With the ever increasing growth of services and the corresponding demand for Quality of Service requirements that are placed on IP-based networks, the essential aspects of network planning will be critical in the coming years. A wide number of problems must be faced in order for the next generation of IP networks to meet their expected performance. With Performance Evaluation and Planning Methods for the Next Generation Internet, the editors have prepared a volume that outlines and illustrates these developing trends.

A number of the problems examined and analyzed in the book are:

-The design of IP networks and guaranteed performance

-Performances of virtual private networks

-Network design and reliability

-The issues of pricing, routing and the management of QoS

-Design problems arising from wireless networks

-Controlling network congestion

-New applications spawned from Internet use

-Several new models are introduced that will lead to better Internet performance

These are a few of the problem areas addressed in the book and only a selective example of some of the coming key areas in networks requiring performance evaluation and network planning.

This book constitutes the refereed proceedings of the 27th IFIP TC 11 International Information Security Conference, SEC 2012, held in Heraklion, Crete, Greece, in June 2012. The 42 revised full papers presented together with 11 short papers were carefully reviewed and selected from 167 submissions. The papers are organized in topical sections on attacks and malicious code, security architectures, system security, access control, database security, privacy attitudes and properties, social networks and social engineering, applied cryptography, anonymity and trust, usable security, security and trust models, security economics, and authentication and delegation.

Ontological Engineering refers to the set of activities that concern the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. During the last decade, increasing attention has been focused on ontologies and Ontological Engineering. Ontologies are now widely used in Knowledge Engineering, Artificial Intelligence and Computer Science; in applications related to knowledge management, natural language processing, e-commerce, intelligent integration information, information retrieval, integration of databases, b- informatics, and education; and in new emerging fields like the Semantic Web. Primary goals of this book are to acquaint students, researchers and developers of information systems with the basic concepts and major issues of Ontological Engineering, as well as to make ontologies more understandable to those computer science engineers that integrate ontologies into their information systems. We have paid special attention to the influence that ontologies have on the Semantic Web. Pointers to the Semantic Web appear in all the chapters, but specially in the chapter on ontology languages and tools.

Multiprocessing: Trade-Offs in Computation and Communication presents an in-depth analysis of several commonly observed regular and irregular computations for multiprocessor systems. This book includes techniques which enable researchers and application developers to quantitatively determine the effects of algorithm data dependencies on execution time, on communication requirements, on processor utilization and on the speedups possible. Starting with simple, two-dimensional, diamond-shaped directed acyclic graphs, the analysis is extended to more complex and higher dimensional directed acyclic graphs. The analysis allows for the quantification of the computation and communication costs and their interdependencies. The practical significance of these results on the performance of various data distribution schemes is clearly explained. Using these results, the performance of the parallel computations are formulated in an architecture independent fashion. These formulations allow for the parameterization of the architecture specitific entities such as the computation and communication rates. This type of parameterized performance analysis can be used at compile time or at run-time so as to achieve the most optimal distribution of the computations. The material in Multiprocessing: Trade-Offs in Computation and Communication connects theory with practice, so that the inherent performance limitations in many computations can be understood, and practical methods can be devised that would assist in the development of software for scalable high performance systems.

The Advanced Planner and Optimiser (APO) is the software from SAP dedicated to supply chain management. This book addresses the question of how to implement APO in a company. It is written from a long years' experience in implementation projects and provides project managers and team members with the necessary know-how for a successful implementation project. The focus is on introducing modeling approaches and explaining the structure and interdependencies of systems, modules and entities of APO. Another concern is the integration with the R/3 system(s), both technically and from a process point of view. Since APO projects differ significantly from other SAP projects, some key issues and common mistakes concerning project management are covered.

The book introduces new techniques that imply rigorous lower bounds on the com plexity of some number-theoretic and cryptographic problems. It also establishes certain attractive pseudorandom properties of various cryptographic primitives. These methods and techniques are based on bounds of character sums and num bers of solutions of some polynomial equations over finite fields and residue rings. Other number theoretic techniques such as sieve methods and lattice reduction algorithms are used as well. The book also contains a number of open problems and proposals for further research. The emphasis is on obtaining unconditional rigorously proved statements. The bright side of this approach is that the results do not depend on any assumptions or conjectures. On the downside, the results are much weaker than those which are widely believed to be true. We obtain several lower bounds, exponential in terms of logp, on the degrees and orders of o polynomials; o algebraic functions; o Boolean functions; o linear recurrence sequences; coinciding with values of the discrete logarithm modulo a prime p at sufficiently many points (the number of points can be as small as pI/2+O: ). These functions are considered over the residue ring modulo p and over the residue ring modulo an arbitrary divisor d of p - 1. The case of d = 2 is of special interest since it corresponds to the representation of the rightmost bit of the discrete logarithm and defines whether the argument is a quadratic residue."

Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.

As a new generation of technologies, frameworks, concepts and practices for information systems emerge, practitioners, academicians, and researchers are in need of a source where they can go to educate themselves on the latest innovations in this area. ""Semantic Web Information Systems: State-of-the-Art Applications"" establishes value-added knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government. ""Semantic Web Information Systems: State-of-the-Art Applications"" covers new semantic Web-enabled tools for the citizen, learner, organization, and business. Real-world applications toward the development of the knowledge society and semantic Web issues, challenges and implications in each of the IS research streams are included as viable sources for this challenging subject.

Logical Data Modeling offers business managers, analysts, and students a clear, basic systematic guide to defining business information structures in relational database terms. The approach, based on Clive Finkelstein s business-side Information Engineering, is hands-on, practical, and explicit in terminology and reasoning. Filled with illustrations, examples, and exercises, Logical Data Modeling makes its subject accessible to readers with only a limited knowledge of database systems. The book covers all essential topics thoroughly but succinctly: entities, associations, attributes, keys and inheritance, valid and invalid structures, and normalization. It also emphasizes communication with business and database specialists, documentation, and the use of Visible Systems' Visible Advantage enterprise modeling tool. The application of design patterns to logical data modeling provides practitioners with a practical tool for fast development. At the end, a chapter covers the issues that arise when the logical data model is translated into the design for a physical database."

This book reports on cutting-edge technologies that have been fostering sustainable development in a variety of fields, including built and natural environments, structures, energy, advanced mechanical technologies as well as electronics and communication technologies. It reports on the applications of Geographic Information Systems (GIS), Internet-of-Things, predictive maintenance, as well as modeling and control techniques to reduce the environmental impacts of buildings, enhance their environmental contribution and positively impact the social equity. The different chapters, selected on the basis of their timeliness and relevance for an audience of engineers and professionals, describe the major trends in the field of sustainable engineering research, providing them with a snapshot of current issues together with important technical information for their daily work, as well as an interesting source of new ideas for their future research. The works included in this book were selected among the contributions to the BUE ACE1, the first event, held in Cairo, Egypt, on 8-9 November 2016, of a series of Annual Conferences & Exhibitions (ACE) organized by the British University in Egypt (BUE).

Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.

Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview.

The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.

This book, based on extensive international collaborative research, highlights the state-of-the-art design of "smart living" for metropolises, megacities, and metacities, as well as at the community and neighbourhood level. Smart living is one of six main components of smart cities, the others being smart people, smart economy, smart environment, smart mobility and smart governance. Smart living in any smart city can only be designed and implemented with active roles for smart people and smart city government, and as a joint effort combining e-Democracy, e-Governance and ICT-IoT systems. In addition to using information and communication technologies, the Internet of Things, Internet of Governance (e-Governance) and Internet of People (e-Democracy), the design of smart living utilizes various domain-specific tools to achieve coordinated, effective and efficient management, development, and conservation, and to improve ecological, social, biophysical, psychological and economic well-being in an equitable manner without compromising the sustainability of development ecosystems and stakeholders. This book presents case studies covering more than 10 cities and centred on domain-specific smart living components. The book is issued in two volumes. and this volume focus on city studies.

This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.

Relational databases hold data, right? They indeed do, but to think of a database as nothing more than a container for data is to miss out on the profound power that underlies relational technology. Use the expressive power of mathematics to precisely specify designs and business rules. Communicate effectively about design using the universal language of mathematics. Develop and write complex SQL statements with confidence. Avoid pitfalls and problems from common relational bugaboos such as null values and duplicate rows. The math that you learn in this book will put you above the level of understanding of most database professionals today. You'll better understand the technology and be able to apply it more effectively. You'll avoid data anomalies like redundancy and inconsistency. Understanding what's in this book will take your mastery of relational technology to heights you may not have thought possible.

Over the last decade, a great amount of effort and resources have been invested in the development of Semantic Web Service (SWS) frameworks. Numerous description languages, frameworks, tools, and matchmaking and composition algorithms have been proposed. Nevertheless, when faced with a real-world problem, it is still very hard to decide which of these different approaches to use.

In this book, the editors present an overall overview and comparison of the main current evaluation initiatives for SWS. The presentation is divided into four parts, each referring to one of the evaluation initiatives. Part I covers the long-established first two tracks of the Semantic Service Selection (S3) Contest - the OWL-S matchmaker evaluation and the SAWSDL matchmaker evaluation. Part II introduces the new S3 Jena Geography Dataset (JGD) cross evaluation contest. Part III presents the Semantic Web Service Challenge. Lastly, Part IV reports on the semantic aspects of the Web Service Challenge. The introduction to each part provides an overview of the evaluation initiative and overall results for its latest evaluation workshops. The following chapters in each part, written by the participants, detail their approaches, solutions and lessons learned.This book is aimed at two different types of readers. Researchers on SWS technology receive an overview of existing approaches in SWS with a particular focus on evaluation approaches; potential users of SWS technologies receive a comprehensive summary of the respective strengths and weaknesses of current systems and thus guidance on factors that play a role in evaluation.

This book discusses theoretical backgrounds, techniques and methodologies, and applications of the current state-of-the-art human dynamics research utilizing social media and geospatial big data. It describes various forms of social media and big data with location information, theory development, data collection and management techniques, and analytical methodologies to conduct human dynamics research including geographic information systems (GIS), spatiotemporal data analytics, text mining and semantic analysis, machine learning, trajectory data analysis, and geovisualization. The book also covers applied interdisciplinary research examples ranging from disaster management, public health, urban geography, and spatiotemporal information diffusion. By providing theoretical foundations, solid empirical research backgrounds, techniques, and methodologies as well as application examples from diverse interdisciplinary fields, this book will be a valuable resource to students, researchers and practitioners who utilize or plan to employ social media and big data in their work.

"Date on Database: Writings 2000 2006" captures some of the freshest thinking from widely known and respected relational database pioneer C. J. Date . Known for his tenacious defense of relational theory in its purest form, Date tackles many topics that are important to database professionals, including the difference between model and implementation, data integrity, data redundancy, deviations in SQL from the relational model, and much more.

Date clearly and patiently explains where many of todays products and practices go wrong, and illustrates some of the trouble you can get into if you don't carefully think through your use of current database technology. In almost every field of endeavor, the writings of the founders and early leaders have had a profound effect. And now is your chance to read Date while his material is fresh and the field is still young. You'll want to read this book because it: Provides C. J. Date's freshest thinking on relational theory versus current products in the field Features a tribute to E. F. Codd, founder of the relational database field Clearly explains how the unwary practitioner can avoid problems with current relational database technology Offers novel insights into classic issues like redundancy and database design

Biometrics such as fingerprint, face, gait, iris, voice and signature, recognizes one's identity using his/her physiological or behavioral characteristics. Among these biometric signs, fingerprint has been researched the longest period of time, and shows the most promising future in real-world applications. However, because of the complex distortions among the different impressions of the same finger, fingerprint recognition is still a challenging problem.

Computational Algorithms for Fingerprint Recognition presents an entire range of novel computational algorithms for fingerprint recognition. These include feature extraction, indexing, matching, classification, and performance prediction/validation methods, which have been compared with state-of-art algorithms and found to be effective and efficient on real-world data. All the algorithms have been evaluated on NIST-4 database from National Institute of Standards and Technology (NIST). Specific algorithms addressed include:
-Learned template based minutiae extraction algorithm,
-Triplets of minutiae based fingerprint indexing algorithm,
-Genetic algorithm based fingerprint matching algorithm,
-Genetic programming based feature learning algorithm for fingerprint classification,
-Comparison of classification and indexing based approaches for identification,
-Fundamental fingerprint matching performance prediction analysis and its validation.

Computational Algorithms for Fingerprint Recognition is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering.

Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign, looking for patterns in financial transactions to discover illegal activities or analyzing genome sequences. From this perspective, it was just a matter of time for the discipline to reach the important area of computer security. Applications Of Data Mining In Computer Security presents a collection of research efforts on the use of data mining in computer security.

Applications Of Data Mining In Computer Security concentrates heavily on the use of data mining in the area of intrusion detection. The reason for this is twofold. First, the volume of data dealing with both network and host activity is so large that it makes it an ideal candidate for using data mining techniques. Second, intrusion detection is an extremely critical activity. This book also addresses the application of data mining to computer forensics. This is a crucial area that seeks to address the needs of law enforcement in analyzing the digital evidence.

An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages. All data for exercises can be found here: https://github.com/tbrown122387/r_and_python_book/tree/master/data. Key features: - Teaches R and Python in a "side-by-side" way. - Examples are tailored to aspiring data scientists and statisticians, not software engineers. - Designed for introductory graduate students. - Does not assume any mathematical background.

Cellular Automata Transforms describes a new approach to using the dynamical system, popularly known as cellular automata (CA), as a tool for conducting transforms on data. Cellular automata have generated a great deal of interest since the early 1960s when John Conway created the Game of Life'. This book takes a more serious look at CA by describing methods by which information building blocks, called basis functions (or bases), can be generated from the evolving states. These information blocks can then be used to construct any data. A typical dynamical system such as CA tend to involve an infinite possibilities of rules that define the inherent elements, neighborhood size, shape, number of states, and modes of association, etc. To be able to build these building blocks an elegant method had to be developed to address a large subset of these rules. A new formula, which allows for the definition a large subset of possible rules, is described in the book. The robustness of this formula allows searching of the CA rule space in order to develop applications for multimedia compression, data encryption and process modeling. Cellular Automata Transforms is divided into two parts. In Part I the fundamentals of cellular automata, including the history and traditional applications are outlined. The challenges faced in using CA to solve practical problems are described. The basic theory behind Cellular Automata Transforms (CAT) is developed in this part of the book. Techniques by which the evolving states of a cellular automaton can be converted into information building blocks are taught. The methods (including fast convolutions) by which forward and inverse transforms of any data can beachieved are also presented. Part II contains a description of applications of CAT. Chapter 4 describes digital image compression, audio compression and synthetic audio generation, three approaches for compressing video data. Chapter 5 contains both symmetric and public-key implementation of CAT encryption. Possible methods of attack are also outlined. Chapter 6 looks at process modeling by solving differential and integral equations. Examples are drawn from physics and fluid dynamics.

This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become "?rst-class citizens" and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.

This book constitutes the Proceedings of the IFIP Working Conference PRO COMET'98, held 8-12 June 1998 at Shelter Island, N.Y. The conference is organized by the t'wo IFIP TC 2 Working Groups 2.2 Formal Description of Programming Concepts and 2.3 Programming Methodology. WG2.2 and WG2.3 have been organizing these conferences every four years for over twenty years. The aim of such Working Conferences organized by IFIP Working Groups is to bring together leading scientists in a given area of computer science. Participation is by invitation only. As a result, these conferences distinguish themselves from other meetings by extensive and competent technical discus sions. PROCOMET stands for Programming Concepts and Methods, indicating that the area of discussion for the conference is the formal description of pro gramming concepts and methods, their tool support, and their applications. At PROCOMET working conferences, papers are presented from this whole area, reflecting the interest of the individuals in WG2.2 and WG2.3."

Data stewards in any organization are the backbone of a successful data governance implementation because they do the work to make data trusted, dependable, and high quality. Since the publication of the first edition, there have been critical new developments in the field, such as integrating Data Stewardship into project management, handling Data Stewardship in large international companies, handling "big data" and Data Lakes, and a pivot in the overall thinking around the best way to align data stewardship to the data-moving from business/organizational function to data domain. Furthermore, the role of process in data stewardship is now recognized as key and needed to be covered. Data Stewardship, Second Edition provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on organizational/company structure, business functions, and data ownership. The book shows data managers how to gain support for a stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort. It includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards.