bluuebunny/crossref_metadata_2025 huggingface dataset:
Le second pilier de la Politique Agricole Commune : une politique à la carte
Authors: Lataste François , Berriet-Solliec Marielle , Trouvé Aurélie , Lépicier Denis
Published: December 2012

Bien plus que le 1 er pilier de la PAC, le 2 nd pilier, consacré au développement rural, offre des marges de manœuvre importantes aux Etats-membres et régions. A partir d’une analyse statistique des maquettes budgétaires 2007-2013 du second pilier et d’une classification des programmes nationaux et régionaux, sont mises en évidence quatre grandes logiques d’intervention : le soutien de la compétitivité agricole, le soutien de l’ensemble des activités rurales, la prise en compte des biens publics et effets externes agricoles, enfin la garantie de l’équité au sein du secteur agricole. Une interprétation plus fine des classes obtenues à l’aide d’indicateurs socio-économiques et agricoles régionaux nous permet ensuite de mieux les caractériser.
Nouvelle régulation concurrentielle et nouveaux jeux de proximités. Les associations d'aide à la personne à la reconquête de leur légitimité territoriale
Authors: Leroux Isabelle , Pujol Laurent , Rigamonti Éric
Published: December 2012

Le secteur de l’aide à la personne, jusque-là essentiellement associatif, a été bouleversé par l’introduction d’une nouvelle régulation concurrentielle en 2005. Auparavant, les associations s’entendaient pour le partage de leurs territoires respectifs d’intervention et bénéficiaient d’une légitimité territoriale forte, constituant ainsi des monopoles territoriaux. Ces monopoles étaient fondés sur des proximités géographique et organisée : contraintes de coûts de déplacement des aidants et liens historiques locaux. Soumises à la concurrence, leur légitimité territoriale est aujourd’hui remise en question. Cet article montre que selon les positionnements des politiques départementales et l’intensité concurrentielle perçue, les associations cherchent à asseoir leur légitimité territoriale en développant de nouvelles articulations de proximités. La proximité organisée y est déterminante : proximité organisée fondée sur une entente de non rivalité et respect des territoires légitimes ; ou bien proximité organisée fondée sur des règles de partage de la demande et sur l’absorption des acteurs les plus fragiles.
Symbolisme et échange symbolique dans la transplantation d'organes
Authors: Steiner Philippe
Published: December 2012

Résumé Cet article examine la place du symbolique dans la transplantation d’organes. Dans un premier temps, l’étude porte sur la symbolique mise en œuvre par l’Agence de la biomédecine dans ses campagnes d’exhortation visant à accroître le nombre d’organes prélevés. Dans un deuxième temps, il s’agit de savoir si le prélèvement d’organes correspond à un échange de biens symboliques. En conclusion, l’article propose d’isoler la notion de don organisationnel comme forme caractéristique de l’échange entre les familles et le monde médical au moment du prélèvement d’organes.
Maintenir l'économie à distance dans l'univers des biens symboliques : le cas de l'édition indépendante « critique »
Authors: Noël Sophie
Published: December 2012

Résumé Les maisons d’édition indépendantes critiques apparues depuis une vingtaine d’années en France permettent d’éclairer certaines stratégies de résistance aux contraintes marchandes dans un secteur culturel confronté à la pression croissante de la rationalité économique, et d’analyser les ressorts de ce type de position hétérodoxe. S’inscrivant dans une économie extrêmement précaire, mais à forte portée symbolique, ces éditeurs développent un discours de mise en avant du désintéressement que se propose d’analyser l’article.
L'exploitation d'un label de qualité dans une industrie culturelle. Le marché de la diffusion des films « Recherche et Découverte » dans les salles de cinéma
Authors: Pinto Aurélie
Published: December 2012

Résumé L’objet de cet article est d’éclairer la problématique de la qualité cinématographique en mettant au jour les conditions de possibilité de la diffusion de films labellisés « Recherche et Découverte », selon les types de marché sur lesquels ils sont offerts. Après avoir décrit le profil de ces films, nous livrerons, en nous appuyant sur les données de programmation de plus de 80 films dans les salles françaises, une analyse de leur parcours entre deux configurations polaires du marché de l’exploitation : marché concurrentiel idéal-typique à Paris et marché « subventionné » en Seine-Saint-Denis.
« Performance », « excellence » et création scientifique
Authors: Karpik Lucien
Published: December 2012

Résumé L’analyse de la réforme de la recherche de 2006 conduit à distinguer deux registres d’action : un discours général et des politiques particulières aux disciplines scientifiques. Dans le premier cas, la volonté d’augmenter les performances des chercheurs s’appuie sur une intensification de la compétition individuelle c’est-à-dire sur un raisonnement emprunté à la théorie économique néoclassique qui, par ce biais, sert de fondement à la nouvelle politique de recherche. Il est montré que cette théorie ne peut s’appliquer aux produits et acteurs de la recherche et que la justification n’est qu’une illusion. Dans le second cas, une comparaison des pratiques d’évaluation de l’économie et de la sociologie met en évidence deux univers non seulement différents mais opposés d’indicateurs et de classement, de modalités de distribution des ressources de la recherche (prime, carrière, financement), de conséquences, qui résultent de l’usage de critères de l’excellence scientifique non seulement différents mais aussi opposés. L’absence de moyens pour prévenir les effets néfastes inattendus est, par contre, commune aux deux disciplines. On peut s’interroger sur les buts réels de la réforme de la recherche.
Critique d'une analyse économique du cinéma
Authors: Duval Julien
Published: December 2012

Résumé Cet article propose une comparaison entre, d’une part, des analyses du cinéma inspirées par l’économie néo-classique et publiées dans le Journal of Cultural Economics et, d’autre part, des recherches sur le cinéma inspirées par le modèle des champs de production culturelle, développé par Pierre Bourdieu. L’article explicite les différences entre les deux approches, qui concernent aussi bien l’analyse de la production cinématographique que l’analyse des consommations.
Le marché de la santé et la reconstruction de l'interaction patient-médecin
Authors: Batifoulier Philippe
Published: December 2012

Résumé L’orientation marchande des réformes transforme profondément l’interaction médecin-patient, qui constitue le noyau dur des systèmes de santé 1 . Les patients actifs et mieux informés ont trouvé un nouveau statut de consommateur. La prise de parole revendiquée par la démocratie sanitaire a été transformée en stratégie « exit » de la logique marchande. Dans la mesure où le consommateur souverain peut plus facilement comparer des produits standardisés, la construction du marché requiert une nouvelle conception du soin, qui tient compte de l’avancée de l’industrialisation de la médecine. Cette normativité marchande influence les règles déontologiques de la profession et active le libéralisme médical qui est source d’inégalités et d’inefficacité.
Du trimorphe aux frontières quasi topologiques
Authors: Desclés Jean-Pierre
Published: December 2012

La topologie est utilisée en linguistique pour des descriptions sémantiques, lexicales et grammaticales. Cependant, la topologie classique, avec la notion de limite, est insuffisante. Il faut donc, pour décrire de nombreuses oppositions sémantiques (comme encore/pas encore, déjà/déjà plus), faire appel à des « frontières épaisses », à des frontières internes et externes. Le parcours des lieux topologiques (temporels, spatiaux, spatio-temporels, notionnels) traverse ces différentes frontières. En ethnologie, des situations d’initiation et de simulacres rituels peuvent être décrits à l’aide de schèmes quasi topologiques.
Quelles frontières pour les populations cholanes ?
Authors: Becquey Cédric
Published: December 2012

Les populations de langues cholanes ont eu longtemps une situation centrale, tant géographiquement que culturellement, au sein de l’aire maya. Le paysage actuel présente une situation bien différente : avec une répartition géographique devenue périphérique et une démographie réduite, deux des trois langues cholanes « survivantes » sont déjà classées parmi les langues en danger par l’Unesco. L’objectif de cet article est de rendre compte des principales phases dans l’évolution des frontières spatiales des populations cholanes à travers leurs histoires et de donner quelques clefs pour comprendre leurs mouvements. Une telle étude aurait certes pu se contenter de données historiques, mais des considérations d’ordres ethnologique et linguistique permettent d’affiner les paramètres nécessaires à la caractérisation de ces frontières et d’apprécier les interrelations de ces populations entre elles et avec l’« extérieur ».
La dynamique des représentations : frontières et paysage sociopolitique postclassique des Hautes Terres mayas
Authors: Annereau-Fulbert Marie
Published: December 2012

Les sources ethnohistoriques des Hautes Terres du Guatemala décrivent le Postclassique, à l’aube de la Conquête espagnole, comme une période tourmentée, nourrie de conflits incessants entre des groupes de nature sociopolitique distincte mais de langues apparentées. Ils partagent pourtant une même histoire évoquée sous la forme de récits de migration et de mythes. Sans interroger la véracité des faits historiques qu’ils pourraient illustrer, nous verrons, grâce à certains passages du Popol Vuh compilé au xvie siècle et d’autres textes de l’époque, qu’ils servent à domestiquer plusieurs moments de rupture radicale avec un ancien ordre du monde, en redéfinissant les frontières de l’altérité et des relations de pouvoir vis-à-vis d’une autorité centrale dont l’identité se déplace également au gré des conjonctures. Interroger la relation entre mythe et frontière revient à examiner ici le rapport entre mythe et politique.
Le passage de la frontière
Authors: Baudez Claude François
Published: December 2012

Chez trois ethnies qui pratiquaient la guerre de capture (Aztèques, Iroquois et Tupinamba), le rituel qui commençait avec la capture et se prolongeait au-delà de l’exécution avait pour fonction d’assimiler le prisonnier à son vainqueur, l’Autre à Soi. Les diverses étapes de cette transformation sont vues comme les divers moments du franchissement d’une frontière épaisse. Alors que celle-ci n’est définitivement franchie qu’avec l’exécution du captif, les étapes qui la précèdent le présentent comme un être de statut ambigu, à la fois ennemi et parent, hostile et impuissant.
Du système de charges aux statuts de prestige individuel
Authors: Chosson Marie
Published: December 2012

Les habitants du village tseltal d’Aguacatenango se trouvent confrontés aujourd’hui à différents bouleversements, dont les plus importants sont la prochaine réorganisation administrative et politique et, depuis une vingtaine d’années, la diversification des référents religieux. Ces transformations les obligent à adapter ou à reformuler ce qui pour eux constitue « la tradition » et, par là même, leur identité. L’une de leur principale préoccupation concernant ces changements est la mise en danger de leur conception de la bonne administration des affaires publiques. Les questions de distribution des pouvoirs sont ainsi au cœur des discours sur les processus actuels de « modernisation » de la communauté. Cet article se propose de revenir sur leur approche particulière de la notion de pouvoir par l’analyse de la forme d’organisation actuelle de leur administration, le système de charges. L’outil conceptuel de « frontières épaisses » permet de faire apparaître, à la marge du système, d’autres espaces de pouvoir, moins visibles mais tout aussi effectifs pour la population. Cette réflexion nous donnera une vision plus générale des différentes sphères de pouvoir et de leurs frontières, permettant pour la suite une lecture plus simple des véritables enjeux des changements actuels et des possibles stratégies d’adaptation qui en découleront.
Penser et construire l’espace tojolabal à travers une expérience rituelle transfrontalière (Mexique-Guatemala)
Authors: Chavarochette Carine
Published: December 2012

Au sud-est de l’État mexicain du Chiapas vivent des populations mayas tojolabal. L’une des origines mythiques de ce groupe se situe au Guatemala dans les montagnes des Cuchumatanes, dans un lieu habité par un autre groupe maya : les Chuj. Depuis la révolution mexicaine de 1910-1920 et la réforme agraire entreprise par les autorités gouvernementales, les Tojolabal entretiennent par l’intermédiaire de rituels des relations avec les Chuj du pays voisin. En choisissant de se rendre au Guatemala, les pèlerins tojolabal ne franchissent pas seulement une frontière politique et étatique, ils construisent et donnent sens à leur territoire par le fait même de traverser des limites. L’évolution de l’itinéraire et des pratiques religieuses tout au long du xxe siècle témoigne des événements politiques vécus par ces populations.
A Study of XML Models for Data Mining
Authors: Kutty Sangeetha , Nayak Richi , Tran Tien
Published: December 2012

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.
An Optimal Categorization of Feature Selection Methods for Knowledge Discovery
Authors: Kaur Harleen , Chauhan Ritu , Alam M.
Published: December 2012

With the continuous availability of massive experimental medical data has given impetus to a large effort in developing mathematical, statistical and computational intelligent techniques to infer models from medical databases. Feature selection has been an active research area in pattern recognition, statistics, and data mining communities. However, there have been relatively few studies on preprocessing data used as input for data mining systems in medical data. In this chapter, the authors focus on several feature selection methods as to their effectiveness in preprocessing input medical data. They evaluate several feature selection algorithms such as Mutual Information Feature Selection (MIFS), Fast Correlation-Based Filter (FCBF) and Stepwise Discriminant Analysis (STEPDISC) with machine learning algorithm naive Bayesian and Linear Discriminant analysis techniques. The experimental analysis of feature selection technique in medical databases has enable the authors to find small number of informative features leading to potential improvement in medical diagnosis by reducing the size of data set, eliminating irrelevant features, and decreasing the processing time.
Parallel Computing for Mining Association Rules in Distributed P2P Networks
Authors: Guan Huiwei
Published: December 2012

Distributed computing and Peer-to-Peer (P2P) systems have emerged as an active research field that combines techniques which cover networks, distributed computing, distributed database, and the various distributed applications. Distributed Computing and P2P systems realize information systems that scale to voluminous information on very large numbers of participating nodes. Data mining on large distributed databases is a very important research area. Recently, most work for mining association rules focused on a single machine or client-server network model. However, this traditional approach does not satisfy the requirements from the large distributed databases and applications in a P2P computing system. Two important challenges are raised, one is how to implement data mining for large distributed databases in P2P computing systems, and the other is how to develop parallel data mining algorithms and tools for the distributed P2P computing systems to improve the efficiency. In this chapter, a parallel association rule mining approach in a P2P computing system is designed and implemented, which satisfies the distribution of the P2P computing system well and makes parallel computing become true. The performance and comparison of the parallel algorithm with the sequential algorithm is analyzed and evaluated, which presents the parallel algorithm features consistent implementation, higher performance, and fine scalable ability.
Online Clustering and Outlier Detection
Authors: Wang Baoying , Dong Aijuan
Published: December 2012

Clustering and outlier detection are important data mining areas. Online clustering and outlier detection generally work with continuous data streams generated at a rapid rate and have many practical applications, such as network instruction detection and online fraud detection. This chapter first reviews related background of online clustering and outlier detection. Then, an incremental clustering and outlier detection method for market-basket data is proposed and presented in details. This proposed method consists of two phases: weighted affinity measure clustering (WC clustering) and outlier detection. Specifically, given a data set, the WC clustering phase analyzes the data set and groups data items into clusters. Then, outlier detection phase examines each newly arrived transaction against the item clusters formed in WC clustering phase, and determines whether the new transaction is an outlier. Periodically, the newly collected transactions are analyzed using WC clustering to produce an updated set of clusters, against which transactions arrived afterwards are examined. The process is carried out continuously and incrementally. Finally, the future research trends on online data mining are explored at the end of the chapter.
Analysis and Integration of Biological Data
Authors: Milone Diego , Stegmayer Georgina , Gerard Matías , Kamenetzky Laura , López Mariana , Carrari Fernando
Published: December 2012

The volume of information derived from post genomic technologies is rapidly increasing. Due to the amount of involved data, novel computational methods are needed for the analysis and knowledge discovery into the massive data sets produced by these new technologies. Furthermore, data integration is also gaining attention for merging signals from different sources in order to discover unknown relations. This chapter presents a pipeline for biological data integration and discovery of a priori unknown relationships between gene expressions and metabolite accumulations. In this pipeline, two standard clustering methods are compared against a novel neural network approach. The neural model provides a simple visualization interface for identification of coordinated patterns variations, independently of the number of produced clusters. Several quality measurements have been defined for the evaluation of the clustering results obtained on a case study involving transcriptomic and metabolomic profiles from tomato fruits. Moreover, a method is proposed for the evaluation of the biological significance of the clusters found. The neural model has shown a high performance in most of the quality measures, with internal coherence in all the identified clusters and better visualization capabilities.
Web Mining and Social Network Analysis
Authors: Marmo Roberto
Published: December 2012

Research on social networks has advanced significantly due to wide variety of on-line social websites and very popular Web 2.0 application. Social network analysis views social relationships in terms of network and graph theory about nodes (individual actors within the network) and ties (relationships between the actors). Using web mining techniques and social networks analysis it is possible to process and analyze large amount of social data (such as blogtagging, online game playing, instant messenger etc.) and by this to discover valuable information from data. In this way, we can understand the social structure, social relationships and social behaviors. This new approach is also denoted as social network mining. These algorithms differ from established set of data mining algorithms developed to analyze individual records, because social network datasets are called relational due to centrality of relations among entities. This chapter also sets out a process to apply web mining.
Intelligent Data Processing Based on Multi-Dimensional Numbered Memory Structures
Authors: Markov Krassimir , Vanhoof Koen , Mitov Iliya , Depaire Benoit , Ivanova Krassimira , Velychko Vitalii , Gladun Victor
Published: December 2012

The Multi-layer Pyramidal Growing Networks (MPGN) are memory structures based on multidimensional numbered information spaces (Markov, 2004), which permit us to create association links (bonds), hierarchically systematizing, and classification the information simultaneously with the input of it into memory. This approach is a successor of the main ideas of Growing Pyramidal Networks (Gladun, 2003), such as hierarchical structuring of memory that allows reflecting the structure of composing instances and gender-species bonds naturally, convenient for performing different operations of associative search. The recognition is based on reduced search in the multi-dimensional information space hierarchies. In this chapter, the authors show the advantages of using the growing numbered memory structuring via MPGN in the field of class association rule mining. The proposed approach was implemented in realization of association rules classifiers and has shown reliable results.
A Framework on Data Mining on Uncertain Data with Related Research Issues in Service Industry
Authors: Hung Edward
Published: December 2012

There has been a large amount of research work done on mining on relational databases that store data in exact values. However, in many real-life applications such as those commonly used in service industry, the raw data are usually uncertain when they are collected or produced. Sources of uncertain data include readings from sensors (such as RFID tagged in products in retail stores), classification results (e.g., identities of products or customers) of image processing using statistical classifiers, results from predictive programs used for stock market or targeted marketing as well as predictive churn model in customer relationship management. However, since traditional databases only store exact values, uncertain data are usually transformed into exact data by, for example, taking the mean value (for quantitative attributes) or by taking the value with the highest frequency or possibility. The shortcomings are obvious: (1) by approximating the uncertain source data values, the results from the mining tasks will also be approximate and may be wrong; (2) useful probabilistic information may be omitted from the results. Research on probabilistic databases began in 1980s. While there has been a great deal of work on supporting uncertainty in databases, there is increasing work on mining on such uncertain data. By classifying uncertain data into different categories, a framework is proposed to develop different probabilistic data mining techniques that can be applied directly on uncertain data in order to produce results that preserve the accuracy. In this chapter, we introduce the framework with a scheme to categorize uncertain data with different properties. We also propose a variety of definitions and approaches for different mining tasks on uncertain data with different properties. The advances in data mining application in this aspect are expected to improve the quality of services provided in various service industries.
A Framework to Detect Disguised Missing Data
Authors: Belen Rahime , Temizel Tugba Taskaya
Published: December 2012

Many manually populated very large databases suffer from data quality problems such as missing, inaccurate data and duplicate entries. A recently recognized data quality problem is that of disguised missing data which arises when an explicit code for missing data such as NA (Not Available) is not provided and a legitimate data value is used instead. Presence of these values may affect the outcome of data mining tasks severely such that association mining algorithms or clustering techniques may result in biased inaccurate association rules and invalid clusters respectively. Detection and elimination of these values are necessary but burdensome to be carried out manually. In this chapter, the methods to detect disguised missing values by visual inspection are explained first. Then, the authors describe the methods used to detect these values automatically. Finally, the framework to detect disguised missing data is proposed and a demonstration of the framework on spatial and categorical data sets is provided.
Visual Data Mining in Physiotherapy Using Self-Organizing Maps
Authors: Alakhdar Yasser , Martínez-Martínez José M. , Guimerà-Tomás Josep , Escandell-Montero Pablo , Benitez Josep , Soria-Olivas Emilio
Published: December 2012

The basis of all clinical science developments is the analysis of the data obtained from a particular problem. In recent decades, however, the capacity of computers to process data has been increasing exponentially, which has created the possibility of applying more powerful methods of data analysis. Among these methods, the multidimensional visual data mining methods are outstanding. These methods show all the variables of one particular problem on the whole allowing to the clinical specialist to extract his own conclusions. In this chapter, a neural approximation to this kind of data mining is shown by means of the valuation analysis of the knee in athletes in the pre- and post-surgery of the anterior cruciate ligament, studying variables of force and measurements at different distances of the knee.
The Use of Prediction Reliability Estimates on Imbalanced Datasets
Authors: Košir Domen , Bosnic Zoran , Kononenko Igor
Published: December 2012

Data mining techniques are extensively used on medical data, which is typically composed of many normal examples and few interesting ones. When presented with highly imbalanced data, some standard classifiers tend to ignore the minority class which leads to poor performance. Various solutions have been proposed to counter this problem. Random undersampling, random oversampling, and SMOTE (Synthetic Minority Oversampling Technique) are the most well-known approaches. In recent years several approaches to evaluate the reliability of single predictions have been developed. Most recently a simple and efficient approach, based on the classifier’s class probability estimates was shown to outperform the other reliability estimates. The authors propose to use this reliability estimate to improve the SMOTE algorithm. In this study, they demonstrate the positive effects of using the proposed algorithms on artificial datasets. The authors then apply the developed methodology on the problem of predicting the maximal wall shear stress (MWSS) in the human carotid artery bifurcation. The results indicate that it is feasible to improve the classifier’s performance by balancing the data with their versions of the SMOTE algorithm.
Heterogeneous Text and Numerical Data Mining with Possible Applications in Business and Financial Sectors
Authors: Bourennani Farid , Rahnamayan Shahryar
Published: December 2012

Nowadays, many world-wide universities, research centers, and companies share their own data electronically. Naturally, these data are from heterogeneous types such as text, numerical data, multimedia, and others. From user side, this data should be accessed in a uniform manner, which implies a unified approach for representing and processing data. Furthermore, unified processing of the heterogeneous data types can lead to richer semantic results. In this chapter, we present a unified pre-processing approach that leads to generation of richer semantics of qualitative and quantitative data.
Frequent Pattern Discovery and Association Rule Mining of XML Data
Authors: Ding Qin , Sundarraj Gnanasekaran
Published: December 2012

Finding frequent patterns and association rules in large data has become a very important task in data mining. Various algorithms have been proposed to solve such problems, but most algorithms are only applicable to relational data. With the increasing use and popularity of XML representation, it is of importance yet challenging to find solutions to frequent pattern discovery and association rule mining of XML data. The challenge comes from the complexity of the structure in XML data. In this chapter, we provide an overview of the state-of-the-art research in content-based and structure-based mining of frequent patterns and association rules from XML data. We also discuss the challenges and issues, and provide our insight for solutions and future research directions.
Extraction of Medical Pathways from Electronic Patient Records
Authors: Antonelli Dario , Baralis Elena , Bruno Giulia , Chiusano Silvia , Mahoto Naeem A. , Petrigni Caterina
Published: December 2012

With the introduction of electronic medical records, a large amount of patients’ medical data has been available. An actual problem in this domain is to perform reverse engineering of the medical treatment process to highlight medical pathways typically adopted for specific health conditions. This chapter addresses the ability of sequential data mining techniques to reconstruct the actual medical pathways followed by patients. Detected medical pathways are in the form of sets of exams frequently done together, sequences of exam sets frequently followed by patients and frequent correlations between exam sets. The analysis shows that the majority of the extracted pathways are consistent with the medical guidelines, but also reveals some unexpected results, which can be useful both to enrich existing guidelines and to improve the public sanitary service.
Implications for Nursing Research and Generation of Evidence
Authors: Bakken Suzanne , Lucero Robert , Yoon Sunmoo , Hardiker Nicholas
Published: December 2012

A sound informatics infrastructure is essential to optimise the application of evidence in nursing practice. A comprehensive review of the infrastructure and associated research methods is supported by an extensive resource of references to point the interested reader to further resources for more in depth study. Information and communication technology (ICT) has been recognized as a fundamental component of applying evidence to practice for several decades. Although the role of ICT in generating knowledge from practice was formally identified as a nursing informatics research priority in the early 1990s (NINR Priority Expert Panel on Nursing Informatics, 1993), it has received heightened interest recently. In this chapter, the authors summarize some important trends in research that motivate increased attention to practice-based generation of evidence. These include an increased emphasis on interdisciplinary, translational, and comparative effectiveness research; novel research designs; frameworks and models that inform generation of evidence from practice; and creation of data sets that include not only variables related to biological and genetic measures, but also social and behavioral variables. The chapter also includes an overview of the ICT infrastructure and informatics processes required to facilitate generation of evidence from practice and across research studies: (1) information structures (e.g., re-usable concept representations, tailored templates for data acquisition), (2) processes (e.g., data mining algorithms, natural language processing), and (3) technologies (e.g., data repositories, visualization tools that optimize cognitive support). In addition, the authors identify key knowledge gaps related to informatics support for nursing research and generation of evidence from practice.
Data Mining, Validation, and Collaborative Knowledge Capture
Authors: Atzmueller Martin , Beer Stephanie , Puppe Frank
Published: December 2012

For large-scale data mining, utilizing data from ubiquitous and mixed-structured data sources, the extraction and integration into a comprehensive data-warehouse is usually of prime importance. Then, appropriate methods for validation and potential refinement are essential. This chapter describes an approach for integrating data mining, information extraction, and validation with collaborative knowledge management and capture in order to improve the data acquisition processes. For collaboration, a semantic wiki-enabled system for knowledge and experience management is presented. The proposed approach applies information extraction techniques together with pattern mining methods for initial data validation and is applicable for heterogeneous sources, i.e., capable of integrating structured and unstructured data. The methods are integrated into an incremental process providing for continuous validation options. The approach has been developed in a health informatics context: The results of a medical application demonstrate that pattern mining and the applied rule-based information extraction methods are well suited for discovering, extracting and validating clinically relevant knowledge, as well as the applicability of the knowledge capture approach. The chapter presents experiences using a case-study in the medical domain of sonography.
User’s Behaviour inside a Digital Library
Authors: Scarnò Marco
Published: December 2012

CASPUR allows many academic Italian institutions located in the Centre-South of Italy to access more than 7 million articles through a digital library platform. The behaviour of its users were analyzed by considering their “traces”, which are stored in the web server log file. Using several web mining and data mining techniques the author discovered a gradual and dynamic change in the way articles are accessed. In particular there is evidence of a journal browsing increase in comparison to the searching mode. Such phenomenon were interpreted using the idea that browsing better meets the needs of users when they want to keep abreast about the latest advances in their scientific field, in comparison to a more generic searching inside the digital library.
Conversation-Oriented Decision Support Systems for Organizations
Authors: Garrido Paulo
Published: December 2012

This chapter proposes concepts for designing and developing decision support systems that acknowledge, explore and exploit the fact that conversations among people are the top-level “supporting device” for decision-making. The goal is to design systems that support, configure and induce increasingly effective and efficient decision-making conversations. This includes allowing and motivating participation in decision-making conversations of any people who may contribute positively to decision-making and to the quality of its outcomes. The proposal sees the sum total of decisions being taken in an organization as the global decision process of the organization. The global decision process of the organization is structured in decision processes corresponding to organizational domains. Each organizational domain has associated a unit decision process. If the organizational domain contains organizational sub-domains, then its compound decision process is the union and composition of its unit decision process and the unit decision processes of its sub-domains. The proposal can be seen as extending, enlarging and integrating group decision support systems into an organization-wide system. The resulting organizational decision support system, by its conversational nature, may become the kernel decision support system of an organization or enterprise. In this way, the global decision process of the organization may be made explicit and monitored. It is believed that this proposal is original.
Text Mining for Analysis of Interviews and Questionnaires
Authors: Yamada Yasuhiro , Kato Kanji , Hirokawa Sachio
Published: December 2012

Interviews and questionnaires are the basis for collecting information about the opinions, concerns and needs of people. Analysis of those texts is crucial to understand the kansei of people. Text mining is an approach to discover useful and interesting patterns, knowledge and information from texts. This chapter contains two sections on text mining for beginners of it. The first section gives a brief survey of basic text mining techniques, such as keyword extraction, word graphs, clustering of texts and association rule mining. The second section demonstrates an example of text mining applied to interview analysis. Two text mining systems - the concept graph system and the matrix search system - are applied to analyze 2,409 remarks about products and services from 19 people. The analysis shows that text mining systems with a search function achieve interactive analysis of texts and an examination of various problems that we targeted.
A Decision Support System for Privacy Compliance
Authors: Pearson Siani , Sander Tomas
Published: December 2012

Regulatory compliance in areas such as privacy has become a major challenge for organizations. In large organizations there can be hundreds or thousands of projects that involve personal information. Ensuring that all those projects properly take privacy considerations into account is a complex challenge for accountable privacy management. Accountable privacy management requires that an organization makes sure that all relevant projects are in compliance and that there is evidence and assurance that this actually is the case. To date, there has been no suitable automated, scalable support for accountable privacy management; it is such a tool that the authors describe in this chapter. Specifically, they describe a privacy risk assessment and compliance tool which they are developing and rolling out within a large, global company – called HP Privacy Advisor (HP PA) – and its generalisation and extension. The authors also bring out those security, privacy, risk, and trust-related aspects they have been researching related to this work in particular.
Financial Early Warning System for Risk Detection and Prevention from Financial Crisis
Authors: Ozgulbas Nermin , Koyuncugil Ali Serhan
Published: December 2012

Risk management has become a vital topic for all enterprises especially in financial crisis periods. All enterprises need systems to warn against risks, detect signs and prevent from financial distress. Before the global financial crisis that began 2008, small and medium-sized enterprises (SMEs) have already fought with important financial issues. The global financial crisis and the ensuring flight away from risk have affected SMEs more than larger enterprises When we consider these effects, besides the issues of poor business performance, insufficient information and insufficiencies of managers in finance education, it is clear that early warning systems (EWS) are vital for SMEs for detection risk and prevention from financial crisis. The aim of this study is to develop and present a financial EWS for risk detection via data mining. For this purpose, data of SMEs listed in Istanbul Stock Exchange (ISE) and Chi-Square Automatic Interaction Detector (CHAID) Decision Tree Algorithm were used. By using EWS, we determined the risk profiles and risk signals for risk detection and road maps for risk prevention from financial crisis.
Role of Data Mining and Knowledge Discovery in Managing Telecommunication Systems
Authors: El Emary Ibrahiem Mahmoud Mohamed
Published: December 2012

This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.
Data Mining Applications in the Electrical Industry
Authors: Vacio Rubén Jaramillo , Ortiz Zezzatti Carlos Alberto Ochoa , Rios Armando
Published: December 2012

This chapter describes the experimental study partial discharges (PD) activities with artificial intelligent tools. The results present different patterns using a hybrid system with Self Organizing Maps (SOM) and Hierarchical clustering, this combination constitutes an excellent tool for exploration analysis of massive data such a partial discharge on underground power cables and electrical equipment. The SOM has been used for nonlinear feature extraction and the hierarchical clustering to visualization. The hybrid system is trained with different dataset using univariate phase-resolved distributions. The results show that the clustering method is fast, robust, and visually efficient.
Discovering Patterns in Order to Detect Weak Signals and Define New Strategies
Authors: El Haddadi Anass , Dousset Bernard , Berrada Ilham
Published: December 2012

Competitive intelligence activities rely on collecting and analyzing data in order to discover patterns from data using sequence data mining. The discovered patterns are used to help decision-makers considering innovation and defining the strategy for their business. In this chapter we present four methods for discovering patterns in the competitive intelligence process: “correspondence analysis,” “multiple correspondence analysis,” “evolutionary graph,” and “multi-term method.”
Data Mining and Economic Crime Risk Management
Authors: Jans Mieke , Lybaert Nadine , Vanhoof Koen
Published: December 2012

Economic crime is a billion dollar business and is substantially present in our current society. Both researchers and practitioners have gone into this problem by looking for ways of fraud mitigation. Data mining is often called in this context. In this chapter, the application of data mining in the field of economic crime, or corporate fraud, is discussed. The classification external versus internal fraud is explained and the major types of fraud within these classifications will be given. Aside from explaining these classifications, some numbers and statistics are provided. After this thorough introduction into fraud, an academic literature review concerning data mining in combination with fraud is given, along with the current solutions for corporate fraud in business practice. At the end, a current state of data mining applications within the field of economic crime, both in the academic world and in business practice, is given.
Evaluation Challenges for Computer-Aided Diagnostic Characterization
Authors: Horsthemke William H. , Raicu Daniela S. , Furst Jacob D. , Armato Samuel G.
Published: December 2012

Evaluating the success of computer-aided decision support systems depends upon a reliable reference standard, a ground truth. The ideal gold standard is expected to result from the marking, labeling, and rating by domain experts of the image of interest. However experts often disagree, and this lack of agreement challenges the development and evaluation of image-based feature prediction of expert-defined “truth.” The following discussion addresses the success and limitation of developing computer-aided models to characterize suspicious pulmonary nodules based upon ratings provided by multiple expert radiologists. These prediction models attempt to bridge the semantic gap between images and medically-meaningful, descriptive opinions about visual characteristics of nodules. The resultant computer-aided diagnostic characterizations (CADc) are directly usable for indexing and retrieving in content-based medical image retrieval and supporting computer-aided diagnosis. The predictive performance of CADc models are directly related to the extent of agreement between radiologists; the models better predict radiologists’ opinions when radiologists agree more with each other about the characteristics of nodules.
Assessing Data Mining Approaches for Analyzing Actuarial Student Success Rate
Authors: Olinsky Alan , Schumacher Phyllis A. , Quinn John
Published: December 2012

One way to enhance the likelihood that more students will graduate within the specific major that they begin with is to attract the type of students who have typically (historically) done well in that field of study. This chapter details a study that utilizes data mining techniques to analyze the characteristics of students who enroll as actuarial students and then either drop out of the major or graduate as actuarial students. Several predictive models including logistic regression, neural networks and decision trees are obtained. The models are then compared and the best fitting model is determined. The regression model turns out to be the best predictor. Since this is a very well understood method, it can easily be explained. The decision tree, although its underpinnings are somewhat difficult to explain, gives a clear and well understood output. Not only is the resulting model a good one for predicting success in the major, it also allows us the ability to better counsel students.
Human-Centred Web Search
Authors: Hoeber Orland
Published: December 2012

People commonly experience difficulties when searching the Web, arising from an incomplete knowledge regarding their information needs, an inability to formulate accurate queries, and a low tolerance for considering the relevance of the search results. While simple and easy to use interfaces have made Web search universally accessible, they provide little assistance for people to overcome the difficulties they experience when their information needs are more complex than simple fact-verification. In human-centred Web search, the purpose of the search engine expands from a simple information retrieval engine to a decision support system. People are empowered to take an active role in the search process, with the search engine supporting them in developing a deeper understanding of their information needs, assisting them in crafting and refining their queries, and aiding them in evaluating and exploring the search results. In this chapter, recent research in this domain is outlined and discussed.