bluuebunny/crossref_metadata_2025 huggingface dataset:
Quality of Knowledge in Virtual Entities
Authors: Analide Cesar , Novais Paulo , Machado Jose , Neves Jose
Published: May 2011

The work done by some authors in the fields of computer science, artificial intelligence, and multi-agent systems foresees an approximation of these disciplines and those of the social sciences, namely, in the areas of anthropology, sociology, and psychology. Much of this work has been done in terms of the humanization of the behavior of virtual entities by expressing human-like feelings and emotions. Some authors (e.g., Ortony, Clore & Collins, 1988; Picard, 1997) suggest lines of action considering ways to assign emotions to machines. Attitudes like cooperation, competition, socialization, and trust are explored in many different areas (Arthur, 1994; Challet & Zhang, 1998; Novais et al., 2004). Other authors (e.g., Bazzan et al., 2000; Castelfranchi, Rosis & Falcone, 1997) recognize the importance of modeling virtual entity mental states in an anthropopathic way. Indeed, an important motivation to the development of this project comes from the author’s work with artificial intelligence in the area of knowledge representation and reasoning, in terms of an extension to the language of logic programming, that is, the Extended Logic Programming (Alferes, Pereira & Przymusinski, 1998; Neves, 1984). On the other hand, the use of null values to deal with imperfect knowledge (Gelfond, 1994; Traylor & Gelfond, 1993) and the enforcement of exceptions to characterize the behavior of intelligent systems (Analide, 2004) is another justification for the adoption of these formalisms in this knowledge arena. Knowledge representation, as a way to describe the real world based on mechanical, logical, or other means, will always be a function of the systems ability to describe the existent knowledge and their associated reasoning mechanisms. Indeed, in the conception of a knowledge representation system, it must be taken into attention different instances of knowledge.
The Role of Technology in Supporting Communities of Practice
Authors: Chua Alton
Published: May 2011

Most KM literature which investigates the role of technology in supporting communities of practice is oriented either technically or socially. Works that adopt a technical orientation tend to focus sharply on issues such as robustness, scalability, interoperability, and security. Furthermore, they invariably include a plethora of KM tools ranging from databases, portals, and search engines. The dynamics that occur in communities of practice, on the other hand, receive cursory treatment. Works that adopt a social orientation tend to delve into cognitive and social processes but are usually confined within the context of distributed communities. They discuss how technology is used to mitigate the geographical separation among members. However, beyond citing the generic capabilities of technology, such as enabling connection among members, holding electronic content, and providing search functions, the role of technology to meet the peculiar needs of communities of practice has rarely been expounded. For this reason, this article seeks to clarify how technology can be used to support communities of practice. First, it develops a conceptual model which provides a parsimonious approach to unravel the nebular properties of communities of practice. Next, it explains the power and limitation of technology within the realm of knowledge management and proposes a suite of capabilities found in extant technology tools which supports communities of practice. Finally, this article briefly discusses emerging technologies that could be used to meet the rising needs of community members.
Use and Methods of Social Network Analysis in Knowledge Management
Authors: Müller-Prothmann Tobias
Published: May 2011

Whilst the primary importance of informal communities of practice and knowledge networks in innovation and knowledge management is widely accepted (see Armbrecht et al., 2001; Brown & Duguid, 1991; Collinson & Gregson, 2003; Jain & Triandis, 1990; Lesser, 2001; Liyanage, Greenfied & Don, 1999; Nahapiet & Ghoshal, 1998; Nohria & Eccles, 1992; Wenger, 1999; Zanfei, 2000), there is less agreement on the most appropriate method for their empirical study and theoretical analysis. In this article it is argued that social network analysis (SNA) is a highly effective tool for the analysis of knowledge networks, as well as for the identification and implementation of practical methods in knowledge management and innovation. Social network analysis is a sociological method to undertake empirical analysis of the structural patterns of social relationships in networks (see, e.g., Scott, 1991; Wasserman & Faust, 1994; Wellman & Berkowitz, 1988). This article aims at demonstrating how it can be used to identify, visualize, and analyze the informal personal networks that exist within and between organizations according to structure, content, and context of knowledge flows. It will explore the benefits of social network analysis as a strategic tool on the example of expert localization and knowledge transfer, and also point to the limits of the method.
Active Disks for Data Mining
Authors: Thomasian Alexander
Published: May 2011

Active disks allow the downloading of certain types of processing from the host computer onto disks, more specifically the disk controller, which has access to a limited amount of local memory serving as the disk cache. Data read from a disk may be sent directly to the host computer as usual or processed locally at the disk. Only filtered information is uploaded to the host computer. In this article, We am interested in downloading data mining and database applications to the disk controller.
Active Learning with Multiple Views
Authors: Muslea Ion
Published: May 2011

Inductive learning algorithms typically use a set of labeled examples to learn class descriptions for a set of user-specified concepts of interest. In practice, labeling the training examples is a tedious, time consuming, error-prone process. Furthermore, in some applications, the labeling of each example also may be extremely expensive (e.g., it may require running costly laboratory tests). In order to reduce the number of labeled examples that are required for learning the concepts of interest, researchers proposed a variety of methods, such as active learning, semi-supervised learning, and meta-learning.
Administering and Managing a Data Warehouse
Authors: Yao James E. , Liu Chang , Chen Qiyang , Lu June
Published: May 2011

As internal and external demands on information from managers are increasing rapidly, especially the information that is processed to serve managers’ specific needs, regular databases and decision support systems (DSS) cannot provide the information needed. Data warehouses came into existence to meet these needs, consolidating and integrating information from many internal and external sources and arranging it in a meaningful format for making accurate business decisions (Martin, 1997). In the past five years, there has been a significant growth in data warehousing (Hoffer, Prescott, & McFadden, 2005). Correspondingly, this occurrence has brought up the issue of data warehouse administration and management. Data warehousing has been increasingly recognized as an effective tool for organizations to transform data into useful information for strategic decision-making. To achieve competitive advantages via data warehousing, data warehouse management is crucial (Ma, Chou, & Yen, 2000).
Aggregation for Predictive Modeling with Relational Data
Authors: Perlich Claudia , Provost Foster
Published: May 2011

Most data mining and modeling techniques have been developed for data represented as a single table, where every row is a feature vector that captures the characteristics of an observation. However, data in most domains are not of this form and consist of multiple tables with several types of entities. Such relational data are ubiquitous; both because of the large number of multi-table relational databases kept by businesses and government organizations, and because of the natural, linked nature of people, organizations, computers, and etc. Relational data pose new challenges for modeling and data mining, including the exploration of related entities and the aggregation of information from multi-sets (“bags”) of related entities.
Association Rule Mining and Application to MPIS
Authors: Chi-Wing Wong Raymond , Wai-Chee fu Ada
Published: May 2011

Association rule mining (Agrawal, Imilienski, & Swami, 1993) has been proposed for understanding the relationships among items in transactions or market baskets. For instance, if a customer buys butter, what is the chance that he/she buys bread at the same time? Such information may be useful for decision makers to determine strategies in a store.
Assocation Rules and Statistics
Authors: Cadot Martine , Maj Jean-Baptiste , Ziade Tarek
Published: May 2011

A manager would like to have a dashboard of his company without manipulating data. Usually, statistics have solved this challenge, but nowadays, data have changed (Jensen, 1992); their size has increased, and they are badly structured (Han & Kamber, 2001). A recent method—data mining—has been developed to analyze this type of data (Piatetski-Shapiro, 2000). A specific method of data mining, which fits the goal of the manager, is the extraction of association rules (Hand, Mannila & Smyth, 2001). This extraction is a part of attribute-oriented induction (Guyon & Elisseeff, 2003).
Bibliomining for Library Decision-Making
Authors: Nicholson Scott , Stanton Jeffrey
Published: May 2011

Most people think of a library as the little brick building in the heart of their community or the big brick building in the center of a college campus. However, these notions greatly oversimplify the world of libraries. Most large commercial organizations have dedicated in-house library operations, as do schools; nongovernmental organizations; and local, state, and federal governments. With the increasing use of the World Wide Web, digital libraries have burgeoned, serving a huge variety of different user audiences. With this expanded view of libraries, two key insights arise. First, libraries are typically embedded within larger institutions. Corporate libraries serve their corporations, academic libraries serve their universities, and public libraries serve taxpaying communities who elect overseeing representatives. Second, libraries play a pivotal role within their institutions as repositories and providers of information resources. In the provider role, libraries represent in microcosm the intellectual and learning activities of the people who comprise the institution. This fact provides the basis for the strategic importance of library data mining: By ascertaining what users are seeking, bibliomining can reveal insights that have meaning in the context of the library’s host institution.
Biomedical Data Mining Using RBF Neural Networks
Authors: Chu Fang , Wang Lipo
Published: May 2011

Accurate diagnosis of cancers is of great importance for doctors to choose a proper treatment. Furthermore, it also plays a key role in the searching for the pathology of cancers and drug discovery. Recently, this problem attracts great attention in the context of microarray technology. Here, we apply radial basis function (RBF) neural networks to this pattern recognition problem. Our experimental results in some well-known microarray data sets indicate that our method can obtain very high accuracy with a small number of genes.
Closed-Itemset Incremental-Mining Problem
Authors: Dumitriu Luminita
Published: May 2011

Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold. According to Agrawal, Imielinski and Swami, this problem is solved in two steps: 1. Find all frequent itemsets in the database. 2. For each frequent itemset I, generate all the association rules I’ÞI\I’, where I’ÌI.
Clustering Analysis and Algorithms
Authors: Huang Xiangji
Published: May 2011

Clustering is the process of grouping a collection of objects (usually represented as points in a multidimensional space) into classes of similar objects. Cluster analysis is a very important tool in data analysis. It is a set of methodologies for automatic classification of a collection of patterns into clusters based on similarity. Intuitively, patterns within the same cluster are more similar to each other than patterns belonging to a different cluster. It is important to understand the difference between clustering (unsupervised classification) and supervised classification.
Condensed Representations for Data Mining
Authors: Boulicaut Jean-Francois
Published: May 2011

Condensed representations have been proposed in Mannila and Toivonen (1996) as a useful concept for the optimization of typical data-mining tasks. It appears as a key concept within the inductive database framework (Boulicaut et al., 1999; de Raedt, 2002; Imielinski & Mannila, 1996), and this article introduces this research domain, its achievements in the context of frequent itemset mining (FIM) from transactional data, and its future trends.
Data Mining and Decision Support for Business and Science
Authors: Ganguly Auroop R. , Gupta Amar , Khan Shiraj
Published: May 2011

Information by itself is no longer perceived as an asset. Billions of business transactions are recorded in enterprise-scale data warehouses every day. Acquisition, storage, and management of business information are commonplace and often automated. Recent advances in remote or other sensor technologies have led to the development of scientific data repositories. Database technologies, ranging from relational systems to extensions like spatial, temporal, time series, text, or media, as well as specialized tools like geographical information systems (GIS) or online analytical processing (OLAP), have transformed the design of enterprise-scale business or large scientific applications. The question increasingly faced by the scientific or business decision-maker is not how one can get more information or design better information systems but what to make of the information and systems already in place. The challenge is to be able to utilize the available information, to gain a better understanding of the past, and to predict or influence the future through better decision making. Researchers in data mining technologies (DMT) and decision support systems (DSS) are responding to this challenge. Broadly defined, data mining (DM) relies on scalable statistics, artificial intelligence, machine learning, or knowledge discovery in databases (KDD). DSS utilize available information and DMT to provide a decision-making tool usually relying on human-computer interaction. Together, DMT and DSS represent the spectrum of analytical information technologies (AIT) and provide a unifying platform for an optimal combination of data dictated and human-driven analytics.
Data Mining and Warehousing in Pharma Industry
Authors: Kusiak Andrew , Shah Shital C.
Published: May 2011

Most processes in pharmaceutical industry are data driven. Company’s ability to capture the data and making use of it will grow in significance and may become the main factor differentiating the industry. Basic concepts of data mining, data warehousing, and data modeling are introduced. These new data-driven concepts lead to a paradigm shift in pharmaceutical industry.
Data Mining for Intrusion Detection
Authors: Lazarevic Aleksandar
Published: May 2011

Today computers control power, oil and gas delivery, communication systems, transportation networks, banking and financial services, and various other infrastructure services critical to the functioning of our society. However, as the cost of the information processing and Internet accessibility falls, more and more organizations are becoming vulnerable to a wide variety of cyber threats. According to a recent survey by CERT/CC (Computer Emergency Response Team/Coordination Center), the rate of cyber attacks has been more than doubling every year in recent times (Figure 1). In addition, the severity and sophistication of the attacks are also growing. For example, Slammer/Sapphire Worm was the fastest computer worm in history. As it began spreading throughout the Internet, it doubled in size every 8.5 seconds and infected at least 75,000 hosts causing network outages and unforeseen consequences such as canceled airline flights, interference with elections, and ATM failures (Moore, 2003).
Data Mining in Human Resources
Authors: Troutt Marvin D. , Long Lori K.
Published: May 2011

In this paper, we briefly review and update our earlier work (Long & Troutt, 2003) on the topic of data mining in the human resources area. To gain efficiency, many organizations have turned to technology to automate many HR processes (Hendrickson, 2003). As a result of this automation, HR professionals are able to make more informed strategic HR decisions (Bussler & Davis, 2002). While HR professionals may no longer need to manage the manual processing of data, they should not abandon their ties to data collected on and about the organization’s employees. Using HR data in decision-making provides a firm with the opportunity to make more informed strategic decisions. If a firm can extract useful or unique information on the behavior and potential of their people from HR data, they can contribute to the firm’s strategic planning process. The challenge is identifying useful information in vast human resources databases that are the result of the automation of HR related transaction processing.
Data Mining Medical Digital Libraries
Authors: Cunningham Colleen , Hu Xiaohua
Published: May 2011

Given the exponential growth rate of medical data and the accompanying biomedical literature, more than 10,000 documents per week (Leroy et al., 2003), it has become increasingly necessary to apply data mining techniques to medical digital libraries in order to assess a more complete view of genes, their biological functions and diseases. Data mining techniques, as applied to digital libraries, are also known as text mining.
Data Mining with Incomplete Data
Authors: Wang Hai , Wang Shouhong
Published: May 2011

Survey is one of the common data acquisition methods for data mining (Brin, Rastogi & Shim, 2003). In data mining one can rarely find a survey data set that contains complete entries of each observation for all of the variables. Commonly, surveys and questionnaires are often only partially completed by respondents. The possible reasons for incomplete data could be numerous, including negligence, deliberate avoidance for privacy, ambiguity of the survey question, and aversion. The extent of damage of missing data is unknown when it is virtually impossible to return the survey or questionnaires to the data source for completion, but is one of the most important parts of knowledge for data mining to discover. In fact, missing data is an important debatable issue in the knowledge engineering field (Tseng, Wang, & Lee, 2003).
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 article provides an overview of two methods for improving quality. The first is data cleaning 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 data-cleaning methods are suitable for files with hundreds of millions of records (Winkler, 1999b, 2003b). The fastest edit/imputation methods are suitable for files with millions of records (Winkler, 1999a, 2004b).
Database Queries, Data Mining, and OLAP
Authors: Hamel Lutz
Published: May 2011

Modern, commercially available relational database systems now routinely include a cadre of data retrieval and analysis tools. Here we shed some light on the interrelationships between the most common tools and components included in today’s database systems: query language engines, data mining components, and online analytical processing (OLAP) tools. We do so by pair-wise juxtaposition, which will underscore their differences and highlight their complementary value.
Database Sampling for Data Mining
Authors: Lutu Patricia E.N.
Published: May 2011

In data mining, sampling may be used as a technique for reducing the amount of data presented to a data mining algorithm. Other strategies for data reduction include dimension reduction, data compression, and discretisation. For sampling, the aim is to draw, from a database, a random sample, which has the same characteristics as the original database. This chapter looks at the sampling methods that are traditionally available from the area of statistics, how these methods have been adapted to database sampling in general and database sampling for data mining in particular.
Evolution of Data Cube Computational Approaches
Authors: Tan Rebecca Boon-Noi
Published: May 2011

Aggregation is a commonly used operation in decision support database systems. Users of decision support queries are interested in identifying trends rather than looking at individual records in isolation. Decision support system (DSS) queries consequently make heavy use of aggregations, and the ability to simultaneously aggregate across many sets of dimensions (in SQL terms, this translates to many simultaneous group-bys) is crucial for Online Analytical Processing (OLAP) or multidimensional data analysis applications (Datta, VanderMeer, & Ramamritham, 2002; Dehne, Eavis, Hambrusch, & Rau-Chaplin, 2002; Elmasri & Navathe, 2004; Silberschatz, Korth & Sudarshan, 2002).
General Model for Data Warehouses
Authors: Schneider Michel
Published: May 2011

Basically, the schema of a data warehouse lies on two kinds of elements: facts and dimensions. Facts are used to memorize measures about situations or events. Dimensions are used to analyse these measures, particularly through aggregation operations (counting, summation, average, etc.). To fix the ideas let us consider the analysis of the sales in a shop according to the product type and to the month in the year. Each sale of a product is a fact. One can characterize it by a quantity. One can calculate an aggregation function on the quantities of several facts. For example, one can make the sum of quantities sold for the product type “mineral water” during January in 2001, 2002 and 2003. Product type is a criterion of the dimension Product. Month and Year are criteria of the dimension Time. A quantity is so connected both with a type of product and with a month of one year. This type of connection concerns the organization of facts with regard to dimensions. On the other hand a month is connected to one year. This type of connection concerns the organization of criteria within a dimension. The possibilities of fact analysis depend on these two forms of connection and on the schema of the warehouse. This schema is chosen by the designer in accordance with the users needs.
Graph-Based Data Mining
Authors: Holder Lawrence B. , Cook Diane J.
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 graph-based data mining are frequent subgraph mining and graph-based relational learning. This article 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.
Hyperbolic Space for Interactive Visualization
Authors: Walter Jörg Andreas
Published: May 2011

For many tasks of exploratory data analysis, visualization plays an important role. It is a key for efficient integration of human expertise — not only to include his background knowledge, intuition and creativity, but also his powerful pattern recognition and processing capabilities. The design goals for an optimal user interaction strongly depend on the given visualization task, but they certainly include an easy and intuitive navigation with strong support for the user’s orientation.
Identifying Single Clusters in Large Data Sets
Authors: Klawonn Frank , Georgieva Olga
Published: May 2011

Most clustering methods have to face the problem of characterizing good clusters among noise data. The arbitrary noise points that just do not belong to any class being searched for are of a real concern. The outliers or noise data points are data that severely deviate from the pattern set by the majority of the data, and rounding and grouping errors result from the inherent inaccuracy in the collection and recording of data. In fact, a single outlier can completely spoil the least squares (LS) estimate and thus the results of most LS based clustering techniques such as the hard C-means (HCM) and the fuzzy C-means algorithm (FCM) (Bezdek, 1999).
Inter-Transactional Association Analysis for Prediction
Authors: Feng Ling , Dillon Tharam
Published: May 2011

The discovery of association rules from large amounts of structured or semi-structured data is an important data-mining problem (Agrawal et al., 1993; Agrawal & Srikant, 1994; Braga et al., 2002, 2003; Cong et al., 2002; Miyahara et al., 2001; Termier et al., 2002; Xiao et al., 2003). It has crucial applications in decision support and marketing strategy. The most prototypical application of association rules is market-basket analysis using transaction databases from supermarkets. These databases contain sales transaction records, each of which details items bought by a customer in the transaction. Mining association rules is the process of discovering knowledge such as, 80% of customers who bought diapers also bought beer, and 35% of customers bought both diapers and beer, which can be expressed as “diaper Þ beer” (35%, 80%), where 80% is the confidence level of the rule, and 35% is the support level of the rule indicating how frequently the customers bought both diapers and beer. In general, an association rule takes the form X Þ Y (s, c), where X and Y are sets of items, and s and c are support and confidence, respectively.
Materialized Hypertext View Maintenance
Authors: Sindoni Giuseppe
Published: May 2011

A hypertext view is a hypertext containing data from an underlying database. The materialization of such hypertexts, that is, the actual storage of their pages in the site server, is often a valid option1. Suitable auxiliary data structures and algorithms must be designed to guarantee consistency between the structures and contents of each heterogeneous component where base data is stored and those of the derived hypertext view.
Mining Chat Discussions
Authors: Loh Stanley , Licthnow Daniel , Borges Thyago , Primo Tiago , Branco Kockhofel Rodrigo , Simoes Gabriel , Piltcher Gustavo , Saldana Ramiro
Published: May 2011

According to Nonaka and Takeuchi (1995), the majority of the organizational knowledge comes from interactions between people. People tend to reuse solutions from other persons in order to gain productivity.
Mining for Image Classification Based on Feature Elements
Authors: Zhang Yu-Jin
Published: May 2011

The growth of the Internet and storage capability not only increasingly makes images a widespread information format on the World Wide Web (WWW), but it also dramatically expands the number of images on WWW and makes the search of required images more complex and time-consuming. To efficiently search images on the WWW, effective image search engines need to be developed.
Predicting Resource Usage for Capital Efficient Marketing
Authors: Mani D. R. , Betz Andrew L. , Drew James H.
Published: May 2011

A structural conflict exists in businesses that sell services whose production costs are discontinuous and whose consumption is continuous but variable. A classic example is in businesses where capital-intensive infrastructure is necessary for provisioning service, but the capacity resulting from capital outlay is not always fully and efficiently utilized. Marketing departments focus on initiatives that increase infrastructure usage to improve both customer retention and ongoing revenue. Engineering and operations departments focus on the cost of service provision to improve the capital efficiency of revenue dollars received. Consequently, a marketing initiative to increase infrastructure usage may be resisted by engineering, if its introduction would require great capital expense to accommodate that increased usage. This conflict is exacerbated when a usage-enhancing initiative tends to increase usage variability so that capital expenditures are triggered with only small increases in total usage.
Privacy and Confidentiality Issues in Data Mining
Authors: Saygin Yücel
Published: May 2011

Data regarding people and their activities have been collected over the years, which has become more pervasive with widespread usage of the Internet. Collected data usually are stored in data warehouses, and powerful data mining tools are used to turn it into competitive advantage. Besides businesses, government agencies are among the most ambitious data collectors, especially in regard to the increase of safety threats coming from global terrorist organizations. For example, CAPPS (Computer Assisted Passenger Prescreening System) collects flight reservation information as well as commercial information about passengers. This data, in turn, can be utilized by government security agencies. Although CAPPS represents US national data collection efforts, it also has an effect on other countries. The following sign at the KLM ticket desk in Amsterdam International Airport illustrates the international level of data collection efforts: “Please note that KLM Royal Dutch Airlines and other airlines are required by new security laws in the US and several other countries to give security customs and immigration authorities access to passenger data. Accordingly, any information we hold about you and your travel arrangements may be disclosed to the concerning authorities of these countries in your itinerary.” This is a very striking example of how the confidential data belonging to citizens of one country could be handed over to authorities of some other country via newly enforced security laws. In fact, some of the largest airline companies in the US, including American, United, and Northwest, turned over millions of passenger records to the FBI, according to the New York Times (Schwartz & Maynard, 2004).
Reinforcing CRM with Data Mining
Authors: Zhu Dan
Published: May 2011

With the explosive growth of information available on the World Wide Web, users must increasingly use automated tools to find, extract, filter, and evaluate desired information and resources. Companies are investing significant amounts of time and money on creating, developing, and enhancing individualized customer relationships, a process called customer relationship management, or CRM (Berry & Linoff, 1999; Buttle, 2003; Rud, 2000). Based on a report by the Aberdeen Group, worldwide CRM spending reached $13.7 billion in 2002 and should be close to $20 billion by 2006.
Resource Allocation in Wireless Networks
Authors: Katsaros Dimitrios , Yavas Gökhan , Nanopoulos Alexandros , Karakaya Murat , Ulusoy Özgür , Manolopoulos Yannis
Published: May 2011

During the past years, we have witnessed an explosive growth in our capabilities to both generate and collect data. Advances in scientific data collection, the computerization of many businesses, and the recording (logging) of clients’ accesses to networked resources have generated a vast amount of data. Various data mining techniques have been proposed and widely employed to discover valid, novel and potentially useful patterns in these data.
Robust Face Recognition for Data Mining
Authors: Lovell Brain C. , Chen Shaokang
Published: May 2011

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the Internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata, such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them — and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, post-production of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the Internet backbone.
Rough Sets and Data Mining
Authors: Grzymala-Busse Jerzy W. , Ziarko Wojciech
Published: May 2011

Discovering useful models capturing regularities of natural phenomena or complex systems until recently was almost entirely limited to finding formulae fitting empirical data. This worked relatively well in physics, theoretical mechanics, and other areas of science and engineering. However, in social sciences, market research, medicine, pharmacy, molecular biology, learning and perception, and in many other areas, the complexity of the natural processes and their common lack of analytical smoothness almost totally exclude the use of standard tools of mathematics for the purpose of data-based modeling. A fundamentally different approach is needed in those areas. The availability of fast data processors creates new possibilities in that respect. This need for alternative approaches to modeling from data was recognized some time ago by researchers working in the areas of neural nets, inductive learning, rough sets, and, more recently, data mining. The empirical models in the form of data-based structures of decision tables or rules play similar roles to formulas in classical analytical modeling. Such models can be analyzed, interpreted, and optimized using methods of rough set theory.
Semi-Supervised Learning
Authors: Scheffer Tobias
Published: May 2011

For many classification problems, unlabeled training data are inexpensive and readily available, whereas labeling training data imposes costs. Semi-supervised classification algorithms aim at utilizing information contained in unlabeled data in addition to the (few) labeled data.
Sequential Pattern Mining
Authors: Masseglia Florent , Teisseire Maguelonne , Poncelet Pascal
Published: May 2011

Sequential pattern mining deals with data represented as sequences (a sequence contains sorted sets of items). Compared to the association rule problem, a study of such data provides “inter-transaction” analysis (Agrawal & Srikant, 1995). Applications for sequential pattern extraction are numerous and the problem definition has been slightly modified in different ways. Associated to elegant solutions, these problems can match with real-life timestamped data (when association rules fail) and provide useful results.
Software Warehouse
Authors: Dai Honghua
Published: May 2011

A software warehouse is a facility providing an effective and yet efficient mechanism to store, manage, and utilize existing software resources (Dai, 2003, 2004a, 2004b; Dai & Li, 2004). It is designed for the automation of software analysis, testing, mining, reuse, evaluation, and system design decision making. It makes it easier to make use of existing software for solving new problems in order to increase software productivity and to reduce the cost of software development.
Subgraph Mining
Authors: Fischer Ingrid , Meinl Thorsten
Published: May 2011

The amount of available data is increasing very fast. With this data the desire for data mining is also growing. More and larger databases have to be searched to find interesting (and frequent) elements and connections between them. Most often, the data of interest is very complex. It is common to model complex data with the help of graphs consisting of nodes and edges that often are labeled to store additional information. Applications can be found in very different fields. For example, the two-dimensional structure of molecules often is modeled as graphs having the atoms as nodes and bonds as edges. The same holds for DNA or proteins. Web pages and links between Web pages also can be represented as graph. Other examples are social networks as citation networks and CAD circuits; graphs can be found in a lot of different application areas.
Temporal Association Rule Mining in Event Sequences
Authors: Harms Sherri K.
Published: May 2011

The emergence of remote sensing, scientific simulation and other survey technologies has dramatically enhanced our capabilities to collect temporal data. However, the explosive growth in data makes the management, analysis, and use of data both difficult and expensive. To meet these challenges, there is an increased use of data mining techniques to index, cluster, classify and mine association rules from time series data (Roddick & Spiliopoulou, 2002; Han, 2001). A major focus of these algorithms is to characterize and predict complex, irregular, or suspicious activity (Han, 2001).
Vertical Data Mining
Authors: Perrizo William , Ding Qiang , Ding Qin , Abidin Taufik
Published: May 2011

The volume of data keeps increasing. There are many data sets that have become extremely large. It is of importance and a challenge to develop scalable methodologies that can be used to perform efficient and effective data mining on large data sets. Vertical data mining strategy aims at addressing the scalability issues by organizing data in vertical layouts and conducting logical operations on vertical partitioned data instead of scanning the entire database horizontally.
Wavelets for Querying Multidimensional Datasets
Authors: Shahabi Cyrus , Sacharidis Dimitris , Jahangiri Mehrdad
Published: May 2011

Following the constant technological advancements that provide more processing power and storage capacity, scientific applications have emerged as a new field of interest for the database community. Such applications, termed Online Science Applications (OSA), require continuous interaction with datasets of multidimensional nature, mainly for performing statistical analysis. OSA can seriously benefit from the ongoing research for OLAP systems and the pre-calculation of aggregate functions for multidimensional datasets. One of the tools that we see fit for the task in hand is the wavelet transformation. Due to its inherent multi-resolution properties, wavelets can be utilized to provide progressively approximate and eventually fast exact answers to complex queries in the context of Online Science Applications.
Benchmarking and Data Generation in Moving Objects Databases
Authors: Tzouramanis Theodoros
Published: May 2011

Moving objects databases (MODs) provide the framework for the efficient storage and retrieval of the changing position of continuously moving objects. This includes the current and past locations of moving objects and the support of spatial queries that refer to historical location information and future projections as well. Nowadays, new spatiotemporal applications that require tracking and recording the trajectories of moving objects online are emerging. Digital battlefields, traffic supervision, mobile communication, navigation systems, and geographic information systems (GIS) are among these applications. Towards this goal, during recent years many efforts have focused on MOD formalism, data models, query languages, visualization, and access methods (Guting et al., 2000; Saltenis & Jensen, 2002; Sistla, Wolfson, Chamberlain, & Dao, 1997). However, little work has appeared on benchmarking.
Biological Data Mining
Authors: Tzanis George , Berberidis Christos , Vlahavas Ioannis
Published: May 2011

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).
Common Information Model
Authors: Fulton James A.
Published: May 2011

A common information model (CIM) defines information that is available for sharing among multiple business processes and the applications that support them. These common definitions are neutral with respect to the processes that produce and use that information, the applications that access the data that express that information, and the technologies in which those applications are implemented. In an architecture based on the CIM, applications map their data only to the CIM and not to other applications, and they interface only to the middleware that implements the CIM (the integration broker or IB), not to other applications. This not only reduces the number of interfaces to be built and maintained, it provides a basis for integrating applications in a way that reduces the coupling among them, thereby allowing them to be upgraded or replaced with minimal functional impact on other applications.
Data Model Versioning and Database Evolution
Authors: Bounif Hassina
Published: May 2011

In the field of computer science, we are currently facing a major problem designing models that evolve over time. This holds in particular for the case of databases: Their data models need to evolve, but their evolution is difficult. User requirements are now changing much faster than before for several reasons, among them the changing perception of the real world and the development of new technologies. Databases show little flexibility in terms of supporting changes in the organization of their schemas and data. Database evolution approaches maintain current populated data and software application functionalities when changing database schema. Data model versioning is one of these chosen approaches used to resolve the evolution of conventional and nonconventional databases such as temporal and spatial-temporal databases. This article provides some background on database evolution and versioning technique fields. It presents the unresolved issues as well.
Data Warehouses
Authors: Badia Antonio
Published: May 2011

Data warehouses (DW) appeared first in industry in the mid 1980s. When their impact on businesses and database practices became clear, a flurry or research took place in academia in the late 1980s and 1990s. However, the concept of DW still remains rooted on its practical origins. This entry describes the basic concepts behind a DW while keeping the discussion at an intuitive level. The entry is meant as an overview to complement more focused and detailed entries, and it assumes only familiarity with the relational data model and relational databases.
Data Warehousing and OLAP
Authors: Hernandez-Orallo Jose
Published: May 2011

Information systems provide organizations with the necessary information to achieve their goals. Relevant information is gathered and stored to allow decision makers to obtain quick and elaborated reports from the data.
A Development Environment for Customer-Oriented Web Business
Authors: Lee Choongseok , Suh Woojong , Lee Heeseok
Published: May 2011

Web business can help companies strengthen the links between customers and suppliers. Under this environment, individual customers are getting much smarter and their needs are changing much faster than ever before. The customers can compare products and services with a variety of rich information provided from Web business systems, so that they can easily move to new products or services. Accordingly, many companies have conceived Web business systems to individual customers as a critical instrument for their business success and so have made a lot of efforts to develop and maintain them.
Digital Media Warehouses
Authors: Windhouwer Menzo , Kersten Martin
Published: May 2011

Due to global trends, like the rise of the Internet, the cheapness of storage space and the ease of digital media acquisition, vast collections of digital media, are becoming ubiquitous. Futuristic usage scenarios, like ambient technologies, strive to open up these collections for the consumer market. However, this requires high-level semantic knowledge of the items in the collections. Experiences in the development and usage of multimedia retrieval systems have shown that within a specific, but still limited domain, semantic knowledge can be automatically extracted and exploited. However, when the domain is unspecified and unrestricted, that is, the collection becomes a warehouse, semantic knowledge quickly boils down to generics. The research on Digital Media Warehouses (dmws) focuses on improving this situation by providing additional support for annotation extraction and maintenance to build semantically rich knowledge bases. The following sections will introduce these dmw topics for closely related topics like multimedia storage, the usage/indexing of the extracted annotations for multimedia retrieval, and so forth, the reader is directed to the wide variety of literature on multimedia databases (for example, Subrahmaniam, 1997).