate intergovernmental, interagencies, intersocietal, as well as private sector cooperation, commitment, dedication, and partnership in the context of an overall framework for the logical development of the ICT sector. The positioning of suitable foundations requires a comprehensive national strategic vision with elaborate plans. They should ensure seamless interlinkages among all sectors and meaningful participation of all citizens in meeting the challenges to transform the human capital through establishment of various e-applications. At the same time, pervasive education/skill development and the provision for life long learning has to be interlaced within all sectors. As the technology is ever renewing, especially in developing countries, the inadequacy of proper management structure and the scarcity of suitably trained managers deserve immediate attention. This article emphasizes broad-based utilization of ICT applications for social developments, focuses on issues and challenges of their implementations with various usages, and provides a brief discussion on a few cases on networked applications.
Open Source Database Technologies
Authors: Udoh Emmanuel
Published: May 2011

The free or open source software (OSS) movement, pioneered by Richard Stallman in 1983, is gaining mainstream acceptance and challenging the established order of the commercial software world. The movement is taking root in various aspects of software development, namely operating systems (Linux), Web servers (Apache), databases (MySQL), and scripting languages (PHP) to mention but a few. The basic tenet of the movement is that the underlying code of any open source software should be freely viewable, modifiable, or redistributable by any interested party, as enunciated under the copyleft concept (Stallman, 2002) This is in sharp contrast to the proprietary software (closed source), in which the code is controlled under the copyright laws. In the contemporary software landscape, the open source movement can no longer be overlooked by any major players in the industry, as the movement portends a paradigm shift and is forcing a major rethinking of strategy in the software business. For instance, companies like Oracle, Microsoft, and IBM now offer the lightweight versions of their proprietary flagship products to small—to-medium businesses at no cost for product trial (Samuelson, 2006). These developments are signs of the success of the OSS movement. Reasons abound for the success of the OSS, viz. the collective effort of many volunteer programmers, flexible and quick release rate, code availability, and security. On the other hand, one of the main disadvantages of OSS is the limited technical support, as it may be difficult to find an expert to help an organization with system setup or maintenance. Due to the extensive nature of OSS, this article will only focus on the database aspects. A database is one of the critical components of the application stack for an organization or a business. Increasingly, open-source databases (OSDBs) such as MYSQL, PostgreSQL, MaxDB, Firebird, and Ingress are coming up against the big three commercial proprietary databases: Oracle, SQL server, and IBM DB (McKendrick, 2006; Paulson, 2004; Shankland, 2004). Big companies like Yahoo and Dell are now embracing OSDBs for enterprise-wide applications. According to the Independent Oracle Users Group (IOUG) survey, 37% of enterprise database sites are running at least one of the major brands of open source databases (McKendrik, 2006). The survey further finds that the OSDBs are mostly used for single function systems, followed by custom home-grown applications and Web sites. But critics maintain that these OSDBs are used for nonmission critical purposes, because IT organizations still have concerns about support, security, and management tools (Harris, 2004; Zhao & Elbaum, 2003)