bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Role of Communities of Practice in Regional Innovation Systems
Authors: Teigland Robin , Schenkel Andrew
Published: May 2011

In the past two decades, the related concepts of regional innovation systems and clusters have become widely circulated in both academic and policy circles. Both concepts depart from the idea that innovations predominantly occur as a result of interactions between various actors, rather than as a result of a solitary genius (Håkansson, 1987; von Hippel; 1988; Lundvall, 1992), and that innovation and industrial transformation result from interactions across sets of actors within a spatially defined territory (e.g., countries, regions). Researchers within this field posit that most innovations are based on some form of problem solving in which someone generally perceives a problem and turns to someone else for help and advice (Teigland, Lindqvist, Malmberg & Waxell, 2004), and that spatial proximity seems to enhance the processes of interactive learning and innovation (Malmberg & Maskell, 2002). These assumptions draw striking parallels to the traditional concept of communities of practice (Brown & Duguid, 1991; Orr, 1990; Wenger, 1998), which are emergent groups of people who know each other relatively intimately and who primarily work together directly in face-to-face situations since learning and knowledge are situated within a physical setting (Teigland, 2003). Thus, the purpose of this short article is to provide a brief discussion of clusters and regional innovation systems, and propose broad areas of future research in which the community of practice concept can contribute to our understanding of clusters and regional innovation systems.
Hybrid Knowledge Networks Supporting the Collaborative Multidisciplinary Research
Authors: Ranguelov Stanislav
Published: May 2011

Virtual networks are becoming increasingly important instruments for knowledge and collaboration management. In addition, research, development, and innovation performances are among the most important activities in modern organisations. These two issues deal with complex problems that companies, universities, and other organisations can only face with multidisciplinary, geographically widespread teams. This article describes the setup of a model of a hybrid knowledge network that can group and connect together universities and researchers and enable them to collaborate. The proposed model for the virtual network is based on the conjunction of the personal and organisational aspects of collaboration. Due to this union within the organisational structure, two main levels of collaboration have been envisaged, namely the institutional one and the individual one.
Networks of People as an Emerging Business Model
Authors: Robinson Lesley
Published: May 2011

Networking as a skill is becoming more and more important as traditional ways of doing business continue to change. Many organisations are moving from the industrial model of culture to a more “knowledge”-based culture, changing from having structured hierarchies to flatter structures with distributed responsibility. This has vast implications for how things get done. Instead of receiving instructions or being expected to work to a strict process, the knowledge-based organisations are giving people looser frameworks, and expect them to take responsibility for contributing ideas and sharing their knowledge.
Tacit Knowledge in Communities of Practice
Authors: Zappavigna Michele Suzanne
Published: May 2011

Harnessing the tacit knowledge latent in communities of practice in organizations is a major impetus in knowledge management research and practice. The concept of practice itself is closely associated with activity that is below-view such as intuition and tacit knowing. Indeed, the features binding the members of the communities are often tacit in nature, including things such as rules of thumb, ideologies, embedded habits, or predispositions. Much research in knowledge management posits a dichotomy of doing and saying: what we can do is, in these frameworks, necessarily distinct from what we can say. Polanyi’s (1966) idea that “we know more than we can tell” (p. 4) is often cited to affirm this differentiation. This article seeks to review this relation with tacit knowledge as a focus and suggests that the skillful practice of communities of practice is carried in the discourse which they produce. We adopt a functional approach to discourse, drawn from Systemic Functional Linguistics, that suggests a realization relationship between doing, meaning, and saying rather than a series of dichotomies involving these three semiotic modes. According to this view, what we can say embodies what we can mean which in turn embodies what we can do (Halliday, 1975). This approach is in accord with Wenger’s (1998) opposition to formalist dichotomies when theorizing social action. This entry is structured to present the potential of discourse analysis as an analytical tool to understand the tacit component of participation in communities of practice. The background section details the issues which theories of practice have raised for knowledge management and information systems research. We then review the analytical tools which the field of linguistics offers to uncover implicit knowledge and assumptions in communities (e.g., Iedema, 2003; Jorderns & Little, 2004; Zappavigna-Lee & Patrick, 2004; Zappavigna-Lee et al., 2003). We conclude by arguing that the nature of our skillful practice may be carried in language: we articulate what we know through patterns and features of language of which we are not consciously aware. Analysis of this kind of language aims to elicit implicit meaning and is allied with psychoanalytical methods that attempt to understand implicit aspects of social experience.
Team-Work Issues in Virtual Teams
Authors: Cargill Barbara J.
Published: May 2011

There has been much written about virtual organisations and virtual teams in the last five years. We have begun to research the shift in work organisation paradigms and structures, translating much of what we knew already about workgroups and teams in conventional workplaces into the new contexts, and adding some new issues and understandings into the mix. We may need to translate a little further to come to grips with the ‘virtual teams’ that are actually communities of practice (CoPs).
Technical Issues Facing Work Groups, Teams, and Knowledge Networks
Authors: Louis Lynda R.
Published: May 2011

Communities of practice have become prevalent in today’s business and educational environments. Loosely defined, communities of practice (CoPs) are informal groups within or across organizations. These groups are viewed to have a common set of information needs or problems. Davies, Duke, and Sure (2003) suggest that while not a typical organizational unit, these informal, work-oriented networks share a common agenda, interests, or issues. Millen and Fontaine (2003) assert that these communities are defined by a common disciplinary background, similar work activities and tools, and have shared stories, contexts, and values. Burk (2000) says CoPs are expansions of one-on-one knowledge sharing. This article discusses first the background to such issues as team formation, member participation, and sustained membership; it then considers future trends in knowledge capture and sharing, and the support such communities require.
Aggregate Query Rewriting in Multidimensional Databases
Authors: Tininini Leonardo
Published: May 2011

An efficient query engine is certainly one of the most important components in data warehouses (also known as OLAP systems or multidimensional databases) and its efficiency is influenced by many other aspects, both logical (data model, policy of view materialization, etc.) and physical (multidimensional or relational storage, indexes, etc). As is evident, OLAP queries are often based on the usual metaphor of the data cube and the concepts of facts, measures and dimensions and, in contrast to conventional transactional environments, they require the classification and aggregation of enormous quantities of data. In spite of that, one of the fundamental requirements for these systems is the ability to perform multidimensional analyses in online response times. Since the evaluation from scratch of a typical OLAP aggregate query may require several hours of computation, this can only be achieved by pre-computing several queries, storing the answers permanently in the database and then reusing them in the query evaluation process. These pre-computed queries are commonly referred to as materialized views and the problem of evaluating a query by using (possibly only) these precomputed results is known as the problem of answering/rewriting queries using views. In this paper we briefly analyze the difference between query answering and query rewriting approach and why query rewriting is preferable in a data warehouse context. We also discuss the main techniques proposed in literature to rewrite aggregate multidimensional queries using materialized views.
API Standardization Efforts for Data Mining
Authors: Zendulka Jaroslav
Published: May 2011

Data mining technology just recently became actually usable in real-world scenarios. At present, the data mining models generated by commercial data mining and statistical applications are often used as components in other systems in such fields as customer relationship management, risk management or processing scientific data. Therefore, it seems to be natural that most data mining products concentrate on data mining technology rather than on the easy-to-use, scalability, or portability. It is evident that employing common standards greatly simplifies the integration, updating, and maintenance of applications and systems containing components provided by other producers (Grossman, Hornick, & Meyer, 2002). Data mining models generated by data mining algorithms are good examples of such components.
Best Practices in Data Warehousing from the Federal Perspective
Authors: Pang Les
Published: May 2011

Data warehousing has been a successful approach for supporting the important concept of knowledge management — one of the keys to organizational success at the enterprise level. Based on successful implementations of warehousing projects, a number of lessons learned and best practices were derived from these project experiences. The scope was limited to projects funded and implemented by federal agencies, military institutions and organizations directly supporting them.
Center-Based Clustering and Regression Clustering
Authors: Zhang Bin
Published: May 2011

Center-based clustering algorithms are generalized to more complex model-based, especially regression-model-based, clustering algorithms. This article briefly reviews three center-based clustering algorithms—K-Means, EM, and K-Harmonic Means—and their generalizations to regression clustering algorithms. More details can be found in the referenced publications.
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.
Data Management in Three-Dimensional Structures
Authors: Wang Xiong
Published: May 2011

Data management in its general term refers to activities that involve the acquisition, storage, and retrieval of data. Traditionally, information retrieval is facilitated through queries, such as exact search, nearest neighbor search, range search, etc. In the last decade, data mining has emerged as one of the most dynamic fields in the frontier of data management. Data mining refers to the process of extracting useful knowledge from the data. Popular data mining techniques include association rule discovery, frequent pattern discovery, classification, and clustering. In this chapter, we discuss data management in a specific type of data i.e., three-dimensional structures. While research on text and multimedia data management has attracted considerable attention and substantial progress has been made, data management in three-dimensional structures is still in its infancy (Castelli & Bergman, 2001; Paquet & Rioux, 1999). Data management in 3D structures raises several interesting problems: 1. Similarity search 2. Pattern discovery 3. Classification 4. Clustering
Data Mining in the Soft Computing Paradigm
Authors: Bala Pradip Kumar , Sural Shamik , Banerjee Rabindra Nath
Published: May 2011

Data mining is a set of tools, techniques and methods that can be used to find new, hidden or unexpected patterns from a large volume of data typically stored in a data warehouse. Results obtained from data mining help an organization in more effective individual and group decision-making. Regardless of the specific technique, data mining methods can be classified by the function they perform or by their class of application.
Fuzzy Information and Data Analysis
Authors: Viertl Reinhard
Published: May 2011

The results of data warehousing and data mining are depending essentially on the quality of data. Usually data are assumed to be numbers or vectors, but this is often not realistic. Especially the result of a measurement of a continuous quantity is always not a precise number, but more or less non-precise. This kind of uncertainty is also called fuzziness and should not be confused with errors. Data mining techniques have to take care of fuzziness in order to avoid unrealistic results.
Graph Transformations and Neural Networks
Authors: Fischer Ingrid
Published: May 2011

As the beginning of the area of artificial neural networks, the introduction of the artificial neuron of McCulloch and Pitts is considered. They were inspired by the biological neuron. Since then, many new networks or new algorithms for neural networks have been invented with the result that this area is not very clearly laid out. In most textbooks on (artificial) neural networks (Rojas, 2000; Silipo, 2002), there is no general definition on what a neural net is, but rather an example-based introduction leading from the biological model to some artificial successors. Perhaps the most promising approach to define a neural network is to see it as a network of many simple processors (units), each possibly having a small amount of local memory. The units are connected by communication channels (connections) that usually carry numeric (as opposed to symbolic) data called the weight of the connection. The units operate only on their local data and on the inputs they receive via the connections. It is typical of neural networks that they have great potential for parallelism, since the computations of the components are largely independent of each other. Neural networks work best if the system modeled by them has a high tolerance to error. Therefore, one would not be advised to use a neural network to balance one’s checkbook. However, they work very well for: • capturing associations or discovering regularities within a set of patterns; • any application where the number of variables or diversity of the data is very great; • any application where the relationships between variables are vaguely understood; or, • any application where the relationships are difficult to describe adequately with conventional approaches.
Imprecise Data and the Data Mining Process
Authors: Brown Marvin L. , Kros John F.
Published: May 2011

Missing or inconsistent data has been a pervasive problem in data analysis since the origin of data collection. The management of missing data in organizations has recently been addressed as more firms implement large-scale enterprise resource planning systems (see Vosburg & Kumar, 2001; Xu et al., 2002). The issue of missing data becomes an even more pervasive dilemma in the knowledge discovery process, in that as more data is collected, the higher the likelihood of missing data becomes.
Inexact Field Learning Approach for Data Mining
Authors: Dai Honghua
Published: May 2011

Inexact fielding learning (IFL) (Ciesieski & Dai, 1994; Dai & Ciesieski, 1994a, 1994b, 1995, 2004; Dai & Li, 2001) is a rough-set, theory-based (Pawlak, 1982) machine learning approach that derives inexact rules from fields of each attribute. In contrast to a point-learning algorithm (Quinlan, 1986, 1993), which derives rules by examining individual values of each attribute, a field learning approach (Dai, 1996) derives rules by examining the fields of each attribute. In contrast to exact rule, an inexact rule is a rule with uncertainty. The advantage of the IFL method is the capability to discover high-quality rules from low-quality data, its property of low-quality data tolerant (Dai & Ciesieski, 1994a, 2004), high efficiency in discovery, and high accuracy of the discovered rules.
Instance Selection
Authors: Liu Huan , Yu Lei
Published: May 2011

The amounts of data have become increasingly large in recent years as the capacity of digital data storage worldwide has significantly increased. As the size of data grows, the demand for data reduction increases for effective data mining. Instance selection is one of the effective means to data reduction. This article introduces the basic concepts of instance selection and its context, necessity, and functionality. The article briefly reviews the state-of-the-art methods for instance selection.
Interval Set Representations of Clusters
Authors: Lingras Pawan , Yan Rui , Hogo Mofreh , West Chad
Published: May 2011

The amount of information that is available in the new information age has made it necessary to consider various summarization techniques. Classification, clustering, and association are three important data-mining features. Association is concerned with finding the likelihood of co-occurrence of two different concepts. For example, the likelihood of a banana purchase given that a shopper has bought a cake. Classification and clustering both involve categorization of objects. Classification processes a previously known categorization of objects from a training sample so that it can be applied to other objects whose categorization is unknown. This process is called supervised learning. Clustering groups objects with similar characteristics. As opposed to classification, the grouping process in clustering is unsupervised. The actual categorization of objects, even for a sample, is unknown. Clustering is an important step in establishing object profiles.
Kernel Methods in Chemoinformatics
Authors: Lodhi Huma
Published: May 2011

Millions of people are suffering from fatal diseases such as cancer, AIDS, and many other bacterial and viral illnesses. The key issue is now how to design lifesaving and cost-effective drugs so that the diseases can be cured and prevented. It would also enable the provision of medicines in developing countries, where approximately 80% of the world population lives. Drug design is a discipline of extreme importance in chemoinformatics. Structure-activity relationship (SAR) and quantitative SAR (QSAR) are key drug discovery tasks.
Lsquare System for Mining Logic Data
Authors: Felici Giovanni , Truemper Klaus
Published: May 2011

The method described in this chapter is designed for data mining and learning on logic data. This type of data is composed of records that can be described by the presence or absence of a finite number of properties. Formally, such records can be described by variables that may assume only the values true or false, usually referred to as logic (or Boolean) variables. In real applications, it may also happen that the presence or absence of some property cannot be verified for some record; in such a case we consider that variable to be unknown (the capability to treat formally data with missing values is a feature of logic-based methods). For example, to describe patient records in medical diagnosis applications, one may use the logic variables healthy, old, has_high_temperature, among many others. A very common data mining task is to find, based on training data, the rules that separate two subsets of the available records, or explains the belonging of the data to one subset or the other. For example, one may desire to find a rule that, based one the many variables observed in patient records, is able to distinguish healthy patients from sick ones. Such a rule, if sufficiently precise, may then be used to classify new data and/or to gain information from the available data. This task is often referred to as machine learning or pattern recognition and accounts for a significant portion of the research conducted in the data mining community. When the data considered is in logic form or can be transformed into it by some reasonable process, it is of great interest to determine explanatory rules in the form of the combination of logic variables, or logic formulas. In the example above, a rule derived from data could be:if (has_high_temperature is true) and (running_nose is true) then (the patient is not healthy).
Marketing Data Mining
Authors: Lo Victor S.Y.
Published: May 2011

Data mining has been widely applied over the past two decades. In particular, marketing is an important application area. Many companies collect large amounts of customer data to understand their customers’ needs and predict their future behavior. This article discusses selected data mining problems in marketing and provides solutions and research opportunities.
Materialized Hypertext Views
Authors: Sindoni Giuseppe
Published: May 2011

A materialized hypertext view can be defined as “a hypertext containing data coming from a database and whose pages are stored in files” (Sindoni, 1999). A Web site presenting data from a data warehouse is an example of such a view. Even if the most popular approach to the generation of such sites is based on dynamic Web pages, a rationale for the materialized approach has produced many research efforts. The topic will cover logical models to describe the structure of the hypertext.
Mining Images for Structure
Authors: Caelli Terry
Published: May 2011

Most data warehousing and mining involves storing and retrieving data either in numerical or symbolic form, varying from tables of numbers to text. However, when it comes to everyday images, sounds, and music, the problem turns out to be far more complex. The major problem with image data mining is not so much image storage, per se, but rather how to automatically index, extract, and retrieve image content (content-based retrieval [CBR]). Most current image data-mining technologies encode image content by means of image feature statistics such as color histograms, edge, texture, or shape densities. Two well- known examples of CBR are IBM’s QBIC system used in the State Heritage Museum and PICASSO (Corridoni, Del Bimbo & Pala, 1999) used for the retrieval of paintings. More recently, there have been some developments in indexing and retrieving images based on the semantics, particularly in the context of multimedia, where, typically, there is a need to index voice and video (semantic-based retrieval [SBR]). Recent examples include the study by Lay and Guan (2004) on artistry-based retrieval of artworks and that of Benitez and Chang (2002) on combining semantic and perceptual information in multimedia retrieval for sporting events.
Mining Microarray Data
Authors: Ge Nanxiang , Liu Li
Published: May 2011

During the last 10 years and in particularly within the last few years, there has been a data explosion associated with the completion of the human genome project (HGP) (IHGMC and Venter et al., 2001) in 2001 and the many sophisticated genomics technologies. The human genome (and genome from other species) now provides an enormous amount of data waiting to be transformed into useful information and scientific knowledge. The availability of genome sequence data also sparks the development of many new technology platforms. Among the available different technology platforms, microarray is one of the technologies that is becoming more and more mature and has been widely used as a tool for scientific discovery. The major application of microarray is for simultaneously measuring the expression level of thousands of genes in the cell. It has been widely used in drug discovery and starts to impact the drug development process.
Mosaic-Based Relevance Feedback for Image Retrieval
Authors: Kao Odej , la Tendresse Ingo
Published: May 2011

A standard approach for content-based image retrieval (CBIR) is based on the extraction and comparison of features usually related to dominant colours, shapes, textures and layout (Del Bimbo, 1999). These features are a-priori defined and extracted, when the image is inserted into the database. At query time the user submits a similar sample image (query-by-sample-image) or draws a sketch (query-by-sketch) of the sought archived image. The similarity degree of the current query image and the target images is determined by calculation of a multidimensional distance between the corresponding features. The computed similarity values allow the creation of an image ranking, where the first k, usually k=32 or k=64, images are considered retrieval hits. These are chained in a list called ranking and then presented to the user. Each of these images can be used as a starting point for a refined search in order to improve the obtained results.
Multimodal Analysis in Multimedia Using Symbolic Kernels
Authors: Aradhye Hrishikesh B. , Dorai Chitra
Published: May 2011

The rapid adoption of broadband communications technology, coupled with ever-increasing capacity-to-price ratios for data storage, has made multimedia information increasingly more pervasive and accessible for consumers. As a result, the sheer volume of multimedia data available has exploded on the Internet in the past decade in the form of Web casts, broadcast programs, and streaming audio and video. However, indexing, search, and retrieval of this multimedia data is still dependent on manual, text-based tagging (e.g., in the form of a file name of a video clip). However, manual tagging of media content is often bedeviled by an inadequate choice of keywords, incomplete and inconsistent terms used, and the subjective biases of the annotator introduced in his or her descriptions of content adversely affecting accuracy in the search and retrieval phase. Moreover, manual annotation is extremely time-consuming, expensive, and unscalable in the face of ever-growing digital video collections. Therefore, as multimedia get richer in content, become more complex in format and resolution, and grow in volume, the urgency of developing automated content analysis tools for indexing and retrieval of multimedia becomes easily apparent.
Onling Analytical Processing Systems
Authors: Tan Rebecca Boon-Noi
Published: May 2011

Since its origin in the 1970’s research and development into databases systems has evolved from simple file storage and processing systems to complex relational databases systems, which have provided a remarkable contribution to the current trends or environments. Databases are now such an integral part of day-to-day life that often people are unaware of their use. For example, purchasing goods from the local supermarket is likely to involve access to a database. In order to retrieve the price of the item, the application program will access the product database. A database is a collection of related data and the database management system (DBMS) is software that manages and controls access to the database (Elmasri & Navathe, 2004).
Path Mining in Web Processes Using Profiles
Authors: Cardoso Jorge
Published: May 2011

Business process management systems (BPMSs) (Smith & Fingar, 2003) provide a fundamental infrastructure to define and manage business processes, Web processes, and workflows. When Web processes and workflows are installed and executed, the management system generates data describing the activities being carried out and is stored in a log. This log of data can be used to discover and extract knowledge about the execution of processes. One piece of important and useful information that can be discovered is related to the prediction of the path that will be followed during the execution of a process. I call this type of discovery path mining. Path mining is vital to algorithms that estimate the quality of service of a process, because they require the prediction of paths. In this work, I present and describe how process path mining can be achieved by using data-mining techniques.
Reasoning about Frequent Patterns with Negation
Authors: Kryszkiewicz Marzena
Published: May 2011

Discovering frequent patterns in large databases is an important data mining problem. The problem was introduced in (Agrawal, Imielinski, & Swami, 1993) for a sales transaction database. Frequent patterns were defined there as sets of items that are purchased together frequently. Frequent patterns are commonly used for building association rules. For example, an association rule may state that 80% of customers who buy fish also buy white wine. This rule is derivable from the fact that fish occurs in 5% of sales transactions and set {fish, white wine} occurs in 4% of transactions. Patterns and association rules can be generalized by admitting negation. A sample association rule with negation could state that 75% of customers who buy coke also buy chips and neither beer nor milk. The knowledge of this kind is important not only for sales managers, but also in medical areas (Tsumoto, 2002). Admitting negation in patterns usually results in an abundance of mined patterns, which makes analysis of the discovered knowledge infeasible. It is thus preferable to discover and store a possibly small fraction of patterns, from which one can derive all other significant patterns when required. In this chapter, we introduce first lossless representations of frequent patterns with negation.
Rule Qualities and Knowledge Combination for Decision-Making
Authors: Bruha Ivan
Published: May 2011

Within the past several years, research in decision-supporting systems has been investigating the possibilities of enhancing their overall performance, particularly their prediction (classification) accuracy, or performance quality, and their time complexity. One such discipline, data mining (DM), processes usually very large databases in a profound and robust way. Since data are collected and stored at a very large acceleration these days, there has been an urgent need for a new generation of robust software packages to extract useful information or knowledge from large volumes of data. Research is supposed to develop methods and techniques to process large data in order to receive knowledge, which is hidden in these databases, that would be compact, more or less abstract, but understandable and useful for further applications.
Statistical Data Editing
Authors: Conversano Claudio , Siciliano Roberta
Published: May 2011

Statistical Data Editing (SDE) is the process of checking data for errors and correcting them. Winkler (1999) defined it as the set of methods used to edit (i.e., clean up) and impute (fill in) missing or contradictory data. The result of SDE is data that can be used for analytic purposes.
Support Vector Machines
Authors: Awad Mamoun , Khan Latifur
Published: May 2011

The availability of reliable learning systems is of strategic importance, as many tasks cannot be solved by classical programming techniques, because no mathematical model of the problem is available. So, for example, no one knows how to write a computer program that performs handwritten character recognition, though plenty of examples are available. It is, therefore, natural to ask if a computer could be trained to recognize the letter A from examples; after all, humans learn to read this way. Given the increasing quantity of data for analysis and the variety and complexity of data analysis problems being encountered in business, industry, and research, demanding the best solution every time is impractical. The ultimate dream, of course, is to have some intelligent agent that can preprocess data, apply the appropriate mathematical, statistical, and artificial intelligence techniques, and then provide a solution and an explanation. In the meantime, we must be content with the pieces of this automatic problem solver. The data miner’s purpose is to use the available tools to analyze data and provide a partial solution to a business problem.
Symbolic Data Clustering
Authors: Diday Edwin , Murthy M. Narasimha
Published: May 2011

In data mining, we generate class/cluster models from large datasets. Symbolic Data Analysis (SDA) is a powerful tool that permits dealing with complex data (Diday, 1988) where a combination of variables and logical and hierarchical relationships among them are used. Such a view permits us to deal with data at a conceptual level, and as a consequence, SDA is ideally suited for data mining. Symbolic data have their own internal structure that necessitates the need for new techniques that generally differ from the ones used on conventional data (Billard & Diday, 2003). Clustering generates abstractions that can be used in a variety of decision-making applications (Jain, Murty, & Flynn, 1999). In this article, we deal with the application of clustering to SDA.
Tree and Graph Mining
Authors: Katsaros Dimitrios , Manolopoulos Yannis
Published: May 2011

During the past decade, we have witnessed an explosive growth in our capabilities to both generate and collect data. Various data mining techniques have been proposed and widely employed to discover valid, novel and potentially useful patterns in these data. Data mining involves the discovery of patterns, associations, changes, anomalies, and statistically significant structures and events in huge collections of data.
Trends in Web Content and Structure Mining
Authors: Lee-Post Anita , Jin Haihao
Published: May 2011

Web mining is the use of data mining techniques to automatically discover and extract information from Web documents and services. This area of research is fast-developing today, drawing attention and interests from both researchers and practitioners. The tremendous growth of information available on the Web and the recent interest in e-commerce have accounted for this phenomenon (Kosala & Blockeel, 2000).
Using Standard APIs for Data Mining in Prediction
Authors: Zendulka Jaroslav
Published: May 2011

There are three standardization initiatives concerning application programming interfaces (API) for data mining — OLE DB for Data Mining (OLE DB for DM), SQL/MM Data Mining (SQL/MM DM), and Java Data Mining (JDM) (Schwenkreis, 2001; Grossman et al., 2002; Grossman, 2004). Their goal is to make it possible for different data mining algorithm providers from various software vendors to be easily plugged into applications. Although the goal is the same for all the APIs, the approach applied in each of them is different. OLE DB for DM is a language-based interface developed by Microsoft, SQL/MM DM is an ISO/IEC standard based on user-defined data types of SQL:1999, and JDM, which is being developed under SUN’s Java Community Process, contains packages of data mining oriented Java interfaces and classes.
Visualization Techniques for Data Mining
Authors: Viktor Herna L. , Paquet Eric
Published: May 2011

The current explosion of data and information, mainly caused by data warehousing technologies as well as the extensive use of the Internet and its related technologies, has increased the urgent need for the development of techniques for intelligent data analysis. Data mining, which concerns the discovery and extraction of knowledge chunks from large data repositories, is aimed at addressing this need. Data mining automates the discovery of hidden patterns and relationships that may not always be obvious. Data mining tools include classification techniques (such as decision trees, rule induction programs and neural networks) (Han & Kamber, 2001), clustering algorithms and association rule approaches, amongst others.
Biometric Databases
Authors: Vatsa Mayank , Singh Richa , Gupta P. , Kaushik A. K.
Published: May 2011

Biometric databases are the set of biometric features collected from a large public domain for the evaluation of biometric systems. For the evaluation of any algorithm for a particular biometric trait, the database should be a large collection of that particular biometric trait. The creation and maintenance of biometric databases involve various steps like enrollment and training.
CASE Tools for Database Engineering
Authors: Hainaut Jean-Luc , Henrard Jean , Hick Jean-Marc , Roland Didier , Englebert Vincent
Published: May 2011

Designing and implementing a database comprising a few tables require a level of expertise that is readily found among most experienced users, provided they are somewhat keen on office productivity tools. Playing a dozen of hours with Microsoft Access should give clever and motivated users sufficient feeling and technical skill to develop small workable databases.
Collective Knowledge Composition in a P2P Network
Authors: Aleman-Meza Boanerges , Halaschek-Wiener Christian , Arpinar I. Budak
Published: May 2011

Today’s data and information management tools enable massive accumulation and storage of knowledge that is produced through scientific advancements, personal and corporate experiences, communications, interactions, and so forth. In addition, the increase in the volume of this data and knowledge continues to accelerate. The willingness and the ability to share and use this information are key factors for realizing the full potential of this knowledge scattered over many distributed computing devices and human beings. By correlating these isolated islands of knowledge, individuals can gain new insights, discover new relations (Sheth, Arpinar & Kashyap, 2003), and produce more knowledge. Despite the abundance of information, knowledge starvation still exists because most of the information cannot be used effectively for decision-making and problem-solving purposes. This is in part due to the lack of easy to use knowledge sharing and collective discovery mechanisms. Thus, there is an emerging need for knowledge tools that will enable users to collectively create, share, browse, and query their knowledge.
Database Query Personalization
Authors: Koutrika Georgia
Published: May 2011

Traditional database and information retrieval systems have followed a query-based information access paradigm (i.e., information is returned to the user on the basis of a query issued). As a result, users issuing the same query are provided with the same answer. With the advent of the World Wide Web and hand-held electronic devices such as palmtops and cellular phones, information access entered a new era. Increasing amounts of information become available to a growing mass of untrained lay users through various access media. A user searching Web-resident information may have to reformulate queries issued several times and sift through many results until a satisfactory, if any, answer is obtained. As purely query-driven approaches may be inappropriate in this context, the need for a shift towards a more user-centered information access paradigm arises. To this end, different approaches aim to the personalization of the overall user experience at different levels: content selection, content presentation, and user interaction.
Discovering Association Rules in Temporal Databases
Authors: Ale Juan M. , Rossi Gustavo H.
Published: May 2011

The problem of the discovery of association rules comes from the need to discover interesting patterns in transaction data in a supermarket. Since transaction data are temporal we expect to find patterns that depend on time. For example, when gathering data about products purchased in a supermarket, the time of the purchase is stamped in the transaction.
E-Mail Data Stores
Authors: Horiuchi Catherine
Published: May 2011

For many people, e-mail has become a running record of their business and personal lives. Somewhere in that big clot of e-mail messages that have accumulated over the years is a wealth of information about people they’ve met, work they’ve done, meetings they’ve held. There are tough calls and tender moments, great debates and funny episodes. When did you first meet a certain person? Just what was the initial offer in a business deal? What was that joke somebody sent you in 1998? The answers lie in your old e-mail. Unfortunately, it’s often easier to search the vast reaches of the World Wide Web than to quickly and accurately search your own stored e-mail. (Mossberg, 2004)
Generic Model Management
Authors: Diskin Zinovy , Kadish Boris
Published: May 2011

Generic model management (gMMt) is a novel view on classical and modern metadata management problems. The present article surveys the goals, components, pros and cons of gMMt, and major problems cited in the literature. It argues that some methodology developed in abstract mathematics can be extremely helpful for the field and is capable of providing it with a convenient notation, semantic foundations and truly generic specification patterns. The two other articles, titled Mathematics of Generic Specifications for Model Management, I (further referred to as Math-I , see p. 351), and Mathematics of Generic Specifications for Model Management, II (further referred to as Math-II, see p. 359), give some evidence to these claims by demonstrating how the machinery works in a series of examples.
Multiparticipant Decision Making and Balanced Scorecard Collaborative
Authors: Xodo Daniel
Published: May 2011

The building of decision support system (DSS) for integrated workgroups sets out problems related to the nature of the decision process as well as its operative implementation.
Signature Files and Signature File Construction
Authors: Chen Yangjun , Shi Yong
Published: May 2011

An important question in information retrieval is how to create a database index which can be searched efficiently for the data one seeks. Today, one or more of the following four techniques have been frequently used: full text searching, B-trees, inversion, and the signature file. Full text searching imposes no space overhead but requires long response time. In contrast, B-trees, inversion, and the signature file work quickl, but need a large intermediary representation structure (index), which provides direct links to relevant data. In this paper, we concentrate on the techniques of signature files and discuss different construction approaches of a signature file.
Storing XML Documents in Databases
Authors: Schmidt Albrecht , Manegold Stefan , Kersten Martin
Published: May 2011

Ever since the Extensible Markup Language (XML) (W3C, 1998b) began to be used to exchange data between diverse sources, interest has grown in deploying data management technology to store and query XML documents. A number of approaches propose to adapt relational database technology to store and maintain XML documents (Deutsch, Fernandez & Suciu, 1999; Florescu & Kossmann, 1999; Klettke & Meyer, 2000; Shanmugasundaram et al., 1999; Tatarinov et al., 2002; O’Neil et al., 2004). The advantage is that the XML repository inherits all the power of mature relational technology like indexes and transaction management. For XML-enabled querying, a declarative query language (Chamberlin et al., 2001) is available.
Transaction Concurrency Methods
Authors: Frank Lars
Published: May 2011

In this article, I will evaluate the most important methods for increasing concurrency between transactions. Because different transactions have different needs, it is important that the evaluation gives an overview of the advantages and disadvantages of the different optimization methods.
Using Views to Query XML Documents
Authors: Cannataro Mario , Cluet Sophie , Tradigo Giuseppe , Veltri Pierangelo , Vodislav Dan
Published: May 2011

We consider using views in databases and in particular their applications to access heterogeneous and large volumes of digital documents, focusing on (1) views in databases and (2) views for data integration.
Adoption of Communication Products and the Individual Critical Mass
Authors: Voeth Markus , Liehr Marcus
Published: May 2011

Communication products are characterized by the fact that the benefit that results from their use is mainly dependent on the number of users of the product, the so-called installed base, and only dependent to a minor degree on the actual product characteristics. The utility of a videoconferencing system, for example, is quite small at the product launch because only a few users are present with whom adopters can communicate. Only the increase in the number of users leads to an enhancement of the utility for each user.
Broadband Solutions for Residential Customers
Authors: Hentea Mariana
Published: May 2011

The term “home networking” implies that electronic network devices work together and communicate amongst themselves. These devices are classified in three categories: appliances, electronics and computers. Home networks include home theater, home office, small office home office (SOHO), intelligent appliances, smart objects, telecommunications products and services, home controls for security, heating/cooling, lighting and so forth. The suite of applications on each device, including the number of connected devices, is specific to each home. The home network configurations are challenges, besides the unpredictable problems that could be higher compared to a traditional business environment. These are important issues that have to be considered by developers supporting home networking infrastructure. In addition, home networks have to operate in an automatically configured plug-and-play mode. Home networks support a diverse suite of applications and services discussed next.
Constructing a Globalized E-Commerce Site
Authors: Chan Tom S.
Published: May 2011

Traditional boundaries and marketplace definitions are fast becoming irrelevant due to globalization. According to recent statistics, there are approximately 208 million English speakers and 608 million non-English speakers online, and 64.2% of Web users speak a native language other than English (Global Reach, 2004). The world outside of English-speaking countries is obviously coming online fast. As with activities such as TV, radio and print, people surf in their own language. A single-language Web site simply could not provide good visibility and accessibility in this age of globalize Internet. In this article, we will focus on the approaches in the construction of an effective globalized e-commerce Web site.
Content-Based Multimedia Retrieval
Authors: Wei Chia-Hung , Li Chang-Tsun
Published: May 2011

In the past decade, there has been rapid growth in the use of digital media such as images, video, and audio. As the use of digital media increases, effective retrieval and management techniques become more important. Such techniques are required to facilitate the effective searching and browsing of large multimedia databases.