bluuebunny/crossref_metadata_2025 huggingface dataset:
The Open University of Israel
Authors: Erlich Zippy , Gal-Ezer Judith
Published: May 2011

The Open University of Israel (OUI) is a distance education institution with open admissions that offers academic studies to students throughout Israel. Its home-study method allows its students to pursue higher education whenever and wherever convenient. The OUI offers over 500 courses in life sciences, natural sciences, mathematics, computer science, social sciences, management, Jewish studies, education, humanities, psychology, communications, music and art, and more, and has about 38,000 students all over Israel and abroad.
Participatory Design of Interactive Computer-Based Learning Systems
Authors: Zaphiris Panayiotis , Zacharia Giorgos
Published: May 2011

We present an iterative evaluation methodology (as part of a participatory design methodology) employed in an online course for learning Modern Greek. Unlike other educational technology evaluation methodologies that focus primarily on questionnaires that administer summative and formative evaluation methods (Kandaswamy, 1990; Scriven, 1980), we propose a hybrid evaluation that is unique in the following ways. 1. It becomes an integral part of all stages of our design methodology, so every stage of the development is based on user feedback and evaluation. 2. In addition to evaluation questionnaires, we also collected feedback from our users through our interaction with the students during our collaborative design and development of the course. The students contributed lecture notes, quizzes, and student diaries that enabled us to evaluate the pedagogical value of our course, ensuring continuous feedback from the students regarding the usability and adequacy of the course.
Participatory Learning Approach
Authors: Bieber Michael , Shen Jia , Wu Dezhi , Hiltz Starr Roxanne
Published: May 2011

The Participatory Learning Approach (PLA, pronounced “play”) engages students as active participants in the full life cycle of homework, projects, and examination. PLA’s core idea is that students design the questions or projects, execute them, and then assess and grade their peers’ solutions. Each stage can be performed by individuals or by teams. Students should be able to observe (read) everything their peers do so they can learn further from others’ efforts.
The Pedagogy of Social Development in Online Learning
Authors: Woods Robert H. , Baker Jason D.
Published: May 2011

Optimal learning outcomes, whether in face-to-face or online settings, are inextricably intertwined with the establishment of social networks among participants engaged in a collaborative enterprise. Students who feel socially connected to other students and faculty are more likely to persist in coursework and report higher levels of learning than those who report being less connected (McDonald, 2002; Rovai, 2002; Tinto, 1987; Wegerif, 1998).
Preservice Teachers Creating Electronic Portfolios
Authors: Wieseman Katherine C.
Published: May 2011

The use of portfolios stored and published in electronic formats is based on at least two assumptions. First, as performance-based assessments, they can extensively represent a preservice teacher’s or teacher’s competence, achievement, learning, and/or professional growth (Bartlett, 2002; Milman, 1999; Wieseman, 2004). Advances in electronic and digital technology permit classroom action to be documented and stored, thus capturing classroom practice and work of teacher for asynchronous viewing by others. Because they require less physical storage space than print media portfolios, electronic portfolios are increasingly being used to document, assess, and evaluate teacher quality, including eligibility for initial teacher licensure/credential or documentation of competence with respect to teaching and technology standards (Bartlett, 2002; Peters, 2000; Wieseman & Wenzlaff, 2004). A variety of models are emerging that are being labeled as electronic portfolios, not all of which portfolio experts would agree are portfolios; for example, an electronic work sample may not truly be an electronic portfolio (Barrett, personal communication, April 14, 2004; Barrett & Wilkerson, 2004). Regardless, reflection in some fashion is necessary in electronic portfolio creation. A second assumption, particularly in longitudinal electronic portfolio approaches, is that reflection will become more rich and complex as preservice teachers continue through a teacher preparation program (Levin & Camp, 2002; Mullen, Doty & Rice, 2002).
Research-Based Distance Learning Services in the Northern Pacific
Authors: Baxendale Steve
Published: May 2011

The remoteness of the northern Pacific region presents significant challenges to providing high-quality, interactive, distance education. These geographic, economic, cultural, linguistic, professional, and technological challenges require creativity and flexibility in the design of courses and in instructional methods. This article explores the experience of a group of educators in providing distance learning opportunities throughout this region during the past six years.
Respecting Diverse Talents and Ways for Learning
Authors: Plekhanova Valentina
Published: May 2011

Information technology is used to help learners gain the knowledge, confidence, and credentials they need to succeed. Contemporary approaches to online learning offer technology-mediated courses. The educational programs offered take full advantage of the power of the Internet to foster communication, learning, and skill and knowledge acquisition.
University History of the Fielding Graduate Institute
Authors: Luskin Bernard J.
Published: May 2011

Fielding Graduate Institute was founded in 1974 in Santa Barbara, California by educators who envisioned a graduate school serving mid-career professionals wanting to pursue an advanced degree. The educator’s learning needs were not being met by traditional institutions of higher education because of style, location or lifestyle fit. Several founders were psychologists and one was deputy director of the Peace Corps, with an understanding of distributed learning. According to university history, the founders recognized that students seeking advanced degrees would generally be mid-career adults wishing to enhance their professional skills. These students would also be interested in being scholar-practitioners participating in a collaborative lifetime-learning community. At first glance, Fielding appears to be a small university having only 1,500 students, with more than 100 members of staff and 150 faculty members. Fielding is actually quite a large graduate university. However, since Fielding does not have an undergraduate program on which to perch, it may appear to some to be small.
Web-Based Remote Laboratory
Authors: Ko C. C. , Chen Ben M. , Cheng C. D.
Published: May 2011

From the angle of computer-based education, the Internet extends the power of the personal computer (PC) from being a standalone machine to one that is connected to the world with uncountable resources. Applications such as library access, information search, educational material download, online tutorials, and even examinations can now be routinely carried out by a low-cost PC with an Internet connection at anytime from practically everywhere.
Communities of Practice and Technology Support
Authors: Coakes Elayne
Published: May 2011

In order to operate successfully, communities of practice (CoPs) require a number of resources and facilities made readily available to them. These facilities can come in both physical and virtual forms. In this article we look at these resources and facility requirements for success, and review the possibilities for technology support (software offerings) that can provide the virtual aspects of these facilities.
Communities of Practice for Organizational Learning
Authors: Wenn Andrew
Published: May 2011

The term community of practice (CoP) arises out of the work of Lave and Wenger (1991) and Wenger (1998) and refers to the way groups of individuals interact and engage in “the sustained pursuit of a shared enterprise” (Wenger, 1998, p. 45). It is the activities of the members of these groups both individually and collectively, the construction of and practices at a local level that allows them “to meet the demands of the institution” (Wenger, 1998, p. 46) which they work for. For the CoP, learning occurs as a form of social practice.
Comparison of the Features of Some CoP Software
Authors: Coakes Elayne
Published: May 2011

There are now significant numbers of software houses supplying services and solutions for community collaboration. In this article we briefly review the requirements for virtual support and the current offerings. This is not intended as a comprehensive survey, but rather an overview of what might be available.
Knowledge Sharing through Communities of Practice in the Voluntary Sector
Authors: Bellarby Lizzie , Orange Graham
Published: May 2011

Over the past few years, the concepts of knowledge management and knowledge sharing have been recognised as cognate areas of study and research. To date research has focussed mainly upon the commercial sector. However, this article looks at knowledge management and sharing through communities of practice within the voluntary sector. The work is based upon research carried out within a UK national voluntary counselling and advisory service. For reasons of privacy and confidentiality, the organisation shall remain anonymous and will be referred to as the ‘organisation’. This article considers the background to the study in terms of knowledge management and communities of practice. It then discusses the study’s methodology and findings. It concludes that knowing and sharing are active processes, and that the natural disposition of the actors was found to be important in how knowledge sharing and learning was undertaken.
Linking Communities of Practice and Project Teams in the Construction Industry
Authors: Ragsdell Gillian , Remington Kaye
Published: May 2011

Continuing from Ragsdell’s (Article, The Contribution of Communities of Practice to Project Management) discussion highlighting potential synergy between project teams and CoPs, Remington and Ragsdell move into the practical arena. This article interrogates the usefulness of CoP in the construction industry and the challenges they pose for project management practice therein. Emphasis is on the role of CoPs in addressing the problem of project knowledge transfer within and between project teams. The concept of community of practice (CoP), defined initially by Lave and Wenger (1991) and later developed by Wenger (1998) and others (Barab & Duffy, 2000; Gherardi & Nicolini, 2002; Skyrme, 1999), has only recently received attention in the project management professional literature (Galarneau & Rose, 2002; Love, Huang, Edards, & Irani, 2005; Morris, 2002). This is perhaps a little surprising since, on initial analysis, the project environment would appear to comply with all three of Wenger’s three dimensions of a CoP (Wenger, 1998, pp. 72-85). There exists a level of mutual engagement in practice between parties who are involved in projects. A project is certainly a joint enterprise between a number of individuals who might come from a variety of organizations and backgrounds to achieve agreed goals. At the wider level, within the project management professional community, there is a shared interest in improving practice. At least among project management professionals, there is a shared repertoire of language, routines, stories, and cultural artifacts. However, the peculiar nature of the range of initiatives that are referred to under the generic name projects suggests a number of structural and organizational barriers to free exchange and development of knowledge within the CoP model described by Wenger and others. This article goes on to explore a number of characteristics, peculiar to construction projects, which might influence effective application of the CoP concept.
Narrative Inquiry and Communities of Practice
Authors: Hunter M. Gordon
Published: May 2011

The kind of research conducted by communities of practice may be related to preliminary investigations of subjects that may not have been extensively investigated in the past. Alternatively, communities of practice may be formed to expand previous investigations into relatively under-researched areas. In either case the approach to conducting these preliminary investigations may be qualitative in nature. An approach that facilitates qualitative investigations is Narrative Inquiry.
Tacit-Explicit and Specific-General Knowledge Interactions in CoPs
Authors: Medeni Tunç
Published: May 2011

Over the last decade the fields of knowledge management and organizational learning have developed rapidly, showing increasing diversity and specialization in the academic literature. Ikujiro Nonaka has played a leading role in setting standards and earning academic legitimacy for the emergent field of “organizational knowledge management” (Easterby-Smith & Lyles, 2003). In the period 1995-2001, the book The Knowledge-Creating Company (Nonaka & Takeuchi 1995) was the most-cited knowledge management work from academic literature (Koenig & Srikantaiah, 2004). Interestingly, in this book and in following works, the authors themselves prefer to use the term “knowledge creation” rather than “knowledge management,” later also dropping the term “organizational” from the initial proposition. Easterby-Smith and Lyles also state (2003, pp. 642-643) that in the field of organizational learning and knowledge management, among the topics of articles published in the last two years, “learning capabilities, experience, and absorptive capacity” is the largest category, including several articles that assess the impact of learning on performance. Seeming to be frequently interrelated, “organizational learning and knowledge management across boundaries,” “knowledge creation and transfer,” and “human resource management and human capital” are the next largest categories for articles. Communities of practice, socio-political processes, and the development of tacit knowledge or social identity are among the other topics frequently addressed in the literature, categorized in terms of “cognition, socio-political aspects, and tacitness.” Using the extant and emerging perspectives in knowledge management, organizational learning, and communities of practice literature, in the following sections of this short article, we will first discuss the importance of specific-general knowledge, and context for knowledge creation and management. Then we will introduce the conceptualization of “specific” and “general” knowledge interactions, and discuss a framework that proposes these interactions as contextual knowledge conversions for learning and practice. The following section will aim to contribute to the representation of our knowledge on these contextual knowledge interactions, using visualization tools like geometric figures. We will conclude our discussion by highlighting future research possibilities in the relevant research fields.
Teamwork Issues in Virtual Teams
Authors: Tremblay Diane-Gabrielle
Published: May 2011

Over the past few decades, there has been much interest in various forms of participation in the workplace and in its impacts on learning from work for individuals and organisations. Teamwork has been the object of much attention in labour economics, in sociology of work, as well as in human resources management (Tremblay, Rolland & Davel, 2000; Davel, Gomez da Silva, Rolland & Tremblay, 2001). Collaborative work and learning have also been the object of much attention in HRM and organisational learning debates, as well as in education circles (Henri & Lundgren, 2000). Much of this interest stems from gains that organisations can expect to obtain from interaction between workers in terms of quality of products, innovation, productivity, and the like. Knowledge management has also spurred interest in recent years, partly on the basis of these expected gains from a better management of the knowledge hidden within organisations. More recently, the concept of communities of practice has been put forward as a form of knowledge management which paves the way to attainment of the various organisational objectives: productivity, quality, innovation, and so forth. In our view, this last concept is closely related to teamwork issues, and we will show how in the following pages.
Training and Articulating Public Agencies in Argentina
Authors: Galivene Graciela Mónica , Kaufman Eater
Published: May 2011

The deep institutional crisis the Argentine state has gone through has led the Heads of the Public Management Training Program (Instituto Nacional de Administración Pública (INAP)) to question the usual way of facing reforms and training policies. Thus, the design of the activities within this program has been carried out from a knowledge management perspective. According to this approach, professional state forums (PSFs) were created in 2002. These PSFs’ practices followed the CoPs and networks conception; their main goals were strengthening public organizations and upgrading professional standards. The PSFs involve public executives who focus on specific practices. These PSFs can cut across agencies or function at an internal level. Their members essentially seek to develop their competencies in the practice considered. In order to recognize the PSFs/CoPs, previous analysis was done regarding the way each process or task is performed in each agency.
Association Rule Mining
Authors: Woon Yew-Kwong , Ng Wee-Keong , Lim Ee-Peng
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.
Association Rule Mining of Regional Data
Authors: Denton Anne , Besemann Christopher
Published: May 2011

Most data of practical relevance are structured in more complex ways than is assumed in traditional data mining algorithms, which are based on a single table. The concept of relations allows for discussing many data structures such as trees and graphs. Relational data have much generality and are of significant importance, as demonstrated by the ubiquity of relational database management systems. It is, therefore, not surprising that popular data mining techniques, such as association rule mining, have been generalized to relational data. An important aspect of the generalization process is the identification of problems that are new to the generalized setting.
Case-Based Recommender Systems
Authors: Lorenzi Fabiana , Ricci Francesco
Published: May 2011

Recommender systems are being used in e-commerce web sites to help the customers in selecting products more suitable to their needs. The growth of Internet and the business to consumer e-Commerce has brought the need for such a new technology (Schafer, Konstan, & Riedl., 2001).
Data Mining with Cubegrades
Authors: Abdulghani Amin A.
Published: May 2011

Much interest has been expressed in database mining by using association rules (Agrawal, Imielinski, & Swami, 1993). In this article, I provide a different view of the association rules, which are referred to as cubegrades (Imielinski, Khachiyan, & Abdulghani, 2002) .
Data Warehouse Performance
Authors: Lin Beixin (Betsy) , Hong Yu , Lee Zu-Hsu
Published: May 2011

A data warehouse is a large electronic repository of information that is generated and updated in a structured manner by an enterprise over time to aid business intelligence and to support decision making. Data stored in a data warehouse is non-volatile and time variant and is organized by subjects in a manner to support decision making (Inmon, Rudin, Buss, & Sousa, 1998). Data warehousing has been increasingly adopted by enterprises as the backbone technology for business intelligence reporting and query performance has become the key to the successful implementation of data warehouses. According to a survey of 358 businesses on reporting and end-user query tools, conducted by Appfluent Technology, data warehouse performance significantly affects the Return on Investment (ROI) on Business Intelligence (BI) systems and directly impacts the bottom line of the systems (Appfluent Technology, 2002). Even though in some circumstances it is very difficult to measure the benefits of BI projects in terms of ROI or dollar figures, management teams are still eager to have a “single version of the truth,” better information for strategic and tactical decision making, and more efficient business processes by using BI solutions (Eckerson, 2003).
Distributed Data Management of Daily Car Pooling Problems
Authors: Calvo Roberto Wolfler , de Luigi Fabio , Haastrup Palle , Maniezzo Vittorio
Published: May 2011

The increased human mobility, combined with high use of private cars, increases the load on the environment and raises issues about the quality of life. The use of private cars lends to high levels of air pollution in cities, parking problems, noise pollution, congestion, and the resulting low transfer velocity (and, thus, inefficiency in the use of public resources). Public transportation service is often incapable of effectively servicing non-urban areas, where cost-effective transportation systems cannot be set up. Based on investigations during the last years, problems related to traffic have been among those most commonly mentioned as distressing, while public transportation systems inherently are incapable of facing the different transportation needs arising in modern societies. A solution to the problem of the increased passenger and freight transportation demand could be obtained by increasing both the efficiency and the quality of public transportation systems, and by the development of systems that could provide alternative solutions in terms of flexibility and costs between the public and private ones. This is the rationale behind so-called Innovative Transport Systems (ITS) (Colorni et al., 1999), like car pooling, car sharing, dial-a-ride, park-and-ride, card car, park pricing, and road pricing, which are characterized by the exploitation of innovative organizational elements and by a large flexibility in their management (e.g., traffic restrictions and fares can vary according with the time of day).
Efficient Computation of Data Cubes and Aggregate Views
Authors: Tininini Leonardo
Published: May 2011

This paper reviews the main techniques for the efficient calculation of aggregate multidimensional views and data cubes, possibly using specifically designed indexing structures. The efficient evaluation of aggregate multidimensional queries is obviously one of the most important aspects in data warehouses (OLAP systems). In particular, a fundamental requirement of such systems is the ability to perform multidimensional analyses in online response times. As multidimensional queries usually involve a huge amount of data to be aggregated, the only way to achieve this is by pre-computing some queries, storing the answers permanently in the database and reusing these almost exclusively when evaluating queries in the multidimensional database. These pre-computed queries are commonly referred to as materialized views and carry several related issues, particularly how to efficiently compute them (the focus of this paper), but also which views to materialize and how to maintain them.
Embedding Bayesian Networks in Sensor Grids
Authors: Vargas Juan E.
Published: May 2011

In their simplest form, sensors are transducers that convert physical phenomena into electrical signals. By combining recent innovations in wireless technology, distributed computing, and transducer design, grids of sensors equipped with wireless communication can monitor large geographical areas. However, just getting the data is not enough. In order to react intelligently to the dynamics of the physical world, advances at the lower end of the computing spectrum are needed to endow sensor grids with some degree of intelligence at the sensor and the network levels. Integrating sensory data into representations conducive to intelligent decision making requires significant effort. By discovering relationships between seemingly unrelated data, efficient knowledge representations, known as Bayesian networks, can be constructed to endow sensor grids with the needed intelligence to support decision making under conditions of uncertainty. Because sensors have limited computational capabilities, methods are needed to reduce the complexity involved in Bayesian network inference. This paper discusses methods that simplify the calculation of probabilities in Bayesian networks and perform probabilistic inference with such a small footprint that the algorithms can be encoded in small computing devices, such as those used in wireless sensors and in personal digital assistants (PDAs).
Employing Neural Networks in Data Mining
Authors: Hamdi Mohamed Salah
Published: May 2011

Data-mining technology delivers two key benefits: (i) a descriptive function, enabling enterprises, regardless of industry or size, in the context of defined business objectives, to automatically explore, visualize, and understand their data and to identify patterns, relationships, and dependencies that impact business outcomes (i.e., revenue growth, profit improvement, cost containment, and risk management); (ii) a predictive function, enabling relationships uncovered and identified through the data-mining process to be expressed as business rules or predictive models. These outputs can be communicated in traditional reporting formats (i.e., presentations, briefs, electronic information sharing) to guide business planning and strategy. Also, these outputs, expressed as programming code, can be deployed or hard wired into business-operating systems to generate predictions of future outcomes, based on newly generated data, with higher accuracy and certainty.
Immersive Image Mining in Cardiology
Authors: Liu Xiaoqiang , Koppelaar Henk , Hamers Ronald , Bruining Nico
Published: May 2011

Buried within the human body, the heart prohibits direct inspection, so most knowledge about heart failure is obtained by autopsy (in hindsight). Live immersive inspection within the human heart requires advanced data acquisition, image mining and virtual reality techniques. Computational sciences are being exploited as means to investigate biomedical processes in cardiology.
Interscheme Properties' Role in Data Warehouses
Authors: De Meo Pasquale , Terracina Giorgio , Ursino Domenico
Published: May 2011

In this article, we illustrate a general approach for the semi-automatic construction and management of data warehouses. Our approach is particularly suited when the number or the size of involved sources is large and/or when it changes quite frequently over time. Our approach is based mainly on the semi-automatic derivation of interschema properties (i.e., terminological and structural relationships holding among concepts belonging to different input schemas). It consists of the following steps: (1) enrichment of schema descriptions obtained by the semi-automatic extraction of interschema properties; (2) exploitation of derived interschema properties for obtaining in a data repository an integrated and abstracted view of available data; and (3) design of a three-level data warehouse having as its core the derived data repository.
Learning Information Extraction Rules for Web Data Mining
Authors: Chang Chia-Hui , Hsu Chun-Nan
Published: May 2011

The explosive growth and popularity of the World Wide Web has resulted in a huge number of information sources on the Internet. However, due to the heterogeneity and the lack of structure of Web information sources, access to this huge collection of information has been limited to browsing and keyword searching. Sophisticated Web-mining applications, such as comparison shopping, require expensive maintenance costs to deal with different data formats. The problem in translating the contents of input documents into structured data is called information extraction (IE). Unlike information retrieval (IR), which concerns how to identify relevant documents from a document collection, IE produces structured data ready for post-processing, which is crucial to many applications of Web mining and search tools.
Material Acquisitions Using Discovery Informatics Approach
Authors: Wu Chien-Hsing , Lee Tzai-Zang
Published: May 2011

Material acquisition is a time-consuming but important task for a library, because the quality of a library is not in the number of materials that are available but in the number of materials that are actually utilized. Its goal is to predict the users’ needs for information with respect to the materials that will most likely be used (Bloss, 1995). Discovery informatics using the technology of knowledge discovery in databases can be used to investigate in-depth how the acquired materials are being used and, in consequence, can be a predictive sign of information needs.
Microarray Databases for Biotechnology
Authors: Segall Richard S.
Published: May 2011

Microarray informatics is a rapidly expanding discipline in which large amounts of multi-dimensional data are compressed into small storage units. Data mining of microarrays can be performed using techniques such as drill-down analysis rather than classical data analysis on a record-by-record basis. Both data and metadata can be captured in microarray experiments. The latter may be constructed by obtaining data samples from an experiment. Extractions can be made from these samples and formed into homogeneous arrays that are needed for higher level analysis and mining.
Mine Rule
Authors: Meo Rosa , Psaila Giuseppe
Published: May 2011

Mining of association rules is one of the most adopted techniques for data mining in the most widespread application domains. A great deal of work has been carried out in the last years on the development of efficient algorithms for association rules extraction. Indeed, this problem is a computationally difficult task, known as NP-hard (Calders, 2004), which has been augmented by the fact that normally association rules are being extracted from very large databases. Moreover, in order to increase the relevance and interestingness of obtained results and to reduce the volume of the overall result, constraints on association rules are introduced and must be evaluated (Ng et al.,1998; Srikant et al., 1997). However, in this contribution, we do not focus on the problem of developing efficient algorithms but on the semantic problem behind the extraction of association rules (see Tsur et al. [1998] for an interesting generalization of this problem).
Mining Assocation Rules Using Frequent Closed Itemsets
Authors: Pasquier Nicolas
Published: May 2011

In the domain of knowledge discovery in databases and its computational part called data mining, many works addressed the problem of association rule extraction that aims at discovering relationships between sets of items (binary attributes). An example association rule fitting in the context of market basket data analysis is cereal Ù milk ® sugar (support 10%, confidence 60%). This rule states that 60% of customers who buy cereals and sugar also buy milk, and that 10% of all customers buy all three items. When an association rule support and confidence exceed some user-defined thresholds, the rule is considered relevant to support decision making. Association rule extraction has proved useful to analyze large databases in a wide range of domains, such as marketing decision support; diagnosis and medical research support; telecommunication process improvement; Web site management and profiling; spatial, geographical, and statistical data analysis; and so forth.
Mining Data with Group Theoretical Means
Authors: Kern-Isberner Gabriele
Published: May 2011

Knowledge discovery refers to the process of extracting new, interesting, and useful knowledge from data and presenting it in an intelligible way to the user. Roughly, knowledge discovery can be considered a three-step process: preprocessing data; data mining, in which the actual exploratory work is done; and interpreting the results to the user. Here, I focus on the data-mining step, assuming that a suitable set of data has been chosen properly.
Mining for Profitable Patterns in the Stock Market
Authors: Sheng Yihua Philip , Hou Wen-Chi , Chen Zhong
Published: May 2011

The stock market, like other economic phenomena, is a very complex system. Many factors, such as company news, interest rates, macro economic data, and investors’ hopes and fears, all affect its behavior (Pring, 1991; Sharpe, Alexander, & Bailey, 1999). Investors have longed for tools and algorithms to analyze and predict stock market movement. In this study, we combine a financial theory, the market efficiency theory, and a data mining technique to explore profitable trading patterns in the stock market. To observe the price oscillation of several consecutive trading days, we examine the K-lines, each of which represents a stock’s one-day movement. We will use a data mining technique with a heuristic rating algorithm to mine for reliable patterns indicating price rise or fall in the near future.
Off-Line Signature Recognition
Authors: Chakravarty Indrani , Mishra Nilesh , Vatsa Mayank , Singh Richa , Gupta P.
Published: May 2011

The most commonly used protection mechanisms today are based on either what a person possesses (e.g. an ID card) or what the person remembers (like passwords and PIN numbers). However, there is always a risk of passwords being cracked by unauthenticated users and ID cards being stolen, in addition to shortcomings like forgotten passwords and lost ID cards (Huang & Yan, 1997). To avoid such inconveniences, one may opt for the new methodology of Biometrics, which though expensive will be almost infallible as it uses some unique physiological and/or behavioral (Huang & Yan, 1997) characteristics possessed by an individual for identity verification. Examples include signature, iris, face, and fingerprint recognition based systems.
Online Signature Recognition
Authors: Chakravarty Indrani , Mishra Nilesh , Vatsa Mayank , Singh Richa , Gupta P.
Published: May 2011

Security is one of the major issues in today’s world and most of us have to deal with some sort of passwords in our daily lives; but, these passwords have some problems of their own. If one picks an easy-to-remember password, then it is most likely that somebody else may guess it. On the other hand, if one chooses too difficult a password, then he or she may have to write it somewhere (to avoid inconveniences due to forgotten passwords) which may again lead to security breaches. To prevent passwords being hacked, users are usually advised to keep changing their passwords frequently and are also asked not to keep them too trivial at the same time. All these inconveniences led to the birth of the biometric field. The verification of handwritten signature, which is a behavioral biometric, can be classified into off-line and online signature verification methods. Online signature verification, in general, gives a higher verification rate than off-line verification methods, because of its use of both static and dynamic features of the problem space in contrast to off-line which uses only the static features. Despite greater accuracy, online signature recognition is not that prevalent in comparison to other biometrics. The primary reasons are: • It cannot be used everywhere, especially where signatures have to be written in ink; e.g. on cheques, only off-line methods will work. • Unlike off-line verification methods, online methods require some extra and special hardware, e.g. electronic tablets, pressure sensitive signature pads, etc. For off-line verification method, on the other hand, we can do the data acquisition with optical scanners. • The hardware for online are expensive and have a fixed and short life cycle.
Recovery of Data Dependencies
Authors: Tan Hee Beng Kuan , Zhao Yuan
Published: May 2011

Today, many companies have to deal with problems in maintaining legacy database applications, which were developed on old database technology. These applications are getting harder and harder to maintain. Reengineering is an important means to address the problems and to upgrade the applications to newer technology (Hainaut, Englebert, Henrard, Hick, J.-M., & Roland, 1995). However, much of the design of legacy databases including data dependencies is buried in the transactions, which update the databases. They are not explicitly stated anywhere else. The recovery of data dependencies designed from transactions is essential to both the reengineering of database applications and frequently encountered maintenance tasks. Without an automated approach, the recovery is difficult and time-consuming. This issue is important in data mining, which entails mining the relationships between data from program source codes. However, until recently, no such approach was proposed in the literature.
Rule Generation Methods Based on Logic Synthesis
Authors: Muselli Marco
Published: May 2011

One of the most relevant problems in artificial intelligence is allowing a synthetic device to perform inductive reasoning, i.e. to infer a set of rules consistent with a collection of data pertaining to a given real world problem. A variety of approaches, arising in different research areas such as statistics, machine learning, neural networks, etc., have been proposed during the last 50 years to deal with the problem of realizing inductive reasoning.
Secure Multiparty Computation for Privacy Preserving Data Mining
Authors: Lindell Yehida
Published: May 2011

The increasing use of data-mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy-preserving data-mining solutions achieve the somewhat paradoxical property of enabling a data-mining algorithm to use data without ever actually seeing it. Thus, the benefits of data mining can be enjoyed without compromising the privacy of concerned individuals.
Semantic Data Mining
Authors: Banerjee Protima , Hu Xiaohua , Yoo Illhoi
Published: May 2011

Over the past few decades, data mining has emerged as a field of research critical to understanding and assimilating the large stores of data accumulated by corporations, government agencies, and laboratories. Early on, mining algorithms and techniques were limited to relational data sets coming directly from Online Transaction Processing (OLTP) systems, or from a consolidated enterprise data warehouse. However, recent work has begun to extend the limits of data mining strategies to include “semi-structured data such as HTML and XML texts, symbolic sequences, ordered trees and relations represented by advanced logics” (Washio & Motoda, 2003).
Spectral Methods for Data Clustering
Authors: Li Wenyuan
Published: May 2011

With the rapid growth of the World Wide Web and the capacity of digital data storage, tremendous amount of data are generated daily from business and engineering to the Internet and science. The Internet, financial real-time data, hyperspectral imagery, and DNA microarrays are just a few of the common sources that feed torrential streams of data into scientific and business databases worldwide. Compared to statistical data sets with small size and low dimensionality, traditional clustering techniques are challenged by such unprecedented high volume, high dimensionality complex data. To meet these challenges, many new clustering algorithms have been proposed in the area of data mining (Han & Kambr, 2001).
Symbiotic Data Mining
Authors: Athappilly Kuriakose , Rea Alan
Published: May 2011

Symbiotic data mining is an evolutionary approach to how organizations analyze, interpret, and create new knowledge from large pools of data. Symbiotic data miners are trained business and technical professionals skilled in applying complex data-mining techniques and business intelligence tools to challenges in a dynamic business environment.
Time Series Analysis and Mining Techniques
Authors: Sayal Mehmet
Published: May 2011

A time series is a sequence of data values that are recorded with equal or varying time intervals. Time series data usually includes timestamps that indicate the time at which each individual value in the times series is recorded. Time series data is usually transmitted in the form of a data stream, i.e., continuous flow of data values. Source of time series data can be any system that measures and records data values over the course of time. Some examples of time series data may be recorded from stock values, blood pressure of a patient, temperature of a room, amount of a product in the inventory, and amount of precipitation in a region. Proper analysis and mining of time series data may yield valuable knowledge about the underlying characteristics of the data source. Time series analysis and mining has applications in many domains, such as financial, biomedical, and meteorological applications, because time series data may be generated by various sources in different domains.
Time Series Data Forecasting
Authors: Cho Vincent
Published: May 2011

Businesses are recognizing the value of data as a strategic asset. This is reflected by the high degree of interest in new technologies such as data mining. Corporations in banking, insurance, retail, and healthcare are harnessing aggregated operational data to help understand and run their businesses (Brockett et al., 1997; Delmater & Hamcock, 2001). Analysts use data-mining techniques to extract business information that enables better decision making (Cho et al., 1998; Cho & Wüthrich, 2002). In particular, time series forecasting is one of the major focuses in data mining. Time series forecasting is used in a variety of fields, such as agriculture, business, economics, engineering, geophysics, medical studies, meteorology, and social sciences. A time series is a sequence of data ordered in time, such as hourly temperature, daily stock prices, monthly sales, quarterly employment rates, yearly population changes, and so forth.
Transferable Belief Model
Authors: Smets Philippe
Published: May 2011

This note is a very short presentation of the transferable belief model (TBM), a model for the representation of quantified beliefs based on belief functions. Details must be found in the recent literature.
Trends in Web Usage Mining
Authors: Lee-Post Anita , Jin Haihao
Published: May 2011

In this paper, we will discuss research efforts devoted to the remaining area of Web mining, namely Web usage mining. Taken together, a complete picture of the trends in Web mining can be discerned.
Unsupervised Mining of Genes Classifying Leukemia
Authors: Liberati Diego , Bittanti Sergio , Garatti Simone
Published: May 2011

Micro-arrays technology has marked a substantial improvement in making available a huge amount of data about gene expression in pathophysiological conditions; among the many papers and books recently devoted to the topic, see, for instance, Hardimann (2003) for a discussion on such a tool.
Business Rules in Databases
Authors: Badia Antonio
Published: May 2011

Though informal, the concept of business rule is very important to the modeling and definition of information systems. Business rules are used to express many different aspects of the representation, manipulation and processing of data (Paton, 1999). However, perhaps due to its informal nature, business rules have been the subject of a limited body of research in academia. There is little agreement on the exact definition of business rule, on how to capture business rules in requirements specification (the most common conceptual models, entity-relationship and UML, have no proviso for capturing business rules), and, if captured at all, on how to express rules in database systems. Usually, business rules are implemented as triggers in relational databases. However, the concept of business rule is more versatile and may require the use of other tools.
Business-to-Business Integration
Authors: Bussler Christoph
Published: May 2011

Businesses worldwide started exchanging electronic business messages with each other around 1970. This coincides with the establishment of wide-area computer networks. Businesses realized the potential immediately to send electronic messages instead of paper letters in order to conduct business electronically. Computer networks provided a significant increase of transmission speed, less failures due to message losses, and direct processing of messages upon receipt without manual transcript from paper to computer terminals or vice versa. Overall, business interactions became a lot more reliable and efficient.
Checking Integrity Constraints in a Distributed Database
Authors: Ibrahim Hamidah
Published: May 2011

Preserving the accuracy and the integrity of information in a database is extremely important for the organization that is maintaining that database. Such an organization is likely to rely heavily upon that accuracy. Applications that consult and use the database expect a guarantee that the database is supplying the correct information. Critical business decisions may be made assuming that information extracted from the database is correct. Thus, incorrect data can lead to incorrect business decisions, which can have serious implications for the people and organizations using it (Codd, 1990).
Consistency in Spatial Databases
Authors: Rodríguez-Tastets M. Andrea
Published: May 2011

During the past several years, traditional databases have been enhanced to include spatially referenced data. Spatial database management (SDBM) systems aim at providing models for the efficient manipulation of data related to space. Such type of manipulation is useful for any type of applications based on large spatial data sets, such as computer-aided design (CAD), very large scale integration (VLSI), robotics, navigation systems, and image processing.
Database Support for Workflow Management Systems
Authors: Pinheiro Francisco A.C.
Published: May 2011

A workflow is a series of work processes performed under rules that reflect the formal structure of the organization in which they are carried out and the relationships between their various parts. Workflow applications are software applications used to automate part of workflow processes. They run under the control of a workflow management system (WfMS). The WfMS usually comprises an organizational model, describing the process structure, and a process model, describing the process logic. The Workflow Management Coalition (WfMC, 2004) publishes a set of workflow definitions and related material, including a reference model.
Enterprise Application Integration
Authors: Bussler Christoph
Published: May 2011

As long as businesses only have one enterprise application or back-end application system, there is no need to share data with any other system in the company. All data that has to be managed is contained within one back-end application system. However, as businesses grow, more back-end application systems find their way into their information technology infrastructure, managing different business data. These back-end application systems are not independent of each other; in general they contain similar business data or are part of the same business processes. This requires their integration for exchanging data between them. The technology that allows this is called enterprise application integration (EAI) technology. EAI technology is able to connect to back-end application systems in order to retrieve and to insert data. Once connected, EAI technology supports the definition of how extracted data is propagated to back-end application systems, solving the integration problem.