bluuebunny/crossref_metadata_2025 huggingface dataset:
An Exploratory Study of Consumer Adoption of Online Shopping
Authors: Kulviwat Songpol , Thakur Ramendra , Guo Chiquan
Published: May 2011

An exploratory study was conducted to investigate consumer adoption of online purchase using a survey data set. Based upon the theory of innovation and self-efficacy theory, risk aversion, online proficiency, shopping convenience, and product choice variety were proposed to influence consumer intention to shop online, which, in turn, affects online purchases. Results of regression analyses revealed that all but shopping convenience were significant predictors of consumer intention to purchase online. In addition, consumer intention directly determines consumer purchases online. Finally, consumer intention to purchase online mediates the relationship of risk aversion, shopping convenience, and product choice variety to online shopping. Research and managerial implications of the findings were discussed.
Multimedia Essentials and Challenges
Authors: Khasawneh Baha A.
Published: May 2011

This chapter introduces multimedia and multimedia elements and technology that are influencing and will continue to influence much of the exchanged data over the Internet today. It presents all the elements of multimedia with an emphasis on the basic building block of each element and how it integrates with the other elements to achieve a better multimedia content. This chapter also presents the close relation between advancements in multimedia technology and the immense use of the Internet. In addition, various popular image, audio, and video standards are presented with more emphasis on compression standards that are influencing multimedia use. Furthermore, we hope the inclusion of a section dealing with copyrights, ownership, and cross-platform issues will shed light on the challenges facing multimedia producers and owners alike. Finally, the chapter includes some of the future challenges and issues facing the research community for the advancement of multimedia uses.
Getting the Big Picture on Small Screens
Authors: Knoche Hendrik , Sasse M. Angela
Published: May 2011

This chapter provides an overview of the key factors that influence the quality of experience (QoE) of mobile TV services. It compiles the current knowledge from empirical studies and recommendations on four key requirements for the uptake of mobile TV services: (1) handset usability and its acceptance by the user, (2) the technical performance and reliability of the service, (3) the usability of the mobile TV service (depending on the delivery of content), and (4) the satisfaction with the content. It illustrates a number of factors that contribute to these requirements ranging from the context of use to the size of the display and the displayed content. The chapter highlights the interdependencies between these factors during the delivery of content in mobile TV services to a heterogeneous set of low resolution devices.
Virtual School Science
Authors: Downing Kevin F. , Holtz Jennifer K.
Published: May 2011

In this chapter, we examine the character and extent of science learning at virtual schools and explore the current deliberations concerning the interconnections and coordination of efforts of online science learning at the critical school-to-college interface. We discuss selected pedagogical approaches popular in virtual school science instruction and express the pronounced correspondence between online technological efforts of schools and those elaborated in later chapters for colleges and universities.
Taking University Science Education Online
Authors: Downing Kevin F. , Holtz Jennifer K.
Published: May 2011

While distance education in various forms has existed for many years, the exponential growth of computer-based, especially web-based, education has presented a challenge for instructors who learned to operate in a traditional classroom environment. Not only must they design engaging, effective learning modules with authentic assessments, promote interaction and gauge understanding throughout the term, they must now do so without being able to see their students and without the students’ ability to participate in typical classroom activities (Edelson, 2001). This is especially problematic for subject areas such as science and mathematics, where demonstration and experiential activities are essential. Despite these complexities, there is tremendous potential for effective, engaging science instruction through distance learning, but also a pronounced, crippling disconnect between the technology being used in other disciplines—computer modeling, for example—and instructional design. Online faculty and instructional designers typically do not collaborate with computer science colleagues who have the equipment required to design sophisticated learning modules. Such sophisticated modules would move beyond the context and resource focused modules that are common—and acceptable, while not ideal, for citizen science-level learning—to those that provide tools and scaffolds that meet the needs of all science learners. A wide range of relevant technologies exist, including games, simulations, modeling, virtual instrumentation and animation (Elgamal, Fraser & McMartin, 2005; Kin, 2004; Young-Suk, 2004). This chapter presents original research that investigates current uses of these web-based instructional technologies.
Investigating Interaction in an EFL Online Environment
Authors: Menezes de Oliveira e Paiva Vera Lucia , Rodrigues-Junior Adail Sebastiao
Published: May 2011

This pedagogical and methodological chapter aims at contributing to increasing Web teachers’ awareness of the different ways teachers and students can make themselves “visible” in the virtual environment by describing three categories of footing in online educational forums, namely, social footing, teaching footing, and cognitive footing. The categories are explained in line with the definition of footing, originally presented by Goffman (1981) and extended to online environments by Paiva and Rodrigues-Junior (2004, 2007). The data stems from a 60-hour online reading and writing course for undergraduate students of English as a Foreign Language in Brazil and was selected having in mind the categories discussed throughout the chapter, with special emphasis on the role played by the teacher and her students during the course. The analysis has shown that footing can be clearly perceived as an interactive category for online environments by means of textual analysis, with special focus on the transitivity model developed by Halliday (2004) and the social, teaching, and cognitive presences investigated by Garrison (2006) and his colleagues.
Assessing the Quality of Online Peer Feedback in L2 Writing
Authors: Rosalia Christine , Llosa Lorena
Published: May 2011

This chapter reports on an instrument that was developed to formatively assess the quality of feedback that second language students give to one another in an online, anonymous, asynchronous learning environment. The Online Peer Feedback (OPF) Assessment was originally developed for a peer online writing center in Japan where student peer advisors jointly compose feedback for a client-writer. The OPF Assessment is composed of two rubrics: (1) a rubric that evaluates the initial feedback drafted by a peer advisor, and (2) a rubric that assesses the contribution that individual peer advisors make to the interactive process of constructing the final feedback for their client-writer. The chapter describes the assessment and discusses its potential uses in a variety of contexts as a formative tool to improve the quality of peer feedback and, ultimately, the writing proficiency of both givers and receivers of the feedback.
Reconfigurable Embedded Medical Systems
Authors: Massey Tammara , Dabiri Foad , Jafari Roozbeh , Noshadi Hyduke , Brisk Philip , Sarrafzadeh Majid
Published: May 2011

This chapter introduces reconfigurable design techniques for light-weight medical systems. The research presented in this chapter demonstrates how the wise use of reconfiguration in small embedded systems is an approach that is beneficial in heterogeneous medical systems. By shrewdly designing embedded systems, one can make efficient use of limited resources through efficient and effective reconfiguration schemes that balance the tradeoffs between power consumption, memory consumption, and interoperability in heterogeneous environments. Furthermore, several reconfigurable architectures and algorithms presented in this chapter will assist researchers in designing efficient embedded systems that can be reconfigured after deployment, which is an essential feature in embedded medical systems.
The Application of Data-Mining to Recommender Systems
Authors: Schafer J. Ben
Published: May 2011

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and knowledge management. For example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations for additional products. These products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This paper will address the technology used to generate recommendations, focusing on the application of data mining techniques.
Association Bundle Identification
Authors: Huang Wenxue , Krneta Milorad , Lin Limin , Wu Jianhong
Published: May 2011

An association pattern describes how a group of items (for example, retail products) are statistically associated together, and a meaningful association pattern identifies ‘interesting’ knowledge from data. A wellestablished association pattern is the association rule (Agrawal, Imielinski & Swami, 1993), which describes how two sets of items are associated with each other. For example, an association rule A-->B tells that ‘if customers buy the set of product A, they would also buy the set of product B with probability greater than or equal to c’. Association rules have been widely accepted for their simplicity and comprehensibility in problem statement, and subsequent modifications have also been made in order to produce more interesting knowledge, see (Brin, Motani, Ullman and Tsur, 1997; Aggarwal and Yu, 1998; Liu, Hsu and Ma, 1999; Bruzzese and Davino, 2001; Barber and Hamilton, 2003; Scheffer, 2005; Li, 2006). A relevant concept is the rule interest and excellent discussion can be found in (Shapiro 1991; Tan, Kumar and Srivastava, 2004). Huang et al. recently developed association bundles as a new pattern for association analysis (Huang, Krneta, Lin and Wu, 2006). Rather than replacing the association rule, the association bundle provides a distinctive pattern that can present meaningful knowledge not explored by association rules or any of its modifications.
Automatic Genre-Specific Text Classification
Authors: Yu Xiaoyan , Tungare Manas , Fan Weiguo , Pérez-Quiñones Manuel , Fox Edward A. , Cameron William , Cassel Lillian
Published: May 2011

Starting with a vast number of unstructured or semistructured documents, text mining tools analyze and sift through them to present to users more valuable information specific to their information needs. The technologies in text mining include information extraction, topic tracking, summarization, categorization/ classification, clustering, concept linkage, information visualization, and question answering [Fan, Wallace, Rich, & Zhang, 2006]. In this chapter, we share our hands-on experience with one specific text mining task — text classification [Sebastiani, 2002]. Information occurs in various formats, and some formats have a specific structure or specific information that they contain: we refer to these as `genres’. Examples of information genres include news items, reports, academic articles, etc. In this paper, we deal with a specific genre type, course syllabus. A course syllabus is such a genre, with the following commonly-occurring fields: title, description, instructor’s name, textbook details, class schedule, etc. In essence, a course syllabus is the skeleton of a course. Free and fast access to a collection of syllabi in a structured format could have a significant impact on education, especially for educators and life-long learners. Educators can borrow ideas from others’ syllabi to organize their own classes. It also will be easy for life-long learners to find popular textbooks and even important chapters when they would like to learn a course on their own. Unfortunately, searching for a syllabus on the Web using Information Retrieval [Baeza-Yates & Ribeiro-Neto, 1999] techniques employed by a generic search engine often yields too many non-relevant search result pages (i.e., noise) — some of these only provide guidelines on syllabus creation; some only provide a schedule for a course event; some have outgoing links to syllabi (e.g. a course list page of an academic department). Therefore, a well-designed classifier for the search results is needed, that would help not only to filter noise out, but also to identify more relevant and useful syllabi.
A Bayesian Based Machine Learning Application to Task Analysis
Authors: Lin Shu-Chiang
Published: May 2011

Many task analysis techniques and methods have been developed over the past decades, but identifying and decomposing a user’s task into small task components remains a difficult, impractically time-consuming, and expensive process that involves extensive manual effort (Sheridan, 1997; Liu, 1997; Gramopadhye and Thaker, 1999; Annett and Stanton, 2000; Bridger, 2003; Stammers and Shephard, 2005; Hollnagel, 2006; Luczak et al., 2006; Morgeson et al., 2006). A practical need exists for developing automated task analysis techniques to help practitioners perform task analysis efficiently and effectively (Lin, 2007). This chapter summarizes a Bayesian methodology for task analysis tool to help identify and predict the agents’ subtasks from the call center’s naturalistic decision making’s environment.
Cluster Analysis in Fitting Mixtures of Curves
Authors: Burr Tom
Published: May 2011

One data mining activity is cluster analysis, which consists of segregating study units into relatively homogeneous groups. There are several types of cluster analysis; one type deserving special attention is clustering that arises due to a mixture of curves. A mixture distribution is a combination of two or more distributions. For example, a bimodal distribution could be a mix with 30% of the values generated from one unimodal distribution and 70% of the values generated from a second unimodal distribution. The special type of mixture we consider here is a mixture of curves in a two-dimensional scatter plot. Imagine a collection of hundreds or thousands of scatter plots, each containing a few hundred points including background noise but also containing from zero to four or five bands of points, each having a curved shape. In one application (Burr et al. 2001), each curved band of points was a potential thunderstorm event (see Figure 1) as observed from a distant satellite and the goal was to cluster the points into groups associated with thunderstorm events. Each curve has its own shape, length, and location, with varying degrees of curve overlap, point density, and noise magnitude. The scatter plots of points from curves having small noise resemble a smooth curve with very little vertical variation from the curve, but there can be a wide range in noise magnitude so that some events have large vertical variation from the center of the band. In this context, each curve is a cluster and the challenge is to use only the observations to estimate how many curves comprise the mixture, plus their shapes and locations. To achieve that goal, the human eye could train a classifier by providing cluster labels to all points in example scatter plots. Each point would either belong to a curved-region or to a catch-all noise category and a specialized cluster analysis would be used to develop an approach for labeling (clustering) the points generated according to the same mechanism in future scatter plots.
Clustering Categorical Data with k-Modes
Authors: Huang Joshua Zhexue
Published: May 2011

A lot of data in real world databases are categorical. For example, gender, profession, position, and hobby of customers are usually defined as categorical attributes in the CUSTOMER table. Each categorical attribute is represented with a small set of unique categorical values such as {Female, Male} for the gender attribute. Unlike numeric data, categorical values are discrete and unordered. Therefore, the clustering algorithms for numeric data cannot be used to cluster categorical data that exists in many real world applications. In data mining research, much effort has been put on development of new techniques for clustering categorical data (Huang, 1997b; Huang, 1998; Gibson, Kleinberg, & Raghavan, 1998; Ganti, Gehrke, & Ramakrishnan, 1999; Guha, Rastogi, & Shim, 1999; Chaturvedi, Green, Carroll, & Foods, 2001; Barbara, Li, & Couto, 2002; Andritsos, Tsaparas, Miller, & Sevcik, 2003; Li, Ma, & Ogihara, 2004; Chen, & Liu, 2005; Parmar, Wu, & Blackhurst, 2007). The k-modes clustering algorithm (Huang, 1997b; Huang, 1998) is one of the first algorithms for clustering large categorical data. In the past decade, this algorithm has been well studied and widely used in various applications. It is also adopted in commercial software (e.g., Daylight Chemical Information Systems, Inc, http://www. daylight.com/).
Data Mining for Lifetime Value Estimation
Authors: Figini Silvia
Published: May 2011

Customer lifetime value (LTV, see e.g. Bauer et al. 2005 and Rosset et al. 2003), which measures the profit generating potential, or value, of a customer, is increasingly being considered a touchstone for administering the CRM (Customer relationship management) process. This in order to provide attractive benefits and retain high-value customers, while maximizing profits from a business standpoint. Robust and accurate techniques for modelling LTV are essential in order to facilitate CRM via LTV. A customer LTV model needs to be explained and understood to a large degree before it can be adopted to facilitate CRM. LTV is usually considered to be composed of two independent components: tenure and value. Though modelling the value (or equivalently, profit) component of LTV, (which takes into account revenue, fixed and variable costs), is a challenge in itself, our experience has revealed that finance departments, to a large degree, well manage this aspect. Therefore, in this paper, our focus will mainly be on modelling tenure rather than value.
Data Quality in Data Warehouses
Authors: Winkler William E.
Published: May 2011

Fayyad and Uthursamy (2002) have stated that the majority of the work (representing months or years) in creating a data warehouse is in cleaning up duplicates and resolving other anomalies. This paper provides an overview of two methods for improving quality. The first is record linkage for finding duplicates within files or across files. The second is edit/imputation for maintaining business rules and for filling-in missing data. The fastest record linkage methods are suitable for files with hundreds of millions of records (Winkler, 2004a, 2008). The fastest edit/imputation methods are suitable for files with millions of records (Winkler, 2004b, 2007a).
Data Warehousing and Mining in Supply Chains
Authors: Mathieu Richard
Published: May 2011

Every finished product has gone through a series of transformations. The process begins when manufacturers purchase the raw materials that will be transformed into the components of the product. The parts are then supplied to a manufacturer, who assembles them into the finished product and ships the completed item to the consumer. The transformation process includes numerous activities (Levary, 2000). Among them are • Designing the product • Designing the manufacturing process • Determining which component parts should be produced in house and which should be purchased from suppliers • Forecasting customer demand • Contracting with external suppliers for raw materials or component parts • Purchasing raw materials or component parts from suppliers • Establishing distribution channels for raw materials and component parts from suppliers to manufacturer • Establishing of distribution channels to the suppliers of raw materials and component parts • Establishing distribution channels from the manufacturer to the wholesalers and from wholesalers to the final customers • Manufacturing the component parts • Transporting the component parts to the manufacturer of the final product • Manufacturing and assembling the final product • Transporting the final product to the wholesalers, retailers, and final customer Each individual activity generates various data items that must be stored, analyzed, protected, and transmitted to various units along a supply chain. A supply chain can be defined as a series of activities that are involved in the transformation of raw materials into a final product, which a customer then purchases (Levary, 2000). The flow of materials, component parts, and products is moving downstream (i.e., from the initial supply sources to the end customers). The flow of information regarding the demand for the product and orders to suppliers is moving upstream, while the flow of information regarding product availability, shipment schedules, and invoices is moving downstream. For each organization in the supply chain, its customer is the subsequent organization in the supply chain, and its subcontractor is the prior organization in the chain.
Discovery of Protein Interaction Sites
Authors: Li Haiquan , Li Jinyan , Zhao Xuechun
Published: May 2011

Physical interactions between proteins are important for many cellular functions. Since protein-protein interactions are mediated via their interaction sites, identifying these interaction sites can therefore help to discover genome-scale protein interaction map, thereby leading to a better understanding of the organization of living cell. To date, the experimentally solved protein interaction sites constitute only a tiny proportion among the whole population due to the high cost and low-throughput of currently available techniques. Computational methods, including many biological data mining methods, are considered as the major approaches in discovering protein interaction sites in practical applications. This chapter reviews both traditional and recent computational methods such as protein-protein docking and motif discovery, as well as new methods on machine learning approaches, for example, interaction classification, domain-domain interactions, and binding motif pair discovery.
Efficient Graph Matching
Authors: Reforgiato Recupero Diego
Published: May 2011

Application domains such as bioinformatics and web technology represent complex objects as graphs where nodes represent basic objects (i.e. atoms, web pages etc.) and edges model relations among them. In biochemical databases proteins are naturally represented as labeled graphs: the nodes are atoms and the edges are chemical links. In computer vision, graphs can be used to represent images at different levels of abstraction. In a low-level representation (Pailloncy, Deruyver, & Jolion, 1999), the nodes of a graph correspond to pixels and the edges to spatial relations between pixels. At higher levels of description (Hong & Huang, 2001), nodes are image regions and edges are spatial relations among them. In a Web graph (Deutsch, Fernandez, Florescu, Levy, & Suciu, 1999) nodes are web pages and edges are links between them. In all these domains, substructure queries that search for all exact or approximate occurrences of a given query graph in the graphs of the database can be useful. Research efforts in graph searching have taken three directions: the first is to study matching algorithms for particular graph structures (planar graphs, bounded valence graphs and association graphs); the second is to use elaborate tricks to reduce the number of generated matching maps (Cordella, Foggia, Sansone, & Vento, 2004); and the third is, since graph searching problem is NP-complete, to provide polynomial approximate algorithms. In the context of querying in a database of graphs many of the existing methods are designed for specific applications. For example, several querying methods for semi-structured databases have been proposed. In addition, commercial products and academic projects (Kelley 2002) for subgraph searching in biochemical databases are available. These two examples have a different underlying data model (a web-page database is a large graph whereas a biochemical database is a collection of graphs). In these two applications regular path expressions and indexing methods are used during query time to respectively locate substructures in the database and to avoid unnecessary traversals of the database. In general graph databases, there are some searching methods where the data graph and the query graph must have the same size. Other methods allow the query to be considerably smaller than the database graphs. A common idea in the above algorithms is to index the subgraph matches in the database and organize them in suitable data structures.
Enclosing Machine Learning
Authors: Wei Xunkai
Published: May 2011

As known to us, the cognition process is the instinct learning ability of the human being. This process is perhaps one of the most complex human behaviors. It is a highly efficient and intelligent information processing process. For a cognition process of the natural world, humans always transfer the feature information to the brain through their perception, and then the brain processes the feature information and remembers it. Due to the invention of computers, scientists are now working toward improving its artificial intelligence, and they hope that one day the computer could have its intelligent “brain” as human does. However, it is still a long way for us to go in order to let a computer truly “think” by itself. Currently, artificial intelligence is an important and active research topic. It imitates the human brain using the idea of function equivalence. Traditionally, the neural computing and neural networks families are the majority parts of the direction (Haykin, 1994). By imitating the working mechanism of the human-brain neuron, scientists have built the neural networks theory following experimental research such as perception neurons and spiking neurons (Gerstner & Kistler, 2002) in order to understand the working mechanism of neurons. Neural-computing and neural networks (NN) families (Bishop, 1995) have made great achievements in various aspects. Recently, statistical learning and support vector machines (SVM) (Vapnik, 1995) have drawn extensive attention and shown better performances in various areas (Li, Wei & Liu, 2004) than NN, which implies that artificial intelligence can also be made via advanced statistical computing theory. Nowadays, these two methods tend to merge under the statistical learning theory framework
Formal Concept Analysis Based Clustering
Authors: Saquer Jamil M.
Published: May 2011

Formal concept analysis (FCA) is a branch of applied mathematics with roots in lattice theory (Wille, 1982; Ganter & Wille, 1999). It deals with the notion of a concept in a given universe, which it calls context. For example, consider the context of transactions at a grocery store where each transaction consists of the items bought together. A concept here is a pair of two sets (A, B). A is the set of transactions that contain all the items in B and B is the set of items common to all the transactions in A. A successful area of application for FCA has been data mining. In particular, techniques from FCA have been successfully used in the association mining problem and in clustering (Kryszkiewicz, 1998; Saquer, 2003; Zaki & Hsiao, 2002). In this article, we review the basic notions of FCA and show how they can be used in clustering.
Graph-Based Data Mining
Authors: Holder Lawrence B.
Published: May 2011

Graph-based data mining represents a collection of techniques for mining the relational aspects of data represented as a graph. Two major approaches to graphbased data mining are frequent subgraph mining and graph-based relational learning. This chapter will focus on one particular approach embodied in the Subdue system, along with recent advances in graph-based supervised learning, graph-based hierarchical conceptual clustering, and graph-grammar induction. Most approaches to data mining look for associations among an entity’s attributes, but relationships between entities represent a rich source of information, and ultimately knowledge. The field of multi-relational data mining, of which graph-based data mining is a part, is a new area investigating approaches to mining this relational information by finding associations involving multiple tables in a relational database. Two main approaches have been developed for mining relational information: logic-based approaches and graph-based approaches. Logic-based approaches fall under the area of inductive logic programming (ILP). ILP embodies a number of techniques for inducing a logical theory to describe the data, and many techniques have been adapted to multi-relational data mining (Dzeroski & Lavrac, 2001; Dzeroski, 2003). Graph-based approaches differ from logic-based approaches to relational mining in several ways, the most obvious of which is the underlying representation. Furthermore, logic-based approaches rely on the prior identification of the predicate or predicates to be mined, while graph-based approaches are more data-driven, identifying any portion of the graph that has high support. However, logic-based approaches allow the expression of more complicated patterns involving, e.g., recursion, variables, and constraints among variables. These representational limitations of graphs can be overcome, but at a computational cost.
Intelligent Query Answering
Authors: Ras Zbigniew W. , Dardzinska Agnieszka
Published: May 2011

One way to make Query Answering System (QAS) intelligent is to assume a hierarchical structure of its attributes. Such systems have been investigated by (Cuppens & Demolombe, 1988), (Gal & Minker, 1988), (Gaasterland et al., 1992) and they are called cooperative. Any attribute value listed in a query, submitted to cooperative QAS, is seen as a node of the tree representing that attribute. If QAS retrieves no objects supporting query q, from a queried information system S, then any attribute value listed in q can be generalized and the same the number of objects supporting q in S can increase. In cooperative systems, these generalizations are controlled either by users (Gal & Minker, 1988), or by knowledge discovery techniques (Muslea, 2004). If QAS for S collaborates and exchanges knowledge with other systems, then it is also called intelligent. In papers (Ras & Dardzinska, 2004, 2006), a guided process of rules extraction and their goal-oriented exchange among systems is proposed. These rules define foreign attribute values for S and they are used to construct new attributes and/or impute null or hidden values of attributes in S. By enlarging the set of attributes from which queries for S can be built and by reducing the incompleteness of S, we not only enlarge the set of queries which QAS can successfully handle but also we increase the overall number of retrieved objects. So, QAS based on knowledge discovery has two classical scenarios which need to be considered: • System is standalone and incomplete. Classification rules are extracted and used to predict what values should replace null values before any query is answered. • System is distributed with autonomous sites (including site S). User needs to retrieve objects from S satisfying query q containing nonlocal attributes for S. We search for definitions of these non-local attributes at remote sites for S and use them to approximate q (Ras & Zytkow, 2000), (Ras & Dardzinska, 2004, 2006). The goal of this article is to provide foundations and basic results for knowledge-discovery based QAS.
Knowledge Discovery in Databases with Diversity of Data Types
Authors: Wu QingXiang , McGinnity Martin , Prasad Girijesh , Bell David
Published: May 2011

Data mining and knowledge discovery aim at finding useful information from typically massive collections of data, and then extracting useful knowledge from the information. To date a large number of approaches have been proposed to find useful information and discover useful knowledge; for example, decision trees, Bayesian belief networks, evidence theory, rough set theory, fuzzy set theory, kNN (k-nearest-neighborhood) classifier, neural networks, and support vector machines. However, these approaches are based on a specific data type. In the real world, an intelligent system often encounters mixed data types, incomplete information (missing values), and imprecise information (fuzzy conditions). In the UCI (University of California – Irvine) Machine Learning Repository, it can be seen that there are many real world data sets with missing values and mixed data types. It is a challenge to enable machine learning or data mining approaches to deal with mixed data types (Ching, 1995; Coppock, 2003) because there are difficulties in finding a measure of similarity between objects with mixed data type attributes. The problem with mixed data types is a long-standing issue faced in data mining. The emerging techniques targeted at this issue can be classified into three classes as follows: (1) Symbolic data mining approaches plus different discretizers (e.g., Dougherty et al., 1995; Wu, 1996; Kurgan et al., 2004; Diday, 2004; Darmont et al., 2006; Wu et al., 2007) for transformation from continuous data to symbolic data; (2) Numerical data mining approaches plus transformation from symbolic data to numerical data (e.g.,, Kasabov, 2003; Darmont et al., 2006; Hadzic et al., 2007); (3) Hybrid of symbolic data mining approaches and numerical data mining approaches (e.g.,, Tung, 2002; Kasabov, 2003; Leng et al., 2005; Wu et al., 2006). Since hybrid approaches have the potential to exploit the advantages from both symbolic data mining and numerical data mining approaches, this chapter, after discassing the merits and shortcomings of current approaches, focuses on applying Self-Organizing Computing Network Model to construct a hybrid system to solve the problems of knowledge discovery from databases with a diversity of data types. Future trends for data mining on mixed type data are then discussed. Finally a conclusion is presented.
Learning Temporal Information from Text
Authors: Pan Feng
Published: May 2011

As an essential dimension of our information space, time plays a very important role in every aspect of our lives. Temporal information is necessarily required in many applications, such as temporal constraint modeling in intelligent agents (Hritcu and Buraga, 2005), semantic web services (Pan and Hobbs, 2004), temporal content modeling and annotation for semantic video retrieval (QasemiZadeh et al., 2006), geographic information science (Agarwal, 2005), data integration of historical stock price databases (Zhu et al., 2004), ubiquitous and pervasive systems for modeling the time dimension of the context (Chen et al., 2004), and so on. Extracting temporal information from text is especially useful for increasing the temporal awareness for different natural language applications, such as question answering, information retrieval, and summarization. For example, in summarizing a story in terms of a timeline, a system may have to extract and chronologically order events in which a particular person participated. In answering a question as to a person’s current occupation, a system may have to selectively determine which of several occupations reported for that person is the most recently reported one (Mani et al., 2004). This chapter presents recent advances in applying machine learning and data mining approaches to automatically extract explicit and implicit temporal information from natural language text. The extracted temporal information includes, for example, events, temporal expressions, temporal relations, (vague) event durations, event anchoring, and event orderings.
Metaheuristics in Data Mining
Authors: García Torres Miguel
Published: May 2011

The Metaheuristics are general strategies for designing heuristic procedures with high performance. The term metaheuristic, which appeared in 1986 for the first time (Glover, 1986), is compound by the terms: “meta”, that means over or behind, and “heuristic”. Heuristic is the qualifying used for methods of solving optimization problems that are obtained from the intuition, expertise or general knowledge (Michalewicz & Fogel, 2000). Nowadays a lot of known strategies can be classified as metaheuristics and there are a clear increasing number of research papers and applications that use this kind of methods. Several optimization methods that already existed when the term appeared have been later interpreted as metaheuristics (Glover & Kochenberger, 2003). Genetic Algorithms, Neural Networks, Local Searches, and Simulated Annealing are some of those classical metaheuristics. Several modern metaheuristics have succeeded in solving relevant optimization problems in industry, business and engineering. The most relevant among them are Tabu Search, Variable Neighbourhood Search and GRASP. New population based evolutionary metaheuristics such as Scatter Search and Estimation Distribution Algorithms are also quite important. Besides Neural Networks and Genetic Algorithms, other nature-inspired metaheuristics such as Ant Colony Optimization and Particle Swarm Optimization are also now well known metaheuristics.
Mining Smart Card Data from an Urban Transit Network
Authors: Agard Bruno
Published: May 2011

In large urban areas, smooth running public transit networks are key to viable development. Currently, economic and environmental issues are fueling the need for these networks to adequately serve travel demand, thereby increasing their competitiveness and their market share. Better balance between transit supply and demand will also help reduce and control operating costs. The fact is, however, that transit operators are finding it extremely difficult to adjust the service to meet the demand, because this demand changes continuously with the time or day of travel (period of the day, day of the week, season or holiday) and other factors like weather and service breakdown. In order to enhance their service, operators need to better understand the travel demand (customer behaviors and the variability of the demand in space and time). This can be achieved only by continuously monitoring the day-to-day activities of users throughout the transit network. Some large cities around the world take advantage of smart card capabilities to manage their transit networks by using Smart Card Automated Fare Collection Systems (SCAFCS). An SCAFCS gives travelers greater flexibility, since a single card may be used by one user at various times and on different parts of the transit network, and may support various fare possibilities (by travel, line, zone, period, etc.). For transit operators, these systems not only validate and collect fares, but also represent a rich source of continuous data regarding the use of their network. Actually, this continuous dataset (developed for fare collection) has the potential to provide new knowledge about transit use. Following the application of various pretreatments which make it possible to extract real-time activity, data mining techniques can reveal interesting patterns. These techniques are aimed at precisely describing customer behavior, identifying sets of customers with similar behaviors, and measuring the spatial and temporal variability of transit use. Patterns are extracted and analyzed to document various issues, such as identifying transit use cycles or homogeneous days and weeks of travel for various periods of the year. This information is required for a better understanding and modeling of customer behavior, and consequently better adjustment of the service to the demand. These adjustments may, for instance, lead to the restructuring of the transit network, to the adaptation of route scheduling or to the definition of new and different subscription options (fares). Below, results from various experiments conducted with a real dataset are provided. They show the potential of data mining to provide useful and novel information about user behavior on a transit network. The data processed in the study are extracted from a system operating in a Canadian city (Gatineau, Quebec).
Modeling Quantiles
Authors: Perlich Claudia , Rosset Saharon , Zadrozny Bianca
Published: May 2011

One standard Data Mining setting is defines by a set of n observations on a variable of interest Y and a set of p explanatory variables, or features, x = (x1,...,xp), with the objective of finding a ‘dependence’ of Y on x. Such dependencies can either be of direct interest by themselves or used in the future to predict a Y given an observed x. This typically leads to a model for a conditional central tendency of Y|x, usually the mean E(Y|x). For example, under appropriate model assumptions, Data Mining based on a least squares loss function (like linear least squares or most regression tree approaches), is as a maximum likelihood approach to estimating the conditional mean. This chapter considers situations when the value of interest is not the conditional mean of a continuous variable, but rather a different property of the conditional distribution P(Y|x), in particular a specific quantile of this distribution. Consider for instance the 0.9th quantile of P(Y|x), which is the function c(x) such that P(Y
Multiple Criteria Optimization in Data Mining
Authors: Kou Gang , Peng Yi , Shi Yong
Published: May 2011

Multiple criteria optimization seeks to simultaneously optimize two or more objective functions under a set of constraints. It has a great variety of applications, ranging from financial management, energy planning, sustainable development, to aircraft design. Data mining is aim at extracting hidden and useful knowledge from large databases. Major contributors of data mining include machine learning, statistics, pattern recognition, algorithms, and database technology (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). In recent years, the multiple criteria optimization research community has actively involved in the field of data mining (See, for example: Yu 1985; Bhattacharyya 2000; Francisci & Collard, 2003; Kou, Liu, Peng, Shi, Wise, & Xu, 2003; Freitas 2004; Shi, Peng, Kou, & Chen, 2005; Kou, Peng, Shi, Wise, & Xu, 2005; Kou, Peng, Shi, & Chen, 2006; Shi, Peng, Kou, & Chen, 2007). Many data mining tasks, such as classification, prediction, clustering, and model selection, can be formulated as multi-criteria optimization problems. Depending upon the nature of problems and the characteristics of datasets, different multi-criteria models can be built. Utilizing methodologies and approaches from mathematical programming, multiple criteria optimization is able to provide effective solutions to large-scale data mining problems. An additional advantage of multi-criteria programming is that it assumes no deterministic relationships between variables (Hand & Henley, 1997).
Program Comprehension through Data Mining
Authors: Kouris Ioannis N.
Published: May 2011

Software development has various stages, that can be conceptually grouped into two phases namely development and production (Figure 1). The development phase includes requirements engineering, architecting, design, implementation and testing. The production phase on the other hand includes the actual deployment of the end product and its maintenance. Software maintenance is the last and most difficult stage in the software lifecycle (Sommerville, 2001), as well as the most costly one. According to Zelkowitz, Shaw and Gannon (1979) the production phase accounts for 67% of the costs of the whole process, whereas according to Van Vliet (2000) the actual cost of software maintenance has been estimated at more than half of the total software development cost. The development phase is critical in order to facilitate efficient and simple software maintenance. The earlier stages should be done by taking into consideration apart from any functional requirements also the later maintenance task. For example the design stage should plan the structure in a way that can be easily altered. Similarly, the implementation stage should create code that can be easily read, understood, and changed, and should also keep the code length to a minimum. According to Van Vliet (2000) the final source code length generated is the determinant factor for the total cost during maintenance, since obviously the less code is written the easier the maintenance becomes. According to Erdil et al. (2003) there are four major problems that can slow down the whole maintenance process: unstructured code, maintenance programmers having insufficient knowledge of the system, documentation being absent, out of date, or at best insufficient, and software maintenance having a bad image. Thus the success of the maintenance phase relies on these problems being fixed earlier in the life cycle. In real life however when programmers decide to perform some maintenance task on a program such as to fix bugs, to make modifications, to create software updates etc. these are usually done in a state of time and commercial pressures and with the logic of cost reduction, thus finally resulting in a problematic system with ever increased complexity. As a consequence the maintainers spend from 50% up to almost 90% of their time trying to comprehend the program (Erdös and Sneed; 1998, Von Mayrhauser and Vans; 1994, Pigoski, 1996). Providing maintainers with tools and techniques to comprehend the programs has become and is receiving a lot of financial and research interest given the widespread of computers and software in all aspects of life. In this work we briefly present some of the most important techniques proposed in the field thus far and focus primarily on the use of data mining techniques in general and especially on association rules. Accordingly we give some possible solutions to problems faced by these methods.