bluuebunny/crossref_metadata_2025 huggingface dataset:
Outsourcing in the Healthcare Industry
Authors: Gupta Amar , Goyal Raj K. , Joiner Keith A. , Saini Sanjay
Published: May 2011

The healthcare industry is being impacted by advances in information technology in four major ways: first, a broad spectrum of tasks that were previously done manually can now be performed by computers; second, some tasks can be outsourced to other countries using inexpensive communications technology; third, longitudinal and societal healthcare data can now be analyzed in acceptable periods of time; and fourth, the best medical expertise can sometimes be made available without the need to transport the patient to the doctor or vice versa. The healthcare industry will increasingly use a portfolio approach comprised of three closely-coordinated components seamlessly interwoven together: healthcare tasks performed by humans on-site; healthcare tasks performed by humans off-site, including tasks performed in other countries; and healthcare tasks performed by computers without direct human involvement. Finally, this chapter deals with intellectual property and legal aspects related to the three-pronged healthcare services paradigm.
New Trends in Global Offshore Outsourcing
Authors: Sharma Suresh , Chen Yuanyuan
Published: May 2011

With the rapid rise of globalization, the challenge of global outsourcing today is not “Why and what to outsource?” but “How to outsource?” The theme today is “Let us do it right the first time.” The barriers to outsourcing are companies’ own mind-sets, local regulations, and the robustness of their internal processes. The domain knowledge in many industries has gone fully global. Likewise, new product development and R&D must be global in order to compete in emerging economies and to tap into global talent to compete globally. Software development and IT outsourcing can be done from anywhere, virtually! The availability of mobile technology and superior digital infrastructure is giving way to “distributed IT,” making “homes” as the future nodes of outsourcing factories. China and India have emerged as the major leaders in this industry due to their capacity, talent pool, and lower cost structure. This chapter compares their strengths, challenges, and growth potential based on the authors’ own hands-on experience of doing outsourcing in these countries for the past 15 years.
The Role of Prisons in Offshoring
Authors: Hollis Whitney
Published: May 2011

Outsourcing and offshoring are popular (and often controversial) trends in American business, yet not all outsourcing is done in foreign countries; many jobs are being sent to prisons, where inmates can provide low-cost, locally based labor. This trend has extended from a role in manufacturing to whitecollar jobs, like telemarketing. This chapter analyzes this type of outsourcing in terms of the costs and benefits for business and consumers, as well as the social implications.
The Role of Information Resource Management in Enabling the 24-Hour Knowledge Factory
Authors: Seshasai Satwik , Gupta Amar
Published: May 2011

The term 24-Hour Knowledge Factory connotes a globally distributed work environment in which teammates work on a project around the clock. The 24-Hour Knowledge Factory is a special case of a globally distributed team in which the different teams work on a sequential basis that has been clearly defined in advance. Whereas a manufactured item was the end product in the case of the factory which emerged as a consequence of the industrial revolution, knowledge-based services and knowledge-based products are the end deliverables in the case of the current information revolution; hence, the term 24-Hour Knowledge Factory. Work can be decomposed by task style or by organizational style, and allows for greater specialization of workers. A case study from IBM details surprising differences between colocated and distributed teams, and leads to a future state analysis for organizations seeking to study or implement the 24-Hour Knowledge Factory.
Research and Methodological Foundations of Transaction Log Analysis
Authors: Jansen Bernard J. , Taksa Isak , Spink Amanda
Published: May 2011

This chapter outlines and discusses theoretical and methodological foundations for transaction log analysis. We first address the fundamentals of transaction log analysis from a research viewpoint and the concept of transaction logs as a data collection technique from the perspective of behaviorism. From this research foundation, we move to the methodological aspects of transaction log analysis and examine the strengths and limitations of transaction logs as trace data. We then review the conceptualization of transaction log analysis as an unobtrusive approach to research, and present the power and deficiency of the unobtrusive methodological concept, including benefits and risks of transaction log analysis specifically from the perspective of an unobtrusive method. Some of the ethical questions concerning the collection of data via transaction log applications are discussed.
From Analysis to Estimation of User Behavior
Authors: Ozmutlu Seda , Ozmutlu Huseyin C. , Spink Amanda
Published: May 2011

This chapter summarizes the progress of search engine user behavior analysis from search engine transaction log analysis to estimation of user behavior. Correct estimation of user information searching behavior paves the way to more successful and even personalized search engines. However, estimation of user behavior is not a simple task. It closely relates to natural language processing and human computer interaction, and requires preliminary analysis of user behavior and careful user profiling. This chapter details the studies performed on analysis and estimation of search engine user behavior, and surveys analytical methods that have been and can be used, and the challenges and research opportunities related to search engine user behavior or transaction log query analysis and estimation.
Semantic-Based Video Transcoding Architectures for Quality of Service Applications in Mobile and Wireless Video Communication
Authors: Ahmad Ashraf M.A.
Published: May 2011

Delivering streaming video over wireless networks is an important component for most interactive multimedia applications running on personal wireless handset devices. Such personal devices have to be inexpensive, compact, and lightweight. Wireless channels have limited bandwidth and a high channel bit error rate and limited bandwidth. Delay variation of packets due to network congestion with the high bit error rate lessens the quality of video at the handheld device. Mobile access to multimedia content requires video transcoding functionality at the edge of the mobile network for interworking with heterogeneous networks and services. Under certain conditions, the bandwidth of a coded video stream needs to be drastically reduced. We present several efficient mechanisms for improving the quality of service (QoS) delivered to the client by deploying content-based transcoding schemes. The proposed approaches are performing the required transcoding based on the video content. Some approaches study the texture and temporal features. Other approaches perform object detection in order to determine the important objects to achieve semantic transcoding. The quality of the reconstructed images is remarkably similar to results that have been processed by the expensive and high performance transcoding approach. Exceptional performance is demonstrated in the experiment results. Extensive experiments have been conducted, and the results of various video clips with different bit rates and frame rates have been provided.
QoE for Mobile TV Services
Authors: Agboma Florence , Liotta Antonio
Published: May 2011

This chapter discusses the various issues that surround the development stage of mobile TV services. It highlights the importance of Quality of Experience (QoE), which is a shift in paradigm away from the widely studied Quality of Service (QoS). We discuss the factors affecting QoE and the types of assessment methods used to evaluate QoE. A QoE-layered model is presented with the aim of ensuring end-to-end user satisfaction. Using a case study, we develop a QoE management framework. We argue that gaining an understanding of users’ perceptions and their service quality expectations may assist in the development of QoE models that are user centric.
Online Life Sciences
Authors: Downing Kevin F. , Holtz Jennifer K.
Published: May 2011

In some ways, the life sciences have surpassed other fields in adoption of instructional technologies, although coverage is by no means uniform. In many cases found, textbooks are posted in traditional, linear format, slides accompany simple audio taped lectures or lectures are videotaped with slides as background. While useful, these resources differ substantially from those that address the best practices described in earlier chapters. Several better sites that used to offer free-use educational programs have now gone commercial, requiring purchase of their programs.
OpenLearn and Knowledge Maps for Language Learning
Authors: Okada Alexandra
Published: May 2011

This chapter presents new methodologies designed to facilitate language acquisition in open learning communities via open educational resources and knowledge mapping. It specifically focuses on the OpenLearn project developed by The Open University. This offers a virtual learning environment based on Moodle platform with free educational materials and knowledge media tools such as the instant messaging MSG, the Web videoconferencing application, FlashMeeting, and the knowledge mapping software tool Compendium. In this chapter, these technologies and mapping techniques are introduced in order to promote open language learning. Ways in which teachers and students can make use of these OpenLearn tools and resources are discussed and some benefits fully described.
A Pliant-Based Software Tool for Courseware Development
Authors: dos Santos Marcus Vinicius , Woungang Isaac , Nyongwa Moses
Published: May 2011

The increasing importance of e-learning has been a boosting element for the emergence of Internet-based educational tools. As we move into the information age, tremendous efforts are made in the development of new information and communication technologies for educational purposes. The ultimate goal is to facilitate e-learning methodologies and acquisition. The chapter’s contribution is in the area of open source software for technology-enhanced learning. First, we report on the capabilities of Pliant, a novel software framework for Web-based courseware development. Pliant’ design features upon which e-learning capabilities are built are presented, showing that Pliant has some advantages over existing software, including flexibility, efficiency, and universal usability. A case study of the use of Pliant in the project “Multilanguage Database for Localization” developed at the CUSB School of Translation is presented. Second, we present Academia,3 a Pliant-based courseware development Web portal, and its use in translation studies at CUSB.
A Lexical Study Based on Corpora, DDL, and Moodle
Authors: Nishina Yasunori
Published: May 2011

This chapter suggests an effective method for lexical studies using Moodle within the framework of data-driven learning based on parallel concordances, and particularly shows how teachers can prepare and compile materials of a specific keyword for Japanese learners of English. It is often the case that knowledge of L1 possessed by EFL learners affects that of L2 when they do L2 writing. The author shows this using the case of the English abstract noun condition, because it differs in its usage (e.g., implied meaning and context) by English native speakers and by Japanese EFL learners. Errors of this kind can be overcome by presenting parallel concordances concerning translation equivalents and their synonyms of the English noun in question. Thus, the several steps in the compilation of classroom materials based on parallel concordances with Moodle are presented here.
Electronic Strategies to Improve Chinese Reading Skills
Authors: Wang Jing
Published: May 2011

This chapter introduces a series of studies carried out with intermediate learners of Chinese regarding the reading of authentic e-materials with hyperlinked dictionaries. The study results indicate that it is practical to let intermediate students read authentic e-materials when aided by hyperlinked dictionaries, which can improve reading comprehension and vocabulary retention. Guided by the findings from these studies, good practices on how to use authentic e-materials and hyperlinked dictionaries to improve reading skills for intermediate students are introduced. It is recommended that in order to achieve optimal results when using technology, instructors need to employ systematic strategies to support and guide students in the reading process.
Social Software and Language Acquisition
Authors: Guth Sarah , Petrucco Corrado
Published: May 2011

This chapter describes how the social software tools that characterize Web 2.0, such as wikis and blogs, can be used as a valid substitute for more traditional Learning Management Systems in the context of e-learning and blended learning language courses. First, we will give a brief overview of how the educational arena is changing and the role social software can play in promoting these changes. Then we will describe two experimental courses carried out at the University of Padova using social software. The chapter ends with a discussion of the role of these tools in formal education. The aim of the chapter is to show how these tools allow language educators to take network-based language teaching beyond the limits of planned classroom activities, offering students new opportunities to access and produce real language in real situations.
Designs for Curriculum-Based Telementoring
Authors: Harris Judi
Published: May 2011

Telementoring for K-12 students is done primarily outside of school, typically addressing topics that are extrinsic to school curricula. As beneficial as extracurricular telementoring can be, bringing mentors virtually into classrooms to interact with students and teachers over time holds great potential—and considerable challenge—for both. How can telementoring be integrated effectively into content-based curricula taught in face-to-face educational contexts like classrooms? What is key to the success of this type of curriculum-based telementoring? Answers to these questions appear below, illustrated by examples from an informal taxonomy of curriculum-based telementoring projects that were facilitated by the Electronic Emissary (http://Emissary.wm.edu/), the longest-running formal telementoring program for K-12 students and their teachers.
The Electronic Global Village
Authors: Targowski Andrew
Published: May 2011

The purpose of this chapter is to define the architecture of information-communication systems which play key roles in the development of the Electronic Global Village (EGV) as the metaphoric mechanism for the implementation of the Information Wave.
Information Societies
Authors: Targowski Andrew
Published: May 2011

The purpose of this chapter is to define the evolution and key indicators of the information society that is being triggered by the Information Wave of the last 25 years. Several types of the information society from the point of view of information-communication technology (ICT) will be reviewed and their developmental paths will be defined. The fast development of the global economy based on information-communication technology (ICT) is supported by the information society, because without this technology it would be rather impossible to perceive information society. Depending on the different levels of a given country’s development, the information society has different levels of complexity and influence on the global economy and vice versa. Hence, it is important to recognize the information society’s different trends of development and their solutions and internal and external consequences. A question appears whether the information society is a new tool of thought or a new way of life. The answer to this question is provided in this chapter.
Architecture for Symbolic Object Warehouse
Authors: González Císaro Sandra Elizabeth
Published: May 2011

Much information stored in current databases is not always present at necessary different levels of detail or granularity for Decision-Making Processes (DMP). Some organizations have implemented the use of central database - Data Warehouse (DW) - where information performs analysis tasks. This fact depends on the Information Systems (IS) maturity, the type of informational requirements or necessities the organizational structure and business own characteristic. A further important point is the intrinsic structure of complex data; nowadays it is very common to work with complex data, due to syntactic or semantic aspects and the processing type (Darmont et al., 2006). Therefore, we must design systems, which can to maintain data complexity to improve the DMP. OLAP systems solve the problem of present different aggregation levels and visualization for multidimensional data through cube’s paradigm. The classical data analysis techniques (factorial analysis, regression, dispersion, etc.) are applied to individuals (tuples or individuals in transactional databases). The classic analysis objects are not expressive enough to represent tuples, which contain distributions, logic rules, multivaluate attributes, and intervals. Also, they must be able to respect their internal variation and taxonomy maintaining the dualism between individual and class. Consequently, we need a new data type holding these characteristics. This is just the mathematical concept model introduced by Diday called Symbolic Object (SO). SO allows modeling physic entities or real world concepts. The former are the tuples stored in transactional databases and the latter are high entities obtained from expert’s analysis, automatic classification or some particular aggregation taken from analysis units (Bock & Diday, 2000). The SO concept helps construct the DW and it is an important development for Data Mining (DM): for the manipulation and analysis of aggregated information (Nigro & González Císaro, 2005). According to Calvanese, data integration is a central problem in the design of DWs and Decision Support Systems (Calvanese, 2003; Cali, et al., 2003); we make the architecture for Symbolic Object Warehouse construction with integrative goal. Also, it combines with Data Analysis tasks or DM. This paper is presented as follows: First, Background: DW concepts are introduced. Second, Main Focus divided into: SOs Basic Concepts, Construing SOs and Architecture. Third, Future Trends, Conclusions, References and Key Terms.
Audio and Speech Processing for Data Mining
Authors: Tan Zheng-Hua
Published: May 2011

The explosive increase in computing power, network bandwidth and storage capacity has largely facilitated the production, transmission and storage of multimedia data. Compared to alpha-numeric database, non-text media such as audio, image and video are different in that they are unstructured by nature, and although containing rich information, they are not quite as expressive from the viewpoint of a contemporary computer. As a consequence, an overwhelming amount of data is created and then left unstructured and inaccessible, boosting the desire for efficient content management of these data. This has become a driving force of multimedia research and development, and has lead to a new field termed multimedia data mining. While text mining is relatively mature, mining information from non-text media is still in its infancy, but holds much promise for the future. In general, data mining the process of applying analytical approaches to large data sets to discover implicit, previously unknown, and potentially useful information. This process often involves three steps: data preprocessing, data mining and postprocessing (Tan, Steinbach, & Kumar, 2005). The first step is to transform the raw data into a more suitable format for subsequent data mining. The second step conducts the actual mining while the last one is implemented to validate and interpret the mining results. Data preprocessing is a broad area and is the part in data mining where essential techniques are highly dependent on data types. Different from textual data, which is typically based on a written language, image, video and some audio are inherently non-linguistic. Speech as a spoken language lies in between and often provides valuable information about the subjects, topics and concepts of multimedia content (Lee & Chen, 2005). The language nature of speech makes information extraction from speech less complicated yet more precise and accurate than from image and video. This fact motivates content based speech analysis for multimedia data mining and retrieval where audio and speech processing is a key, enabling technology (Ohtsuki, Bessho, Matsuo, Matsunaga, & Kayashi, 2006). Progress in this area can impact numerous business and government applications (Gilbert, Moore, & Zweig, 2005). Examples are discovering patterns and generating alarms for intelligence organizations as well as for call centers, analyzing customer preferences, and searching through vast audio warehouses.
A Case Study of a Data Warehouse in the Finnish Police
Authors: Juntunen Arla
Published: May 2011

The high level objectives of public authorities are to create value at minimal cost, and achieve ongoing support and commitment from its funding authority. Similar to the private sector, today’s government agencies face a rapidly changing operating environment and many challenges. Where public organizations differ is that they need to manage this environment while answering to demands for increased service, reduced costs, fewer resources and at the same time increased efficiency and accountability. Public organization must cope with changing expectations of multiple contact groups, emerging regulation, changes in politics, decentralization of organization, and centralization of certain functions providing similar services, and growing demand for better accountability. The aim of public management is to create public value. Public sector managers create value through their organization’s performance and demonstrated accomplishments. The public value is difficult to define: it is something that exists within each community. It is created and affected by the citizens, businesses and organizations of that community (cf. also Moore, 1995). This increased interest to questions of value is partly due to the adoption of values and value-related concepts taken from business, like value creation and added value. It is argued that the public sector adopts business-like techniques to increase efficiency (Khademian, 1995; cf. Turban et al. 2007; Chen et al. 2005). In addition, there is a growing concern to the non-tangible, political, and ethical aspects of the public sector governance and actions (See Berg, 2001) Decision making that turns the resources in to public value is a daily challenge in the government (Khademian, 1999; Flynn, 2007) and not only because of the social or political factors. Most of decision problems are no longer well-structured problems that are easy to be solved by experience. Even problems that used to be fairly simple to define and solve are now much more complex because of the globalization of the economy, and rapid pace of changes in the technology and political and social environment. Therefore, modern decision makers often need to integrate quickly and reliably knowledge from different areas of data sources to use it in their decision making process. Moreover, the tools and applications developed for knowledge representations in key application areas are extremely diversified, therefore knowledge and data modeling and integration is important (See also the decision support systems (DSS) modeling methods and paradigms: Ruan et al., 2001; Carlsson & Fuller, 2002; Fink, 2002; Makowski & Wierzbicki, 2003). The application s of real-world problems and the abundance of different software tools allow to integrate several methods, specifications and analysis and to apply them to new, arising, complex problems.
Cluster Analysis with General Latent Class Model
Authors: Qiu Dingxi , Malthouse Edward C.
Published: May 2011

Cluster analysis is a set of statistical models and algorithms that attempt to find “natural groupings” of sampling units (e.g., customers, survey respondents, plant or animal species) based on measurements. The observable measurements are sometimes called manifest variables and cluster membership is called a latent variable. It is assumed that each sampling unit comes from one of K clusters or classes, but the cluster identifier cannot be observed directly and can only be inferred from the manifest variables. See Bartholomew and Knott (1999) and Everitt, Landau and Leese (2001) for a broader survey of existing methods for cluster analysis. Many applications in science, engineering, social science, and industry require grouping observations into “types.” Identifying typologies is challenging, especially when the responses (manifest variables) are categorical. The classical approach to cluster analysis on those data is to apply the latent class analysis (LCA) methodology, where the manifest variables are assumed to be independent conditional on the cluster identity. For example, Aitkin, Anderson and Hinde (1981) classified 468 teachers into clusters according to their binary responses to 38 teaching style questions. This basic assumption in classical LCA is often violated and seems to have been made out of convenience rather than it being reasonable for a wide range of situations. For example, in the teaching styles study two questions are “Do you usually allow your pupils to move around the classroom?” and “Do you usually allow your pupils to talk to one another?” These questions are mostly likely correlated even within a class.
Constrained Data Mining
Authors: Morantz Brad
Published: May 2011

Mining a large data set can be time consuming, and without constraints, the process could generate sets or rules that are invalid or redundant. Some methods, for example clustering, are effective, but can be extremely time consuming for large data sets. As the set grows in size, the processing time grows exponentially. In other situations, without guidance via constraints, the data mining process might find morsels that have no relevance to the topic or are trivial and hence worthless. The knowledge extracted must be comprehensible to experts in the field. (Pazzani, 1997) With time-ordered data, finding things that are in reverse chronological order might produce an impossible rule. Certain actions always precede others. Some things happen together while others are mutually exclusive. Sometimes there are maximum or minimum values that can not be violated. Must the observation fit all of the requirements or just most. And how many is “most?” Constraints attenuate the amount of output (Hipp & Guntzer, 2002). By doing a first-stage constrained mining, that is, going through the data and finding records that fulfill certain requirements before the next processing stage, time can be saved and the quality of the results improved. The second stage also might contain constraints to further refine the output. Constraints help to focus the search or mining process and attenuate the computational time. This has been empirically proven to improve cluster purity. (Wagstaff & Cardie, 2000)(Hipp & Guntzer, 2002) The theory behind these results is that the constraints help guide the clustering, showing where to connect, and which ones to avoid. The application of user-provided knowledge, in the form of constraints, reduces the hypothesis space and can reduce the processing time and improve the learning quality.
Constraint-Based Association Rule Mining
Authors: Leung Carson Kai-Sang
Published: May 2011

The problem of association rule mining was introduced in 1993 (Agrawal et al., 1993). Since then, it has been the subject of numerous studies. Most of these studies focused on either performance issues or functionality issues. The former considered how to compute association rules efficiently, whereas the latter considered what kinds of rules to compute. Examples of the former include the Apriori-based mining framework (Agrawal & Srikant, 1994), its performance enhancements (Park et al., 1997; Leung et al., 2002), and the tree-based mining framework (Han et al., 2000); examples of the latter include extensions of the initial notion of association rules to other rules such as dependence rules (Silverstein et al., 1998) and ratio rules (Korn et al., 1998). In general, most of these studies basically considered the data mining exercise in isolation. They did not explore how data mining can interact with the human user, which is a key component in the broader picture of knowledge discovery in databases. Hence, they provided little or no support for user focus. Consequently, the user usually needs to wait for a long period of time to get numerous association rules, out of which only a small fraction may be interesting to the user. In other words, the user often incurs a high computational cost that is disproportionate to what he wants to get. This calls for constraint-based association rule mining.
Control-Based Database Tuning Under Dynamic Workloads
Authors: Tu Yi-Cheng , Ding Gang
Published: May 2011

Database administration (tuning) is the process of adjusting database configurations in order to accomplish desirable performance goals. This job is performed by human operators called database administrators (DBAs) who are generally well-paid, and are becoming more and more expensive with the increasing complexity and scale of modern databases. There has been considerable effort dedicated to reducing such cost (which often dominates the total ownership cost of missioncritical databases) by making database tuning more automated and transparent to users (Chaudhuri et al, 2004; Chaudhuri and Weikum, 2006). Research in this area seeks ways to automate the hardware deployment, physical database design, parameter configuration, and resource management in such systems. The goal is to achieve acceptable performance on the whole system level without (or with limited) human intervention. According to Weikum et al. (2002), problems in this category can be stated as: workload × configuration (?) ? performance which means that, given the features of the incoming workload to the database, we are to find the right settings for all system knobs such that the performance goals are satisfied The following two are representatives of a series of such tuning problems in different databases: • Problem 1: Maintenance of multi-class servicelevel agreements (SLA) in relational databases. Database service providers usually offer various levels of performance guarantees to requests from different groups of customers. Fulfillment of such guarantees (SLAs) is accomplished by allocating different amounts of system resources to different queries. For example, query response time is negatively related to the amount of memory buffer assigned to that query. We need to dynamically allocate memory to individual queries such that the absolute or relative response times of queries from different users are satisfied. • Problem 2: Load shedding in stream databases. Stream databases are used for processing data generated continuously from sources such as a sensor network. In streaming databases, data processing delay, i.e., the time consumed to process a data point, is the most critical performance metric (Tatbul et al., 2003). The ability to remain within a desired level of delay is significantly hampered under situations of overloading (caused by bursty data arrivals and time-varying unit data processing cost). When overloaded, some data is discarded (i.e., load shedding) in order to keep pace with the incoming load. The system needs to continuously adjust the amount of data to be discarded such that 1) delay is maintained under a desirable level; 2) data is not discarded unnecessarily.
Data Mining for Fraud Detection System
Authors: Marmo Roberto
Published: May 2011

As a conseguence of expansion of modern technology, the number and scenario of fraud are increasing dramatically. Therefore, the reputation blemish and losses caused are primary motivations for technologies and methodologies for fraud detection that have been applied successfully in some economic activities. The detection involves monitoring the behavior of users based on huge data sets such as the logged data and user behavior. The aim of this contribution is to show some data mining techniques for fraud detection and prevention with applications in credit card and telecommunications, within a business of mining the data to achieve higher cost savings, and also in the interests of determining potential legal evidence. The problem is very difficult because fraudsters takes many different forms and are adaptive, so they will usually look for ways to avoid every security measures.
Data Mining for Model Identification
Authors: Liberati Diego
Published: May 2011

In many fields of research, as well as in everyday life, it often turns out that one has to face a huge amount of data, without an immediate grasp of an underlying simple structure, often existing. A typical example is the growing field of bio-informatics, where new technologies, like the so-called Micro-arrays, provide thousands of gene expressions data on a single cell in a simple and fast integrated way. On the other hand, the everyday consumer is involved in a process not so different from a logical point of view, when the data associated to his fidelity badge contribute to the large data base of many customers, whose underlying consuming trends are of interest to the distribution market. After collecting so many variables (say gene expressions, or goods) for so many records (say patients, or customers), possibly with the help of wrapping or warehousing approaches, in order to mediate among different repositories, the problem arise of reconstructing a synthetic mathematical model capturing the most important relations between variables. To this purpose, two critical problems must be solved: 1 To select the most salient variables, in order to reduce the dimensionality of the problem, thus simplifying the understanding of the solution 2 To extract underlying rules implying conjunctions and/or disjunctions between such variables, in order to have a first idea of their even non linear relations, as a first step to design a representative model, whose variables will be the selected ones When the candidate variables are selected, a mathematical model of the dynamics of the underlying generating framework is still to be produced. A first hypothesis of linearity may be investigated, usually being only a very rough approximation when the values of the variables are not close to the functioning point around which the linear approximation is computed. On the other hand, to build a non linear model is far from being easy: the structure of the non linearity needs to be a priori known, which is not usually the case. A typical approach consists in exploiting a priori knowledge to define a tentative structure, and then to refine and modify it on the training subset of data, finally retaining the structure that best fits a cross-validation on the testing subset of data. The problem is even more complex when the collected data exhibit hybrid dynamics, i.e. their evolution in time is a sequence of smooth behaviors and abrupt changes.
A Data Mining Methodology for Product Family Design
Authors: Ki Moon Seung
Published: May 2011

Many companies strive to maximize resource utilization by sharing and reusing distributed design knowledge and information when developing new products. By sharing and reusing assets such as components, modules, processes, information, and knowledge across a family of products and services, companies can efficiently develop a set of differentiated products by improving the flexibility and responsiveness of product development (Simpson, 2004). Product family planning is a way to achieve cost-effective mass customization by allowing highly differentiated products to be developed from a shared platform while targeting products to distinct market segments (Shooter et al., 2005). In product design, data mining can be used to help identify customer needs, to find relationships between customer needs and functional requirements, and to cluster products based on functional similarity to facilitate modular design (Braha, 2001). The objective in this chapter is to introduce a methodology for identifying a platform along with variant and unique modules in a product family using design knowledge extracted with data mining techniques. During conceptual design, data mining can facilitate decision-making when selecting design concepts by extracting design knowledge and rules, clustering design cases, and exploring conceptual designs in large product design databases interactively (Braha, 2001). Moreover, since design knowledge for a product depends on the experience and knowledge of designers, representation of design knowledge, such as linguistic representation, may fail to describe a crisp representation completely. When clustering design knowledge, the knowledge is needed to assign to clusters with varying degrees of membership. Fuzzy membership can be used to represent and model the fuzziness of design knowledge (Braha, 2001). Design knowledge can be defined as linguistic variables based on the fuzzy set theory to support decision-making in product development (Ma et al., 2007).
Data Mining Tool Selection
Authors: Giraud-Carrier Christophe
Published: May 2011

It is sometimes argued that all one needs to engage in Data Mining (DM) is data and a willingness to “give it a try.” Although this view is attractive from the perspective of enthusiastic DM consultants who wish to expand the use of the technology, it can only serve the purposes of one-shot proofs of concept or preliminary studies. It is not representative of the complex reality of deploying DM within existing business processes. In such contexts, one needs two additional ingredients: a process model or methodology, and supporting tools. Several Data Mining process models have been developed (Fayyad et al, 1996; Brachman & Anand, 1996; Mannila, 1997; Chapman et al, 2000), and although each sheds a slightly different light on the process, their basic tenets and overall structure are essentially the same (Gaul & Saeuberlich, 1999). A recent survey suggests that virtually all practitioners follow some kind of process model when applying DM and that the most widely used methodology is CRISP-DM (KDnuggets Poll, 2002). Here, we focus on the second ingredient, namely, supporting tools. The past few years have seen a proliferation of DM software packages. Whilst this makes DM technology more readily available to non-expert end-users, it also creates a critical decision point in the overall business decision-making process. When considering the application of Data Mining, business users now face the challenge of selecting, from the available plethora of DM software packages, a tool adequate to their needs and expectations. In order to be informed, such a selection requires a standard basis from which to compare and contrast alternatives along relevant, business-focused dimensions, as well as the location of candidate tools within the space outlined by these dimensions. To meet this business requirement, a standard schema for the characterization of Data Mining software tools needs to be designed.
Direction-Aware Proximity on Graphs
Authors: Tong Hanghang , Koren Yehuda , Faloutsos Christos
Published: May 2011

In many graph mining settings, measuring node proximity is a fundamental problem. While most of existing measurements are (implicitly or explicitly) designed for undirected graphs; edge directions in the graph provide a new perspective to proximity measurement: measuring the proximity from A to B; rather than between A and B. (See Figure 1 as an example). In this chapter, we study the role of edge direction in measuring proximity on graphs. To be specific, we will address the following fundamental research questions in the context of direction-aware proximity: 1. Problem definitions: How to define a directionaware proximity? 2. Computational issues: How to compute the proximity score efficiently? 3. Applications: How can direction-aware proximity benefit graph mining?
Discovering an Effective Measure in Data Mining
Authors: Ito Takao
Published: May 2011

One of the most important issues in data mining is to discover an implicit relationship between words in a large corpus and labels in a large database. The relationship between words and labels often is expressed as a function of distance measures. An effective measure would be useful not only for getting the high precision of data mining, but also for time saving of the operation in data mining. In previous research, many measures for calculating the one-to-many relationship have been proposed, such as the complementary similarity measure, the mutual information, and the phi coefficient. Some research showed that the complementary similarity measure is the most effective. The author reviewed previous research related to the measures in one-to-many relationships and proposed a new idea to get an effective one, based on the heuristic approach in this article.
Dynamical Feature Extraction from Brain Activity Time Series
Authors: Liu Chang-Chia , Chaovalitwongse W. Art , Pardalos Panos M. , Uthman Basim M.
Published: May 2011

Neurologists typically study the brain activity through acquired biomarker signals such as Electroencephalograms (EEGs) which have been widely used to capture the interactions between neurons or groups of neurons. Detecting and identifying the abnormal patterns through visual inspection of EEG signals are extremely challenging and require constant attention for well trained and experienced specialists. To resolve this problem, data mining techniques have been successfully applied to the analysis for EEG recordings and other biomarker data sets. For example, Chaovalitwongse et al., (2006; 2007), Prokopyev et al., (2007) and Pardalos et al., (2007) reported the EEG patterns can be classified through dynamical features extracted from the underlying EEG dynamical characteristics. Moreover, in the area of cancer research, Busygin et al., (2006) showed promising results via Bi-clustering data classification technique using selected features from DNA microarrays. Ceci et al., (2007); Krishnamoorthy et al., (2007) also reported that data mining techniques enable protein structure characterization and protein structure prediction. From data mining aspects, feature extraction and selection for time series data sets not only play an important role in data preprocessing but also provide opportunities to uncover the underlying mechanisms of data under study. It also keeps the essential data structure and makes a better representation of acquired data sets that need to be classified. In this work, the properties and descriptions of the most common neurological biomarker namely EEG data sets will be given as well as the motivations and challenges for abnormal EEG classification. The dynamical features for EEG classification will be reviewed and described in the second part of this work. The described dynamical features can also be applied to other types of classification applications for discovering the useful knowledge from obtained data sets. Finally, the potential applications for EEG classification will be discussed and comments for further research directions will be given in the future trends and conclusion sections.
Evolutionary Mining of Rule Ensembles
Authors: Muruzábal Jorge
Published: May 2011

Ensemble rule based classification methods have been popular for a while in the machine-learning literature (Hand, 1997). Given the advent of low-cost, high-computing power, we are curious to see how far can we go by repeating some basic learning process, obtaining a variety of possible inferences, and finally basing the global classification decision on some sort of ensemble summary. Some general benefits to this idea have been observed indeed, and we are gaining wider and deeper insights on exactly why this is the case in many fronts of interest. There are many ways to approach the ensemblebuilding task. Instead of locating ensemble members independently, as in Bagging (Breiman, 1996), or with little feedback from the joint behavior of the forming ensemble, as in Boosting (see, e.g., Schapire & Singer, 1998), members can be created at random and then made subject to an evolutionary process guided by some fitness measure. Evolutionary algorithms mimic the process of natural evolution and thus involve populations of individuals (rather than a single solution iteratively improved by hill climbing or otherwise). Hence, they are naturally linked to ensemble-learning methods. Based on the long-term processing of the data and the application of suitable evolutionary operators, fitness landscapes can be designed in intuitive ways to prime the ensemble’s desired properties. Most notably, beyond the intrinsic fitness measures typically used in pure optimization processes, fitness can also be endogenous, that is, it can prime the context of each individual as well.
A Genetic Algorithm for Selecting Horizontal Fragments
Authors: Bellatreche Ladjel
Published: May 2011

Decision support applications require complex queries, e.g., multi way joins defining on huge warehouses usually modelled using star schemas, i.e., a fact table and a set of data dimensions (Papadomanolakis & Ailamaki, 2004). Star schemas have an important property in terms of join operations between dimensions tables and the fact table (i.e., the fact table contains foreign keys for each dimension). None join operations between dimension tables. Joins in data warehouses (called star join queries) are particularly expensive because the fact table (the largest table in the warehouse by far) participates in every join and multiple dimensions are likely to participate in each join. To speed up star join queries, many optimization structures were proposed: redundant structures (materialized views and advanced index schemes) and non redundant structures (data partitioning and parallel processing). Recently, data partitioning is known as an important aspect of physical database design (Sanjay, Narasayya & Yang, 2004; Papadomanolakis & Ailamaki, 2004). Two types of data partitioning are available (Özsu & Valduriez, 1999): vertical and horizontal partitioning. Vertical partitioning allows tables to be decomposed into disjoint sets of columns. Horizontal partitioning allows tables, materialized views and indexes to be partitioned into disjoint sets of rows that are physically stored and usually accessed separately. Contrary to redundant structures, data partitioning does not replicate data, thereby reducing storage requirement and minimizing maintenance overhead. In this paper, we concentrate only on horizontal data partitioning (HP). HP may affect positively (1) query performance, by performing partition elimination: if a query includes a partition key as a predicate in the WHERE clause, the query optimizer will automatically route the query to only relevant partitions and (2) database manageability: for instance, by allocating partitions in different machines or by splitting any access paths: tables, materialized views, indexes, etc. Most of database systems allow three methods to perform the HP using PARTITION statement: RANGE, HASH and LIST (Sanjay, Narasayya & Yang, 2004). In the range partitioning, an access path (table, view, and index) is split according to a range of values of a given set of columns. The hash mode decomposes the data according to a hash function (provided by the system) applied to the values of the partitioning columns. The list partitioning splits a table according to the listed values of a column. These methods can be combined to generate composite partitioning. Oracle currently supports range-hash and range-list composite partitioning using PARTITION - SUBPARTITION statement. The following SQL statement shows an example of fragmenting a table Student using range partitioning.