bluuebunny/crossref_metadata_2025 huggingface dataset:
Virtual Teams and Communities of Practice
Authors: Hunter M. Gordon
Published: May 2011

Interdisciplinary research is being supported by universities and funding agencies, which in turn require a collaborative approach by researchers with complimentary yet different sets of expertise. Communities of practice are also facilitated by a collaborative approach, with groups of researchers investigating an area of common interest. It is important to note that collaborative research is not an extension of the single researcher approach. Goode (1973) originally suggested that collaborative research may be depicted as a delicate balance of collegiality and bureaucracy. Bradley (1982) supported this idea and further suggested that to increase the probability of group success, it is important to reach, as early as possible, a mutually acceptable and explicit agreement about group members’ responsibilities.
Agent-Based Mining of User Profiles for E-Services
Authors: De Meo Pasquale , Quattrone Giovanni , Terracina Giorgio , Ursino Domenico
Published: May 2011

An electronic service (e-service) can be defined as a collection of network-resident software programs that collaborate for supporting users in both accessing and selecting data and services of their interest present in a provider site. Examples of e-services are e-commerce, e-learning, and e-government applications. E-services are undoubtedly one of the engines presently supporting the Internet revolution (Hull, Benedikt, Christophides & Su, 2003). Indeed, nowadays, a large number and a great variety of providers offer their services also or exclusively via the Internet.
Automated Anomaly Detection
Authors: Morantz Brad
Published: May 2011

Preparing a dataset is a very important step in data mining. If the input to the process contains problems, noise, or errors, then the results will reflect this, as well. Not all possible combinations of the data should exist, as the data represent real-world observations. Correlation is expected among the variables. If all possible combinations were represented, then there would be no knowledge to be gained from the mining process.
Clustering in the Identification of Space Models
Authors: Santos Maribel Yasmina , Moreira Adriano , Carneiro Sofia
Published: May 2011

Clustering is the process of grouping a set of objects into clusters so that objects within a cluster have high similarity with each other, but are as dissimilar as possible to objects in other clusters. Dissimilarities are measured based on the attribute values describing the objects (Han & Kamber, 2001).
Combining Induction Methods with the Multimethod Approach
Authors: Lenic Mitja , Kokol Peter , Povalej Petra , Zorman Milan
Published: May 2011

The aggressive rate of growth of disk storage and, thus, the ability to store enormous quantities of data have far outpaced our ability to process and utilize that. This challenge has produced a phenomenon called data tombs—data is deposited to merely rest in peace, never to be accessed again. But the growing appreciation that data tombs represent missed opportunities in cases supporting scientific discovering, business exploitation, or complex decision making has awakened the growing commercial interest in knowledge discovery and data-mining techniques. That, in order, has stimulated new interest in the automatic knowledge induction from cases stored in large databases—a very important class of techniques in the data-mining field. With the variety of environments, it is almost impossible to develop a single-induction method that would fit all possible requirements. Thereafter, we constructed a new so-called multi-method approach, trying out some original solutions.
Content-Based Image Retrieval
Authors: Bretschneider Timo R. , Kao Odej
Published: May 2011

Sensing and processing multimedia information is one of the basic traits of human beings: The audiovisual system registers and transports surrounding images and sounds. This complex re­cording system, complemented by the senses of touch, taste, and smell, enables perception and provides humans with data for analysing and interpreting the environment. Imitating this perception and the simulation of the processing was and still is one of the major leitmotifs of multimedia technology developments. The goal is to find a representation for every type of knowledge, which makes the reception and processing of information as easy as possible. The need to process given information, deliver it, and explain it to a certain audience exists in nearly all areas of day-to-day life: commerce, science, education, and entertainment (Smeulders, Worring, Santini, Gupta, & Jain, 2000).
Continuous Auditing and Data Mining
Authors: Garrity Edward J. , O’Donnell Joseph B. , Sanders G. Lawrence
Published: May 2011

Investor confidence in the financial markets has been rocked by recent corporate frauds and many in the investment community are searching for solutions. Meanwhile, due to changes in technology, organizations are increasingly able to produce financial reports on a real-time basis. Access to this timely information can help investors, shareholders, and other third parties, but only if this information is accurate and verifiable. Real-time financial reporting requires real-time or continuous auditing (CA) to ensure integrity of the reported information. Continuous auditing “ is a type of auditing which produces audit results simultaneously, or a short period of time after, the occurrence of relevant events” (Kogan, Sudit, & Vasarhelyi, 2003, p. 1). CA is facilitated by eXtensible Business Reporting Language (XBRL), which enables seamless transmission of company financial information to auditor data warehouses. Data mining of these warehouses provides opportunities for the auditor to determine financial trends and identify erroneous transactions.
Data Quality in Cooperative Information Systems
Authors: Marchetti Carla , Mecella Massimo , Scannapieco Monica , Virgillito Antoninio
Published: May 2011

A Cooperative Information System (CIS) is a large-scale information system that interconnects various systems of different and autonomous organizations, geographically distributed and sharing common objectives (De Michelis et al., 1997). Among the different resources that are shared by organizations, data are fundamental; in real world scenarios, organization A may not request data from organization B, if it does not trust B’s data (i.e., if A does not know that the quality of the data that B can provide is high). As an example, in an e-government scenario in which public administrations cooperate in order to fulfill service requests from citizens and enterprises (Batini & Mecella, 2001), administrations very often prefer asking citizens for data rather than from other administrations that have stored the same data, because the quality of such data is not known. Therefore, lack of cooperation may occur due to lack of quality certification.
Decision Tree Inudction
Authors: Siciliano Roberta , Conversano Claudio
Published: May 2011

Decision Tree Induction (DTI) is an important step of the segmentation methodology. It can be viewed as a tool for the analysis of large datasets characterized by high dimensionality and nonstandard structure. Segmentation follows a nonparametric approach, since no hypotheses are made on the variable distribution. The resulting model has the structure of a tree graph. It is considered a supervised method, since a response criterion variable is explained by a set of predictors.
Diabetic Data Warehouses
Authors: Breault Joseph L.
Published: May 2011

The National Academy of Sciences convened in 1995 for a conference on massive data sets. The presentation on health care noted that “massive applies in several dimensions . . . the data themselves are massive, both in terms of the number of observations and also in terms of the variables . . . there are tens of thousands of indicator variables coded for each patient” (Goodall, 1995, paragraph 18). We multiply this by the number of patients in the United States, which is hundreds of millions.
Discovering Unknown Patterns in Free Text
Authors: Kroeze Jan H.
Published: May 2011

A very large percentage of business and academic data is stored in textual format. With the exception of metadata, such as author, date, title and publisher, these data are not overtly structured like the standard, mainly numerical, data in relational databases. Parallel to data mining, which finds new patterns and trends in numerical data, text mining is the process aimed at discovering unknown patterns in free text. Owing to the importance of competitive and scientific knowledge that can be exploited from these texts, “text mining has become an increasingly popular and essential theme in data mining” (Han & Kamber, 2001, p. 428).
Discovery Informatics
Authors: Agresti William W.
Published: May 2011

Discovery informatics is an emerging methodology that brings together several threads of research and practice aimed at making sense out of massive data sources. It is defined as “the study and practice of employing the full spectrum of computing and analytical science and technology to the singular pursuit of discovering new information by identifying and validating patterns in data” (Agresti, 2003).
Enhancing Web Search through Query Log Mining
Authors: Wen Ji-Rong
Published: May 2011

Web query log is a type of file keeping track of the activities of the users who are utilizing a search engine. Compared to traditional information retrieval setting in which documents are the only information source available, query logs are an additional information source in the Web search setting. Based on query logs, a set of Web mining techniques, such as log-based query clustering, log-based query expansion, collaborative filtering and personalized search, could be employed to improve the performance of Web search.
Enhancing Web Search through Web Structure Mining
Authors: Wen Ji-Rong
Published: May 2011

The Web is an open and free environment for people to publish and get information. Everyone on the Web can be either an author, a reader, or both. The language of the Web, HTML (Hypertext Markup Language), is mainly designed for information display, not for semantic representation. Therefore, current Web search engines usually treat Web pages as unstructured documents, and traditional information retrieval (IR) technologies are employed for Web page parsing, indexing, and searching. The unstructured essence of Web pages seriously blocks more accurate search and advanced applications on the Web. For example, many sites contain structured information about various products. Extracting and integrating product information from multiple Web sites could lead to powerful search functions, such as comparison shopping and business intelligence. However, these structured data are embedded in Web pages, and there are no proper traditional methods to extract and integrate them. Another example is the link structure of the Web. If used properly, information hidden in the links could be taken advantage of to effectively improve search performance and make Web search go beyond traditional information retrieval (Page, Brin, Motwani, & Winograd, 1998, Kleinberg, 1998).
Evolutionary Computation and Genetic Algorithms
Authors: Hsu William H.
Published: May 2011

A genetic algorithm (GA) is a procedure used to find approximate solutions to search problems through the application of the principles of evolutionary biology. Genetic algorithms use biologically inspired techniques, such as genetic inheritance, natural selection, mutation, and sexual reproduction (recombination, or crossover). Along with genetic programming (GP), they are one of the main classes of genetic and evolutionary computation (GEC) methodologies.
Evolutionary Data Mining for Genomics
Authors: Jourdan Laetitia , Dhaenens Clarisse , Talbi El-Ghazali
Published: May 2011

Knowledge discovery from genomic data has become an important research area for biologists. Nowadays, a lot of data is available on the Web, but it is wrong to say that corresponding knowledge is also available. For example, the first draft of the human genome, which contains 3,000,000,000 letters, was achieved in June 2000, but, up to now, only a small part of the hidden knowledge has been discovered. This is the aim of bioinformatics, which brings together biology, computer science, mathematics, statistics, and information theory to analyze biological data for interpretation and prediction. Hence, many problems encountered while studying genomic data may be modeled as data mining tasks, such as feature selection, classification, clustering, or association rule discovery.
Evolutionary Mining of Rule Ensembles
Authors: Muruzabal Jorge
Published: May 2011

Ensemble rule based classification methods have been popular for a while in the machine-learning literature (Hand, 1997). Given the advent of low-cost, high-computing power, we are curious to see how far can we go by repeating some basic learning process, obtaining a variety of possible inferences, and finally basing the global classification decision on some sort of ensemble summary. Some general benefits to this idea have been observed indeed, and we are gaining wider and deeper insights on exactly why this is the case in many fronts of interest.
Flexible Mining of Association Rules
Authors: Shen Hong
Published: May 2011

The discovery of association rules showing conditions of data co-occurrence has attracted the most attention in data mining. An example of an association rule is the rule “the customer who bought bread and butter also bought milk,” expressed by T(bread; butter)®T(milk).
Group Pattern Discovery Systems for Multiple Data Sources
Authors: Zhang Shichao , Zhang Chengqi
Published: May 2011

Multiple data source mining is the process of identifying potentially useful patterns from different data sources, or datasets (Zhang et al., 2003). Group pattern discovery systems for mining different data sources are based on local pattern-analysis strategy, mainly including logical systems for information enhancing, a pattern discovery system, and a post-pattern-analysis system.
Hierarchical Document Clustering
Authors: Fung Benjamin C.M. , Wang Ke , Ester Martin
Published: May 2011

Document clustering is an automatic grouping of text documents into clusters so that documents within a cluster have high similarity in comparison to one another, but are dissimilar to documents in other clusters. Unlike document classification (Wang, Zhou, & He, 2001), no labeled documents are provided in clustering; hence, clustering is also known as unsupervised learning. Hierarchical document clustering organizes clusters into a tree or a hierarchy that facilitates browsing. The parent-child relationship among the nodes in the tree can be viewed as a topic-subtopic relationship in a subject hierarchy such as the Yahoo! directory.
Information Extraction in Biomedical Literature
Authors: Song Min , Song Il-Yeol , Hu Xiaohua , Han Hyoil
Published: May 2011

Information extraction (IE) technology has been defined and developed through the US DARPA Message Understanding Conferences (MUCs). IE refers to the identification of instances of particular events and relationships from unstructured natural language text documents into a structured representation or relational table in databases. It has proved successful at extracting information from various domains, such as the Latin American terrorism, to identify patterns related to terrorist activities (MUC-4). Another domain, in the light of exploiting the wealth of natural language documents, is to extract the knowledge or information from these unstructured plain-text files into a structured or relational form. This form is suitable for sophisticated query processing, for integration with relational databases, and for data mining. Thus, IE is a crucial step for fully making text files more easily accessible.
Intelligent Query Answering
Authors: Ras Zbigniew W. , Dardzinska Agnieszka
Published: May 2011

One way to make query answering system (QAS) intelligent is to assume a hierarchical structure of its attributes. Such systems have been investigated by Cuppens & Demolombe (1988), Gal & Minker (1988), and Gaasterland et al. (1992), and they are called cooperative. Any attribute value listed in a query, submitted to cooperative QAS, is seen as a node of the tree representing that attribute. If QAS retrieves an empty set of objects, which match query q in a target information system S, then any attribute value listed in q can be generalized and the same the number of objects that possibly can match q in S can increase. In cooperative systems, these generalizations are usually controlled by users.
Knowledge Discovery with Artificial Neural Networks
Authors: Rabuñal Dopico Juan R. , Cebrian Daniel Rivero , de la Calle Julián Dorado , Souto Nieves Pedreira
Published: May 2011

The world of Data Mining (Cios, Pedrycz & Swiniarrski, 1998) is in constant expansion. New information is obtained from databases thanks to a wide range of techniques, which are all applicable to a determined set of domains and count with a series of advantages and inconveniences. The Artificial Neural Networks (ANNs) technique (Haykin, 1999; McCulloch & Pitts, 1943; Orchad, 1993) allows us to resolve complex problems in many disciplines (classification, clustering, regression, etc.), and presents a series of advantages that convert it into a very powerful technique that is easily adapted to any environment. The main inconvenience of ANNs, however, is that they can not explain what they learn and what reasoning was followed to obtain the outputs. This implies that they can not be used in many environments in which this reasoning is essential.
Mining Associations Rules on a NCR Teradata System
Authors: Chung Soon M. , Mangamuri Murali
Published: May 2011

Data mining from relations is becoming increasingly important with the advent of parallel database systems. In this paper, we propose a new algorithm for mining association rules from relations. The new algorithm is an enhanced version of the SETM algorithm (Houtsma & Swami 1995), and it reduces the number of candidate itemsets considerably. We implemented and evaluated the new algorithm on a parallel NCR Teradata database system. The new algorithm is much faster than the SETM algorithm, and its performance is quite scalable.
Mining for Web-Enabled E-Business Applications
Authors: Nayak Richi
Published: May 2011

A small shop owner builds a relationship with its customers by observing their needs, preferences and buying behaviour. A Web-enabled e-business will like to accomplish something similar. It is an easy job for the small shop owner to serve his customers better in future by learning from past interactions. But, this may not be easy for Web-enabled e-businesses when most customers may never interact personally, and the number of customers is much higher than of the small shop owner.
Mining Group Differences
Authors: Butler Shane M. , Webb Geoffrey I.
Published: May 2011

Finding differences among two or more groups is an important data-mining task. For example, a retailer might want to know what the different is in customer purchasing behaviors during a sale compared to a normal trading day. With this information, the retailer may gain insight into the effects of holding a sale and may factor that into future campaigns. Another possibility would be to investigate what is different about customers who have a loyalty card compared to those who don’t. This could allow the retailer to better understand loyalty cardholders, to increase loyalty revenue, or to attempt to make the loyalty program more appealing to non-cardholders.
Model Indentification through Data Mining
Authors: Liberati Diego
Published: May 2011

In many fields of research as in everyday life, one has to face a huge amount of data, often not completely homogeneous and many times without an immediate grasp of an underlying simple structure. A typical example is the growing field of bio-informatics, where new technologies, like the so-called micro-arrays, provide thousands of gene expression data on a single cell in a simple and quickly integrated way. Further, the everyday consumer is involved in a process not so different from a logical point of view, when the data associated with the consumer’s identity contributes to a large database of many customers whose underlying consumer trends are of interest to the distribution market.
Music Information Retrieval
Authors: Wieczorkowska Alicja A.
Published: May 2011

Music information retrieval is a multi-disciplinary research on retrieving information from music. This research involves scientists from traditional, music, and digital libraries; information science; computer science; law; business; engineering; musicology; cognitive psychology; and education (Downie, 2001).
Negative Association Rules in Data Mining
Authors: Daly Olena , Taniar David
Published: May 2011

Data Mining is a process of discovering new, unexpected, valuable patterns from existing databases (Chen, Han & Yu, 1996; Fayyad et. al., 1996; Frawley, Piatetsky-Shapiro & Matheus, 1991; Savasere, Omiecinski & Navathe, 1995). Though data mining is the evolution of a field with a long history, the term itself was introduced only relatively recently in the 1990s. Data mining is best described as the union of historical and recent developments in statistics, artificial intelligence, and machine learning. These techniques then are used together to study data and find previously hidden trends or patterns within.
Organizational Data Mining
Authors: Nemati Hamid R. , Barko Christopher D.
Published: May 2011

Data mining is now largely recognized as a business imperative and considered essential for enabling the execution of successful organizational strategies. The adoption rate of data mining by enterprises is growing quickly, due to noteworthy industry results in applications such as credit assessment, risk management, market segmentation, and the ever-increasing volumes of corporate data available for analysis. The quantity of data being captured is staggering—data experts estimate that in 2002, the world generated five exabytes of information. This amount of data is more than all the words ever spoken by human beings (Hardy, 2004). The rate of growth is just as astounding—the amount of data produced in 2002 was up 68% from just two years earlier. The size of the typical business database has grown a hundred-fold during the past five years as a result of Internet commerce, ever-expanding computer systems, and mandated recordkeeping by government regulations (Hardy, 2004). Following this trend, a recent survey of corporations across 23 countries revealed that the largest transactional database almost doubled in size to 18 TB (terabytes), while the largest decision-support database grew to almost 30 TB (Reddy, 2004).
Privacy Protection in Association Rule Mining
Authors: Jha Neha , Sural Shamik
Published: May 2011

Data mining technology has emerged as a means for identifying patterns and trends from large sets of data. Mining encompasses various algorithms, such as discovery of association rules, clustering, classification and prediction. While classification and prediction techniques are used to extract models describing important data classes or to predict future data trends, clustering is the process of grouping a set of physical or abstract objects into classes of similar objects.
Profit Mining
Authors: Zhou Senqiang , Wang Ke
Published: May 2011

A major obstacle in data mining applications is the gap between the statistic-based pattern extraction and the value-based decision-making. “Profit mining” aims to reduce this gap. In profit mining, given a set of past transactions and pre-determined target items, we like to build a model for recommending target items and promotion strategies to new customers, with the goal of maximizing profit. Though this problem is studied in the context of retailing environment, the concept and techniques are applicable to other applications under a general notion of “utility”. In this short article, we review existing techniques and briefly describe the profit mining approach recently proposed by the authors. The reader is referred to (Wang, Zhou & Han, 2002) for the details.
Sampling Methods in Approximate Query Answering Systems
Authors: Das Gautam
Published: May 2011

In recent years, advances in data collection and management technologies have led to a proliferation of very large databases. These large data repositories typically are created in the hope that, through analysis such as data mining and decision support, they will yield new insights into the data and the real-world processes that created them. In practice, however, while the collection and storage of massive datasets has become relatively straightforward, effective data analysis has proven more difficult to achieve. One reason that data analysis successes have proven elusive is that most analysis queries, by their nature, require aggregation or summarization of large portions of the data being analyzed. For multi-gigabyte data repositories, this means that processing even a single analysis query involves accessing enormous amounts of data, leading to prohibitively expensive running times. This severely limits the feasibility of many types of analysis applications, especially those that depend on timeliness or interactivity.
Semi-Structured Document Classification
Authors: Denoyer Ludovic , Gallinari Patrick
Published: May 2011

Document classification developed over the last 10 years, using techniques originating from the pattern recognition and machine-learning communities. All these methods operate on flat text representations, where word occurrences are considered independents. The recent paper by Sebastiani (2002) gives a very good survey on textual document classification. With the development of structured textual and multimedia documents and with the increasing importance of structured document formats like XML, the document nature is changing. Structured documents usually have a much richer representation than flat ones. They have a logical structure. They are often composed of heterogeneous information sources (e.g., text, image, video, metadata, etc.). Another major change with structured documents is the possibility to access document elements or fragments. The development of classifiers for structured content is a new challenge for the machine-learning and IR communities. A classifier for structured documents should be able to make use of the different content information sources present in an XML document and to classify both full documents and document parts. It should adapt easily to a variety of different sources (e.g., different document type definitions). It should be able to scale with large document collections.
Statistical Metadata in Data Processing and Interchange
Authors: Vardaki Maria
Published: May 2011

The term metadata is frequently considered in many different sciences. Statistical metadata is a term generally used to denote data about data. Modern statistical information systems (SIS) use metadata templates or complex object-oriented metadata models, making an extensive and active usage of metadata.
Storage Strategies in Data Warehouses
Authors: Lu Xinjian
Published: May 2011

A data warehouse stores and manages historical data for on-line analytical processing, rather than for on-line transactional processing. Data warehouses with sizes ranging from gigabytes to terabytes are common, and they are much larger than operational databases. Data warehouse users tend to be more interested in identifying business trends rather than individual values. Queries for identifying business trends are called analytical queries. These queries invariably require data aggregation, usually according to many different groupings. Analytical queries are thus much more complex than transactional ones. The complexity of analytical queries combined with the immense size of data can easily result in unacceptably long response times. Effective approaches to improving query performance are crucial to a proper physical design of data warehouses.
Web Usage Mining and Its Applications
Authors: Fu Yongjian
Published: May 2011

With the rapid development of the World Wide Web or the Web, many organizations now put their information on the Web and provide Web-based services such as online shopping, user feedback, technical support, and so on. Understanding Web usage through data mining techniques is recognized as an important area.
Component-Based Generalized Database Index Model
Authors: Gaffar Ashraf
Published: May 2011

The performance of a database is greatly affected by the performance of its indexes. An industrial quality database typically has several indexes associated with it. Therefore, the design of a good quality index is essential to the success of any nontrivial database. Parallel to their significance, indexed data structures are inherently complex applications that require a lot of effort and consume a considerable amount of resources. Index frameworks rely on code reuse to reasonably reduce the costs associated with them (Lynch & Stonebraker, 1988; Stonebraker, 1986). Generalized database systems have further addressed this challenge by offering databases with indexes that can be adjusted to different data/key types, different queries, or both. The generalized search tree (GiST; Hellerstein, Naughton, & Pfeffer, 1995; Hellerstein, Papadimitriou, & Koutsoupias, 1997) is a good example of a database system with a generalized index, or generalized index database for simplicity. Additional improvements extended the concept of generalized index databases to work on different domains by having generalized access methods (search criteria). For example, based on the work of Hellerstein et al. (1995), Aoki (1998) provides a generalized framework that allows users to adjust the index to different search criteria like equality, similarity, or nearest neighbor search. This makes the database system customizable to not only finding the exact records, but also to finding records that are “similar” or “close” to a given record. Users can customize their own criteria of “similarity” and let the index apply it to the database and return all “similar” results. This is particularly important for more challenging domains like multimedia applications, where there is always the need to find a “close image,” a “similar sound,” or “matching fingerprints.”
Database Engineering Focusing on Modern Dynamism Crises
Authors: Camolesi Jr. Luiz , Vieira Marina Teresa Pires
Published: May 2011

Researchers in several areas (sociology, philosophy, and psychology), among them Herbert Spencer and Abraham Maslow, attribute human actions resulting in continual environmental changes to the search for the satisfaction of individual and collective needs. In other fields of science, this behavior represents a challenge in ethical researches on concepts, methodologies, and technologies aimed at optimizing and qualifying the actions involved in these continual changes to obtain better results.
Databases for Mobile Applications
Authors: Bose Indranil , Ping Wang , Shan Mok Wai , Shing Wong Ka , Shing Yip Yee , Tin Chan Lit , Wai Shiu Ka
Published: May 2011

Owing to the rapid development of mobile technology over the past few decades, there have been many different kinds of mobile devices emerging in the market, and most of them work with databases seamlessly. Mobile phone gaming, downloading of ringtones, and e-calendaring are some of the prominent examples of mobile applications that require the close integration of mobile devices with databases. Mobile devices take various forms and configurations. The packaging, form factors, hardware platforms, operating system support, and functional capabilities vary across these devices. There are, however, many common attributes shared by the devices, such as notebook computers, pen-based computers, handheld computers, and the like, all of which are used in mobile computing. These devices can be categorized into the following categories according to their functionalities and features, as detailed in Dhawan (1997).
E-Government Databases
Authors: Horiuchi Catherine
Published: May 2011

The new face of government is electronic. Prior to the development of e-government, adoption of small-scale computing and networking in homes and businesses created the world of e-business, where computer technologies mediate transactions once performed face-to-face. Through the use of computers large and small, companies reduce costs, standardize performance, extend hours of service, and increase the range of products available to consumers. These same technological advances create opportunities for governments to improve their capacity to meet growing public service mandates. Tasks that formerly required a trip to city hall can be accomplished remotely. Government employees can post answers to frequently asked questions online, and citizens can submit complex questions through the same electronic mail (e-mail) systems already used at home and in businesses. This developing e-government increases the number and complexity of electronic databases that must be managed according to the roles information plays in government operations.
Normalizing Multimedia Databases
Authors: Chang Shi Kuo , Deufemia Vincenzo , Polese Giuseppe
Published: May 2011

Multimedia databases have been used in many application fields. As opposed to traditional alphanumeric databases, they need enhanced data models and DBMSs to enable the modeling and management of complex data types. After an initial anarchy, multimedia DBMSs (MMDBMS) have been classified based on standard issues, such as the supported data model, the indexing techniques to support content-based retrieval, the query language, the support for distributed multimedia information management, and the flexibility of their architecture (Narasimhalu, 1996).
Object Modeling of RDBMS Based Applications
Authors: Polese Giuseppe , Deufemia Vincenzo , Costagliola Gennaro , Tortora Genny
Published: May 2011

As the development of industrial software applications become more complex, the benefits of developing a comprehensive “blueprint” enabling developers to visualize the scope of a project increase substantially.
Object-Relational Modeling in the UML
Authors: Zendulka Jaroslav
Published: May 2011

Modeling techniques play an important role in the development of database applications. One of the trends in current database management systems is that they become object-relational (Stonebraker & Brown, 1999). The most recent version of the SQL standard, SQL:1999, includes object-relational features, and a number of leading companies have already released packages that incorporate them.
Online Data Mining
Authors: Nigro Héctor Oscar , González Císaro Sandra Elizabeth
Published: May 2011

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).
Ontological Assumptions in Information Modeling
Authors: Artz John M.
Published: May 2011

Information modeling is a technique by which a database designer develops a conceptual model of a database depicting the entity classes that will be represented in the database. There are three competing ontological assumptions that guide the modeling process. The broadest characterization of these assumptions is realism vs. conceptualism, with social realism occupying a middle ground. The realist believes that object classes exist in the real world, waiting to be discovered. The conceptualist believes that object classes are constructed in the mind of the modeler, based on observations about the application domain and the objectives of the information model. The social realist believes that classes exist as shared meanings among stakeholders in an application domain. This article explores these assumptions and then reviews selected literature in information modeling to determine which assumptions are held by key authors. It concludes that most authors hold inconsistent views, and this inconsistency provides some important insights into information modeling while presenting serious problems for practitioners and students of information modeling.
Ontology-Based Data Integration
Authors: Buccella Agustina , Cechich Alejandra , Rodríguez Brisaboa Nieves
Published: May 2011

Nowadays, different areas of large modern enterprises use different database management systems to store and search their critical data. Competition, evolving technology, geographical distribution, and the inevitable growing decentralization contribute to this diversity. All of these databases are very important to an enterprise, but the their different interfaces make their administration difficult. Therefore, recovering information through a common interface becomes crucial to realize, for instance, the full value of data contained in the databases (Hass & Lin, 2002).
Open Source Database Management Systems
Authors: Sowe Sulayman K. , Samoladas Ioannis , Stamelos Ioannis
Published: May 2011

This article discusses open source database management systems (OSDBMS) trends from two broad perspectives. First, the software engineering discipline platform on which databases are built has recently witnessed a new form of software development—Free/Open Source Software Development (F/OSSD). Methodically, the F/OSSD paradigm has changed the way relational databases, initiated in the 1960s and 1970s, are developed, distributed, supported, and maintained. Second, commercial relational database management systems (RDBMS) still dominate the database market because, on one hand, vendors and users are skeptical of the boon of applications developed and distributed under the F/OSSD paradigm, and on the other hand, it has been argued that OSDBMS are not likely to follow the successful trend of other robust Free/Open Source Software (F/OSS) systems (Linux, Apache, etc.). This article presents trends in OSDBMS by looking at the morphology and landscape of the type of applications developed by the F/OSS community. Implementation of F/OSS strategies and factors mitigating the adoption and utilization of OSDBMS are explored by looking at the interactions between the F/OSSD process and database firms, vendors, and users.
Querical Data Networks
Authors: Shahabi Cyrus , Banaei-Kashani Farnoush
Published: May 2011

Recently, a family of massive self-organizing data networks has emerged. These networks mainly serve as large-scale distributed query-processing systems. We term these networks querical data networks (QDN). A QDN is a federation of a dynamic set of peer, autonomous nodes communicating through a transient-form interconnection. Data is naturally distributed among the QDN nodes in extra-fine grain, where a few data items are dynamically created, collected, and/or stored at each node. Therefore, the network scales linearly to the size of the data set. With a dynamic data set, a dynamic and large set of nodes, and a transient-form communication infrastructure, QDNs should be considered as the new generation of distributed database systems with significantly less constraining assumptions as compared to their ancestors. Peer-to-peer networks (Daswani, Garcia-Molina, & Yang, 2003) and sensor networks (Akyildiz, Su, Sankarasubramaniam, & Cayirci, 2002; Estrin, Govindan, Heidemann, & Kumar, 1999) are well-known examples of QDNs.
Query Operators in Temporal XML Databases
Authors: Nørvåg Kjetil
Published: May 2011

The amount of data available in XML is rapidly increasing and at the same time the price of mass storage is rapidly decreasing, and this makes it possible to store larger amounts of data. The contents of a database or data warehouse are seldom static. New documents are created, documents are deleted and, more important, documents are updated. In many cases, one wants to be able to search in historical (old) versions, retrieve documents that were valid at a certain time, query changes to documents, and so forth. (Note that although this process is somewhat similar to general document versioning maintenance, the aspect of time makes possibilities and appropriate solutions different.) The “easiest” way to do this is to store all versions of all documents in the database and use a middleware layer to convert temporal query language statements into conventional statements, executed by an underlying database system (an example of such a system is TeXOR; Nørvåg, Limstrand, & Myklebust, 2003). Although this approach makes the introduction of temporal support easier, it can be difficult to achieve good performance: temporal query processing is in general costly, and the cost of storing the complete document versions can be high. Thus, a temporal XML database system is necessary.
Repairing Inconsistent XML Data with Functional Dependencies
Authors: Flesca Sergio , Furfaro Fillippo , Greco Sergio , Zumpano Ester
Published: May 2011

The World Wide Web is of strategic importance as a global repository for information and a means of communicating and sharing knowledge. Its explosive growth has caused deep changes in all the aspects of human life, has been a driving force for the development of modern applications (e.g., Web portals, digital libraries, wrapper generators, etc.), and has greatly simplified the access to existing sources of information, ranging from traditional DBMS to semi-structured Web repositories. The adoption by the WWW consortium (W3C) of XML (eXtensible Markup Language) as the new standard for information exchange among Web applications has led researchers to investigate classical problems in the new environment of repositories containing large amounts of data in XML format.
A Rhetorical Perspective on Localization and International Outsourcing
Authors: St. Amant Kirk
Published: May 2011

Globalization is increasingly integrating the economies and the societies of the world. Now, products created in one nation are often marketed to a wide range of international consumers. Similarly, the rapid diffusion of online media has led to an increase in cross-border interactions on both a social and a professional level. Differing cultural expectations, however, can result in miscommunications within this new paradigm of global discourse. Individuals working within this international framework therefore need to understand the process of localization in order to operate more effectively under such a global paradigm. This paper overviews localization, why it is important, and how it might affect professional activities in the future.
Rough Sets
Authors: Pawlak Zdzislaw , Polkowski Lech , Skowron Andrzej
Published: May 2011

Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge, tackled for a long time by philosophers, logicians, and mathematicians, has become also a crucial issue for computer scientists, particularly in the area of artificial intelligence. There are many approaches to the problem of how to understand and manipulate imperfect knowledge. The most successful one is, no doubt, fuzzy set theory proposed by Zadeh (1965). Rough set theory (Pawlak, 1982) presents still another attempt at this problem. This theory has attracted the attention of many researchers and practitioners all over the world, who contributed essentially to its development and applications. The rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning, and pattern recognition.
Semantic Enrichment of Geographical Databases
Authors: Faïz Sami , Mahmoudi Khaoula
Published: May 2011

The distributed Web-based multi-document summarization system is conceived to enrich semantically Geographic Databases (GDB) (Faïz, 1999; Scholl et al., 1996). In fact, in a traditional database, for instance, a city is described by its alphanumeric features: name, population count, and so forth; however, in a GDB, it is further described by spatial attributes which indicate its position (coordinates) in the space and its shape (point, line, polygon, etc.). Although the use of this myriad of information (alphanumeric and spatial data), the GDB suffers from the lack of an exhaustive set of information describing in a quasi-complete way the entities handled by it (Faïz, 2001). Hence, Geographic Information System (GIS) is not able to provide the end-user with information not fed into the GDB and that is not inherent to the application for which the GIS is designed (Bâazaoui, Faïz & Ben Ghezala, 2001, 2003; Faïz, Abbassi & Boursier, 1998). For instance, given a map displayed on the screen, it is not possible to get economic or historical information about cities for a given country whenever the GIS is concerned only with administrative boundaries. Having this idea in mind, our intention is to profit from the huge mass of information available online to enrich semantically a GDB. To fulfill this purpose and to manage the great amount of documents retrieved from the Web in a quick and convenient fashion, we adopted the Text Mining techniques (Tan 1999; Weiss, Apte & Damerau, 1999) and more precisely the summarization. Indeed, with the fast growth in the amount of textual information available online and the multitude of documents reporting almost the same thing, there is clearly a strong need for automatic summarization that copes with not only one document at one time but a set of topically similar ones.
Transformation-Based Database Engineering
Authors: Hainaut Jean-Luc
Published: May 2011

Modelling software design as the systematic transformation of formal specifications into efficient programs and building CASE tools that support it has long been considered one of the ultimate goals of software engineering. For instance, Balzer (1981) and Fikas (1985) consider that the process of developing a program [can be] formalized as a set of correctness-preserving transformations [...] aimed to compilable and efficient program production. In this context, according to Partsch and Steinbrüggen (1983), a transformation is a relation between two program schemes P and P’ (a program scheme is the [parameterized] representation of a class of related programs; a program of this class is obtained by instantiating the scheme parameters). It is said to be correct if a certain semantic relation holds between P and P’.
Ubiquitous Computing and Databases
Authors: Roussos George , Zoumboulakis Michael
Published: May 2011

The concept of the so-called ubiquitous computing was introduced in the early 1990s as the third wave of computing to follow the eras of the mainframe and the personal computer. Unlike previous technology generations, ubiquitous computing recedes into the background of everyday life: It activates the world, makes computers so imbedded, so fitting, so natural, that we use it without even thinking about it, and is invisible, everywhere computing that does not live on a personal device of any sort, but is in the woodwork everywhere. (Weiser & Brown, 1997, p. 81)
Biometrics, A Critical Consideration in Information Security Management
Authors: Lowry Paul Benjamin , Stephens Jackson , Moyes Aaron , Wilson Sean , Mitchell Mark
Published: May 2011

The need for increased security management in organizations has never been greater. With increasing globalization and the spread of the Internet, information-technology (IT) related risks have multiplied, including identity theft, fraudulent transactions, privacy violations, lack of authentication, redirection and spoofing, data sniffing and interception, false identities, and fraud.
Challenges and Perspectives for Web-Based Applications in Organizations
Authors: Lalopoulos George K. , Chochliouros Ioannis P. , Spiliopoulou-Chochliourou Anastasia S.
Published: May 2011

The last decade is characterized by the tempestuous evolution, growth, and dissemination of information and communication technologies in the economy and society. As a result, information and communication technologies have managed to open new broad horizons and markets for enterprises, whose installation and operation costs are rapidly amortizing through the proper usage of these technologies.