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Books > Computing & IT > Applications of computing > Databases
Privacy requirements have an increasing impact on the realization of modern applications. Commercial and legal regulations demand that privacy guarantees be provided whenever sensitive information is stored, processed, or communicated to external parties. Current approaches encrypt sensitive data, thus reducing query execution efficiency and preventing selective information release. Preserving Privacy in Data Outsourcing presents a comprehensive approach for protecting highly sensitive information when it is stored on systems that are not under the data owner's control. The approach illustrated combines access control and encryption, enforcing access control via structured encryption. This solution, coupled with efficient algorithms for key derivation and distribution, provides efficient and secure authorization management on outsourced data, allowing the data owner to outsource not only the data but the security policy itself. To reduce the amount of data to be encrypted the book also investigates data fragmentation as a possible way to protect privacy of data associations and provide fragmentation as a complementary means for protecting privacy: associations broken by fragmentation will be visible only to users authorized (by knowing the proper key) to join fragments. The book finally investigates the problem of executing queries over possible data distributed at different servers and which must be controlled to ensure sensitive information and sensitive associations be visible only to parties authorized for that. Case Studies are provided throughout the book. Privacy, data mining, data protection, data outsourcing, electronic commerce, machine learning professionals and others working in these related fields will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science. This book is also suitable for advanced level students and researchers concentrating on computer science as a secondary text or reference book.
This book presents new approaches that advance research in all aspects of agent-based models, technologies, simulations and implementations for data intensive applications. The nine chapters contain a review of recent cross-disciplinary approaches in cloud environments and multi-agent systems, and important formulations of data intensive problems in distributed computational environments together with the presentation of new agent-based tools to handle those problems and Big Data in general. This volume can serve as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary work in the areas of data intensive computing and Big Data systems using emergent large-scale distributed computing paradigms. It will also allow newcomers to grasp key concepts and potential solutions on advanced topics of theory, models, technologies, system architectures and implementation of applications in Multi-Agent systems and data intensive computing.
Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature. This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection. The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.
This book constitutes the refereed proceedings of the 15th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2014, held in Amsterdam, The Netherlands, in October 2014. The 73 revised papers were carefully selected from 190 submissions. They provide a comprehensive overview of identified challenges and recent advances in various collaborative network (CN) domains and their applications, with a particular focus on the following areas in support of smart networked environments: behavior and coordination; product-service systems; service orientation in collaborative networks; engineering and implementation of collaborative networks; cyber-physical systems; business strategies alignment; innovation networks; sustainability and trust; reference and conceptual models; collaboration platforms; virtual reality and simulation; interoperability and integration; performance management frameworks; performance management systems; risk analysis; optimization in collaborative networks; knowledge management in networks; health and care networks; and mobility and logistics.
This book offers means to handle interference as a central problem of operating wireless networks. It investigates centralized and decentralized methods to avoid and handle interference as well as approaches that resolve interference constructively. The latter type of approach tries to solve the joint detection and estimation problem of several data streams that share a common medium. In fact, an exciting insight into the operation of networks is that it may be beneficial, in terms of an overall throughput, to actively create and manage interference. Thus, when handled properly, "mixing" of data in networks becomes a useful tool of operation rather than the nuisance as which it has been treated traditionally. With the development of mobile, robust, ubiquitous, reliable and instantaneous communication being a driving and enabling factor of an information centric economy, the understanding, mitigation and exploitation of interference in networks must be seen as a centrally important task.
""As organizations have become more sophisticated, pressure to
provide information sharing across dissimilar platforms has
mounted. In addition, advances in distributed computing and
networking combined with the affordable high level of connectivity,
are making information sharing across databases closer to being
accomplished...With the advent of the internet, intranets, and
affordable network connectivity, business reengineering has become
a necessity for modern corporations to stay competitive in the
global market...An end-user in a heterogeneous computing
environment should be able to not only invoke multiple exiting
software systems and hardware devices, but also coordinate their
interactions.""--From the Introduction Seventeen leaders in the field contributed chapters specifically
for this unique book, together providing the most comprehensive
resource on managing multidatabase systems involving heterogeneous
and autonomous databases available today. The book covers virtually
all fundamental issues, concepts, and major research topics.
The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.
In our world of ever-increasing Internet connectivity, there is an on-going threat of intrusion, denial of service attacks, or countless other abuses of computer and network resources. In particular, these threats continue to persist due to the flaws of current commercial intrusion detection systems (IDSs). Intrusion Detection Systems is an edited volume by world class leaders in this field. This edited volume sheds new light on defense alert systems against computer and network intrusions. It also covers integrating intrusion alerts within security policy framework for intrusion response, related case studies and much more. This volume is presented in an easy-to-follow style while including a rigorous treatment of the issues, solutions, and technologies tied to the field. Intrusion Detection Systems is designed for a professional audience composed of researchers and practitioners within the computer network and information security industry. It is also suitable as a reference or secondary textbook for advanced-level students in computer science.
Big data technologies are used to achieve any type of analytics in a fast and predictable way, thus enabling better human and machine level decision making. Principles of distributed computing are the keys to big data technologies and analytics. The mechanisms related to data storage, data access, data transfer, visualization and predictive modeling using distributed processing in multiple low cost machines are the key considerations that make big data analytics possible within stipulated cost and time practical for consumption by human and machines. However, the current literature available in big data analytics needs a holistic perspective to highlight the relation between big data analytics and distributed processing for ease of understanding and practitioner use. This book fills the literature gap by addressing key aspects of distributed processing in big data analytics. The chapters tackle the essential concepts and patterns of distributed computing widely used in big data analytics. This book discusses also covers the main technologies which support distributed processing. Finally, this book provides insight into applications of big data analytics, highlighting how principles of distributed computing are used in those situations. Practitioners and researchers alike will find this book a valuable tool for their work, helping them to select the appropriate technologies, while understanding the inherent strengths and drawbacks of those technologies.
The Semantic Web, which is intended to establish a machine-understandable Web, is currently changing from being an emerging trend to a technology used in complex real-world applications. A number of standards and techniques have been developed by the World Wide Web Consortium (W3C), e.g., the Resource Description Framework (RDF), which provides a general method for conceptual descriptions for Web resources, and SPARQL, an RDF querying language. Recent examples of large RDF data with billions of facts include the UniProt comprehensive catalog of protein sequence, function and annotation data, the RDF data extracted from Wikipedia, and Princeton University's WordNet. Clearly, querying performance has become a key issue for Semantic Web applications. In his book, Groppe details various aspects of high-performance Semantic Web data management and query processing. His presentation fills the gap between Semantic Web and database books, which either fail to take into account the performance issues of large-scale data management or fail to exploit the special properties of Semantic Web data models and queries. After a general introduction to the relevant Semantic Web standards, he presents specialized indexing and sorting algorithms, adapted approaches for logical and physical query optimization, optimization possibilities when using the parallel database technologies of today's multicore processors, and visual and embedded query languages. Groppe primarily targets researchers, students, and developers of large-scale Semantic Web applications. On the complementary book webpage readers will find additional material, such as an online demonstration of a query engine, and exercises, and their solutions, that challenge their comprehension of the topics presented.
This comprehensive book focuses on better big-data security for healthcare organizations. Following an extensive introduction to the Internet of Things (IoT) in healthcare including challenging topics and scenarios, it offers an in-depth analysis of medical body area networks with the 5th generation of IoT communication technology along with its nanotechnology. It also describes a novel strategic framework and computationally intelligent model to measure possible security vulnerabilities in the context of e-health. Moreover, the book addresses healthcare systems that handle large volumes of data driven by patients' records and health/personal information, including big-data-based knowledge management systems to support clinical decisions. Several of the issues faced in storing/processing big data are presented along with the available tools, technologies and algorithms to deal with those problems as well as a case study in healthcare analytics. Addressing trust, privacy, and security issues as well as the IoT and big-data challenges, the book highlights the advances in the field to guide engineers developing different IoT devices and evaluating the performance of different IoT techniques. Additionally, it explores the impact of such technologies on public, private, community, and hybrid scenarios in healthcare. This book offers professionals, scientists and engineers the latest technologies, techniques, and strategies for IoT and big data.
A modern information retrieval system must have the capability to find, organize and present very different manifestations of information - such as text, pictures, videos or database records - any of which may be of relevance to the user. However, the concept of relevance, while seemingly intuitive, is actually hard to define, and it's even harder to model in a formal way. Lavrenko does not attempt to bring forth a new definition of relevance, nor provide arguments as to why any particular definition might be theoretically superior or more complete. Instead, he takes a widely accepted, albeit somewhat conservative definition, makes several assumptions, and from them develops a new probabilistic model that explicitly captures that notion of relevance. With this book, he makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism; second, a new method for modeling exchangeable sequences of discrete random variables which does not make any structural assumptions about the data and which can also handle rare events. Thus his book is of major interest to researchers and graduate students in information retrieval who specialize in relevance modeling, ranking algorithms, and language modeling.
This book presents the outcomes of the Third National Conference on Communication, Cloud and Big Data (CCB) held on November 2-3, 2018, at Sikkim Manipal Institute of Technology, Majitar, Sikkim. Featuring a number of papers from the conference, it explores various aspects of communication, computation, cloud, and big data, including routing in cognitive radio wireless sensor networks, big data security issues, routing in ad hoc networks, routing protocol for Internet of things (IoT), and algorithm for imaging quality enhancement.
This book explores all relevant aspects of net scoring, also known as uplift modeling: a data mining approach used to analyze and predict the effects of a given treatment on a desired target variable for an individual observation. After discussing modern net score modeling methods, data preparation, and the assessment of uplift models, the book investigates software implementations and real-world scenarios. Focusing on the application of theoretical results and on practical issues of uplift modeling, it also includes a dedicated chapter on software solutions in SAS, R, Spectrum Miner, and KNIME, which compares the respective tools. This book also presents the applications of net scoring in various contexts, e.g. medical treatment, with a special emphasis on direct marketing and corresponding business cases. The target audience primarily includes data scientists, especially researchers and practitioners in predictive modeling and scoring, mainly, but not exclusively, in the marketing context.
Since their invention in the late seventies, public key cryptosystems have become an indispensable asset in establishing private and secure electronic communication, and this need, given the tremendous growth of the Internet, is likely to continue growing. Elliptic curve cryptosystems represent the state of the art for such systems. Elliptic Curves and Their Applications to Cryptography: An Introduction provides a comprehensive and self-contained introduction to elliptic curves and how they are employed to secure public key cryptosystems. Even though the elegant mathematical theory underlying cryptosystems is considerably more involved than for other systems, this text requires the reader to have only an elementary knowledge of basic algebra. The text nevertheless leads to problems at the forefront of current research, featuring chapters on point counting algorithms and security issues. The Adopted unifying approach treats with equal care elliptic curves over fields of even characteristic, which are especially suited for hardware implementations, and curves over fields of odd characteristic, which have traditionally received more attention. Elliptic Curves and Their Applications: An Introduction has been used successfully for teaching advanced undergraduate courses. It will be of greatest interest to mathematicians, computer scientists, and engineers who are curious about elliptic curve cryptography in practice, without losing the beauty of the underlying mathematics.
In the last ten years, a true explosion of investigations into fuzzy modeling and its applications in control, diagnostics, decision making, optimization, pattern recognition, robotics, etc. has been observed. The attraction of fuzzy modeling results from its intelligibility and the high effectiveness of the models obtained. Owing to this the modeling can be applied for the solution of problems which could not be solved till now with any known conventional methods. The book provides the reader with an advanced introduction to the problems of fuzzy modeling and to one of its most important applications: fuzzy control. It is based on the latest and most significant knowledge of the subject and can be used not only by control specialists but also by specialists working in any field requiring plant modeling, process modeling, and systems modeling, e.g. economics, business, medicine, agriculture,and meteorology.
The International Federation for Information Processing (IFIP) series publishes state-of-the-art results in the sciences and technologies of information and communication. The IFIP series encourages education and the dissemination and exchange of information on all aspects of computing. This particular volume presents the most up-to-date research findings from leading experts from around the world on information security education.
This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
This work takes a critical look at the current concept of isotopic landscapes ("isoscapes") in bioarchaeology and its application in future research. It specifically addresses the research potential of cremated finds, a somewhat neglected bioarchaeological substrate, resulting primarily from the inherent osteological challenges and complex mineralogy associated with it. In addition, for the first time data mining methods are applied. The chapters are the outcome of an international workshop sponsored by the German Science Foundation and the Centre of Advanced Studies at the Ludwig-Maximilian-University in Munich. Isotopic landscapes are indispensable tracers for the monitoring of the flow of matter through geo/ecological systems since they comprise existing temporally and spatially defined stable isotopic patterns found in geological and ecological samples. Analyses of stable isotopes of the elements nitrogen, carbon, oxygen, strontium, and lead are routinely utilized in bioarchaeology to reconstruct biodiversity, palaeodiet, palaeoecology, palaeoclimate, migration and trade. The interpretive power of stable isotopic ratios depends not only on firm, testable hypotheses, but most importantly on the cooperative networking of scientists from both natural and social sciences. Application of multi-isotopic tracers generates isotopic patterns with multiple dimensions, which accurately characterize a find, but can only be interpreted by use of modern data mining methods.
The present economic and social environment has given rise to new situations within which companies must operate. As a first example, the globalization of the economy and the need for performance has led companies to outsource and then to operate inside networks of enterprises such as supply chains or virtual enterprises. A second instance is related to environmental issues. The statement about the impact of ind- trial activities on the environment has led companies to revise processes, to save - ergy, to optimize transportation.... A last example relates to knowledge. Knowledge is considered today to be one of the main assets of a company. How to capitalize, to manage, to reuse it for the benefit of the company is an important current issue. The three examples above have no direct links. However, each of them constitutes a challenge that companies have to face today. This book brings together the opinions of several leading researchers from all around the world. Together they try to develop new approaches and find answers to those challenges. Through the individual ch- ters of this book, the authors present their understanding of the different challenges, the concepts on which they are working, the approaches they are developing and the tools they propose. The book is composed of six parts; each one focuses on a specific theme and is subdivided into subtopics.
Botnets have become the platform of choice for launching attacks and committing fraud on the Internet. A better understanding of Botnets will help to coordinate and develop new technologies to counter this serious security threat. Botnet Detection: Countering the Largest Security Threat consists of chapters contributed by world-class leaders in this field, from the June 2006 ARO workshop on Botnets. This edited volume represents the state-of-the-art in research on Botnets.
Database Recovery presents an in-depth discussion on all aspects of database recovery. Firstly, it introduces the topic informally to set the intuitive understanding, and then presents a formal treatment of recovery mechanism. In the past, recovery has been treated merely as a mechanism which is implemented on an ad-hoc basis. This book elevates the recovery from a mechanism to a concept, and presents its essential properties. A book on recovery is incomplete if it does not present how recovery is practiced in commercial systems. This book, therefore, presents a detailed description of recovery mechanisms as implemented on Informix, OpenIngres, Oracle, and Sybase commercial database systems. Database Recovery is suitable as a textbook for a graduate-level course on database recovery, as a secondary text for a graduate-level course on database systems, and as a reference for researchers and practitioners in industry.
Mining of Data with Complex Structures: - Clarifies the type and nature of data with complex structure including sequences, trees and graphs - Provides a detailed background of the state-of-the-art of sequence mining, tree mining and graph mining. -Defines the essential aspects of the tree mining problem: subtree types, support definitions, constraints. - Outlines the implementation issues one needs to consider when developing tree mining algorithms (enumeration strategies, data structures, etc.) - Details the Tree Model Guided (TMG) approach for tree mining and provides the mathematical model for the worst case estimate of complexity of mining ordered induced and embedded subtrees. - Explains the mechanism of the TMG framework for mining ordered/unordered induced/embedded and distance-constrained embedded subtrees. - Provides a detailed comparison of the different tree mining approaches highlighting the characteristics and benefits of each approach. - Overviews the implications and potential applications of tree mining in general knowledge management related tasks, and uses Web, health and bioinformatics related applications as case studies. - Details the extension of the TMG framework for sequence mining - Provides an overview of the future research direction with respect to technical extensions and application areas The primary audience is 3rd year, 4th year undergraduate students, Masters and PhD students and academics. The book can be used for both teaching and research. The secondary audiences are practitioners in industry, business, commerce, government and consortiums, alliances and partnerships to learn how to introduce and efficiently make use of the techniques for mining of data with complex structures into their applications. The scope of the book is both theoretical and practical and as such it will reach a broad market both within academia and industry. In addition, its subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains."
Security Education and Critical Infrastructures presents the most recent developments in research and practice on teaching information security, and covers topics including: -Curriculum design; |
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