bluuebunny/crossref_metadata_2025 huggingface dataset:
L’habitation maya
Authors: Pierrebourg Fabienne de , Arnauld Marie-Charlotte , Figuerola Helios , Hoppan Jean-Michel , Le Guen Olivier , Petrich Perla
Published: December 2012

En ce début de recherche qui met en œuvre informations archéologiques, épigraphiques et ethnologiques, nous proposons certains arguments montrant une continuité linguistique et conceptuelle de l’habitation maya en tant qu’espace délimité. Les frontières visibles et invisibles qui ferment ou fractionnent son espace temporairement ou durablement semblent assez similaires dans les diverses régions et époques envisagées. À travers l’accueil des personnes étrangères à l’intérieur du groupe domestique, nous voyons en outre apparaître des séries de frontières séparant des lieux internes, tout autant qu’elles sont des lieux de passage.
Data Discovery Approaches for Vague Spatial Data
Authors: Petry Frederick E.
Published: December 2012

This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining and uncertainty representations using rough set and fuzzy set techniques is provided. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets is described. Finally, an example of rule extraction for both types of uncertainty representations is given.
An Extraction, Transformation, and Loading Tool Applied to a Fuzzy Data Mining System
Authors: Carrasco Ramón A. , Hornos Miguel J. , Villar Pedro , Aguilar María A.
Published: December 2012

In this chapter, we address the problem of integrating semantically heterogeneous data (including data expressed in natural language), which are collected from various questionnaires published in different websites, into a Data Warehouse. We present an extension of the sentences and architecture of data mining Fuzzy Structured Query Language as an extraction, transformation, and loading tool to integrate semantically heterogeneous data from these websites. Moreover, we show a case study using the questionnaires (carried out during several years) about the courses on Information and Communication Technologies which are taught in the Business Studies implanted at the University of Granada (Spain). With this integrated information, the Data Warehouse user can make several analyses with the benefit of an easy linguistic interpretability. The solution proposed here can be used to similar integration problems.
A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I
Authors: Dileep A. D. , Veena T. , Sekhar C. Chandra
Published: December 2012

Sequential data mining involves analysis of sequential patterns of varying length. Sequential pattern analysis is important for pattern discovery from sequences of discrete symbols as in bioinformatics and text analysis, and from sequences or sets of continuous valued feature vectors as in processing of audio, speech, music, image, and video data. Pattern analysis techniques using kernel methods have been explored for static patterns as well as sequential patterns. The main issue in sequential pattern analysis using kernel methods is the design of a suitable kernel for sequential patterns of varying length. Kernel functions designed for sequential patterns are known as dynamic kernels. In this chapter, we present a brief description of kernel methods for pattern classification and clustering. Then we describe dynamic kernels for sequences of continuous feature vectors. We then present a review of approaches to sequential pattern classification and clustering using dynamic kernels.
Mining Lifecycle Event Logs for Enhancing Service-Based Applications
Authors: Dustdar Schahram , Leitner Philipp , Nardini Franco Maria , Silvestri Fabrizio , Tolomei Gabriele
Published: December 2012

Service-Oriented Architectures (SOAs), and traditional enterprise systems in general, record a variety of events (e.g., messages being sent and received between service components) to proper log files, i.e., event logs. These files constitute a huge and valuable source of knowledge that may be extracted through data mining techniques. To this end, process mining is increasingly gaining interest across the SOA community. The goal of process mining is to build models without a priori knowledge, i.e., to discover structured process models derived from specific patterns that are present in actual traces of service executions recorded in event logs. However, in this work, the authors focus on detecting frequent sequential patterns, thus considering process mining as a specific instance of the more general sequential pattern mining problem. Furthermore, they apply two sequential pattern mining algorithms to a real event log provided by the Vienna Runtime Environment for Service-oriented Computing, i.e., VRESCo. The obtained results show that the authors are able to find services that are frequently invoked together within the same sequence. Such knowledge could be useful at design-time, when service-based application developers could be provided with service recommendation tools that are able to predict and thus to suggest next services that should be included in the current service composition.
Database Analysis with ANNs by means of Graph Evolution
Authors: Rivero Daniel , Dorado Julián , Rabuñal Juan , Pazos Alejandro
Published: December 2012

Traditionally, the development of Artificial Neural Networks (ANNs) is a slow process guided by the expert knowledge. This expert usually has to test several architectures until he finds one suitable for solving a specific problem. This makes the development of ANNs a slow process in which the expert has to do much effort. This chapter describes a new method for the development of Artificial Neural Networks, so it becomes completely automated. Since ANNs are complex structures with very high connectivity, traditional algorithms are not suitable to represent them. For this reason, in this work graphs with high connectivity that represent ANNs are evolved. In order to measure the performance of the system and to compare the results with other ANN development methods by means of Evolutionary Computation (EC) techniques, several tests were performed with problems based on some of the most used test databases in Data Mining. These comparisons show that the system achieves good results that are not only comparable to those of the already existing techniques but, in most cases, improve them.
Quantization based Sequence Generation and Subsequence Pruning for Data Mining Applications
Authors: Babu T. Ravindra , Murty M. Narasimha , Subrahmanya S. V.
Published: December 2012

Data Mining deals with efficient algorithms for dealing with large data. When such algorithms are combined with data compaction, they would lead to superior performance. Approaches to deal with large data include working with representatives of data instead of entire data. The representatives should preferably be generated with minimal data scans. In the current chapter we discuss working with methods of lossy and non-lossy data compression methods combined with clustering and classification of large datasets. We demonstrate the working of such schemes on two large data sets.
Mammogram Mining Using Genetic Ant-Miner
Authors: Thangavel K. , Roselin R.
Published: December 2012

Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. It is an extension of data mining to image domain and an interdisciplinary endeavour. This chapter focuses on mammogram classification using genetic Ant-Miner. The key idea is to generate classifier for classifying mammograms as normal or abnormal using the proposed Genetic Ant-Miner algorithm. The Genetic Algorithm has been employed to optimize some of the ant parameters. A comparative analysis is performed in order to achieve the efficiency of the proposed algorithm. Further, the experimental results reveals that the improvement of the proposed Genetic Ant-Miner in the domain of Biomedical image Analysis.
A Dynamic Privacy Manager for Compliance in Pervasive Computing
Authors: Bonazzi Riccardo , Liu Zhan , Ganière Simon , Pigneur Yves
Published: December 2012

In this chapter we propose a decision support system for privacy management of context-aware technologies, which requires the alignment of four dimensions: business, regulation, technology, and user behavior. We have developed a middleware model able to achieve compliance with privacy policies within a dynamic and context-aware risk management situation. We illustrate our model in more details by means of a small prototype that we developed, and we present the current outcomes of its implementation to derive some pointers for the direction of future investigation.
ANGEL Mining
Authors: Swanger Tyler , Whitlock Kaitlyn , Scime Anthony , Post Brendan P.
Published: December 2012

This chapter data mines the usage patterns of the ANGEL Learning Management System (LMS) at a comprehensive college. The data includes counts of all the features ANGEL offers its users for the Fall and Spring semesters of the academic years beginning in 2007 and 2008. Data mining techniques are applied to evaluate which LMS features are used most commonly and most effectively by instructors and students. Classification produces a decision tree which predicts the courses that will use the ANGEL system based on course specific attributes. The dataset undergoes association mining to discover the usage of one feature’s effect on the usage of another set of features. Finally, clustering the data identifies messages and files as the features most commonly used. These results can be used by this institution, as well as similar institutions, for decision making concerning feature selection and overall usefulness of LMS design, selection and implementation.
Virtual Telemedicine and Virtual Telehealth
Authors: Kareem Shazia , Bajwa Imran Sarwar
Published: December 2012

Telemedicine is modern technology that is employed to provide low cost, high standard medical facilities to the people of remote areas. Store-and-Forward method of telemedicine suits more to the progressive countries like Pakistan as not only is it easy to set up but it also has a very cheap operating cost. However, the high response time taken by store & forward telemedicine becomes a critical factor in emergency cases, where each minute has a price. The response time factor can be overcome by using virtual telemedicine approach. In virtual telemedicine, a Clinical Decision Support System (CDSS) is deployed at rural station. The CDSS is intelligent enough to diagnose a patient’s disease and prescribe proper medication. In case the CDSS cannot answer a query, the CDSS immediately sends an e-mail to a medical expert (doctor), and when the response is received, the CDSS knowledge-base is updated for future queries. In this chapter, the authors not only report a NL-based CDSS that can answer NL queries, but also present a complete architecture of a virtual telemedicine setup.
Machine Learning in Studying the Organism’s Functional State of Clinically Healthy Individuals Depending on Their Immune Reactivity
Authors: Sambukova Tatiana V.
Published: December 2012

The work is devoted to the decision of two interconnected key problems of Data Mining: discretization of numerical attributes, and inferring pattern recognition rules (decision rules) from training set of examples with the use of machine learning methods. The method of discretization is based on a learning procedure of extracting attribute values’ intervals the bounds of which are chosen in such a manner that the distributions of attribute’s values inside of these intervals should differ in the most possible degree for two classes of samples given by an expert. The number of intervals is defined to be not more than 3. The application of interval data analysis allowed more fully than by traditional statistical methods of comparing distributions of data sets to describe the functional state of persons in healthy condition depending on the absence or presence in their life of the episodes of secondary deficiency of their immunity system. The interval data analysis gives the possibility (1) to make the procedure of discretization to be clear and controlled by an expert, (2) to evaluate the information gain index of attributes with respect to the distinguishing of given classes of persons before any machine learning procedure (3) to decrease crucially the machine learning computational complexity.
A Comparative Study of Associative Classifiers in Mesenchymal Stem Cell Differentiation Analysis
Authors: Wang Weiqi , Wang Yanbo J. , Xin Qin , Bañares-Alcántara René , Coenen Frans , Cui Zhanfeng
Published: December 2012

Discovering how Mesenchymal Stem Cells (MSCs) can be differentiated is an important topic in stem cell therapy and tissue engineering. In a general context, such differentiation analysis can be modeled as a classification problem in data mining. Specifically, this is concerned with the single-label multi-class classification task. Previous studies on this topic suggests the Associative Classification (AC) rather than other alternative (Classification) techniques, and presented classification results based on the CMAR (Classification based on Multiple Association Rules) associative classifier. Other AC algorithms include: CBA (Classification Based on Associations), PRM (Predictive Rule Mining), CPAR (Classification based on Predictive Association Rules) and TFPC (Total From Partial Classification). The main aim of this chapter is to compare the performance of different associative classifiers, in terms of classification accuracy, efficiency, number of rules to be generated, quality of such rules, and the maximum number of attributes in rule-antecedents, with respect to MSC differentiation analysis.
Cloud-Based Intelligent DSS Design for Emergency Professionals
Authors: Miah Shah J.
Published: December 2012

Computational Intelligence (CI) has become a well-established research field of computer science in which multi-disciplinary problems are studied to design an effective computing solution. As a known computer-based CI approach, decision support systems (DSS) has gained popularity as a computing solution to structured and unstructured problems in organizations’ managerial improvement. DSS design needs to meet the domain-specific demands of emergency professionals on both an on-site and a real-time basis using the support of the most up-to-date technological provisioning platform. The advantages of cloud computing may offer promising support (e.g. Internet or web-based provisioning) for DSS services to meet the emergency professionals’ decision needs. This chapter introduces requirements of a cloud-based CI approach for domain-specific decision support through the functionalities on an anywhere and anytime basis. The chapter highlights the context of intelligent DSS design in terms of support in determining the priorities of taking action, both for medical emergency professionals and natural disasters workers, as potential application areas identified in this study.
Classification of Biological Sequences
Authors: Rani Pratibha , Pudi Vikram
Published: December 2012

The rapid progress of computational biology, biotechnology, and bioinformatics in the last two decades has led to the accumulation of tremendous amounts of biological data that demands in-depth analysis. Data mining methods have been applied successfully for analyzing this data. An important problem in biological data analysis is to classify a newly discovered sequence like a protein or DNA sequence based on their important features and functions, using the collection of available sequences. In this chapter, we study this problem and present two Bayesian classifiers RBNBC (Rani & Pudi, 2008a) and REBMEC (Rani & Pudi, 2008c). The algorithms used in these classifiers incorporate repeated occurrences of subsequences within each sequence (Rani, 2008). Specifically, Repeat Based Naive Bayes Classifier (RBNBC) uses a novel formulation of Naive Bayes, and the second classifier, Repeat Based Maximum Entropy Classifier (REBMEC) uses a novel framework based on the classical Generalized Iterative Scaling (GIS) algorithm.
From Data to Knowledge
Authors: Wijaya Tri
Published: December 2012

This chapter will discuss a very useful technique to get (or to mine) a hidden information or knowledge which is lie in our data namely, data mining, which is a powerful and automatic (or semi-automatic) technique. Not only about the concept and theory, this chapter will also discuss about the application and implementation of data mining. Firstly, the authors will talk about data, information, and knowledge, whether they are different or not. After understand the term, they will discuss about what data mining is and what the importance of it. Second, they describe the process of gaining the hidden knowledge, how it is done, from the beginning until presenting the result. The authors will go through it step by step. In the next section, they will discuss about the several different tasks of data mining. In addition, to get a better understanding, the authors will compare data mining with other terminology which closely related so called data warehouse, and OLAP. For the last, but not the least, as stated before, this chapter will tell us about the real implementation of data mining in several different areas.
Knowledge Discovery from Online Communities
Authors: Cagliero Luca , Fiori Alessandro
Published: December 2012

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.
A Data Mining Service to Assist Instructors Involved in Virtual Education
Authors: Zorrilla Marta E. , García Diego
Published: December 2012

In this chapter we present a BI application delivered as a service on-demand. In particular, it is a data mining service that aims to help instructors involved in distance education to discover their students’ behavior profiles and models about how they navigate and work in their virtual courses offered in Learning Content Management Systems such as Blackboard or Moodle. The main characteristic is that the users do not require data mining knowledge to use the service; they only have to send a data file according to one of the templates provided by the system and request the results. The service carries out the KDD process itself. Furthermore, the service provides an interface based on Web services, which can be called by external software. In short, the chapter talks about the necessity of a service with these characteristics and includes the description of its architecture and its method of operation as well as a discussion about some of the patterns it offers and how these provide instructors valuable knowledge to make decisions.
Mobile Purchase Decision Support Systems for In-Store Shopping Environments
Authors: Kowatsch Tobias , Maass Wolfgang
Published: December 2012

Purchase decision-making is influenced by product information available in online or in-store shopping environments. In online shopping environments, the use of decision support systems increases the value of product information as information becomes adaptive and thus more relevant to consumers’ information needs. Correspondingly, mobile purchase decision support systems (MP-DSSs) may also increase the value of product information in in-store shopping environments. In this chapter, we investigate the use of a MP-DSS that is bound to a physical product. Based on Theory of Planned Behaviour, Innovation Diffusion Theory, and Technology Acceptance Model, we propose and evaluate a model to better understand MP-DSSs. Results indicate that perceived usefulness influences product purchases and predicts usage intentions and store preferences of consumers. We therefore discuss new business models for retail stores in which MP-DSSs satisfy both the information needs of consumers and the communication needs of retailers.
Structuring and Facilitating Online Learning through Learning/Course Management Systems
Authors: Hai-Jew Shalin
Published: December 2012

Online learning—whether it is human-facilitated or automated, hybrid/blended, asynchronopus or synchronous or mixed--often relies on learning/course management systems (L/CMSes). These systems have evolved in the past decade-and-a-half of popular use to integrate powerful tools, third-party software, Web 2.0 functionalities (blogs, wikis, virtual worlds, and tag clouds), and a growing set of capabilities (eportfolios, data management, back-end data mining, information assurance, and other elements). This chapter highlights learning/course management systems, their functionalities and structures (including some integrated technologies), their applied uses in adult e-learning, and extra-curricular applications. A concluding section explores future L/CMSes based on current trends.
Innovative Approaches for Efficiently Warehousing Complex Data from the Web
Authors: Bentayeb Fadila , Maïz Nora , Mahboubi Hadj , Favre Cécile , Loudcher Sabine , Harbi Nouria , Boussaïd Omar , Darmont Jérôme
Published: December 2012

Research in data warehousing and OLAP has produced important technologies for the design, management, and use of Information Systems for decision support. With the development of Internet, the availability of various types of data has increased. Thus, users require applications to help them obtaining knowledge from the Web. One possible solution to facilitate this task is to extract information from the Web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. In this chapter, we present three innovative researches recently introduced to extend the capabilities of decision support systems, namely (1) the use of XML as a logical and physical model for complex data warehouses, (2) associating data mining to OLAP to allow elaborated analysis tasks for complex data and (3) schema evolution in complex data warehouses for personalized analyses. Our contributions cover the main phases of the data warehouse design process: data integration and modeling, and user driven-OLAP analysis.
Coastal Atlas Interoperability
Authors: Lassoued Yassine , Pham Trung T. , Bermudez Luis , Stocks Karen , O’Grady Eoin , Isenor Anthony , Alexander Paul
Published: December 2012

This chapter defines the coastal web atlases interoperability problem, introduces interoperability standards, and describes the development of a semantic mediator prototype to provide a common access point to coastal data, maps and information from distributed coastal web atlases. The prototype showcases how ontologies and ontology mappings can be used to integrate different heterogeneous and autonomous atlases (or information systems), using international standards such as ISO-19139 for metadata encoding and the Open Geospatial Consortium’s Catalogue Service for the Web specification. Lessons learned from this prototype will help build regional atlases and improve decision support systems as part of a new International Coastal Atlas Network (ICAN).
A Perspective on Data Mining Integration with Business Intelligence
Authors: Azevedo Ana , Santos Manuel Filipe
Published: December 2012

Business Intelligence (BI) is an emergent area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. The purpose of this chapter is to discuss the relevance of DM integration with BI, and its importance to business users. From the literature review, it was observed that the definition of an underlying structure for BI is missing, and therefore a framework is presented. It was also observed that some efforts are being done that seek the establishment of standards in the DM field, both by academics and by people in the industry. Supported by those findings, this chapter introduces an architecture that can conduct to an effective usage of DM in BI. This architecture includes a DM language that is iterative and interactive in nature. This chapter suggests that the effective usage of DM in BI can be achieved by making DM models accessible to business users, through the use of the presented DM language.
Challenges to Use Recommender Systems to Enhance Meta-Cognitive Functioning in Online Learners
Authors: Zhou Mingming , Xu Yabo
Published: December 2012

A wealth of research has shown that meta-cognition plays a crucial role in the promotion of effective school learning. In most of the e-learning environment designs, however, meta-cognitive strategies have generally been neglected, and therefore, satisfactory uses of these strategies have rarely been realized. Most learners are not even aware of what they have been studying. If the learning system could automatically guide and intelligently recommend learning activities or strategies to facilitate student monitoring and control of their learning, it would favor and improve their learning process and performance. Unfortunately, nearly no e-learning systems to date have attempted to do so. In this chapter, we first described the need for enhancing meta-cognitive skills in e-learning environment, followed by an outline of major challenges for meta-cognitive activity recommendations. We then proposed to adopt data mining algorithms (i.e., content-based and sequence-based recommendation techniques) to meet the identified issues with a toy example.
Research Challenge of Locally Computed Ubiquitous Data Mining
Authors: Cayci Aysegul , Gomes João Bártolo , Zanda Andrea , Menasalvas Ernestina , Eibe Santiago
Published: December 2012

Advances in wireless, sensor, mobile and wearable technologies present new challenges for data mining research on providing mobile applications with intelligence. Autonomy and adaptability requirements are the two most important challenges for data mining in this new environment. In this chapter, in order to encourage the researchers on this area, we analyzed the challenges of designing ubiquitous data mining services by examining the issues and problems while paying special attention to context and resource awareness. We focused on the autonomous execution of a data mining algorithm and analyzed the situational factors that influence the quality of the result. Already existing solutions in this area and future directions of research are also covered in this chapter.
From Non-Invasive Hemodynamic Measurements towards Patient-Specific Cardiovascular Diagnosis
Authors: Bernhard Stefan , Al Zoukra Kristine , Schtte Christof
Published: December 2012

The past two decades have seen impressive success in medical technology, generating novel experimental data at an unexpected rate. However, current computational methods cannot sufficiently manage the data analysis for interpretation, so clinical application is hindered, and the benefit for the patient is still small. Even though numerous physiological models have been developed to describe complex dynamical mechanisms, their clinical application is limited, because parameterization is crucial, and most problems are ill-posed and do not have unique solutions. However, this information deficit is imminent to physiological data, because the measurement process always contains contamination like artifacts or noise and is limited by a finite measurement precision. The lack of information in hemodynamic data measured at the outlet of the left ventricle, for example, induces an infinite number of solutions to the hemodynamic inverse problem (possible vascular morphologies that can represent the hemodynamic conditions) (Quick, 2001). Within this work, we propose that, despite these problems, the assimilation of morphological constraints, and the usage of statistical prior knowledge from clinical observations, reveals diagnostically useful information. If the morphology of the vascular network, for example, is constrained by a set of time series measurements taken at specific places of the cardiovascular system, it is possible to solve the hemodynamic inverse problem by a carefully designed mathematical forward model in combination with a Bayesian inference technique. The proposed cardiovascular system identification procedure allows us to deduce patient-specific information that can be used to diagnose a variety of cardiovascular diseases in an early state. In contrast to traditional inversion approaches, the novel method produces a distribution of physiologically interpretable models (patient-specific parameters and model states) that allow the identification of disease specific patterns that correspond to clinical diagnoses, enabling a probabilistic assessment of human health condition on the basis of a broad patient population. In the ongoing work we use this technique to identify arterial stenosis and aneurisms from anomalous patterns in signal and parameter space. The novel data mining procedure provides useful clinical information about the location of vascular defects like aneurisms and stenosis. We conclude that the Bayesian inference approach is able to solve the cardiovascular inverse problem and to interpret clinical data to allow a patient-specific model-based diagnosis of cardiovascular diseases. We think that the information-based approach provides a useful link between mathematical physiology and clinical diagnoses and that it will become constituent in the medical decision process in near future.
Towards Spatial Decision Support System for Animals Traceability
Authors: Visoli Marcos , Bimonte Sandro , Ternes Sônia , Pinet François , Chanet Jean-Pierre
Published: December 2012

Animal traceability is a very important question for several government and private institutions from many points of view: economical, sanitary, etc. Traditional systems are able to memorize the main bovine movements, or to capture the geolocation of an animal using RFID. Now it should be possible to envisage a new generation of traceability systems in which the different locations are automatically recorded several times per day for each animal. These systems should also be coupled with analysis techniques to help decision-makers to take decisions, validate and/or reformulate their hypothesis. In this chapter the authors present a spatial decision support system dedicated to the animal geolocation acquisitions and analysis of possible sanitary problems. Indeed, in case of sanitary alerts, the system is able to determine the animals which have been in contact with a diseased animal exploiting historical trajectories of animals. It is applied to traceability of beef cattle using the Brazilian production system as a case study. OTAG focuses on improving methods and geotechnologies for recording reliable and accurate data on beef production.
Space-Time Analytics for Spatial Dynamics
Authors: Yuan May , Bothwell James
Published: December 2012

The so-called Big Data Challenge poses not only issues with massive volumes of data, but issues with the continuing data streams from multiple sources that monitor environmental processes or record social activities. Many statistics tools and data mining methods have been developed to reveal embedded patterns in large data sets. While patterns are critical to data analysis, deep insights will remain buried unless we develop means to associate spatiotemporal patterns to the dynamics of spatial processes that essentially drive the formation of patterns in the data. This chapter reviews the literature with the conceptual foundation for space-time analytics dealing with spatial processes, discusses the types of dynamics that have and have not been addressed in the literature, and identifies needs for new thinking that can systematically advance space-time analytics to reveal dynamics of spatial processes. The discussion is facilitated by an example to highlight potential means of space-time analytics in response to the Big Data Challenge. The example shows the development of new space-time concepts and tools to analyze data from two common General Circulation Models for climate change predictions. Common approaches compare temperature changes at locations from the NCAR CCSM3 and from the CNRM CM3 or animate time series of temperature layers to visualize the climate prediction. Instead, new space-time analytics methods are shown here the ability to decipher the differences in spatial dynamics of the predicted temperature change in the model outputs and apply the concepts of change and movement to reveal warming, cooling, convergence, and divergence in temperature change across the globe.
An Approach for Land-Use Suitability Assessment Using Decision Support Systems, AHP and GIS
Authors: Polat Erkan
Published: December 2012

Nowadays, analysis of land-use suitability requires consideration of variety of criteria including not only natural/physical capacity of a land unit but also socio-economic and environmental impact implications. This chapter suggests an approach instead of typical synthesis and land-use suitability assessment methods that is used in the urban and regional planning. Using the decision support systems with AHP and GIS, a participative, GIS-supported, different, new, flexible, and soft approach is proposed for land-use suitability assessment of cities and regions in particular. The chapter presents a technique integrating SWOT-CATWOE analysis, the Delphi and Inquiry Technique, the Analytical Hierarchy Process (AHP), and a Geographic Information System (GIS) to evaluate the land-use suitability for cities. By the help of this study, settlement suitability analyses have been achieved according to the socio-economic and infrastructure, environmental or physical thresholds of the settlement, and this integration could benefit urban planners and decision makers. The proposed method begins with the identification of settlement requirements, followed by the derivation of settlement evaluation criteria with SWOT-CATWOE analysis and the Delphi and Inquiry Technique. Then, pairwise comparisons (PC matrices) are formed between each pair of settlement criteria. The AHP is used to measure the relative importance or weight of each settlement criterion. This chapter has intended a theoretical and scientific base for an AHP and GIS combination with decision support systems. Thus, when land-use suitability assessment has taken as this combination of decision support systems, AHP and GIS, more realistic, more accurate, and applicable results emerge.
Remote Fault Diagnosis System for Marine Power Machinery System
Authors: Yuan Chengqing , Yan Xinping , Li Zhixiong , Zhang Yuelei , Sheng Chenxing , Zhao Jiangbin
Published: December 2012

Marine power machinery parts are key equipments in ships. Ships always work in rigorous conditions such as offshore, heavy load, et cetera. Therefore, the failures in marine power machinery would badly threaten the safety of voyages. Keeping marine power machineries running reliably is the guarantee of voyage safety. For the condition monitoring and fault diagnosis of marine power machinery system, this study established the systemic condition identification approach for the tribo-system of marine power machinery and developed integrated diagnosis method by combining on-line and off-line ways for marine power machinery. Lastly, the remote fault diagnosis system was developed for practical application in marine power machinery, which consists of monitoring system in the ship, diagnosis system in laboratory centre, and maintenance management & maintenance decision support system.
Role of Small and Medium Sized Enterprises in E-Supply Chain Management
Authors: Zhao Fang
Published: December 2012

This chapter studies the role that small and medium-sized enterprises (SMEs) play in the e-supply chain management. It has two objectives: (1) it explores how a SME embraces and implements electronic supply chain management (e-SCM) and the challenges facing it, and (2) it develops strategy to deal with the challenges. The chapter draws upon a case study of IFC Global Logistics (IFC), a small-to-medium-sized third party logistics provider. The case study illustrates how the SME embraces enabling technologies, the Internet, and modern business practices to integrate its supply chain management processes and to create for itself differentiation and a competitive advantage in the tough logistics industry. Based upon a literature review and the case study, the chapter explores effective strategy for SMEs in e-supply chain management.
Small Manufacturers vs. Large Retailers on RFID Adoption in the Apparel Supply Chain
Authors: Tajima May
Published: December 2012

The apparel industry is one of the most rapidly growing sectors of the radio frequency identification (RFID) market, and within it, large retailers have been driving RFID adoption. However, the continuation of this industry’s fast-paced growth is questionable due to the uncertainty associated with how manufacturers, especially small ones, would react to the retailer-led RFID initiative. The literature suggests that the relationship between small manufacturers and large retailers could promote or inhibit RFID adoption among the manufacturers. In order to study the impact of the relationship between small manufacturers and large retailers on the small manufacturers’ RFID adoption decisions, this research develops a 2×2 (two-by-two) game model and conducts outcome stability analysis. The results show that, in the 2×2 game framework, (i) the retailer’s opportunistic behavior is unlikely to occur due to the strong stability associated with the manufacturer’s do-nothing option; (ii) the do-nothing option, however, may lead to missed opportunities for both parties; (iii) the retailer’s pressure tactic is not effective in persuading the small manufacturer to adopt RFID; and (iv) the retailer’s collaborative strategy also does not guarantee the manufacturer’s RFID adoption. The discussion of these results concludes with specific suggestions for how to encourage RFID adoption among the small apparel manufacturers.
Digital Marketplaces as a Viable Model for SME Networking
Authors: Ndou Valentina , Sadguy Nezha
Published: December 2012

In the networked world supply chain, integration and networking is being echoed in various industries as a strategic imperative solution for firms. Firms are recognizing the power of the Internet as a platform for creating different forms of relationships and collaborations aimed to enhance value and achieve a sustainable competitive advantage. Digital Marketplaces represent one of the most powerful solutions adopted by firms to manage their supply chains and to support the networking practices among firms, especially among SMEs. In fact they enable firms to more efficiently buy, sell, and manage their supply chain processes on a global scale. However, SMEs Supply chains may differ in the modalities of coordination and collaboration, in the intensity of relations between actor and more specifically in the technological infrastructure and competencies available. These factors may affect the success of digital marketplaces and their potentialities for firms networking. The authors suggest that digital marketplaces could provide a viable model for SME networking; however, successful path toward networking requires a harmonization of digital marketplace business model with supply chain characteristics.
A Conceptual Model for Greening a Supply Chain through Greening of Suppliers and Green Innovation
Authors: Chan H. K. , Chiou T.-Y. , Lettice F.
Published: December 2012

Nowadays, more organisations are focusing on how to improve their environmental performance, partly driven by recent regulations in this area. This means that green supply chain management plays an important role over traditional supply chain management. Companies could gain competitive advantage through the proper management of their supply chain activities, for example, purchasing management. In fact, organisations can now generate more business opportunities than their competitors by addressing environmental management successfully. More specifically, it has been identified that implementation of green innovation can become a company’s order winner. However, not many studies have investigated the relationships between the greening of suppliers, green innovation, environmental performance and competitive advantage. The objective of this article is to propose a conceptual model, developed from a review of relevant literature and performance indicators, and to identify how future research can address these issues.
Quantitative Risk Management Models for Newsvendor Supply Chains
Authors: Vlachos Dimitrios
Published: December 2012

As the practices of offshoring and outsourcing force the supply chain networks to keep on expanding geographically in the globalised environment, the logistics processes are becoming more exposed to risk and disruptions. Thus, modern supply chains seem to be more vulnerable than ever. It is clear that efficient logistics risk and security management emerges as an issue of pivotal importance in such competitive, demanding and stochastic environment and is thus vital for the viability and profitability of a company. In this context, this chapter focuses on a set of stochastic quantitative models that study the impact of one or more supply chain disruptions on optimal determination of single period inventory control policies. The purpose of this research is to provide a critical review of state-of-the-art methodologies to be used as a starting point for further research efforts.
Strategic Imperatives for Customer Centric Approach
Authors: Baporikar Neeta
Published: December 2012

Organizations have been forced to transform themselves from “product-centric” to “customer-centric” for various reasons. Becoming customer centric is one of the most important aims of any organization. Customer Relationship Management has attracted the attention of both marketing practitioners and researchers over the last decade. Despite, or maybe due to, the attention drawn to the subject, a clear agreement on what CRM is and especially how CRM should be developed remains lacking. It is now commonly understood that CRM involves technology, drives marketing, sales, and customer services, and is often approached as a business strategy to create value for an organization through the creation of profitable long-term customer relationships. Significant progress has been made in identifying and researching the components of CRM individually. The roles of CRM applications, customer knowledge, customer satisfaction, and a customer-centric strategy have all been subject of research lately. However, to the best of the author’s knowledge, no comprehensive research has been conducted to identify and understand the strategic imperatives for developing customer centric approach, and that is the core objective of this chapter. Moreover, the advancements in society and technology leading to convergence of information, communication, and technology, coupled with acceleration in globalization, competitive environments, and changing customer’s preferences have created new challenges as well as opportunities for leveraging knowledge about customers. Also we have yet to find research that shows a relationship between strategic imperatives and customer centric approach.
CRM, SRM and Marketing in Supply Chain
Authors: Kamran Mehdi Abdollahi
Published: December 2012

The first goal of this chapter is to disclose different aspects of this customer-focused strategy, such as CRM definitions, goals, perspectives, and SCM and CRM integration features. But due to the continual needs of cutting costs, organizations realized the necessity of long term buyer-seller relationships, and they were forced to improve their supply chain and leverage their supply base. The key solution to this necessity is Supplier Relationship Management (SRM). After describing various aspects of CRM, therefore in the second part the different characteristics of SRM and the way going from CRM to SRM is being discussed.
Protecting Privacy by Secure Computation
Authors: Kerschbaum Florian , Funke Daniel
Published: December 2012

We consider collaborative social network analysis without revealing private inputs of the participants. This problem arises in criminal investigations of federal police organization where single organizations may not reveal their data without probable cause, but the aggregation of all data entails new information, such as the entire social network structure. We present algorithms for securely computing either the entire, anonymized graph or only specific metrics for individuals. We use secure computation protocols to disclose nothing, but the output of the analysis, i.e. anything that cannot be derived from one’s input and output – including other parties’ input – remains private. We have implemented a prototype for SAP’s investigative case management system – a derivate of its customer relationship management.
Interaction Mining
Authors: Pallotta Vincenzo , Vrieling Lammert , Delmonte Rodolfo
Published: December 2012

In this chapter we present the major challenges of a new trend in business analytics, namely Interaction Mining. With the proliferation of unstructured data as the result of people interacting with each other using digital networked devices, classical methods in text business analytics are no longer effective. We identified the causes of their failure as being related to the inadequacy of dealing with conversational data. We propose then to move from Text Mining towards Interaction Mining, and we make several cases for this transition in areas such as marketing research, social media analytics, and customer relationship management. We also propose a roadmap for the future development of Interaction Mining by challenging the current practices in business intelligence and information visualization.
Application of RFID Technology in Banking Sector
Authors: Dehkordi Lotfollah Forozandeh , Ghorbani Ali , Aliahmadi Ali Reza
Published: December 2012

Nowadays, the banks are using new technologies to provide better services to customers. One of these new technologies is RFID. In this chapter first a brief introduction presented about RFID technology and its components. Then, some applications of RFID in banking sector such as RFID applications in the cheques between banks, reducing the manual operation, customer relationship management, tracking and tracing, money transferring system, countering counterfeiting, contactless smart cards, people identification, phone banking, establishing security, checking purpose and so on are explained. Finally some of the barriers to technology acceptance by the customers and some methods to data protection and increasing security in RFID systems are described.