bluuebunny/crossref_metadata_2025 huggingface dataset:
Collaborative Technology
Authors: Yilmaz Levent
Published: May 2011

This chapter presents a set of requirements for next generation groupware systems to improve team cooperation and awareness in distance learning settings. The premise of the chapter is based on the observation that in distance learning online asynchronous (e.g., e-mail, conference tools) or synchronous (e.g., chat) mechanisms are used to facilitate collaboration and coordination to complete necessary tasks. However, students are neither trained in basic principles regarding how effective cooperation takes place, nor means for their realization. Basic methods of cooperation are delineated along with a set of requirements based on a critical analysis of the elements of cooperation and team awareness. The means for realizing these elements are also discussed to present strategies to develop the proposed elements. Two scenarios are examined to demonstrate the utility of collaboration to provide deep integration of communication and task accomplishment within a unified coherent framework.
What Determines the World
Authors: Abdoullaev Azamat
Published: May 2011

The world or reality is the totality of things diversified into a multitude of collections of subworlds with the characteristic constraints and boundaries; all determined by the fundamental causal laws. Accordingly, the logic of reality is ultimately one; for there cannot be many logics of the universe. Although many personal perspectives allowed, and particular truths, special things and meanings, the global logic of reality is a single universal consistent system relied on a set of ontological classes and relationships, fundamental rules and laws. The universe is a deep, dark secret, mysterious and mystifying to human minds. And the fundamental challenge to the human mind is to provide a comprehensive account and model that explains in noncryprical terms how this unbounded environment changes and how its basic constituents causally related, and so forth. And this is all the legal responsibility of the UFO as a causal dynamic ontology, formulating the causal rules of world behavior as fundamental laws by establishing underlying regularities, uniformities, invariants, and correlations. So guiding science and technology, the task of dynamic ontology is to give us the overall structures, uniformities, patterns, laws, constraints, and invariants within which the many changes in the world take place, finding out what it is outside and inside, defining the natures and essences common to the denumerable multitude of individuals, by classifying the whole universe of things into the prime categories, classes, kinds and relationships.
Introduction to Services
Authors: Karakostas Bill , Zorgios Yannis
Published: May 2011

Over the past few decades, organizations have witnessed dramatic changes in their environment. The globalisation of the economy with the emergence of Asia’s new economic powerhouses, the rise of the knowledge economy, the rapid growth and sometimes equally rapid decline of disruptive technologies and business models are some examples of the modern economic environment, within which organizations must survive. At the same time, requirements from shareholders have become increasingly pressing. Stock markets demand high returns on investments, while strong competition narrows the profit margins. On the other hand, consumers continue to evolve in a process that began in the 1980s, to become more health and image conscious, eclectic in their purchasing choices, and not conforming to one-size-fits-all solutions, preferring individuality and customization, even of commodity products.
Service Coordination
Authors: Karakostas Bill , Zorgios Yannis
Published: May 2011

Chapter III introduced standards for Web service specification, such as WSDL and SOAP. With the use of such standards, Web service designers can model the functionality of a service in terms of inputs and outputs, thus allowing the consumers of the service to understand what to expect (and what not to expect) from the service, before actually using it. As we have said already, a Web service offers some well defined functionality to its consumers, which, however, due to the very essence of the Web service, has a rather focused and narrow scope. The reason is that, in general, we aim to develop Web services that are as much useful as possible for a wide range of consumers. Thus, the Web service becomes a reusable building.block for something more complex such as a business service. It is unlikely that a business level service can be provided by a single Web service. Instead, a complex business service has to be layered on top of several Web services that coordinate.with each other to deliver the business service. The capability for coordination however, is not unique to Web services. Business resources (people, activities, equipment, etc.) must be coordinated to deliver processes (Dayal, Hsu, & Ladin, 2001). Business processes themselves must be coordinated within a single company or even across several companies, in order to deliver higher level business results such as the fulfillment of a supply chain.
Exploring Multimedia Performance in Educational Research
Authors: Bagley Carl
Published: May 2011

This chapter draws on the analysis and (re)presentation of qualitative research undertaken on a UK government project known as Sure Start, an initiative based on the principle of bottom up community-based engagement dealing with social exclusion. The data from interviews, observation, and documentation, arise from ethnographic immersion by the author in a local working class community over a period of two years. Based on these data the chapter explores the (re)telling of the same research tale through the lens of bounded and unbounded (re)presentational forms and in so doing envisions some of the challenges and opportunities for multimedia performance as a methodological approach within the academy.
Toward Theory and Technique for Online Focus Groups
Authors: Bosio Albino Claudio , Graffigna Guendaline , Lozza Edoardo
Published: May 2011

Starting from a review of current dominant points of view about online focus groups, the authors outline the results of a research project in which they compared face-to-face discussion groups with different formats of online focus groups (forum; chat; forum plus chat) in order to identify their methodological specificities. The comparison was conducted with young adults on three health-related topics with different levels of social sensitivity. Systematic analysis of the conversational and thematic characteristics of all discussion transcripts revealed interesting characteristics of the four focus group techniques considered. The results corroborate the view that the research setting influences the findings production process in qualitative research, and might be the basis for a theory of online focus group techniques that can orient the researcher in choosing the online focus group technique best suited to his or her study aims and topics.
Communication and Relation Building in Social Systems
Authors: Hansson Thomas
Published: May 2011

Research into human behaviour have produced much innovative modelling, some respectable instrumentation, but little empirical theory-testing. This chapter follows suit, but rather than pursuing a traditional division between abstract conceptualisation and pragmatic procedures in the analysis of systemic human behaviour, focus is on social and psychological systems defined as information-processing entities in a context of verbal organisation, communication and control.
Design and Implementation of Trust Enabling Functions
Authors: Lemmergaard Jeanette , Brigth Damien , Gersbo-Møller Christopher , Hansson Tim
Published: May 2011

Through a case study based on a knowledge-sharing community of Danish plant growers, this chapter examines how an IT system can be designed to support strategic knowledge-sharing between firms participating in an industry-based virtual community. A suitable environment for trust is seen as an important part of making the community function effectively. Therefore, the system aims to support community members in making trust decisions related to knowledge-sharing. In the presented system decisions are based on digital evidence in the form of system-managed credentials. The chapter presents a model for trust, reputation, and performance management which supports the needs of the specific type of knowledge-sharing community. Further, the model is linked to an underlying public key infrastructure framework which supports the secure exchange of information and credentials between community members.
Evolving Relationship Between Law, Offshoring of Professional Services, Intellectual Property, and International Organizations
Authors: Gupta Amar , Gantz David A. , Sreecharana Devin , Kreyling Jeremy
Published: May 2011

This chapter covers four issues. First, it examines evolving international conventions to determine whether countries, especially developed countries, can take any steps to inhibit offshoring with the objective of protecting jobs in their respective countries. Second, it looks at statistics from independent sources to see if outsourcing exceeds insourcing, or vice versa, in the case of the U.S. Third, it looks at trends in outsourcing in the legal arena. Fourth, it looks at the intellectual property aspects of outsourcing and presents a long-term vision on how this ticklish issue is likely to be addressed in the long-term.
Changing IT Skills
Authors: Bullen Christine V. , Abraham Thomas , Gallagher Kevin , Kaiser Kate M. , Simon Judith
Published: May 2011

The increasingly global sourcing of IT work and other socio-economic trends are prompting fundamental changes in the availability of IT skills needed in both client and vendor organizations. This chapter analyzes the results of a survey conducted in 2005, in which IT executives were asked to describe the skills they felt were critical to keep in house now and in 2008. The top ten current skills included three in project management, five in business domain and three in technical. In 2008, the top five emerging skills are almost all business domain while the top five exiting skills are all technical. Our findings indicate that the critical skills to keep in-house are primarily client-facing skills, even when they are technical. Findings indicate that IT professionals need to have a balance that demonstrates a foundation in the traditional “hard skills” and experience with “softer” business-oriented skills.
Information Systems, Offshore Outsourcing, and Relevance in the Business School Curriculum
Authors: Tastle Willaim J. , White Bruce A. , Valfells Ársaell , Shackleton Peter
Published: May 2011

Offshore outsourcing has been a growing phenomenon in recent years. Rarely will an IT professional pick up a trade publication or journal without some article relating to outsourcing or offshore outsourcing. This in turn raises the question for IS educators—what should we be doing to better prepare our graduates for a future where offshore outsourcing is a reality? This chapter looks at the following topics as they relate to IS curriculum matters for outsourcing: Offshore outsourcing and success factors, the skills needed to effective manage offshore outsourcing, a look at offshore outsourcing and the IS2002 model curriculum, suggested changes to IS2002 to incorporate offshore outsourcing education, and what skills from IS2002 are vital in preparing students for the future.
Watching the Web
Authors: Ladner Sam
Published: May 2011

This chapter aims to improve the rigor and legitimacy of Web-traffic measurement as a social research method. I compare two dominant forms of Web-traffic measurement and discuss the implicit and largely unexamined ontological and epistemological claims of both methods. Like all research methods, Webtraffic measurement has implicit ontological and epistemological assumptions embedded within it. An ontology determines what a researcher is able to discover, irrespective of method, because it provides a frame within which phenomena can be rendered intelligible. I argue that Web-traffic measurement employs an ostensibly quantitative, positivistic ontology and epistemology in hopes of cementing the “scientific” legitimacy they engender. But these claims to “scientific” method are unsubstantiated, thereby limiting the efficacy and adoption rates of log-file analysis in general. I offer recommendations for improving these measurement tools, including more reflexivity and an explicit rejection of truth claims based on positivistic science.
Recommendations for Reporting Web Usage Studies
Authors: Hawkey Kirstie , Kellar Melanie
Published: May 2011

This chapter presents recommendations for reporting context in studies of Web usage including Web browsing behavior. These recommendations consist of eight categories of contextual information crucial to the reporting of results: user characteristics, temporal information, Web browsing environment, nature of the Web browsing task, data collection methods, descriptive data reporting, statistical analysis, and results in the context of prior work. This chapter argues that the Web and its user population are constantly growing and evolving. This changing temporal context can make it difficult for researchers to evaluate previous work in the proper context, particularly when detailed information about the user population, experimental methodology, and results is not presented. The adoption of these recommendations will allow researchers in the area of Web browsing behavior to more easily replicate previous work, make comparisons between their current work and previous work, and build upon previous work to advance the field.
Machine Learning Approach to Search Query Classification
Authors: Taksa Isak , Zelikovitz Sarah , Spink Amanda
Published: May 2011

Search query classification is a necessary step for a number of information retrieval tasks. This chapter presents an approach to non-hierarchical classification of search queries that focuses on two specific areas of machine learning: short text classification and limited manual labeling. Typically, search queries are short, display little class specific information per single query and are therefore a weak source for traditional machine learning. To improve the effectiveness of the classification process the chapter introduces background knowledge discovery by using information retrieval techniques. The proposed approach is applied to a task of age classification of a corpus of queries from a commercial search engine. In the process, various classification scenarios are generated and executed, providing insight into choice, significance and range of tuning parameters.
Online Science
Authors: Downing Kevin F. , Holtz Jennifer K.
Published: May 2011

In this chapter, we provide an overview of the state of global science capacity and online science education initiatives designed to increase that capital, with emphasis on developing countries. We see the online environment as a connective tool to bridge very large gaps in wealth and capacity, an educational bootstrapping mechanism that has not yet been fully tapped. We briefly describe the valuation of science education, and establish a base from which advances in science education will be explored in the remaining chapters.
The Role of Practical Work in Online Science
Authors: Downing Kevin F. , Holtz Jennifer K.
Published: May 2011

There are many educational strategies to achieve learning objectives to prepare students to adapt and survive more effectively in life. Many of these approaches involve, to some degree, practical learning experiences structured to emulate meaningful situations, tasks, and the problem solving required of the real world. In science, educators have long held and place particular importance in the idea that hands-on experiential activities are a fundamental tenet of learning. The portion of scientific instruction devoted to learn by ‘doing’ is called ‘practical work’. In this chapter, we explore the concept of practical work in science instruction, including categories of practical work, the historical basis and development of practical work, its purpose and value, controversies concerning practical work’s utility in science instruction, the importance of practical work in online science instruction, and the design of practical work learning environments. This chapter builds a rationale for the broad value and integral importance of practical work in science education at both the K-12 and university level and as such the necessity for its intentional implementation in online science learning environments. In this way, the practical work discussion of Chapter 5 provides the underpinnings to later chapters that review current and emerging forms and technologies to support online practical work.
The Cutting Edge
Authors: Downing Kevin F. , Holtz Jennifer K.
Published: May 2011

The evolution of online education will continue to be coupled to and constrained by innovations in Communication and Information Technologies (CIT). Only a few years ago, web-based courses were characterized by slow data transmission, at 56 kbps with dial-up lines that dramatically limited the styles of communication and the amount of multimedia that could be incorporated directly through the internet to support learning. As a result, course development was regularly compromised by technical limitations. For online education, the ideal threshold in data transmission speed is that point at which an author’s course vision and creativity is unrestricted by the instructional hardware and software permitting the effortless incorporation of interactivity styles and multimedia. This ideal will be met at different times by particular course authors, institutions, and even between disciplines (e.g., English versus science).
A Non-Language Learning Courseware and its Challenges
Authors: Juhary Jowati
Published: May 2011

This chapter analyses the challenges in adapting a non-language learning courseware (NLLC) for a military learning environment. The National Defense University Malaysia, Kuala Lumpur (NDUM) was the subject for observations and an informal survey. The findings of this chapter argue that there are technical, theoretical, and pedagogical challenges that need to be overcome when using an NLLC in language classrooms. With the rise of information and communication technology (ICT) and the Internet, tertiary military institutions are pressured to implement e-learning technologies in their learning environments. However, not many institutions have the capacity to do so. The author argues that the adoption of an NLLC for an institution that has resources constraints can help determine the potential of using e-learning technologies to help students acquire the target language better.
(B) Liberating the Past from the Future
Authors: Targowski Andrew
Published: May 2011

The purpose of this chapter is to define intrinsic values of information-communication processes in human development. The development of civilization depends upon the accumulation of wisdom, knowledge and cultural and infrastructural gain. Man is prouder of his heritage than of that which he can eventually achieve in the future. The future is often the threat of the imminent unknown, something that can destroy our stability, qualifications and position within society. On the other hand, the “future” is also the hope of the desperate for a better life.
Association Rule Hiding Methods
Authors: Verykios Vassilios S.
Published: May 2011

The enormous expansion of data collection and storage facilities has created an unprecedented increase in the need for data analysis and processing power. Data mining has long been the catalyst for automated and sophisticated data analysis and interrogation. Recent advances in data mining and knowledge discovery have generated controversial impact in both scientific and technological arenas. On the one hand, data mining is capable of analyzing vast amounts of information within a minimum amount of time, an analysis that has exceeded the expectations of even the most imaginative scientists of the last decade. On the other hand, the excessive processing power of intelligent algorithms which is brought with this new research area puts at risk sensitive and confidential information that resides in large and distributed data stores. Privacy and security risks arising from the use of data mining techniques have been first investigated in an early paper by O’ Leary (1991). Clifton & Marks (1996) were the first to propose possible remedies to the protection of sensitive data and sensitive knowledge from the use of data mining. In particular, they suggested a variety of ways like the use of controlled access to the data, fuzzification of the data, elimination of unnecessary groupings in the data, data augmentation, as well as data auditing. A subsequent paper by Clifton (2000) made concrete early results in the area by demonstrating an interesting approach for privacy protection that relies on sampling. A main result of Clifton’s paper was to show how to determine the right sample size of the public data (data to be disclosed to the public where sensitive information has been trimmed off), by estimating at the same time the error that is introduced from the sampling to the significance of the rules. Agrawal and Srikant (2000) were the first to establish a new research area, the privacy preserving data mining, which had as its goal to consider privacy and confidentiality issues originating in the mining of the data. The authors proposed an approach known as data perturbation that relies on disclosing a modified database with noisy data instead of the original database. The modified database could produce very similar patterns with those of the original database.
On Association Rule Mining for the QSAR Problem
Authors: Dumitriu Luminita
Published: May 2011

The concept of Quantitative Structure-Activity Relationship (QSAR), introduced by Hansch and co-workers in the 1960s, attempts to discover the relationship between the structure and the activity of chemical compounds (SAR), in order to allow the prediction of the activity of new compounds based on knowledge of their chemical structure alone. These predictions can be achieved by quantifying the SAR. Initially, statistical methods have been applied to solve the QSAR problem. For example, pattern recognition techniques facilitate data dimension reduction and transformation techniques from multiple experiments to the underlying patterns of information. Partial least squares (PLS) is used for performing the same operations on the target properties. The predictive ability of this method can be tested using cross-validation on the test set of compounds. Later, data mining techniques have been considered for this prediction problem. Among data mining techniques, the most popular ones are based on neural networks (Wang, Durst, Eberhart, Boyd, & Ben-Miled, 2004) or on neuro-fuzzy approaches (Neagu, Benfenati, Gini, Mazzatorta, & Roncaglioni, 2002) or on genetic programming (Langdon, &Barrett, 2004). All these approaches predict the activity of a chemical compound, without being able to explain the predicted value. In order to increase the understanding on the prediction process, descriptive data mining techniques have started to be used related to the QSAR problem. These techniques are based on association rule mining. In this chapter, we describe the use of association rule-based approaches related to the QSAR problem.
Automatic Music Timbre Indexing
Authors: Zhang Xin
Published: May 2011

Music information indexing based on timbre helps users to get relevant musical data in large digital music databases. Timbre is a quality of sound that distinguishes one music instrument from another among a wide variety of instrument families and individual categories. The real use of timbre-based grouping of music is very nicely discussed in (Bregman, 1990). Typically, an uncompressed digital music recording, in form of a binary file, contains a header and a body. A header stores file information such as length, number of channels, rate of sample frequency, etc. Unless being manually labeled, a digital audio recording has no description on timbre, pitch or other perceptual properties. Also, it is a highly nontrivial task to label those perceptual properties for every piece of music object based on its data content. Lots of researchers have explored numerous computational methods to identify the timbre property of a sound. However, the body of a digital audio recording contains an enormous amount of integers in a time-order sequence. For example, at a sample frequency rate of 44,100Hz, a digital recording has 44,100 integers per second, which means, in a oneminute long digital recording, the total number of the integers in the time-order sequence will be 2,646,000, which makes it a very big data item. Being not in form of a record, this type of data is not suitable for most traditional data mining algorithms. Recently, numerous features have been explored to represent the properties of a digital musical object based on acoustical expertise. However, timbre description is basically subjective and vague, and only some subjective features have well defined objective counterparts, like brightness, calculated as gravity center of the spectrum. Explicit formulation of rules of objective specification of timbre in terms of digital descriptors will formally express subjective and informal sound characteristics. It is especially important in the light of human perception of sound timbre. Time-variant information is necessary for correct classification of musical instrument sounds because quasi-steady state, where the sound vibration is stable, is not sufficient for human experts. Therefore, evolution of sound features in time should be reflected in sound description as well. The discovered temporal patterns may better express sound features than static features, especially that classic features can be very similar for sounds representing the same family or pitch, whereas changeability of features with pitch for the same instrument makes sounds of one instrument dissimilar. Therefore, classical sound features can make correct identification of musical instruments independently on the pitch very difficult and erroneous.
Behavioral Pattern-Based Customer Segmentation
Authors: Yang Yinghui
Published: May 2011

Customer segmentation is the process of dividing customers into distinct subsets (segments or clusters) that behave in the same way or have similar needs. Because each segment is fairly homogeneous in their behavior and needs, they are likely to respond similarly to a given marketing strategy. In the marketing literature, market segmentation approaches have often been used to divide customers into groups in order to implement different strategies. It has been long established that customers demonstrate heterogeneity in their product preferences and buying behaviors (Allenby & Rossi 1999) and that the model built on the market in aggregate is often less efficient than models built for individual segments. Much of this research focuses on examining how variables such as demographics, socioeconomic status, personality, and attitudes can be used to predict differences in consumption and brand loyalty. Distance-based clustering techniques, such as k-means, and parametric mixture models, such as Gaussian mixture models, are two main approaches used in segmentation. While both of these approaches have produced good results in various applications, they are not designed to segment customers based on their behavioral patterns. There may exist natural behavioral patterns in different groups of customers or customer transactions (e.g. purchase transactions, Web browsing sessions, etc.). For example, a set of behavioral patterns that distinguish a group of wireless subscribers may be as follows: Their call duration during weekday mornings is short, and these calls are within the same geographical area. They call from outside the home area on weekdays and from the home area on weekends. They have several “data” calls on weekdays. The above set of three behavioral patterns may be representative of a group of consultants who travel frequently and who exhibit a set of common behavioral patterns. This example suggests that there may be natural clusters in data, characterized by a set of typical behavioral patterns. In such cases, appropriate “behavioral pattern-based segmentation” approaches can constitute an intuitive method for grouping customer transactions.
Bridging Taxonomic Semantics to Accurate Hierarchical Classification
Authors: Tang Lei , Liu Huan , Zhang Jiangping
Published: May 2011

The unregulated and open nature of the Internet and the explosive growth of the Web create a pressing need to provide various services for content categorization. The hierarchical classification attempts to achieve both accurate classification and increased comprehensibility. It has also been shown in literature that hierarchical models outperform flat models in training efficiency, classification efficiency, and classification accuracy (Koller & Sahami, 1997; McCallum, Rosenfeld, Mitchell & Ng, 1998; Ruiz & Srinivasan ,1999; Dumais & Chen, 2000; Yang, Zhang & Kisiel, 2003; Cai & Hofmann, 2004; Liu, Yang, Wan, Zeng, Cheng & Ma, 2005). However, the quality of the taxonomy attracted little attention in past works. Actually, different taxonomies can result in differences in classification. So the quality of the taxonomy should be considered for real-world classifications. Even a semantically sound taxonomy does not necessarily lead to the intended classification performance (Tang, Zhang & Liu 2006). Therefore, it is desirable to construct or modify a hierarchy to better suit the hierarchical content classification task.
Computation of OLAP Data Cubes
Authors: Abdulghani Amin A.
Published: May 2011

The focus of online analytical processing (OLAP) is to provide a platform for analyzing data (e.g., sales data) with multiple dimensions (e.g., product, location, time) and multiple measures (e.g., total sales or total cost). OLAP operations then allow viewing of this data from a number of perspectives. For analysis, the object or data structure of primary interest in OLAP is a data cube. A detailed introduction to OLAP is presented in (Han & Kambler, 2006).
A Data Distribution View of Clustering Algorithms
Authors: Wu Junjie , Chen Jian , Xiong Hui
Published: May 2011

Cluster analysis (Jain & Dubes, 1988) provides insight into the data by dividing the objects into groups (clusters), such that objects in a cluster are more similar to each other than objects in other clusters. Cluster analysis has long played an important role in a wide variety of fields, such as psychology, bioinformatics, pattern recognition, information retrieval, machine learning, and data mining. Many clustering algorithms, such as K-means and Unweighted Pair Group Method with Arithmetic Mean (UPGMA), have been wellestablished. A recent research focus on clustering analysis is to understand the strength and weakness of various clustering algorithms with respect to data factors. Indeed, people have identified some data characteristics that may strongly affect clustering analysis including high dimensionality and sparseness, the large size, noise, types of attributes and data sets, and scales of attributes (Tan, Steinbach, & Kumar, 2005). However, further investigation is expected to reveal whether and how the data distributions can have the impact on the performance of clustering algorithms. Along this line, we study clustering algorithms by answering three questions: 1. What are the systematic differences between the distributions of the resultant clusters by different clustering algorithms? 2. How can the distribution of the “true” cluster sizes make impact on the performances of clustering algorithms? 3. How to choose an appropriate clustering algorithm in practice? The answers to these questions can guide us for the better understanding and the use of clustering methods. This is noteworthy, since 1) in theory, people seldom realized that there are strong relationships between the clustering algorithms and the cluster size distributions, and 2) in practice, how to choose an appropriate clustering algorithm is still a challenging task, especially after an algorithm boom in data mining area. This chapter thus tries to fill this void initially. To this end, we carefully select two widely used categories of clustering algorithms, i.e., K-means and Agglomerative Hierarchical Clustering (AHC), as the representative algorithms for illustration. In the chapter, we first show that K-means tends to generate the clusters with a relatively uniform distribution on the cluster sizes. Then we demonstrate that UPGMA, one of the robust AHC methods, acts in an opposite way to K-means; that is, UPGMA tends to generate the clusters with high variation on the cluster sizes. Indeed, the experimental results indicate that the variations of the resultant cluster sizes by K-means and UPGMA, measured by the Coefficient of Variation (CV), are in the specific intervals, say [0.3, 1.0] and [1.0, 2.5] respectively. Finally, we put together K-means and UPGMA for a further comparison, and propose some rules for the better choice of the clustering schemes from the data distribution point of view.
Data Mining and Privacy
Authors: Aïmeur Esma , Gambs Sébastien
Published: May 2011

With the emergence of Internet, it is now possible to connect and access sources of information and databases throughout the world. At the same time, this raises many questions regarding the privacy and the security of the data, in particular how to mine useful information while preserving the privacy of sensible and confidential data. Privacy-preserving data mining is a relatively new but rapidly growing field that studies how data mining algorithms affect the privacy of data and tries to find and analyze new algorithms that preserve this privacy. At first glance, it may seem that data mining and privacy have orthogonal goals, the first one being concerned with the discovery of useful knowledge from data whereas the second is concerned with the protection of data’s privacy. Historically, the interactions between privacy and data mining have been questioned and studied since more than a decade ago, but the name of the domain itself was coined more recently by two seminal papers attacking the subject from two very different perspectives (Agrawal & Srikant, 2000; Lindell & Pinkas, 2000). The first paper (Agrawal & Srikant, 2000) takes the approach of randomizing the data through the injection of noise, and then recovers from it by applying a reconstruction algorithm before a learning task (the induction of a decision tree) is carried out on the reconstructed dataset. The second paper (Lindell & Pinkas, 2000) adopts a cryptographic view of the problem and rephrases it within the general framework of secure multiparty computation. The outline of this chapter is the following. First, the area of privacy-preserving data mining is illustrated through three scenarios, before a classification of privacy- preserving algorithms is described and the three main approaches currently used are detailed. Finally, the future trends and challenges that await the domain are discussed before concluding.
Data Mining for Obtaining Secure E-Mail Communications
Authors: del Castillo Mª Dolores
Published: May 2011

Email is now an indispensable communication tool and its use is continually growing. This growth brings with it an increase in the number of electronic threats that can be classified into five categories according to their inner behavior: virus, trojans, pharming, spam, and phishing. Viruses, trojans and pharming threats represent an attack to the user’s computer while the focus of attack of spam and phishing threats is mainly the user, that is, these last two threats involve a kind of intellectual attack. A virus is a small program that replicates itself and inserts copies into other executable code or documents using e-mails as a means of transport. Trojans can not replicate themselves and they are used to open a network port giving other users a means of controlling the infected computer. Other more dangerous trojans are called spy programs (spyware) which wait until users visit some websites and then capture all the keys typed and mouse movements and make screenshots to obtain information. Pharming is a technique used to redirect users to illegitimate websites. These three threats, in spite of being present in e-mails, can be solved by an anti virus program. The next two threats need e-mail filters to be solved and this chapter focuses on them: spam and phishing. Spam consists on the massive sending of unsolicited commercial e-mail to a large number of recipients. Unlike legitimate commercial e-mail, spam is sent without the explicit permission of the recipients. Spammers obtain e-mail addresses by different ways such as guessing common names at known domains or searching addresses in web pages. A report from the Commission of European Communities (“Communication from”, 2004) shows that more than 25 percent of all e-mail currently received is spam. More recent reliable data shows that spam represents 60-80 percent of e-mail volume. Spam is widely recognized as one of the most significant problems facing the Internet today. Spam has evolved to a new and dangerous form known as ‘phishing’. Phishing differs from spam in that it is generated by a criminal intent on stealing personal data for financial gain (“Spyware”, 2007). Phishing is the term used to describe emails which trick recipients into revealing their personal or their company’s confidential information such as social security and financial account numbers, account passwords and other identity or security information. According to Anti-Phishing Working Group (“June Phishing”, 2006) the number of phishing reports has increased from 20,109 in May 2006 to 28,571 in June 2006 and it is the most ever recorded. Phishing attacks increase despite of the efforts of e-mail filters. Although only 0.001 percent of e-mail sent is responded to, this percentage is enough to return on the investment and keep the phishing industry alive. Further research has estimated that the costs of these phishing attacks on consumers in 2003 ranged from $500 million to an amazing $2.4 billion.
Data Mining in Security Applications
Authors: Lazarevic Aleksandar
Published: May 2011

In recent years, research in many security areas has gained a lot of interest among scientists in academia, industry, military and governmental organizations. Researchers have been investigating many advanced technologies to effectively solve acute security problems. Data mining has certainly been one of the most explored technologies successfully applied in many security applications ranging from computer and physical security and intrusion detection to cyber terrorism and homeland security. For example, in the context of homeland security, data mining can be a potential means to identify terrorist activities, such as money transfers and communications, and to identify and track individual terrorists themselves, such as through travel and immigration records (Seifert, 2007). In another data mining’s success story related to security, credit card fraud detection, all major credit card companies mine their transaction databases, looking for spending patterns that indicate a stolen card. In addition, data mining has also effectively been utilized in many physical security systems (e.g. in efficient system design tools, sensor fusion for false alarm reduction) and video surveillance applications, where many data mining based algorithms have been proposed to detect motion or intruder at monitored sites or to detect suspicious trajectories at public places. This chapter provides an overview of current status of data mining based research in several security applications including cyber security and intrusion detection, physical security and video surveillance.
Data Preparation for Data Mining
Authors: Kamel Magdi
Published: May 2011

Practical experience of data mining has revealed that preparing data is the most time-consuming phase of any data mining project. Estimates of the amount of time and resources spent on data preparation vary from at least 60% to upward of 80% (SPSS, 2002a). In spite of this fact, not enough attention is given to this important task, thus perpetuating the idea that the core of the data mining effort is the modeling process rather than all phases of the data mining life cycle. This article presents an overview of the most important issues and considerations for preparing data for data mining.
Database Security and Statistical Database Security
Authors: Weippl Edgar R.
Published: May 2011

In this article we will present an introduction to issues relevant to database security and statistical database security. We will briefly cover various security models, elaborate on how data analysis in data warehouses (DWH) might compromise an individual’s privacy, and explain which safeguards can be used to prevent attacks. In most companies, databases are an essential part of IT infrastructure since they store critical business data. In the last two decades, databases have been used to process increasing amounts of transactional data, such as, a complete account of a person’s purchases from a retailer or connection data from calls made on a cell phone. As soon as this data became available from transactional databases and online transactional processing (OLTP) became well established, the next logical step was to use the knowledge contained in the vast amounts of data. Today, data warehouses (DWH) store aggregated data in an optimal way to serve queries related to business analysis. In recent years, most people have begun to focus their attention on security. Early OLTP applications were mainly concerned with integrity of data during transactions; today privacy and secrecy are more important as databases store an increasing amount of information about individuals, and data from different systems can be aggregated. Thuraisingham (2002) summarizes the requirements briefly as “However, we do not want the information to be used in an incorrect manner.” All security requirements stem from one of three basic requirements: confidentiality (aka secrecy), integrity, and availability (CIA). Confidentiality refers to the requirement that only authorized subjects, that is, people or processes should be permitted to read data. Integrity means that unauthorized modifications must not be permitted. This includes both modifications by unauthorized people and incorrect modification by authorized users. To correctly perform the services requested, the system needs to remain available; a denial-of-service compromises the requirement of availability. Other security requirements may include privacy, non-repudiation, and separation of duties. These requirements are, however, composite requirements that can be traced back to one of the three basic requirements. Privacy, for instance, is the non-disclosure (=confidentiality) of personal data; non-repudiation refers to the integrity of transaction logs and integrity of origin. Throughout this article we will focus only on technical attacks and safeguards and not on social engineering. Social engineering is often the easiest and, in many cases, a very successful attack vector. For an in-depth coverage of social engineering we recommend (Böck, 2007). In Section 2 we cover the most relevant access control models; in Section 3 we provide an overview of security in statistical databases. Finally, in Section 4 we highlight the essentials of securing not only the transactional and the statistical databases but the entire system.
Data-Driven Revision of Decision Models
Authors: Žnidaršic Martin , Bohanec Marko , Zupan Blaž
Published: May 2011

Computer models are representations of problem environment that facilitate analysis with high computing power and representation capabilities. They can be either inferred from the data using data mining techniques or designed manually by experts according to their knowledge and experience. When models represent environments that change over time, they must be properly updated or periodically rebuilt to remain useful. The latter is required when changes in the modelled environment are substantial. When changes are slight, models can be merely adapted by revision. Model revision is a process that gathers knowledge about changes in the modelled environment and updates the model accordingly. When performed manually, this process is demanding, expensive and time consuming. However, it can be automated to some extent if current data about the modelled phenomena is available. Databased revision is a procedure of changing the model so as to better comply with new empirical data, but which at the same time keeps as much of the original contents as possible. In the following we describe the model revision principles in general and then focus on a solution for a specific type of models, the qualitative multi-attribute decision models as used in DEX methodology.
Decision Tree Induction
Authors: Siciliano Roberta , Conversano Claudio
Published: May 2011

Decision Tree Induction (DTI) is a tool to induce a classification or regression model from (usually large) datasets characterized by n objects (records), each one containing a set x of numerical or nominal attributes, and a special feature y designed as its outcome. Statisticians use the terms “predictors” to identify attributes and “response variable” for the outcome. DTI builds a model that summarizes the underlying relationships between x and y. Actually, two kinds of model can be estimated using decision trees: classification trees if y is nominal, and regression trees if y is numerical. Hereinafter we refer to classification trees to show the main features of DTI. For a detailed insight into the characteristics of regression trees see Hastie et al. (2001). As an example of classification tree, let us consider a sample of patients with prostate cancer on which data Figure 1. The prostate cancer dataset such as those summarized in Figure 1 have been collected. Suppose a new patient is observed and we want to determine if the tumor has penetrated the prostatic capsule on the basis of the other available information. Posing a series of questions about the characteristic of the patient can help to predict the tumor’s penetration. DTI proceeds in such a way, inducing a series of follow- up (usually binary) questions about the attributes of an unknown instance until a conclusion about what is its most likely class label is reached. Questions and their alternative answers can be represented hierarchically in the form of a decision tree, such as the one depicted in Figure 2.