bluuebunny/crossref_metadata_2025 huggingface dataset:
Service Science and Automation Laws
Authors: Targowski Andrew
Published: May 2011

The purpose of this chapter is to define a scope of service science and service automation in service economy based on ideal generic service systems originally developed by the author. There are two goals of this study: 1) to develop generic service categories and their generic systems, and 2) to define a scope of service science based upon the presented generic models of service systems, which determine the required support from emerging system science. The research methodology is based on the architectural modeling according the paradigm of enterprise-wide systems (Targowski, 2003). The architectural system approach is based on the philosophy of the system approach (Klir, 1985), and management cybernetics (Beer, 1981) which provide comprehensive and cohesive solutions to the problems of systems design, thus eliminating the fuziveness of the “application portfolio” and the “information archipelago” (McFarlan, 1981; Targowski, 1990). The mission of the architectural system approach is to find the ultimate synthesis of the whole system structure that involves appropriate logic, appropriate technological accommodation, operational quality, a positive user involvement, and co-existence with nature (Targowski, 1990). In its nature, the architectural system approach is of deductive rather than inductive nature. It looks for the ideal model of a solution, which in practice is far away from its perfect level. The difference between the architectural system approach and the engineering approach is in the level of abstraction. The architectural models are more conceptual whereas engineering outcomes are more technical and specific. The architectural system approach is the response to the complexity of expected outcomes. Prior to spending a few million dollars for a new information system, one must provide its information architecture and the business and social implications associated with it (Targowski, 2003). In this sense, this study will define service systems’ architectures.
Association Rule Mining
Authors: Woon Yew-Kwong
Published: May 2011

Association Rule Mining (ARM) is concerned with how items in a transactional database are grouped together. It is commonly known as market basket analysis, because it can be likened to the analysis of items that are frequently put together in a basket by shoppers in a market. From a statistical point of view, it is a semiautomatic technique to discover correlations among a set of variables. ARM is widely used in myriad applications, including recommender systems (Lawrence, Almasi, Kotlyar, Viveros, & Duri, 2001), promotional bundling (Wang, Zhou, & Han, 2002), Customer Relationship Management (CRM) (Elliott, Scionti, & Page, 2003), and cross-selling (Brijs, Swinnen, Vanhoof, & Wets, 1999). In addition, its concepts have also been integrated into other mining tasks, such as Web usage mining (Woon, Ng, & Lim, 2002), clustering (Yiu & Mamoulis, 2003), outlier detection (Woon, Li, Ng, & Lu, 2003), and classification (Dong & Li, 1999), for improved efficiency and effectiveness. CRM benefits greatly from ARM as it helps in the understanding of customer behavior (Elliott et al., 2003). Marketing managers can use association rules of products to develop joint marketing campaigns to acquire new customers. The application of ARM for the cross-selling of supermarket products has been successfully attempted in many cases (Brijs et al., 1999). In one particular study involving the personalization of supermarket product recommendations, ARM has been applied with much success (Lawrence et al., 2001). Together with customer segmentation, ARM helped to increase revenue by 1.8%. In the biology domain, ARM is used to extract novel knowledge on protein-protein interactions (Oyama, Kitano, Satou, & Ito, 2002). It is also successfully applied in gene expression analysis to discover biologically relevant associations between different genes or between different environment conditions (Creighton & Hanash, 2003).
Data Mining for Internationalization
Authors: Dalla Valle Luciana
Published: May 2011

The term “internationalization” refers to the process of international expansion of firms realized through different mechanisms such as export, strategic alliances and foreign direct investments. The process of internationalization has recently received increasing attention mainly because it is at the very heart of the globalization phenomenon. Through internationalization firms strive to improve their profitability, coming across new opportunities but also facing new risks. Research in this field mainly focuses on the determinants of a firms’ performance, in order to identify the best entry mode for a foreign market, the most promising locations and the international factors that explain an international firms’ performance. In this way, scholars try to identify the best combination of firms’ resources and location in order to maximize profit and control for risks (for a review of the studies on the impact of internationalization on performance see Contractor et al., 2003). The opportunity to use large databases on firms’ international expansion has raised the interesting question concerning the main data mining tools that can be applied in order to define the best possible internationalization strategies. The aim of this paper is to discuss the most important statistical techniques that have been implemented to show the relationship among firm performance and its determinants. These methods belong to the family of multivariate statistical methods and can be grouped into Regression Models and Causal Models. The former are more common and easy to interpret, but they can only describe direct relationships among variables; the latter have been used less frequently, but their complexity allows us to identify important causal structures, that otherwise would be hidden.
Data Mining in Genome Wide Association Studies
Authors: Burr Tom
Published: May 2011

The genetic basis for some human diseases, in which one or a few genome regions increase the probability of acquiring the disease, is fairly well understood. For example, the risk for cystic fibrosis is linked to particular genomic regions. Identifying the genetic basis of more common diseases such as diabetes has proven to be more difficult, because many genome regions apparently are involved, and genetic effects are thought to depend in unknown ways on other factors, called covariates, such as diet and other environmental factors (Goldstein and Cavalleri, 2005). Genome-wide association studies (GWAS) aim to discover the genetic basis for a given disease. The main goal in a GWAS is to identify genetic variants, single nucleotide polymorphisms (SNPs) in particular, that show association with the phenotype, such as “disease present” or “disease absent” either because they are causal, or more likely, because they are statistically correlated with an unobserved causal variant (Goldstein and Cavalleri, 2005). A GWAS can analyze “by DNA site” or “by multiple DNA sites. ” In either case, data mining tools (Tachmazidou, Verzilli, and De Lorio, 2007) are proving to be quite useful for understanding the genetic causes for common diseases.
Data Mining on XML Data
Authors: Ding Qin
Published: May 2011

With the growing usage of XML data for data storage and exchange, there is an imminent need to develop efficient algorithms to perform data mining on semistructured XML data. Mining on XML data is much more difficult than mining on relational data because of the complexity of structure in XML data. A naïve approach to mining on XML data is to first convert XML data into relational format. However the structure information may be lost during the conversion. It is desired to develop efficient and effective data mining algorithms that can be directly applied on XML data.
Data Mining with Cubegrades
Authors: Abdulghani Amin A.
Published: May 2011

A lot of interest has been expressed in database mining using association rules (Agrawal, Imielinski, & Swami, 1993). In this chapter, we provide a different view of the association rules, referred to as cubegrades (Imielinski, Khachiyan, & Abdulghani, 2002) . An example of a typical association rule states that, say, 23% of supermarket transactions (so called market basket data) which buy bread and butter buy also cereal (that percentage is called confidence) and that 10% of all transactions buy bread and butter (this is called support). Bread and butter represent the body of the rule and cereal constitutes the consequent of the rule. This statement is typically represented as a probabilistic rule. But association rules can also be viewed as statements about how the cell representing the body of the rule is affected by specializing it by adding an extra constraint expressed by the rule’s consequent. Indeed, the confidence of an association rule can be viewed as the ratio of the support drop, when the cell corresponding to the body of a rule (in our case the cell of transactions buying bread and butter) is augmented with its consequent (in this case cereal). This interpretation gives association rules a “dynamic flavor” reflected in a hypothetical change of support affected by specializing the body cell to a cell whose description is a union of body and consequent descriptors. For example, our earlier association rule can be interpreted as saying that the count of transactions buying bread and butter drops to 23% of the original when restricted (rolled down) to the transactions buying bread, butter and cereal. In other words, this rule states how the count of transactions supporting buyers of bread and butter is affected by buying cereal as well. With such interpretation in mind, a much more general view of association rules can be taken, when support (count) can be replaced by an arbitrary measure or aggregate and the specialization operation can be substituted with a different “delta” operation. Cubegrades capture this generalization. Conceptually, this is very similar to the notion of gradients used in calculus. By definition the gradient of a function between the domain points x1 and x2 measures the ratio of the delta change in the function value over the delta change between the points. For a given point x and function f(), it can be interpreted as a statement of how a change in the value of x (?x), affects a change of value in the function (? f(x)).
Discovering Knowledge from XML Documents
Authors: Nayak Richi
Published: May 2011

XML is the new standard for information exchange and retrieval. An XML document has a schema that defines the data definition and structure of the XML document (Abiteboul et al., 2000). Due to the wide acceptance of XML, a number of techniques are required to retrieve and analyze the vast number of XML documents. Automatic deduction of the structure of XML documents for storing semi-structured data has been an active subject among researchers (Abiteboul et al., 2000; Green et al., 2002). A number of query languages for retrieving data from various XML data sources also has been developed (Abiteboul et al., 2000; W3c, 2004). The use of these query languages is limited (e.g., limited types of inputs and outputs, and users of these languages should know exactly what kinds of information are to be accessed). Data mining, on the other hand, allows the user to search out unknown facts, the information hidden behind the data. It also enables users to pose more complex queries (Dunham, 2003). Figure 1 illustrates the idea of integrating data mining algorithms with XML documents to achieve knowledge discovery. For example, after identifying similarities among various XML documents, a mining technique can analyze links between tags occurring together within the documents. This may prove useful in the analysis of e-commerce Web documents recommending personalization of Web pages.
Evaluation of Decision Rules by Qualities for Decision-Making Systems
Authors: Bruha Ivan
Published: May 2011

A ‘traditional’ learning algorithm that can induce a set of decision rules usually represents a robust and comprehensive system that discovers a knowledge from usually large datasets. We call this discipline Data Mining (DM). Any classifier, expert system, or generally a decision-supporting system can then utilize this decision set to derive a decision (prediction) about given problems, observations, diagnostics. DM can be defined as a nontrivial process of identifying valid, novel, and ultimately understandable knowledge in data. It is understood that DM as a multidisciplinary activity points to the overall process of determining a useful knowledge from databases, i.e. extracting highlevel knowledge from low-level data in the context of large databases. A rule-inducing learning algorithm may yield either an ordered or unordered set of decision rules. The latter seems to be more understandable by humans and directly applicable in most expert systems or decisionsupporting ones. However, classification utilizing the unordered-mode decision rules may be accompanied by some conflict situations, particularly when several rules belonging to different classes match (‘fire’ for) an input to-be-classified (unseen) object. One of the possible solutions to this conflict is to associate each decision rule induced by a learning algorithm with a numerical factor which is commonly called the rule quality. The chapter first surveys empirical and statistical formulas of the rule quality and compares their characteristics. Statistical tools such as contingency tables, rule consistency, completeness, quality, measures of association, measures of agreement are introduced as suitable vehicles for depicting a behaviour of a decision rule. After that, a very brief theoretical methodology for defining rule qualities is acquainted. The chapter then concludes by analysis of the formulas for rule qualities, and exhibits a list of future trends in this discipline.
Hybrid Genetic Algorithms in Data Mining Applications
Authors: Salcedo-Sanz Sancho , Camps-Valls Gustavo , Bousoño-Calzón Carlos
Published: May 2011

Genetic algorithms (GAs) are a class of problem solving techniques which have been successfully applied to a wide variety of hard problems (Goldberg, 1989). In spite of conventional GAs are interesting approaches to several problems, in which they are able to obtain very good solutions, there exist cases in which the application of a conventional GA has shown poor results. Poor performance of GAs completely depends on the problem. In general, problems severely constrained or problems with difficult objective functions are hard to be optimized using GAs. Regarding the difficulty of a problem for a GA there is a well established theory. Traditionally, this has been studied for binary encoded problems using the so called Walsh Transform (Liepins & Vose, 1991), and its associated spectrum (Hordijk & Stadler, 1998), which provides an idea of the distribution of the important schemas (building blocks) in the search space. Several methods to enhance the performance of GAs in difficult applications have been developed. Firstly, the encoding of a problem determines the search space where the GA must work. Therefore, given a problem, the selection of the best encoding is an important pre-processing step. Operators which reduce the search space are then interesting in some applications. Secondly, variable length or transformed encodings are schemes, which can be successfully applied to some difficult problems. The hybridization of a GA with local search algorithms can also improve the performance of the GA in concrete applications. There are two types of hybridization: • If the GA is hybridized with a local search heuristic in order to tackle the problem constraints, it is usually known as a hybrid genetic algorithm. • If the GA is hybridized with a local search heuristic in order to improve its performance, then it is known as a memetic algorithm. In this chapter we revise several hybrid methods involving GAs that have been applied to data mining problems. First, we provide a brief background with several important definitions on genetic algorithms, hybrid algorithms and operators for improving its performance. In the Main Trust section, we present a survey of several hybrid algorithms, which use GAs as search heuristic, and their main applications in data mining. Finally, we finish the chapter giving some conclusions and future trends.
Learning from Data Streams
Authors: Gama João , Rodrigues Pedro Pereira
Published: May 2011

In the last two decades, machine learning research and practice has focused on batch learning usually with small datasets. In batch learning, the whole training data is available to the algorithm that outputs a decision model after processing the data eventually (or most of the times) multiple times. The rationale behind this practice is that examples are generated at random accordingly to some stationary probability distribution. Also, most learners use a greedy, hill-climbing search in the space of models. What distinguishes current data sets from earlier ones are the continuous flow of data and the automatic data feeds. We do not just have people who are entering information into a computer. Instead, we have computers entering data into each other. Nowadays there are applications in which the data is modelled best not as persistent tables but rather as transient data streams. In some applications it is not feasible to load the arriving data into a traditional DataBase Management Systems (DBMS), and traditional DBMS are not designed to directly support the continuous queries required in these application (Babcock et al., 2002). These sources of data are called Data Streams. There is a fundamental difference between learning from small datasets and large datasets. As pointed-out by some researchers (Brain & Webb, 2002), current learning algorithms emphasize variance reduction. However, learning from large datasets may be more effective when using algorithms that place greater emphasis on bias management. Algorithms that process data streams deliver approximate solutions, providing a fast answer using few memory resources. They relax the requirement of an exact answer to an approximate answer within a small error range with high probability. In general, as the range of the error decreases the space of computational resources goes up. In some applications, mostly database oriented, an approximate answer should be within an admissible error margin. Some results on tail inequalities provided by statistics are useful to accomplish this goal.
Materialized View Selection for Data Warehouse Design
Authors: Theodoratos Dimitri , Xu Wugang , Simitsis Alkis
Published: May 2011

A Data Warehouse (DW) is a repository of information retrieved from multiple, possibly heterogeneous, autonomous, distributed databases and other information sources for the purpose of complex querying, analysis and decision support. Data in the DW are selectively collected from the sources, processed in order to resolve inconsistencies, and integrated in advance (at design time) before data loading. DW data are usually organized multidimensionally to support On-Line Analytical Processing (OLAP). A DW can be abstractly seen as a set of materialized views defined over the source relations. During the initial design of a DW, the DW designer faces the problem of deciding which views to materialize in the DW. This problem has been addressed in the literature for different classes of queries and views and with different design goals.
Mining Generalized Association Rules in an Evolving Environment
Authors: Lin Wen-Yang , Tseng Ming-Cheng
Published: May 2011

The mining of Generalized Association Rules (GARs) from a large transactional database in the presence of item taxonomy has been recognized as an important model for data mining. Most previous studies on mining generalized association rules, however, were conducted on the assumption of a static environment, i.e., static data source and static item taxonomy, disregarding the fact that the taxonomy might be updated as new transactions are added into the database over time, and as such, the analysts may have to continuously change the support and confidence constraints, or to adjust the taxonomies from different viewpoints to discover more informative rules. In this chapter, we consider the problem of mining generalized association rules in such a dynamic environment. We survey different strategies incorporating state-of-the-art techniques for dealing with this problem and investigate how to efficiently update the discovered association rules when there are transaction updates to the database along with item taxonomy evolution and refinement of support constraint.
New Opportunities in Marketing Data Mining
Authors: Lo Victor S.Y.
Published: May 2011

Data mining has been widely applied in many areas over the past two decades. In marketing, many firms collect large amount of customer data to understand their needs and predict their future behavior. This chapter discusses some of the key data mining problems in marketing and provides solutions and research opportunities.
Outlier Detection Techniques for Data Mining
Authors: Angiulli Fabrizio
Published: May 2011

Data mining techniques can be grouped in four main categories: clustering, classification, dependency detection, and outlier detection. Clustering is the process of partitioning a set of objects into homogeneous groups, or clusters. Classification is the task of assigning objects to one of several predefined categories. Dependency detection searches for pairs of attribute sets which exhibit some degree of correlation in the data set at hand. The outlier detection task can be defined as follows: “Given a set of data points or objects, find the objects that are considerably dissimilar, exceptional or inconsistent with respect to the remaining data”. These exceptional objects as also referred to as outliers. Most of the early methods for outlier identification have been developed in the field of statistics (Hawkins, 1980; Barnett & Lewis, 1994). Hawkins’ definition of outlier clarifies the approach: “An outlier is an observation that deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism”. Indeed, statistical techniques assume that the given data set has a distribution model. Outliers are those points that satisfy a discordancy test, that is, that are significantly far from what would be their expected position given the hypothesized distribution. Many clustering, classification and dependency detection methods produce outliers as a by-product of their main task. For example, in classification, mislabeled objects are considered outliers and thus they are removed from the training set to improve the accuracy of the resulting classifier, while in clustering, objects that do not strongly belong to any cluster are considered outliers. Nevertheless, it must be said that searching for outliers through techniques specifically designed for tasks different from outlier detection could not be advantageous. As an example, clusters can be distorted by outliers and, thus, the quality of the outliers returned is affected by their presence. Moreover, other than returning a solution of higher quality, outlier detection algorithms can be vastly more efficient than non ad-hoc algorithms. While in many contexts outliers are considered as noise that must be eliminated, as pointed out elsewhere, “one person’s noise could be another person’s signal”, and thus outliers themselves can be of great interest. Outlier mining is used in telecom or credit card frauds to detect the atypical usage of telecom services or credit cards, in intrusion detection for detecting unauthorized accesses, in medical analysis to test abnormal reactions to new medical therapies, in marketing and customer segmentations to identify customers spending much more or much less than average customer, in surveillance systems, in data cleaning, and in many other fields.
Real-Time Face Detection and Classification for ICCTV
Authors: Lovell Brian C. , Chen Shaokang , Shan Ting
Published: May 2011

Data mining is widely used in various areas such as finance, marketing, communication, web service, surveillance and security. The continuing growth in computing hardware and consumer demand has led to a rapid increase of multimedia data searching. With the rapid development of computer vision and communication techniques, real-time multimedia data mining is becoming increasingly prevalent. A motivating application is Closed-Circuit Television (CCTV) surveillance systems. However, most data mining systems mainly concentrate on text based data because of the relative mature techniques available, which are not suitable for CCTV systems. Currently, CCTV systems rely heavily on human beings to monitor screens physically. An emerging problem is that with thousands of cameras installed, it is uneconomical and impractical to hire the required numbers of people for monitoring. An Intelligent CCTV (ICCTV) system is thus required for automatically or semi-automatically monitoring the cameras.
Receiver Operating Characteristic (ROC) Analysis
Authors: Lachiche Nicolas
Published: May 2011

Receiver Operating Characteristic (ROC curves) have been used for years in decision making from signals, such as radar or radiology. Basically they plot the hit rate versus the false alarm rate. They were introduced recently in data mining and machine learning to take into account different misclassification costs, or to deal with skewed class distributions. In particular they help to adapt the extracted model when the training set characteristics differ from the evaluation data. Overall they provide a convenient way to compare classifiers, but also an unexpected way to build better classifiers.
Business Decisions through Mobile Computing
Authors: Rao N. Raghavendra
Published: May 2011

The existing ways of doing business are constantly changing. This is due to rapid changes in global economy. The opportunities in the present global markets have to be exploited at a rapid pace. The large centralized organizations which have established themselves over a considerable period may find it very difficult to introduce or diversify their product range in the present globalization scenario. They need to realize that managing technical knowledge, as well as innovative process in conducting business, is the way to remain competitive in the global market. Every business enterprise needs unique challenges to face in its sector. It is high time that they take advantage of the opportunities available across the globe by making use of the expertise of the global virtual teams. This chapter talks about a model for creation of global innovation model by global virtual teams who can design a product through the components of Information and Communication Technologies (ICT). Schelda Debowski (2006) rightly states that “Virtual Knowledge teams rely on Information technology to communicate” (p. 73).
A Second Look at Improving Student Interaction with Internet and Peer Review
Authors: de Abreu Moreira Dilvan , Quintino da Silva Elaine
Published: May 2011

In the last few years, education has been going through an important change: the introduction of information technology in the educational process. Many efforts have been conducted to realize the benefits of such technologies, such as the MIT-Media Lab One Laptop per Child initiative (MIT, 2007) in education. As a result of these efforts, there are many tools available today to produce multimedia educational material for the Web such as WebCT (WebCT, 2004). However, teachers are not sure how to use these tools to create effective models for teaching over the Internet. After a teacher puts classroom slides, schedules, and other static information on Web pages, what more can this technology offer? A possible response to this question is to use Internet technologies to promote collaborative learning. Collaborative learning (CL) is an educational strategy based on social theories in which students, joined in small groups, are responsible for the learning experience of each other (Gokhale, 1995; Panitz, 2002). In CL, the main goal of the teacher is to organize collective activities that can stimulate the development of skills such as creativity, oral expression, and critical thinking, among others. When supported by computers and Internet technologies, collaborative learning is referenced as computer supported collaborative learning (CSCL). The main goal of CSCL is to use software and hardware to support and increase group work and learning. The peer review method, known by almost everyone in the academic world, when applied as an educational tool, can be considered a kind of collaborative learning activity. This article describes an educational method that uses peer review and the Internet to promote interaction among students. This method, which has been used and refined since 1997 (by the first author), has been used in different computer science courses at the ICMC-USP. Software tools, such as the WebCoM—Web Course Manager tool (Silva & Moreira, 2003)—are used to support the peer review method and to improve interaction among students. The main advantages of the peer review method and the WebCoM tool over other works in this context are that they: • Allow debate between groups (workers and reviewers) to improve interaction and social abilities among students; • Focus on the interaction among students and their social skills; • Also offer support for group activities (such as reports and assignments) without peer review. Results generated by the experience of managing classes with the WebCoM tool are also presented.
Sociocultural Implications of Wikipedia
Authors: Singh Ramanjit
Published: May 2011

Wikipedia is a free encyclopedia that operates worldwide on the Internet. Articles on Wikipedia are developed with close collaboration of volunteers and anyone can edit the content (Wikipedia, 2006e). Although there are many advantages of using Wikipedia as a group collaboration tool, there are important implications. First, Wikipedia community is diverse and intercultural differences can distort the communication process. Second, the neutral point of view (NPOV) policy can lead to disputes. Third, lack of supervision and open source policy can be another source of conflict. Forth, administration of articles can be complex due to differing cultural and political stand points (Smith & Kollock, 1999). Laslty, differences in time and space as well as low level of access to the Internet can significantly impede collaboration efforts at Wikipedia (Berry, 2006; Madon, 2000; Parayil, 2006; Sahay, Nicholson, & Krishna, 2003). Hence, the aim of this paper is to examine sociocultural implications of using Wikipedia as a group collaboration tool spanning multiple countries and how social and cultural climate, differences in time and space, as well as technological infrastructure of countries affect collaboration between individuals given the distinctive operational and administration policies at Wikipedia. It is believed that findings from this research will increase the awareness of the underlying cause of many disputes arising at Wikipedia. In addition, this research will lead to cultural relativism and provide neutral grounds for collaborative efforts at Wikipedia in the future.
SPIT
Authors: Curran Kevin
Published: May 2011

Spam in the computer does not simply mean ads. Spam is any message, article, or ad that repeats itself an unacceptable number of times so that it causes annoyance. The content of the spam is of no importance. It could contain your simple “Make Money Fast” hyperlink or a beautiful piece of poetry, but if the message is continuously repeated it becomes spam. The term spam is thought to have been taken from a famous Monty Python sketch. In that sketch spam came with everything the people ordered and the waitress would be constantly saying the word spam. Therefore the meaning of spam is something that repeats itself causing much anger or annoyance. Spam can be categorized as follows: • Junk mail: Mass mailings from legitimate businesses that is unwanted. • Noncommercial spam: Mass mailings of unsolicited messages without an apparent commercial motive including chain letters, urban legends, and joke collections. • Offensive and pornographic spam: Mass mailings of “adult” advertisements or pornographic pictures. • Spam scams: Mass mailings of fraudulent messages or those designed to con people out of personal information for the purpose of identity theft and other criminal acts. • Virus spam: Mass mailings that contain viruses, Trojans, malicious scripts, and so forth. Spoofing (Schwartz & Garfinkel, 1998) is a technique often used by spammers to make them harder to trace. Trojan viruses embedded in e-mail messages also employ spoofing techniques to ensure the source of the message is more difficult to locate (Ishibashi, Yamai, Abe, & Matsuura, 2003). Spam filters and virus scanners can only eliminate a certain amount of spam and also risk catching legitimate e-mails. As the SoBig virus has demonstrated, virus scanners themselves actually add to the e-mail traffic through notification and bounceback messages. SMTP is flawed in that it allows these e-mail headers to be faked, and does not allow for the sender to be authenticated as the “real” sender of the message (Geer, 2004). This article looks at a new type of spam known as spam over Internet telephony (SPIT).
Ethical, Cultural and Socio- Economic Factors of Software Piracy Determinants in a Developing Country
Authors: Butt Arsalan
Published: May 2011

Consumer software piracy is widespread in many parts of the world. P2P based websites have made it easier to access pirated software, which has resulted in an increased emphasis on the issue of software piracy in both the software industry and research community. Some factors that determine piracy include poverty, cultural values, ethical attitudes, and education. Earlier empirical studies have looked at software piracy as an intentional behaviour. This study explores the demographic, ethical and socioeconomical factors that can represent software piracy as a social norm among a developing country’s university students. The authors have conducted a comparative analysis of university students from Pakistan and Canada, two countries that differ economically, socially, and culturally. The results of the study indicate that software piracy behaviour is different in both groups of students, but that there are also some similarities. Future research directions and implications are also presented.
Autognomic Intellisite
Authors: Hamann Jon Ray
Published: May 2011

The 20th century saw the beginning of the evolution of learning machines from the growth of Boolean computers into Bayesian inference machines (Knuth, 2003). For some this is the crux of Artificial Intelligence (AI); however, AI research generally has yielded a plethora of specifically engineered, but formally unrelated, theories/models with varied levels of applications successes/failures, but without a commonly-explicatable conceptual foundation (i.e., it has left a theory-glut). Despite these many approaches to AI, including Automated Neural Nets, Natural Language Processing, Genetic Algorithms, Fuzzy Logic and Fractal Mathematical computational approaches, to identify only a few, AI itself has remained an elusive goal to achieve by means of a systems architecture relying on an implementation based on the systemic computer paradigm. The 21st century experience is overwhelmingly one of an ever-accelerating, dynamically changing world. Just staying in place seems nearly impossible—getting ahead is becoming increasing unfathomable in a world now characterized by an evolving dominance of Information Science and Technology Development in exponentially tighter (shorter) innovation cycles (IBM, 2008). In business, for example, there is the continuous challenge to ensure that the business’s products appear obviously differentiated from the competition, while staying current with the never-ending hot new trends that buffet the industry. A prime case in point is that of staying current with the trends in the computer solutions industry since adapting a computer dependent business (and most are) for the next big trend can be expected to be mitigated, if not made completely obsolete, by the next next big trend already on the radar screen.
Business Relationships and Organizational Structures in E-Business
Authors: Zhao Fang
Published: May 2011

In today’s e-business, context, technology, customers, competitors, and partners can change rapidly. Technology can become obsolete in the blink of an eye and customers can appear and disappear with a keystroke. There are practically no barriers to new entrants (competitors) in an e-business world. Likewise, e-business partnerships and virtual organizations become ephemeral and opportunistic in nature. This article explores the dynamics of the changing nature, process, and practice of business relationships and network form of organizations in the cyberspace. It also identifies and discusses a series of management issues raised in the processes of e-partnerships and virtual organizations.
Database Benchmarks
Authors: Darmont Jérôme
Published: May 2011

Performance measurement tools are very important, both for designers and users of Database Management Systems (DBMSs). Performance evaluation is useful to designers to determine elements of architecture, and, more generally, to validate or refute hypotheses regarding the actual behavior of a DBMS. Thus, performance evaluation is an essential component in the development process of well-designed and efficient systems. Users may also employ performance evaluation, either to compare the efficiency of different technologies before selecting a DBMS, or to tune a system. Performance evaluation by experimentation on a real system is generally referred to as benchmarking. It consists of performing a series of tests on a given DBMS to estimate its performance in a given setting. Typically, a benchmark is constituted of two main elements: a database model (conceptual schema and extension), and a workload model (set of read and write operations) to apply on this database, following a predefined protocol. Most benchmarks also include a set of simple or composite performance metrics such as response time, throughput, number of input/output, disk or memory usage, and so forth. The aim of this article is to present an overview of the major families of state-of-the-art database benchmarks, namely, relational benchmarks, object and object-relational benchmarks, XML benchmarks, and decision-support benchmarks; and to discuss the issues, tradeoffs, and future trends in database benchmarking. We particularly focus on XML and decision-support benchmarks, which are currently the most innovative tools that are developed in this area.
Distributed Geospatial Processing Services
Authors: Granell Carlos , Díaz Laura , Gould Michael
Published: May 2011

The development of geographic information systems (GISs) has been highly influenced by the overall progress of information technology (IT). These systems evolved from monolithic systems to become personal desktop GISs, with all or most data held locally, and then evolved to the Internet GIS paradigm in the form of Web services (Peng & Tsou, 2001). The highly distributed Web services model is such that geospatial data are loosely coupled with the underlying systems used to create and handle them, and geospatial processing functionalities are made available as remote, interoperable, discoverable geospatial services. In recent years the software industry has moved from tightly coupled application architectures such as CORBA (Common Object Request Broker Architecture?Vinoski, 1997) toward service-oriented architectures (SOAs) based on a network of interoperable, well-described services accessible via Web protocols. This has led to de facto standards for delivery of services such as Web Service Description Language (WSDL) to describe the functionality of a service, Simple Object Access Protocol (SOAP) to encapsulate Web service messages, and Universal Description, Discovery, and Integration (UDDI) to register and provide access to service offerings. Adoption of this Web services technology as an option to monolithic GISs is an emerging trend to provide distributed geospatial access, visualization, and processing. The GIS approach to SOA-based applications is perhaps best represented by the spatial data infrastructure (SDI) paradigm, in which standardized interfaces are the key to allowing geographic services to communicate with each other in an interoperable manner. This article focuses on standard interfaces and also on current implementations of geospatial data processing over the Web, commonly used in SDI environments. We also mention several challenges yet to be met, such as those concerned with semantics, discovery, and chaining of geospatial processing services and also with the extension of geospatial processing capabilities to the SOA world.
E-Collaboration in Organizations
Authors: Carstens Deborah S. , Rockfield Stephanie M.
Published: May 2011

Organizations are shedding conventional work team structures in favor of virtual team structures that are increasing in popularity (Lee-Kelley, Crossman, & Cannings, 2004). Ecollaboration enables collaboration between individuals not constrained by geographical distance or time. The emergence of the virtual team concept provides organizations with an alternate approach to managing work and individuals that are geographically separated (Gatlin-Watts, Carson, Horton, Maxwell, & Marltby, 2007). An advantage of virtual teams is that organizations can tap into resources rapidly to create a specialized work team that acts like a team, works like a team but doesn’t look like a typical team because team members may not be co-located (Stough, Eom, & Buckenmyer, 2000). E-collaborative technologies such as computer-based conferencing systems are of critical importance to the success of a virtual team (Arnison & Miller, 2002). In the absence of water-cooler philosophizing, virtual teams rely on technology to build trust between team members, resulting in greater synergy and ultimately team success in carrying out work tasks (Arnison & Miller, 2002; Stough et al., 2000). The article focus is on technological and organizational aspects of e-collaboration occurring today and forecasted for tomorrow. The specific topics addressed are e-collaboration in organizations, e-collaboration in organizations of today, specific e-collaboration success factors and future trends of e-collaboration in organizations of tomorrow.
E-Government and E-Democracy in the Making
Authors: Jaeger Birgit
Published: May 2011

The development of electronic or digital government (egovernment) has varied throughout the world. Although we give it the same name, we know from different studies that, for example, the concept of Information Society can be interpreted in different ways in different cultural settings (Jaeger, Slack, & Williams, 2000; Sancho, 2002). This article provides a general outline of the development of e-government in the West and is primarily based on European and Scandinavian experiences. It is only possible to give an introduction to e-government if we can define what we are talking about. E-government is still a rather new concept, but most people agree that egovernment includes the following features: • E-government is based on information and communication technologies (ICTs). • E-government is taking place in public administration. • E-government concerns electronic ways to perform all kinds of internal administrative tasks. • E-government also concerns the communication between the public administration and the citizens and other actors in the surrounding society (Jaeger, 2003: 50).
ERP and the Best-of-Breed Alternative
Authors: Bradley Joseph
Published: May 2011

Enterprise resource planning (ERP) systems are off-theshelf software systems that claim to meet the information needs of organizations. These systems are usually adopted to replace hard-to-maintain legacy systems developed by IS departments or older off-the-shelf packages that often provided only piecemeal solutions to the organization’s information needs. ERP systems evolved in the 1990s from material requirements planning (MRP) systems developed in the 1970s and manufacturing resources planning (MRPII) systems developed in the 1980s. ERP systems serve the entire organization, not just material or manufacturing planning. One advantage of ERP is that it integrates all the information for the entire organization into a single database. Implementation of ERP systems has proven expensive and time consuming. Failed and abandoned projects have been well publicized in the business press. ERP systems are “expensive and difficult to implement, often imposing their own logic on a company’s strategy and existing culture” (Pozzebon, 2000, p. 105). Most firms utilize a single software vendor for the complete ERP system throughout their organizations. The integrated nature of ERP software favors this single-vendor approach. An alternative strategy adopted by some firms is the best-of-breed approach, where the adopting organization picks and chooses ERP functional modules from the vendor whose software best supports its business processes. Organizations adopting best of breed believe that this approach will create a better fit with existing or required business processes, reduce or eliminate the need to customize a single-vendor solution, and reduce user resistance. Jones and Young (2006) found that 18% of companies used this approach to select ERP software packages. This article examines what the best-of-breed strategy is, when it is used, what advantage adopting companies seek, examples of best-of-breed implementations, and differences in implementation methods.
Evolution of Post-Secondary Distance Education
Authors: Miliszewska Iwona
Published: May 2011

Distance education is an increasingly common educational alternative, as well as a key contributor to the newly competitive landscape in higher education. Once regarded as an experimental alternative outside mainstream university education, distance education has attained new levels of legitimacy and expansion and has grown into a higher education industry of its own. This article discusses the history and transformation of distance education to create a framework for the sequence of events that have contributed to the distance education movements and shaped modern post-secondary distance education programs. The article outlines the evolution of post-secondary distance education from its inception to the present: its progression from informal programs offered by individual providers to a well-organised formal educational alternative; its purpose and characteristics; its expansion and internationalisation; and the various forces that have shaped its growth. While noting that technology has its limitations—it can facilitate teaching but not replace it—the article highlights the crucial role that advancements in technology have played in propelling the evolution of distance education, and points to the role of technology in blurring the conceptual divide between distance and traditional education.