bluuebunny/crossref_metadata_2025 huggingface dataset:
What Makes Reality
Authors: Abdoullaev Azamat
Published: May 2011

The ultimate purpose of standard ontology is to describe and represent all the things in the world in comprehensive and consistent ways, whereby making the fundamental knowledge explicit to the formal reason of semantic systems and cognitive agents, natural, or artificial. To build such a formal universal framework capable of including the representation of anything, one can design a general system that includes a set-theoretic (logical) ontology constructed as a formal logical system composed of its objects-primitives (classes, individuals, and properties), logical syntax (notation techniques, formation and transformation rules), and formal semantics (model theory), as currently the Semantic Web formal languages and upper ontologies usually are constructing.
Natural Language Intelligences
Authors: Abdoullaev Azamat
Published: May 2011

Of all possible intelligent NL applications and semantic artifacts, a special value is today ascribed to building the question answering systems (Q&A) with broad and wide ontological learning (Onto Query Project, 2004), classified as open-domain Q&A knowledge systems [Question Answering, From Wikipedia, 2006]. This line of research is considered as upgrading of a traditional keyword query processing in database systems, as endowing the Web search engines with answering deduction capacities. Ideally, such a general-purpose Q&A agent should be able to cover questions (matters, subjects, topics, issues, themes) from any branch of knowledge and domain of interest by giving answers to any meaningful questions, like the Digital Aristotle, “an application that will encompass much of the world’s scientific knowledge and be capable of answering novel questions and advanced problemsolving” (Project Halo, 2004). The trade name of the Digital Aristotle was inspired by the scholar mostly admired for the depth and width of his perception, whose mind spread over ontology, physics, logics, epistemology, biology, zoology, medicine, psychology, literary theory, politics, and art.
Transformations of the Language Laboratory
Authors: Bo-Kristensen Mads , Meyer Bente
Published: May 2011

This chapter focuses on the relationship between remediation and educational innovation in Information and Communications Technology- (ICT) intensive learning environments, as exemplified by the language laboratory (language lab) and its digital descendants. Historically, the language laboratory has been affected by a number of technological and instructional changes, transforming the dead-end of behavioural methodology into the current socio-collaborative paradigms of language learning. The language laboratory has thus been transformed into a learning environment that incorporates and refers to several generations of technology-based language learning. The principles of Computer-Assisted Language Learning (CALL) will serve as this chapter’s theoretical framework. It will also deal with the issues of repurposing and remediation raised by Bolter & Grusin (1999). Remediation is defined as a process of transformation wherein older media are represented and refashioned in new media contexts. It will also be argued that there is a dialogical relationship between the processes of remediation inherent in the genealogy of the language laboratory, and the processes of educational innovation. In addition to this, the chapter will suggest ways of rethinking and reforming the language laboratory through Mobile-Assisted Language Learning (MALL).
Authentic E-Learning in a Virtual Scientific Conference
Authors: Basque Josianne , Dao Kim , Contamines Julien
Published: May 2011

The goal of this chapter is to illustrate how the concept of authentic learning can be implemented in a web-based distance course. We present a collaborative e-learning scenario, inspired by socio-constructivist and situated learning theories, which encourages authentic learning. Developed as the main learning scenario of a graduate distance education course, it requires students to participate asynchronously in a simulation of an online scientific conference. We describe the learning scenario, the technological environment developed to implement this scenario, as well as some results of a course evaluation completed by students.
Human Factors and Innovation with Mobile Devices
Authors: Kukulska-Hulme Agnes
Published: May 2011

Advancements in technology are a significant driving force in educational innovation, but a strong focus on technology means that human aspects and implications may not be given the attention they deserve. This chapter examines usability issues surrounding the use of mobile devices in learning. A key aim is to empower educators and learners to take control of personal devices and realise their potential in relation to teaching and learning. The background section reviews the development of usability studies and explores why mobile device usability presents specific new challenges. The impact of changing requirements in education, and new visions for ways of thinking and competences that learners should be acquiring, are also examined. Finally, the chapter provides a set of concepts that can inform conversations between educators and learners, mobile system engineers, developers, support staff and others.
Ethical Issues in Digital Information Technology
Authors: Morgan Konrad , Morgan Madeleine
Published: May 2011

Since the inception of the Internet in the late 1960´s, technological advances in the field of Information Communications Technology (ICT) have created an ever-expanding digital arena for the development of human innovation, education, expression, communication and interaction. However, the creation and use of this vast network of knowledge, whether it is for educational, commercial, entertainment or creative purposes, has also produced its own set of ethical challenges. This paper discusses the ethical implications associated with the topics of veracity, identity and ownership and the impact of these fundamental ethical issues on human behaviour in emerging digital technologies.
Innovative Technological Paradigms for Corporate Offshoring
Authors: Patki Tapasya , Patki A. B.
Published: May 2011

Internet technology has impelled us to develop faith in the modern practices of business, commerce, and trade. Offshoring has been viewed as a global phenomenon on the economic frontier. While new technologies need to be framed, stopgap arrangements in the form of transient solutions to upgrade the current systems are also desired. Newer regulations and multi-jurisdictional compliance have profound impacts on the growth of outsourcing projects. The development of new technological solutions must challenge the myth that legislation and statutory practices are the only possible mechanisms to counter the unscrupulous activities in the context of outsourcing. A change in the outlook toward such methodologies is essential to shed away the technological inertia and latency. This chapter opens up discussion issues in the perspective of hardware and software requirements for efficient offshoring. The aim is to achieve higher precision, protection, and throughput by applying core-computing techniques to the existing practices of outsourcing.
An Integrated Approach to Interaction Design and Log Analysis
Authors: Muresan Gheorghe
Published: May 2011

In this chapter, we describe and discuss a methodological framework that integrates analysis of interaction logs with the conceptual design of the user interaction. It is based on (i) formalizing the functionality that is supported by an interactive system and the valid interactions that can take place; (ii) deriving schemas for capturing the interactions in activity logs; (iii) deriving log parsers that reveal the system states and the state transitions that took place during the interaction; and (iv) analyzing the user activities and the system’s state transitions in order to describe the user interaction or to test some research hypotheses. This approach is particularly useful for studying user behavior when using highly interactive systems. We present the details of the methodology, and exemplify its use in a mediated retrieval experiment, in which the focus of the study is on studying the information-seeking process and on finding interaction patterns.
Query Log Analysis in Biomedicine
Authors: Bernstam Elmer V. , Herskovic Jorge R. , Hersh William R.
Published: May 2011

Clinicians, researchers and members of the general public are increasingly using information technology to cope with the explosion in biomedical knowledge. This chapter describes the purpose of query log analysis in the biomedical domain as well as features of the biomedical domain such as controlled vocabularies (ontologies) and existing infrastructure useful for query log analysis. We focus specifically on MEDLINE, which is the most comprehensive bibliographic database of the world’s biomedical literature, the PubMed interface to MEDLINE, the Medical Subject Headings vocabulary and the Unified Medical Language System. However, the approaches discussed here can also be applied to other query logs. We conclude with a look toward the future of biomedical query log analysis.
Processing and Analysis of Search Query Logs in Chinese
Authors: Chau Michael , Lu Yan , Fang Xiao , Yang Christopher C.
Published: May 2011

More non-English contents are now available on the World Wide Web and the number of non-English users on the Web is increasing. While it is important to understand the Web searching behavior of these non-English users, many previous studies on Web query logs have focused on analyzing English search logs and their results may not be directly applied to other languages. In this Chapter we discuss some methods and techniques that can be used to analyze search queries in Chinese. We also show an example of applying our methods on a Chinese Web search engine. Some interesting findings are reported.
Query Log Analysis for Adaptive Dialogue-Driven Search
Authors: Kruschwitz Udo , Webb Nick , Sutcliffe Richard
Published: May 2011

The theme of this chapter is the improvement of Information Retrieval and Question Answering systems by the analysis of query logs. Two case studies are discussed. The first describes an intranet search engine working on a university campus which can present sophisticated query modifications to the user. It does this via a hierarchical domain model built using multi-word term co-occurrence data. The usage log was analysed using mutual information scores between a query and its refinement, between a query and its replacement, and between two queries occurring in the same session. The results can be used to validate refinements in the domain model, and to suggest replacements such as domain-dependent spelling corrections. The second case study describes a dialogue-based question answering system working over a closed document collection largely derived from the Web. Logs here are based around explicit sessions in which an analyst interacts with the system. Analysis of the logs has shown that certain types of interaction lead to increased precision of the results. Future versions of the system will encourage these forms of interaction. The conclusions of this chapter are firstly that there is a growing literature on query log analysis, much of it reviewed here, secondly that logs provide many forms of useful information for improving a system, and thirdly that mutual information measures taken with automatic term recognition algorithms and hierarchy construction techniques comprise one approach for enhancing system performance.
Analysis and Evaluation of the Connector Website
Authors: DiPerna Paul
Published: May 2011

This chapter proposes a new theoretical construct for evaluating Websites that facilitate online social networks. The suggested model considers previous academic work related to social networks and online communities. This study’s main purpose is to define a new kind of social institution, called a “connector Website”, and provide a means for objectively analyzing Web-based organizations that empower users to form online social networks. Several statistical approaches are used to gauge Website-level growth, trend lines, and volatility. This project sets out to determine whether or not particular connector Websites can be mechanisms for social change, and to quantify the nature of the observed social change. The author hopes this chapter introduces new applications for Web log analysis by evaluating connector Websites and their organizations.
Using "Blackbox" Algorithms Such AS TreeNET and Random Forests for Data-Ming and for Finding Meaningful Patterns, Relationships and Outliers in Complex Ecological Data
Authors: Craig Erica , Huettmann Falk
Published: May 2011

The use of machine-learning algorithms capable of rapidly completing intensive computations may be an answer to processing the sheer volumes of highly complex data available to researchers in the field of ecology. In spite of this, the continued use of less effective, simple linear, and highly labor intensive techniques such as stepwise multiple regression continue to be widespread in the ecological community. Herein we describe the use of data-mining algorithms such as TreeNet and Random Forests (Salford Systems), which can rapidly and accurately identify meaningful patterns and relationships in subsets of data that carry various degrees of outliers and uncertainty. We use satellite data from a wintering Golden Eagle as an example application; judged by the consistency of the results, the resultant models are robust, in spite of 30 % faulty presence data. The authors believe that the implications of these findings are potentially far-reaching and that linking computational software with wildlife ecology and conservation management in an interdisciplinary framework cannot only be a powerful tool, but is crucial toward obtaining sustainability.
Semantic Multimedia Information Analysis for Retrieval Applications
Authors: Magalhães João , Rüger Stefan
Published: May 2011

Most of the research in multimedia retrieval applications has focused on retrieval by content or retrieval by example. Since the classical review by Smeulders, Worring, Santini, Gupta, and Jain (2000), a new interest has grown immensely in the multimedia information retrieval community: retrieval by semantics. This exciting new research area arises as a combination of multimedia understanding, information extraction, information retrieval, and digital libraries. This chapter presents a comprehensive review of analysis algorithms in order to extract semantic information from multimedia content. We discuss statistical approaches to analyze images and video content and conclude with a discussion regarding the described methods.
Adaptation and Personalization of Web-Based Multimedia Content
Authors: Germanakos Panagiotis , Mourlas Constantinos
Published: May 2011

A traditional multimedia system presents the same static content and suggests the same next page to all users, even though they might have widely differing knowledge of the subject. Such a system suffers from an inability to be all things to all people, especially when the user population is relatively diverse. The rapid growth of mobile and wireless communication allowed service providers to develop new ways of interactions, enabling users to become accustomed to new means of multimedia-based service consumption in an anytime, anywhere, and anyhow manner. This chapter investigates the new multichannel constraints and opportunities emerged by these technologies, as well as the new user-demanding requirements that arise. It further examines the relationship between the adaptation and personalization research considerations, and proposes a three-layer architecture for adaptation and personalization of Web-based multimedia content based on the “new” user profile, with visual, emotional, and cognitive processing parameters incorporated.
Wireless Collaborative Virtual Environments Applied to Language Education
Authors: Garcia-Ruiz Miguel A. , El-Seoud Samir A.
Published: May 2011

This chapter provides an overview of second language learning and an approach on how wireless collaborative virtual reality can contribute to resolving important pedagogical challenges. Second language learning provides an exceptional opportunity to employ mobility and multimedia in the context of just-in-time-learning in formal learning situations, or ubiquitous and lifelong learning in more informal settings. We hypothesize that virtual reality is a tool that can help teach languages in a collaborative manner in that it permits students to use visual, auditory, and kinesthetic stimuli to provide a more “real-life” context, based in large part on Computer-Supported Collaborative Learning. Studies are being conducted in which we assess usability, wireless multimedia technology, and collaborative learning aspects to discover how virtual reality can help students overcome language and anxiety barriers. Furthermore, we suggest carrying out longitudinal studies to determine to what extent wireless, mobile, and collaborative virtual reality can contribute to language instruction.
Politeness in Intercultural E-Mail Communication
Authors: Murphy Margaret , Matas Cristina Poyatos
Published: May 2011

This chapter argues that politeness is an important component of e-mail language. Many people are uncertain about how to make their e-mail polite according to the norms of the receiving culture. Different views on the nature of politeness in e-mail can contribute to personal offence and online miscommunication. As a result, an instrument was designed to assess politeness in e-mail communication. The Tool-Kit, An Instrument to Assess Politeness in Intercultural E-Mail Communication, provides a common ground among e-mail users to discuss and evaluate politeness in English e-mail communication, as well as the metalanguage necessary to reflect on the use of politeness indicators. It also provides clear guidelines to consider politeness protocols. This innovative instrument promotes a practical approach for assessing politeness in e-mail which could then assist the effective online interactions in e-learning methodologies. Its limitations are discussed here, as well as its pedagogical applications for online teaching to second language learners.
Project-Based Instruction for ESP in Higher Education
Authors: Mamakou Irene
Published: May 2011

Interest in the integration of language learning with knowledge/content construction is growing around the world. In this line, an instructional innovation to be applied in language for specific purposes (LSP) undergraduate courses is introduced. Project-based learning has been enhanced by an electronic platform; namely, e-Class, a learning management system, to amplify its impact. A threefold research into current theories on (a) key issues in LSP framework, (b) adult education, and (c) foreign language learning and pedagogical orientations leads us to this model. The result is an e-methodology, pedagogically rather than technology driven, for teaching/learning LSP in the framework of discipline-related, content-based instruction (CBI) and project work that will eventually enhance and modernize teaching and learning LSP and develop new study habits for learners by promoting self-directed, autonomous, active, and out-of-classroom learning, and by encouraging student engagement and academic socialization in the higher educational institution (HEI) to which they belong.
Language Teaching in Live Online Environments
Authors: Swertz Christian , Schultz Rosa , Toifl Katharina
Published: May 2011

This chapter reports the concept development and evaluation results from the project LANCELOT (LANguage learning with CErtified Live Online Teachers). LANCELOT is funded by the Leonardo da Vinci program of the European Commission. LANCELOT, a training program for live online language teachers, covers the technological, methodological, and intercultural aspects of live online language training. Concepts for the use of current online communication technologies and suitable language teaching methods in a virtual language classroom have also been developed within the project. These concepts are integrated by means of an innovative pedagogical concept for online teaching and learning called Web-Didactics. In order to verify that it is ready for the market, the training program recently underwent thorough testing and evaluation.
Portable Handheld Language Learning from CALL MALL to PALL
Authors: Chaka Chaka
Published: May 2011

This chapter explores aspects of portable handheld language learning that are likely to benefit many mobile assisted language learning (MALL) practitioners. Portable handheld language learning refers to mobile, virtual, and ubiquitous language learning mediated through mobile handheld devices. Currently, both computer assisted language learning (CALL) and MALL seem to dominate the act of language learning. Against this background the chapter first provides a brief review of CALL, highlighting CALL technologies helping mediate language learning. Second, it delineates features typifying e-Learning and contends that CALL is more closely linked to traditional e-Learning than MALL. Third, it provides empirical instances of MALL and argues that the future of language learning lies more with MALL and especially with pen assisted language learning (PALL) than with CALL. Finally, it maintains that an all-encompassing and multidimensional definition of mobile learning is necessary if MALL is to evolve into a mainstream virtual learning enterprise.
Civilization Life Cycle
Authors: Targowski Andrew
Published: May 2011

The purpose of this study is to define the role of civilization’s critical powers in the civilization life cycle. The role of information-communication processes is particularly crucial in this quest. The terms “rise” and “fall” of civilization reflect this chronic issue in comparative civilization studies. Spengler, in his book The Decline of the West (1918), argued that all cultures are subject to the same cycle of growth and decay in accordance with predetermined “historical destiny.” Toynbee in his Study of History (1934), compared civilizations to organisms and perceived their existence in a life cycle of four stages: genesis, growth, breakdown, and disintegration. A mechanism of “challenge-response” facing civilizations influences their abilities at self-determination and self-direction. However, according to him, all civilizations that grow eventually reach a peak, from which they begin to decline. It seems that Toynbee’s civilization life cycle is too short, since his “breakdown of growth” phase is in fact a point in time and the “disintegration” phase is too pessimistic in its title, only perceiving the “universal state,” often under a form of “empire,” as an ancient regime which only wants to maintain the status quo and is doomed to fail. But history shows that some civilizations may last a long time in relatively good shape without being in imminent danger of disintegration. Sorokin argued in Social and Cultural Dynamics (1937) that three cultural mentalities, ideational (spiritual needs and goals), sensate (“wine, women, and song”), and idealistic (a balance of needs and ends) are the central organizing principles of a civilization’s life cycle, and that they succeed each other always in the same order according to super-rhythms of history. According to Sorokin, Western civilization has for the last 500 years been in the sensate stage, reaching now its limit, and will soon pass to the next idealistic stage (which, according to this author, could be the universal civilization).
Theory of Critical Total History of Civilization
Authors: Targowski Andrew
Published: May 2011

The purpose of this chapter is to define information- based tools for the study of the human story in order to “informate” traditional historic findings. By “informate” one may understand a gain of additional information above that found by traditional processing of historical information, by applying modern cybernetic techniques that allow for the modeling and understanding of complexity. After literature, history is the most universal discipline of knowledge, passionately held (in their own particular versions) by millions of people on Earth. History makes us curious, perhaps because in it resides the puzzle of human existence, its successes and failures. We want to know the past because we want to learn “lessons of history” (Howard, 1991). Hence, history is popular and rich in its public role and its scientific methods are even the subject of philosophical debates. It is still debated, as Hegel (1956) stated, whether history is not chance but is rather a rational process operating according to laws of evolution and embodying the spirit of freedom. The 19th century’s positivism stipulated two roles for historians: to be disinterested observers and to find, in the records of the past, laws of human behavior. The 20th century’s tremendous progress in research and technology has influenced historians to consider history as a pure science with the emphasis on large-scale forces or structures instead of individuals (Breisach, 1983). As we move into the 21st century, new trends in the evolution of civilization, informatization and globalization, guide our awareness. These trends emphasize the application of information engineering skills and offer an expanded picture of human undertakings. The emerging world’s history of civilization in the making is no longer “sequential” and “slow” but now “instant” and “fast.” To understand such a dynamic civilization and take a pro-active role in it, one must develop new skills and new approaches to its study. Perhaps one should take examples from other sciences, for example, physics and chemistry, where modeling is applied in order to discover some common observations, rules, and laws. Of course, models do not completely reflect reality, but they are useful tools in grasping its essence and suggesting further investigations and quests for truth.
Information and Organization
Authors: Targowski Andrew
Published: May 2011

The purpose of this chapter is to define information, mainly in terms of cognition units, and also to find out its other perspectives and images. Once we understand information, it becomes possible to define its role in an organization, particularly at the level of information systems. The issue of how more complex information systems may advance an organization to higher levels of structure (configuration) will be investigated. Modern complex organization is still very recent, about 50 years old, but can already be perceived to have some evolutionary phases. Finally, the transformation from the industrial to the informated model of an enterprise is described and both models are compared, with some conclusions about meaning for civilization’s well-being.
Tailoring CobiT for Public Sector IT Audit
Authors: Gerke Lynne , Ridley Gail
Published: May 2011

This chapter examines the potential to use an audit program based on the Control Objectives for Information and related Technologies (CobiT) framework for IT audit within a public sector audit office. It documents research that derives, implements and evaluates such a program with the cooperation of the public sector audit office in an Australian state. Additionally a comparison of the study results was undertaken with those of Guldentops, van Grembergen and de Haes (2002), Liu and Ridley (2005) and the European Organisation of Supreme Audit Institutions (EUROSAI) IT Working Group CobiT Selfassessment Project. The results suggest that the CobiT-derived instrument was effective for IT audit, and was able to be tailored to the needs of Tasmanian state public sector organization, when evaluated against a number of criteria.
Analytical Competition for Managing Customer Relations
Authors: Zhu Dan
Published: May 2011

With the advent of technology, information is available in abundance on the World Wide Web. In order to have appropriate and useful information users must increasingly use techniques and automated tools to search, extract, filter, analyze and evaluate desired information and resources. Data mining can be defined as the extraction of implicit, previously unknown, and potentially useful information from large databases. On the other hand, text mining is the process of extracting the information from an unstructured text. A standard text mining approach will involve categorization of text, text clustering, and extraction of concepts, granular taxonomies production, sentiment analysis, document summarization, and modeling (Fan et al, 2006). Furthermore, Web mining is the discovery and analysis of useful information using the World Wide Web (Berry, 2002; Mobasher, 2007). This broad definition encompasses “web content mining,” the automated search for resources and retrieval of information from millions of websites and online databases, as well as “web usage mining,” the discovery and analysis of users’ website navigation and online service access patterns. Companies are investing significant amounts of time and money on creating, developing, and enhancing individualized customer relationship, a process called customer relationship management or CRM. Based on a report by the Aberdeen Group, worldwide CRM spending reached close to $20 billion by 2006. Today, to improve the customer relationship, most companies collect and refine massive amounts of data available through the customers. To increase the value of current information resources, data mining techniques can be rapidly implemented on existing software and hardware platforms, and integrated with new products and systems (Wang et al., 2008). If implemented on high-performance client/server or parallel processing computers, data mining tools can analyze enormous databases to answer customer-centric questions such as, “Which clients have the highest likelihood of responding to my next promotional mailing, and why.” This paper provides a basic introduction to data mining and other related technologies and their applications in CRM.
Analytical Knowledge Warehousing for Business Intelligence
Authors: Huang Chun-Che , Tseng Tzu-Liang ("Bill")
Published: May 2011

The Information Technology and Internet techniques are rapidly developing. Interaction between enterprises and customers has dramatically changed. It becomes critical that enterprises are able to perform rapid diagnosis and quickly respond to market change. How to apply business intelligence (BI), manage, and diffuse discovered knowledge efficiently and effectively has attracted much attention (Turban et al., 2007). In this chapter, an “analytical knowledge warehousing” approach is proposed to apply business intelligence, and solve the knowledge management and diffusion issues for decision-making. Analytical knowledge is referred to a set of discovered knowledge, i.e., core of BI, which is extricated from databases, knowledge bases, and other data storage systems through aggregating data analysis techniques and domain experts from business perspective. The solution approach includes conceptual framework of analytical knowledge, analytical knowledge externalization, design and implementation of analytical knowledge warehouse. The methodology has integrated with multi-dimensional analytical techniques to efficiently search analytical knowledge documents. The techniques include static and dynamic domains and solve problems from the technical and management standpoints. The use of analytical knowledge warehouse and multidimensional analysis techniques shows the promising future to apply BI and support decision-making in business.
Audio Indexing
Authors: Richard Gaël
Published: May 2011

The enormous amount of unstructured audio data available nowadays and the spread of its use as a data source in many applications are introducing new challenges to researchers in information and signal processing. The continuously growing size of digital audio information increases the difficulty of its access and management, thus hampering its practical usefulness. As a consequence, the need for content-based audio data parsing, indexing and retrieval techniques to make the digital information more readily available to the user is becoming ever more critical. The lack of proper indexing and retrieval systems is making de facto useless significant portions of existing audio information (and obviously audiovisual information in general). In fact, if generating digital content is easy and cheap, managing and structuring it to produce effective services is clearly not. This applies to the whole range of content providers and broadcasters which can amount to terabytes of audio and audiovisual data. It also applies to the audio content gathered in private collection of digital movies or music files stored in the hard disks of conventional personal computers. In summary, the goal of an audio indexing system will then be to automatically extract high-level information from the digital raw audio in order to provide new means to navigate and search in large audio databases. Since it is not possible to cover all applications of audio indexing, the basic concepts described in this chapter will be mainly illustrated on the specific problem of musical instrument recognition.
Classification Methods
Authors: An Aijun
Published: May 2011

Generally speaking, classification is the action of assigning an object to a category according to the characteristics of the object. In data mining, classification refers to the task of analyzing a set of pre-classified data objects to learn a model (or a function) that can be used to classify an unseen data object into one of several predefined classes. A data object, referred to as an example, is described by a set of attributes or variables. One of the attributes describes the class that an example belongs to and is thus called the class attribute or class variable. Other attributes are often called independent or predictor attributes (or variables). The set of examples used to learn the classification model is called the training data set. Tasks related to classification include regression, which builds a model from training data to predict numerical values, and clustering, which groups examples to form categories. Classification belongs to the category of supervised learning, distinguished from unsupervised learning. In supervised learning, the training data consists of pairs of input data (typically vectors), and desired outputs, while in unsupervised learning there is no a priori output. Classification has various applications, such as learning from a patient database to diagnose a disease based on the symptoms of a patient, analyzing credit card transactions to identify fraudulent transactions, automatic recognition of letters or digits based on handwriting samples, and distinguishing highly active compounds from inactive ones based on the structures of compounds for drug discovery.
Compression-Based Data Mining
Authors: Keogh Eamonn , Keogh Li , Handley John C.
Published: May 2011

Compression-based data mining is a universal approach to clustering, classification, dimensionality reduction, and anomaly detection. It is motivated by results in bioinformatics, learning, and computational theory that are not well known outside those communities. It is based on an easily computed compression dissimilarity measure (CDM) between objects obtained by compression. The basic concept is easy to understand, but its foundations are rigorously formalized in information theory. The similarity between any two objects (XML files, time series, text strings, molecules, etc.) can be obtained using a universal lossless compressor. The compression dissimilarity measure is the size of the compressed concatenation of the two objects divided by the sum of the compressed sizes of each of the objects. The intuition is that if two objects are similar, lossless compressor will remove the redundancy between them and the resulting size of the concatenated object should be close the size of the larger of the two compressed constituent objects. The larger the CDM between two objects, the more dissimilar they are. Classification, clustering and anomaly detection algorithms can then use this dissimilarity measure in a wide variety of applications. Many of these are described in (Keogh et al., 2004), (Keogh et al. 2007), and references therein. This approach works well when (1) objects are large and it is computationally expensive to compute other distances (e.g., very long strings); or (2) there are no natural distances between the objects or none that are reasonable from first principles. CDM is “parameter-free” and thus avoids over-fitting the data or relying upon assumptions that may be incorrect (Keogh et al., 2004).
Constraint-Based Pattern Discovery
Authors: Bonchi Francesco
Published: May 2011

Devising fast and scalable algorithms, able to crunch huge amount of data, was for many years one of the main goals of data mining research. But then we realized that this was not enough. It does not matter how efficient such algorithms can be, the results we obtain are often of limited use in practice. Typically, the knowledge we seek is in a small pool of local patterns hidden within an ocean of irrelevant patterns generated from a sea of data. Therefore, it is the volume of the results itself that creates a second order mining problem for the human expert. This is, typically, the case of association rules and frequent itemset mining (Agrawal & Srikant, 1994), to which, during the last decade a lot of researchers have dedicated their (mainly algorithmic) investigations. The computational problem is that of efficiently mining from a database of transactions, those itemsets which satisfy a user-defined constraint of minimum frequency. Recently the research community has turned its attention to more complex kinds of frequent patterns extracted from more structured data: sequences, trees, and graphs. All these different kinds of pattern have different peculiarities and application fields, but they all share the same computational aspects: a usually very large input, an exponential search space, and a too large solution set. This situation—too many data yielding too many patterns—is harmful for two reasons. First, performance degrades: mining generally becomes inefficient or, often, simply unfeasible. Second, the identification of the fragments of interesting knowledge, blurred within a huge quantity of mostly useless patterns, is difficult. The paradigm of constraintbased pattern mining was introduced as a solution to both these problems. In such paradigm, it is the user who specifies to the system what is interesting for the current application: constraints are a tool to drive the mining process towards potentially interesting patterns, moreover they can be pushed deep inside the mining algorithm in order to fight the exponential search space curse, and to achieve better performance (Srikant et al., 1997; Ng et al. 1998; Han et al., 1999; Grahne et al., 2000).
Data Analysis for Oil Production Prediction
Authors: Chan Christine W.
Published: May 2011

An economic evaluation of a new oil well is often required, and this evaluation depends heavily on how accurately production of the well can be estimated. Unfortunately, this kind of prediction is extremely difficult because of complex subsurface conditions of reservoirs. The industrial standard approach is to use either curve-fitting methods or complex and timeconsuming reservoir simulations. In this study, we attempted to improve upon the standard techniques by using a variety of neural network and data mining approaches. The approaches differ in terms of prediction model, data division strategy, method, tool used for implementation, and the interpretability of the models. The objective is to make use of the large amount of data readily available from private companies and public sources to enhance understanding of the petroleum production prediction task. Additional objectives include optimizing timing for initiation of advanced recovery processes and identifying candidate wells for production or injection.
Data Mining for the Chemical Process Industry
Authors: Yew Seng Ng , Srinivasan Rajagopalan
Published: May 2011

Advancements in sensors and database technologies have resulted in the collection of huge amounts of process data from chemical plants. A number of process quantities such as temperature, pressure, flow rates, level, composition, and pH can be easily measured. Chemical processes are dynamic systems and are equipped with hundreds or thousands of sensors that generate readings at regular intervals (typically seconds). In addition, derived quantities that are functions of the sensor measurements as well as alerts and alarms are generated regularly. Several commercial data warehouses, referred to as plant historians in chemical plants, such as the DeltaV Continuous Historian (from Emerson Process Management), InfoPlus.21TM (from AspenTech), Uniformance® PHD (from Honeywell), and Industrial SQL (from Wonderware) are in common use today around the world. These historians store large amount (weeks) of historical process operation data at their original resolution and an almost limitless amount (years) in compressed form. This data is available for mining, analysis and decision support – both real-time and offline. Process measurements can be classified based on their nature as binary (on/off) or continuous. However, both are stored in discrete form in the historians. Measurements can also be classified based on their role during operation as controlled, manipulated, and non-control related variables. Controlled variables are directly or indirectly related to the plant’s quality, production, or safety objectives and are maintained at specified setpoints, even in the face of disturbances, by analog or digital controllers. This regulation is achieved by altering manipulated variables such as flow-rates. Chemical plants are typically well-integrated – a change in one variable would propagate across many others. Non-control related variables do not have any role in plant control, but provide information to plant personnel regarding the state of the process. In general, a plant can operate in a number of states which can be broadly classified into steady-states and transitions (Srinivasan et al., 2005b). Large scale plants such as refineries typically run for long periods in steady-states but undergo transition if there is a change in feedstock or product grades. Transitions also result due to large process disturbances, maintenance activities, and abnormal events. During steady-states, the process variables vary within a narrow range. In contrast, transitions correspond to large changes / discontinuities in the plant operations; i.e., change of set points, turning on or idling of equipments, valve manipulations, etc. A number of decisions are needed on the part of the plant personnel to keep the plant running safely and efficiently during steady states as well as transitions. Data mining and analysis tools that facilitate humans to uncover information, knowledge, patterns, trends, and relationships from the historical data are therefore crucial.
Data Warehouse Back-End Tools
Authors: Simitsis Alkis , Theodoratos Dimitri
Published: May 2011

The back-end tools of a data warehouse are pieces of software responsible for the extraction of data from several sources, their cleansing, customization, and insertion into a data warehouse. In general, these tools are known as Extract – Transformation – Load (ETL) tools and the process that describes the population of a data warehouse from its sources is called ETL process. In all the phases of an ETL process (extraction and transportation, transformation and cleaning, and loading), individual issues arise, and, along with the problems and constraints that concern the overall ETL process, make its lifecycle a very complex task.
Deep Web Mining through Web Services
Authors: Maceli Monica , Song Min
Published: May 2011

With the increase in Web-based databases and dynamically- generated Web pages, the concept of the “deep Web” has arisen. The deep Web refers to Web content that, while it may be freely and publicly accessible, is stored, queried, and retrieved through a database and one or more search interfaces, rendering the Web content largely hidden from conventional search and spidering techniques. These methods are adapted to a more static model of the “surface Web”, or series of static, linked Web pages. The amount of deep Web data is truly staggering; a July 2000 study claimed 550 billion documents (Bergman, 2000), while a September 2004 study estimated 450,000 deep Web databases (Chang, He, Li, Patel, & Zhang, 2004). In pursuit of a truly searchable Web, it comes as no surprise that the deep Web is an important and increasingly studied area of research in the field of Web mining. The challenges include issues such as new crawling and Web mining techniques, query translation across multiple target databases, and the integration and discovery of often quite disparate interfaces and database structures (He, Chang, & Han, 2004; He, Zhang, & Chang, 2004; Liddle, Yau, & Embley, 2002; Zhang, He, & Chang, 2004). Similarly, as the Web platform continues to evolve to support applications more complex than the simple transfer of HTML documents over HTTP, there is a strong need for the interoperability of applications and data across a variety of platforms. From the client perspective, there is the need to encapsulate these interactions out of view of the end user (Balke & Wagner, 2004). Web services provide a robust, scalable and increasingly commonplace solution to these needs. As identified in earlier research efforts, due to the inherent nature of the deep Web, dynamic and ad hoc information retrieval becomes a requirement for mining such sources (Chang, He, & Zhang, 2004; Chang, He, Li, Patel, & Zhang, 2004). The platform and program-agnostic nature of Web services, combined with the power and simplicity of HTTP transport, makes Web services an ideal technique for application to the field of deep Web mining. We have identified, and will explore, specific areas in which Web services can offer solutions in the realm of deep Web mining, particularly when serving the need for dynamic, ad-hoc information gathering.