bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-Term Evolution (LTE)
Authors: He Bing , Xie Bin , Agrawal Sanjuli , Zhao David , Reddy Ranga
Published: December 2012

With the ever growing demand on high throughput for mobile users, 3G cellular networks are limited in their network capacity for offering high data services to a large number of users. Consequently, many Internet services such as on-demand video and mobile TV are hard to be satisfactorily supported by the current 3G cellular networks. 3GPP Long Term Evolution (LTE) is a recently proposed 4G standard, representing a significant advance of 3G cellular technology. Attractively, LTE would offer an uplink data speed up to 50 Mbps and a downlink speed up to 100 Mbps for various services such as traditional voice, high-speed data, multimedia unicast, and multimedia broadcasting. In such a short time, it has been broadly accepted by major wireless vendors such as Verizon-Vodafone, AT&T, NTT-Docomo, KDDI, T-Mobile, and China Mobile. In order for high data link speed, LTE adapts new technologies that are new to 3G network such as Orthogonal Frequency Division Multiplexing (OFDM) and Multiple-Input Multiple-Output (MIMO). MIMO allows the use of more than one antenna at the transmitter and receiver for higher data transmission. The LTE bandwidth can be scalable from 1.25 to 20 MHz, satisfying the need of different network operators that may have different bandwidth allocations for services, based on its managed spectrum. In this chapter, we discuss the major advance of the LTE and its recent research efforts in improving its performance. Our illustration of LTE is comprehensive, spanning from the LTE physical layer to link layer. In addition, the LTE security is also discussed.
Soft Computing and its Applications
Authors: Bhattacharyya Siddhartha , Maulik Ujjwal , Bandyopadhyay Sanghamitra
Published: December 2012

Soft Computing is a relatively new computing paradigm bestowed with tools and techniques for handling real world problems. The main components of this computing paradigm are neural networks, fuzzy logic and evolutionary computation. Each and every component of the soft computing paradigm operates either independently or in coalition with the other components for addressing problems related to modeling, analysis and processing of data. An overview of the essentials and applications of the soft computing paradigm is presented in this chapter with reference to the functionalities and operations of its constituent components. Neural networks are made up of interconnected processing nodes/neurons, which operate on numeric data. These networks posses the capabilities of adaptation and approximation. The varied amount of uncertainty and ambiguity in real world data are handled in a linguistic framework by means of fuzzy sets and fuzzy logic. Hence, this component is efficient in understanding vagueness and imprecision in real world knowledge bases. Genetic algorithms, simulated annealing algorithm and ant colony optimization algorithm are representative evolutionary computation techniques, which are efficient in deducing an optimum solution to a problem, thanks to the inherent exhaustive search methodologies adopted. Of late, rough sets have evolved to improve upon the performances of either of these components by way of approximation techniques. These soft computing techniques have been put to use in wide variety of problems ranging from scientific to industrial applications. Notable among these applications include image processing, pattern recognition, Kansei information processing, data mining, web intelligence etc.
A Particle Filtering Based Approach for Gear Prognostics
Authors: He David , Bechhoefer Eric , Ma Jinghua , Zhu Junda
Published: December 2012

In this chapter, a particle filtering based gear prognostics method using a one-dimensional health index for spiral bevel gear subject to pitting failure mode is presented. The presented method effectively addresses the issues in applying particle filtering to mechanical component remaining useful life (RUL) prognostics by integrating a couple of new components into particle filtering: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by a whitening transform; and (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The presented prognostics method is validated using data from a spiral bevel gear case study.
Science and Water Policy Interface
Authors: Hashemi Mukhtar , O’Connell Enda
Published: December 2012

Despite many advances in the field of hydroinformatics, the policy and decision-making world is unable to use these highly technical decision support systems (DSSs) because there has been an undue emphasis on the technological aspects. The historical analysis of hydroinformatics concepts and modelling shows that the technical aspects have been incorporated far better than the social aspects. Hence, there have been calls for the development of ‘socio-technical’ DSSs. However, far greater effort is required to incorporate social and political sciences into the domain of DSSs. The goal of this chapter is to elaborate on the illusive interface between science and water policy within the context of DSSs. It is an attempt to address one main question: how to link or find an interface between policy (institutional matters) and science (technical and natural environment aspects). To achieve this goal, a new paradigm for the DSS modelling approach has been envisaged based on combining multiple theoretical and analytical frameworks into a single methodological framework to attain a linkage between science and policy-making. The integrated methodological framework comprises of: (1) two ‘conceptual’ frameworks: (a) decision-making perspectives and (b) IWRM interface frameworks; (2) analytical frameworks: (a) DPSIR socio-technical assessment and (b) institutional analysis (IA) frameworks; (3) core engine of the DSS consisting of coupled decision support tools (DST) such as process, planning and evaluation models; and (4) a stakeholder participation interface framework consisting of (a(a multi-windowed dynamic cyber stakeholder interface (MDCSI) system and (b) DSS performance assessment (uncertainty and risk analysis) tools, within a shell of a graphical user interface (GUI). From experience, it can be concluded that DSSs are not just about software packages but they are a participatory communication platform for an interactive multi-stakeholder decision-making process. The required science-policy interface can be achieved by using a unique analytical approach in which technical, policy and institutional frameworks are combined within a DSS platform with an output framework, the MDCSI system, that facilitate policy dialogue by having a dynamic and interactive policy interface which can be linked to other technical and non-technical systems. DSSs should be integrated with institutional and socio-political frameworks to help attain both financial and institutional sustainability.
On the MDBSCAN Algorithm in a Spatial Data Mining Context
Authors: Schoier Gabriella
Published: December 2012

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular, spatial clustering algorithms, which group similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this chapter is to present a density based algorithm for the discovery of clusters of units in large spatial data sets (MDBSCAN). This algorithm is a modification of the DBSCAN algorithm (see Ester (1996)). The modifications regard the consideration of spatial and non spatial variables and the use of a Lagrange-Chebychev metrics instead of the usual Euclidean one. The applications concern a synthetic data set and a data set of satellite images
Framework of Knowledge and Intelligence Base
Authors: Rabaey Marc , Mercken Roger
Published: December 2012

This chapter introduces the concept of the “Intelligence Base,” developed in a study on the information requirement of the management of an (military) organization. The purpose of the study was to conceive, for each level of an organization, an appropriate Decision Support System (DSS) and/or Knowledge and Information System. All systems would eventually have been integrated in an overall Enterprise Architecture (EA). By discussing the OODA-cycle of John Boyd and the Naturalistic Decision Making (NDM) the authors show that the concept of Intelligence Base can be a DSS for very demanding environments. Related topics are knowledge, culture, and real options (business example). The proposed framework is based on Service-Oriented Architecture (SOA) and Cloud Computing, which will determine the configuration of the Information Technology (IT) systems.
Using Association Rules for Query Reformulation
Authors: Biskri Ismaïl , Rompré Louis
Published: December 2012

In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.
A Highly Scalable and Adaptable Co-Learning Framework on Multimodal Data Mining in a Multimedia Database
Authors: Zhang Zhongfei (Mark) , Guo Zhen , Faloutsos Christos , Pan Jia-Yu
Published: December 2012

This chapter presents a highly scalable and adaptable co-learning framework on multimodal data mining in a multimedia database. The co-learning framework is based on the multiple instance learning theory. The framework enjoys a strong scalability in the sense that the query time complexity is a constant, independent of the database scale, and the mining effectiveness is also independent of the database scale, allowing facilitating a multimodal querying to a very large scale multimedia database. At the same time, this framework also enjoys a strong adaptability in the sense that it allows incrementally updating the database indexing with a constant operation when the database is dynamically updated with new information. Hence, this framework excels many of the existing multimodal data mining methods in the literature that are neither scalable nor adaptable at all. Theoretic analysis and empirical evaluations are provided to demonstrate the advantage of the strong scalability and adaptability. While this framework is general for multimodal data mining in any specific domains, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance. They have compared the framework with a state-of-the-art multimodal data mining method to demonstrate the effectiveness and the promise of the framework.
Temporal Association Rule Mining in Large Databases
Authors: Kumar A. V. Senthil , Alrabea Adnan , Sekhar Pedamallu Chandra
Published: December 2012

Over the last couple of years, data mining technology has been successfully employed to various business domains and scientific areas. One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. A thorough understanding of this concept requires that the data should be viewed as a sequence of events. Temporal sequences exist extensively in different areas that include economics, finance, communication, engineering, medicine, weather forecast and so on. This chapter proposes a technique that is developed to explore frequent temporal itemsets in the database. The basic idea of this technique is to first partition the database into sub-databases in light of either common starting time or common ending time. Then for each partition, the proposed technique is used progressively to accumulate the number of occurrences of each candidate 2-itemsets. A Directed graph is built using the support of these candidate 2-itemsets (combined from all the sub-databases) as a result of generating all candidate temporal k- itemsets in the database. The above technique may help researchers not only to understand about generating frequent large temporal itemsets but also helps in understanding of finding temporal association rules among transactions within relational databases.
XML Mining for Semantic Web
Authors: Berlanga Rafael , Nebot Victoria
Published: December 2012

This chapter describes the convergence of two influential technologies in the last decade, namely data mining (DM) and the Semantic Web (SW). The wide acceptance of new SW formats for describing semantics-aware and semistructured contents have spurred on the massive generation of semantic annotations and large-scale domain ontologies for conceptualizing their concepts. As a result, a huge amount of both knowledge and semantic-annotated data is available in the web. DM methods have been very successful in discovering interesting patterns which are hidden in very large amounts of data. However, DM methods have been largely based on simple and flat data formats which are far from those available in the SW. This chapter reviews and discusses the main DM approaches proposed so far to mine SW data as well as those that have taken into account the SW resources and tools to define semantics-aware methods.
Modeling, Querying, and Mining Uncertain XML Data
Authors: Kharlamov Evgeny , Senellart Pierre
Published: December 2012

This chapter deals with data mining in uncertain XML data models, whose uncertainty typically comes from imprecise automatic processes. We first review the literature on modeling uncertain data, starting with well-studied relational models and moving then to their semistructured counterparts. We focus on a specific probabilistic XML model, which allows representing arbitrary finite distributions of XML documents, and has been extended to also allow continuous distributions of data values. We summarize previous work on querying this uncertain data model and show how to apply the corresponding techniques to several data mining tasks, exemplified through use cases on two running examples.
Data Mining System Execution Traces to Validate Distributed System Quality-of-Service Properties
Authors: Hill James H.
Published: December 2012

System Execution Modeling (SEM) tools enable distributed system testers to validate Quality-of-Service (QoS) properties, such as end-to-end response time, throughput, and scalability, during early phases of the software lifecycle. Analytical capabilities of QoS properties, however, are traditionally bounded by a SEM tool’s capabilities. This chapter discusses how to mine system execution traces, which are a collection of log messages describing events and states of a distributed system throughout its execution lifetime, generated by distributed systems so that the validation of QoS properties is not dependent on a SEM tool’s capabilities. The author uses a real-life case study to illustrate how data mining system execution traces can assist in discovering potential performance bottlenecks using system execution traces.
Data Mining Used for Analyzing the Bankruptcy Risk of the Romanian SMEs
Authors: Vasilescu Laura Giurca , Siminica Marian , Pirvu Cerasela , Ionascu Costel , Mehedintu Anca
Published: December 2012

The small and medium enterprises (SMEs) represent the backbone of the economy, playing a major economic and social role in the process of developing a dynamic economy. But the recent evolutions in the financial markets, the international financial crisis, the increased competition on markets, the lack of financial resources and the insufficient adaptation of many firms to the requests of the European market are new threats which can determine the bankruptcies of the Romanian SMEs. In this context, starting from the necessity to design an early warning system, we will elaborate a new model for analysis of bankruptcy risk for the Romanian SMEs that combine two main categories of indicators: financial ratios and non-financial indicators. The authors’ analysis is based on data mining techniques (CHAID) in order to identify the firms’ categories accordingly to the bankruptcy risk levels. Through the proposed analysis model they try to offer a real surveillance system for the Romanian SMEs which can allow an early signal regarding the bankruptcy risk.
Applications of Pattern Discovery Using Sequential Data Mining
Authors: Gupta Manish , Han Jiawei
Published: December 2012

Sequential pattern mining methods have been found to be applicable in a large number of domains. Sequential data is omnipresent. Sequential pattern mining methods have been used to analyze this data and identify patterns. Such patterns have been used to implement efficient systems that can recommend based on previously observed patterns, help in making predictions, improve usability of systems, detect events, and in general help in making strategic product decisions. In this chapter, we discuss the applications of sequential data mining in a variety of domains like healthcare, education, Web usage mining, text mining, bioinformatics, telecommunications, intrusion detection, et cetera. We conclude with a summary of the work.
Modeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support
Authors: Alonso Jose M. , Castiello Ciro , Lucarelli Marco , Mencar Corrado
Published: December 2012

Decision support systems in Medicine must be easily comprehensible, both for physicians and patients. In this chapter, the authors describe how the fuzzy modeling methodology called HILK (Highly Interpretable Linguistic Knowledge) can be applied for building highly interpretable fuzzy rule-based classifiers (FRBCs) able to provide medical decision support. As a proof of concept, they describe the case study of a real-world scenario concerning the development of an interpretable FRBC that can be used to predict the evolution of the end-stage renal disease (ESRD) in subjects affected by Immunoglobin A Nephropathy (IgAN). The designed classifier provides users with a number of rules which are easy to read and understand. The rules classify the prognosis of ESRD evolution in IgAN-affected subjects by distinguishing three classes (short, medium, long). Experimental results show that the fuzzy classifier is capable of satisfactory accuracy results – in comparison with Multi-Layer Perceptron (MLP) neural networks – and high interpretability of the knowledge base.
Content-Based Image Retrieval for Medical Image Analysis
Authors: Yao Jianhua , Summers Ronald M.
Published: December 2012

The growing repositories of clinical imaging data generate a need for effective image management and access that demands more than simple text-based queries. Content-Based Image Retrieval (CBIR) is an active research field and has drawn attention in recent years. It is a technique to organize and search image archives by their visual content. It is a multi-discipline field that integrates technologies from computer vision, machine learning, information retrieval, human-machine interaction, database systems, and data mining. CBIR consists of four main components: database and indexing, feature extraction, query formation and interface, and similarity measures. The applications of CBIR to the medical field include PACS integration, image annotation/codification, computer-aided diagnosis, case-based reasoning, and teaching tools. This chapter intends to disseminate the CBIR techniques to their applications to medical image management and analysis and to attract greater interest from various research communities to advance research in this field.
Intelligent Decision Support System for Fetal Delivery using Soft Computing Techniques
Authors: Janghel R. R. , Shukla Anupam , Tiwari Ritu
Published: December 2012

In the present work an attempt is made to develop an intelligent Decision support system (IDSS) using the pathological attributes to predict the fetal delivery to be done normal or by surgical procedure. The pathological tests like Blood Sugar (BR), Blood pressure (BP), Resistivity Index (RI) and systolic / Diastolic (S/P) ratio will be recorded at the time of delivery. All attributes lie within a specific range for normal patient. The database consists of the attributes for cases 2 (i.e. normal and surgical delivery). Soft computing technique namely Artificial Neural Networks (ANN) are used for simulator. The attributes from dataset are used for training & testing of ANN models. Three models of ANN are trained using Back-Propagation Algorithm (BPA), Radial Basis Function Network (RBFN), Learning Vector Quantization Network (LVQN) and one hybrid approach is Adaptive Neuro-Fuzzy Inference System (ANFIS). The designing factors have been changed to get the optimized model, which gives highest recognition score. The optimized models of BPA, RBFN, LVQN and ANFIS gave accuracies of 93.75, 99.00, 87.50 and 99.50% respectively. Hence in our present research the ANFIS is the model whom efficiency and result are best .The ANFIS is the best network for mentioned problem. This system will assist doctor to take decision at the critical time of fetal delivery.
Systems Biology-Based Approaches Applied to Vaccine Development
Authors: Manque Patricio A. , Woehlbier Ute
Published: December 2012

Vaccines represent one of the most cost-effective ways to prevent and treat diseases. The use of vaccines in the control of viral diseases represents an important milestone in the history of medicine. The genomic revolution brought us the possibility to scan genomes in the search of new and more effective vaccine candidates and the advancement of bioinformatics provided the framework for the application of strategies that were focused not only on antigen discovery but also on comparative genomics, and pathogenic factor identification and data mining. In addition, the progress in post-genomic technologies including gene expression technologies such as microarray and proteomics gave us the opportunity to explore the host responses to vaccines leading to a better understanding of immune responses to pathogens and/or to vaccines, assisting in the development of new and better vaccines and adjuvants. This chapter will review how systems biology-based approaches including genomics, gene expression technologies, and bioinformatics have changed the way of thinking about antigen discovery and vaccine development. In addition, the chapter will discuss how the study of the host responses in combination with “in silico” approaches could help predict immunogenicity and improve the efficacy of vaccines.
Enterprise Architecture for Personalization of e-Government Services
Authors: Erdem Alpay , Medeni Ihsan Tolga , Medeni Tunç D.
Published: December 2012

As there has not yet been enough work on enterprise architectures for fully integrated knowledge-based, highly-sophisticated (citizen-oriented) personalized services, this chapter aims to articulate a perspective to design architectures for the development and provision of sophisticated, personalized services. Doing so, the authors benefit from their knowledge and experience in the Turkish e-Government Gateway (eGG) and general e-Government services development and provision. First providing an introduction and background information, the chapter discusses the development of eGG services in Turkey, and then provides a visionary suggestion for knowledge-based personalized, citizen-centric e-Government. Among the suggested perspectives, an E-Citizen Decision Support System, and Entity-Utility and Information Flow Model could be useful for eGG development in Turkey and elsewhere.
Customer Decision Making in Web Services
Authors: Sun Zhaohao , Zhang Ping , Dong Dong
Published: December 2012

Web services play an important role in successful business integration and other application fields such as e-commerce and e-business. Customer decision making (CDM) is an indispensable factor for e-business and Web services. This chapter examines customer decision making in Web services. More specifically, it first looks at decision making in Web services, and proposes a novel P6 model for CDM in Web services, which consists of 6 Ps: privacy, perception, propensity, preference, personalization, and promised experience. This model integrates the existing 6 P elements of marketing mix as the environment of customer decision making in Web services. The new integrated P6 model deals with the inner world of the customer for decision making (DM) and incorporates what the customer sees and thinks during a DM process. The purpose of this novel P6 model is to assist customers in the decision process to acquire the most satisfactory Web service. This chapter also examines case-based decision making in Web services and provides a theoretical foundation for case-based decision making under the condition of one problem with multiple solutions in Web services. The proposed approach will facilitate research and development of e-business, Web services, decision support systems, intelligent systems, and soft computing.
A Predictive Modeling of Retail Satisfaction
Authors: Hemalatha M.
Published: December 2012

Data mining allows managers to make more knowledgeable decisions by predicting future trends and behaviors. One of the most widely used areas of data mining for the retail industry is in marketing. There are also at least seven methods of analysis or statistical techniques that are commonly used in data mining. It is obvious that the approach to the data mining is the key determinant of the statistical technique to be used. Predictive modeling uses variety of techniques such as linear regression, logistic regression, and their extensions can be used to identify patterns, which can be used to predict the future. This research specifically focuses on the application of multiple regression technique a data mining tool in Indian retail industries to predict the retail satisfaction using store attributes as independent variables.
Behavioral Targeting Online Advertising
Authors: Yan Jun , Shen Dou , Mah Teresa , Liu Ning , Chen Zheng , Li Ying
Published: December 2012

With the rapid growth of the online advertising market, Behavioral Targeting (BT), which delivers advertisements to users based on understanding of their needs through their behaviors, is attracting more attention. The amount of spend on behaviorally targeted ad spending in the US is projected to reach $4.4 billion in 2012 (Hallerman, 2008). BT is a complex technology, which involves data collection, data mining, audience segmentation, contextual page analysis, predictive modeling and so on. This chapter gives an overview of Behavioral Targeting by introducing the Behavioral Targeting business, followed by classic BT research challenges and solution proposals. We will also point out BT research challenges which are currently under-explored in both industry and academia.
Detecting Pharmaceutical Spam in Microblog Messages
Authors: Liszka Kathy J. , Chan Chien-Chung , Shekar Chandra
Published: December 2012

Microblogs are one of a growing group of social network tools. Twitter is, at present, one of the most popular forums for microblogging in online social networks, and the fastest growing. Fifty million messages flow through servers, computers, and cell phones on a wide variety of topics exchanged daily. With this considerable volume, Twitter is a natural and obvious target for spreading spam via the messages, called tweets. The challenge is how to determine if a tweet is a spam or not, and more specifically a special category advertising pharmaceutical products. The authors look at the essential characteristics of spam tweets and what makes microblogging spam unique from email or other types of spam. They review methods and tools currently available to identify general spam tweets. Finally, this work introduces a new methodology of applying text mining and data mining techniques to generate classifiers that can be used for pharmaceutical spam detection in the context of microblogging.
Clinical Decision Support Systems for ‘Making It Easy to Do It Right’
Authors: Scheepers-Hoeks Anne-Marie , Klijn Floor , van der Linden Carolien , Grouls Rene , Ackerman Eric , Minderman Niels , Bergmans Jan , Korsten Erik
Published: December 2012

Medical guidelines and best practises are used in medicine to increase the quality of the health-care delivery system. To support implementation and application of these guidelines, clinical decision support systems (CDSS) have been developed. These systems are defined as ‘Computer-based information systems used to integrate clinical and patient information and provide support for decision-making in patient care’ (MeSH) These are integrated with so-called Electronic Health Records (EHR), which have been developed by companies and National Governmental Institutes, and are used to register and present the patient medical data. The integration of an EHR with CDSS modules will revolutionize the way medicine will be practiced. In pediatrics, as well as geriatrics, such systems might prove to be even more needed. The development, use, and maintenance of CDSS in a hospital are complex and far from trivial. This chapter focuses on several aspects and challenges of EHR’s and CDSS-modules in daily clinical practice in the hospital.
Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study
Authors: Lombardo Rosaria
Published: December 2012

By the early 1990s, the term “data mining” had come to mean the process of finding information in large data sets. In the framework of the Total Quality Management, earlier studies have suggested that enterprises could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk customers/consumers and allow for more timely interventions (Macfadyen & Dawson, 2009). The Learning Management System data and the subsequent Customer Interaction System data can help to provide “early warning system data” for risk detection in enterprises. This chapter confirms and extends this proposition by providing data from an international research project investigating on customer satisfaction in services to persons of public utility, like education, training services and health care services, by means of explorative multivariate data analysis tools as Ordered Multiple Correspondence Analysis, Boosting regression, Partial Least Squares regression and its generalizations.
Supporting Companies Management and Improving their Productivity through Mining Customers Transactions
Authors: Omari Asem
Published: December 2012

Selling products or services online plays an important role in the success of businesses that have a physical presence, like a retail business. For many businesses, a retail website is an effective line of communication between the businesses and their customers. Even if the business does not present all of its products and services in the website, the website may be just what the customer needs to see to choose it over a competitor. Therefore, it is important to have a successful website to serve as a sales and marketing tool to participate in meeting the core requirements of the business. Clustering and classification are two important data mining techniques that are widely used to assign customers to different categories. Those categories are used to analyze customer behavior and interestingness. In this chapter, we use clustering and classification to support web designers to have better designed retail websites. This is done during the design phase by improving the structure of the website depending on the extracted patterns in a way that makes it easy for the website’s navigator to find his target products in an efficient time, give him the opportunity to have a look at some products that may be of interest for him, and encourage him to buy more from the available products which will consequently increase the business’s overall profit. This approach will open the eyes of business leaders to adapt new efficient technological tool that when invested in their organizations will improve the strategic goals and meet their basic requirements to be successful, productive, and competitive. The experimental work shows very promising results that can positively change the traditional techniques of the process of designing retail websites.
A Hybrid Multilayer Perceptron Neural Network for Direct Marketing
Authors: Govindarajan M. , Chandrasekaran RM.
Published: December 2012

Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.
Mining Climate and Remote Sensing Time Series to Improve Monitoring of Sugar Cane Fields
Authors: Romani Luciana , de Sousa Elaine , Ribeiro Marcela , de Ávila Ana , Zullo Jurandir , Traina Caetano , Traina Agma
Published: December 2012

This chapter discusses how to take advantage of computational models to analyze and extract useful information from time series of climate data and remote sensing images. This kind of data has been used for researching on climate changes, as well as to help on improving yield forecasting of agricultural crops and increasing the sustainable usage of the soil. The authors present three techniques based on the Fractal Theory, data streams and time series mining: the FDASE algorithm, to identify correlated attributes; a method that combines intrinsic dimension measurements with statistical analysis, to monitor evolving climate and remote sensing data; and the CLIPSMiner algorithm applied to multiple time series of continuous climate data, to identify relevant and extreme patterns. The experiments with real data show that data mining is a valuable tool to help agricultural entrepreneurs and government on monitoring sugar cane areas, helping to make the production more useful to the country and to the environment.
Privacy Expectations in Passive RFID Tagging of Motor Vehicles
Authors: Desierto Diane A.
Published: December 2012

This paper describes Bayan Muna et al. v. Mendoza et al., a 2009 Philippine Supreme Court petition involving the first and ongoing certiorari challenge to the Philippine government’s implementation of passive Radio Frequency Identification (RFID) technology in the registration of all motor vehicles in the Philippines. As a matter of constitutional jurisprudence and policy, the passive use of RFID technology in this context does not infringe constitutionally-protected privacy expectations, entirely consistent with the Executive Branch’s law enforcement powers. The paper shows how the proposed RFID tagging of motor vehicles in the Philippines satisfies the tests of reasonable expectations, and by dealing only with already publicly available information, avoids spectral fears of data mining and government abuse.
Gridifying Neuroscientific Pipelines
Authors: Manset David
Published: December 2012

In recent times, innovative new e-Infrastructures have materialized all around the globe to address the compelling and unavoidably increasing demand on computing power and storage capacity. All fields of science have entered an era of digital explosion and thus need to face it with appropriate and scalable instruments. Amongst century’s cutting-edge technologies, the grid has become a tangible candidate which several initiatives have harnessed and demonstrated the added value of. Turning the concept into a concrete solution for Neurosciences, the neuGRID project aims to establish a grid-based e-Infrastructure providing neuroscientists with a powerful tool to address the challenge of developing and testing new markers of neurodegenerative diseases. In order to optimize the resulting grid and to deliver a user-friendly environment, neuGRID has engaged the process of migrating existing imaging and data mining toolkits to the grid, the so-called gridification, while developing a surrounding service oriented architecture of agnostic biomedical utilities. This chapter reports on a preliminary analysis of the issues faced in the gridification of neuroimaging pipelines and attempts to sketch an integration model able to cope with the several and heterogeneous applications used by neuroscientists.
Cooperation between Expert Knowledge and Data Mining Discovered Knowledge
Authors: Alonso Fernando , Martínez Loïc , Pérez Aurora , Valente Juan Pedro
Published: December 2012

Although expert elicited knowledge and data mining discovered knowledge appear to be completely opposite and competing solutions to the same problems, they are actually complementary concepts. Besides, together they maximize their individual qualities. This chapter highlights how each one profits from the other and illustrates their cooperation in existing systems developed in the medical domain. The authors have identified different types of cooperation that combine elicitation and data mining for knowledge acquisition, use expert knowledge to enact the knowledge discovery, use discovered knowledge to validate expert knowledge, and use discovered knowledge to improve the usability of an expert system. The chapter also describes their experience in combining expert and discovered knowledge in the development of a system for processing medical isokinetics data.
Tailoring FOS-ERP Packages
Authors: Wölfel Klaus , Smets Jean-Paul
Published: December 2012

Free/Open Source software (FOSS) has made Enterprise Resource Planning (ERP) systems more accessible for Small and Medium Enterprises (SMEs) including overseas subsidiaries of large companies. However, the consulting required to configure an ERP to meet the specific needs of an organization remains a major financial and organizational burden for SMEs. Automatic ERP package configuration based on knowledge engineering, machine learning and data mining could be a solution to lessen the burden of the implementation process. This chapter presents two approaches to an automation of selected configuration options of the FOS-ERP package ERP5. These approaches are based on knowledge engineering with decision trees and machine learning with classifiers. The design of the ERP5 Artificial intelligence Toolkit (EAT) aims at the integration of these approaches into ERP5. The chapter also shows how FOS-ERP can boost Information System (IS) research. The investigation of the automation approaches was only possible because the free source code and technical documentation of ERP5 was accessible for TU Dresden researchers.
Optimization of a Hybrid Methodology (CRISP-DM)
Authors: Nava José , Hernández Paula
Published: December 2012

Data mining is a complex process that involves the interaction of the application of human knowledge and skills and technology. This must be supported by clearly defined processes and procedures. This Chapter describes CRISP-DM (Cross-Industry Standard Process for Data Mining), a fully documented, freely available, robust, and non proprietary data mining model. The chapter analyzes the contents of the official Version 1.0 Document, and it is a guide through all the implementation process. The main purpose of data mining is the extraction of hidden and useful knowledge from large volumes of raw data. Data mining brings together different disciplines like software engineering, computer science, business intelligence, human-computer interaction, and analysis techniques. Phases of these disciplines must be combined for data mining project outcomes. CRISP-DM methodology defines its processes hierarchically at four levels of abstraction allowing a project to be structured modularly, being more maintainable, scalable and the most important, to reduce complexity. CRISP-DM describes the life cycle of a data mining project consisting of six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
A Hybrid Approach Based on Self-Organizing Neural Networks and the K-Nearest Neighbors Method to Study Molecular Similarity
Authors: Amine Abdelmalek , Elberrichi Zakaria , Simonet Michel , Rahmouni Ali
Published: December 2012

The “Molecular Similarity Principle” states that structurally similar molecules tend to have similar properties—physicochemical and biological. The question then is how to define “structural similarity” algorithmically and confirm its usefulness. Within this framework, research by similarity is registered, which is a practical approach to identify molecule candidates (to become drugs or medicines) from databases or virtual chemical libraries by comparing the compounds two by two. Many statistical models and learning tools have been developed to correlate the molecules’ structure with their chemical, physical or biological properties. The role of data mining in chemistry is to evaluate “hidden” information in a set of chemical data. Each molecule is represented by a vector of great dimension (using molecular descriptors), the applying a learning algorithm on these vectors. In this paper, the authors study the molecular similarity using a hybrid approach based on Self-Organizing Neural Networks and Knn Method.
Can Information and Communication Technologies Improve the Performance of Microfinance Programs?
Authors: Jawadi Fredj , Jawadi Nabila , Ziane Ydriss
Published: December 2012

Given the important role played by microfinance institutions (MFIs), a growing number of studies have been recorded in this field. More specifically, researchers and practitioners are interested in factors that influence their performance in order to enhance the way they function and to ensure their sustainability. One of these factors concerns the use of information and communication technologies (ICT) in MFI programs. Despite the relevance of the topic, few studies have looked at the links between ICT use and the enhancement of MFI performance. In addition, ICT use in developing and emerging financial markets is also under studied. The purpose of this chapter is to fill this gap in the literature and to analyse how ICT can influence MFI performance. In this regard, three aspects of MFI performance are studied: efficiency, risk management and customer relationship management. The results of the survey conducted with MFI professionals in developing and emerging countries show that the implementation of ICTs enables MFIs to significantly improve their microfinance services, while enhancing work efficiency and customer relationship management in the same way as in other branches of financial services in general. However, our results give no empirical support to the contribution of ICTs in risk management or risk reduction.
Understanding the Progressive Nature of Inter-Organizational Systems (IOS) Adoption
Authors: Ali Mazen , Kurnia Sherah , Johnston Robert B.
Published: December 2012

Inter-organizational Systems (IOS) cannot be adopted by any organizations in isolation from their trading partner. Their adoption requires cooperation and collaboration between trading partners and, therefore, is reliant on the nature of relationships between the parties involved. For organizations to progress in their IOS adoption, improvement in relationships between trading partners is required before they can adopt a more sophisticated IOS. In addition, through IOS adoption, trading partners can actually improve their relationships overtime. There has been some research that investigates relationships and how organizations progress from one level to the next level of adoption. However, these studies do not clearly define the concepts of relationship, IOS adoption and IOS adoption maturity. Furthermore, they do not adequately justify the exclusion of other variables in defining the relevant concepts and are not theoretically based. This research extends the Kurnia and Johnston (2000) process model of IOS adoption by incorporating the notion of IOS adoption maturity and reducing the scope from a supply chain to a dyadic level to enable better evaluations of IOS adoption progression. We argue that with the proposed model, the dynamics of IOS adoption maturity can be better examined empirically.
E-Novation Customer Relationship Management
Authors: Boujena Othman , Johnston Wesley J.
Published: December 2012

In the early 1990’s, marketing theory experienced a paradigm shift from a transactional approach focusing on sales to a relationship one. This shift was due to several limits of traditional mix marketing based on the “four P’s” and to the change in the market business models. In fact, the growing role of branding in mass markets, the development of services marketing, and the importance of network and human interactions in business are some of the main reasons behind relationship marketing emergence. Relationship marketing is then aimed at developing and maintaining mutually profitable relationships with customers and even stakeholders. In the era of technology evolution and Internet, customer relationship management (CRM) is moving forward to better manage, drive, and keep value-added relationships. However, CRM is, first of all, a company strategy and a shared vision that involves organization, people, and processes in satisfying and retaining customers. This chapter deals with the concept of customer centricity and its development, customer lifecycle with acquisition and retention, and finally the issue of CRM implementation.
The Future of Newspapers
Authors: Kurniawan Antonius Ruddy , Mervin Kok Wai Mun , Qiushi Zhang
Published: December 2012

There are many challenges faced by newspaper companies to gain competitive advantage over the internet and increasing digital media consumption. How do newspaper companies face these challenges? How can they refine and strategize their business model to remain competitive? What are the issues that newspaper industries face? With all these questions, this chapter discusses key areas and conceptual models of future newspapers’ strategy framework and supply chain management through literature review and analysis of technologies and innovations.