Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned.

Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases.

Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

Web services and Service-Oriented Computing (SOC) have become thriving areas of academic research, joint university/industry research projects, and novel IT products on the market. SOC is the computing paradigm that uses Web services as building blocks for the engineering of composite, distributed applications out of the reusable application logic encapsulated by Web services. Web services could be considered the best-known and most standardized technology in use today for distributed computing over the Internet. This book is the second installment of a two-book collection covering the state-of-the-art of both theoretical and practical aspects of Web services and SOC research and deployments. Advanced Web Services specifically focuses on advanced topics of Web services and SOC and covers topics including Web services transactions, security and trust, Web service management, real-world case studies, and novel perspectives and future directions. The editors present foundational topics in the first book of the collection, Web Services Foundations (Springer, 2013). Together, both books comprise approximately 1400 pages and are the result of an enormous community effort that involved more than 100 authors, comprising the world's leading experts in this field.

This book provides the most complete formal specification of the semantics of the Business Process Model and Notation 2.0 standard (BPMN) available to date, in a style that is easily understandable for a wide range of readers - not only for experts in formal methods, but e.g. also for developers of modeling tools, software architects, or graduate students specializing in business process management. BPMN - issued by the Object Management Group - is a widely used standard for business process modeling. However, major drawbacks of BPMN include its limited support for organizational modeling, its only implicit expression of modalities, and its lack of integrated user interaction and data modeling. Further, in many cases the syntactical and, in particular, semantic definitions of BPMN are inaccurate, incomplete or inconsistent. The book addresses concrete issues concerning the execution semantics of business processes and provides a formal definition of BPMN process diagrams, which can serve as a sound basis for further extensions, i.e., in the form of horizontal refinements of the core language. To this end, the Abstract State Machine (ASMs) method is used to formalize the semantics of BPMN. ASMs have demonstrated their value in various domains, e.g. specifying the semantics of programming or modeling languages, verifying the specification of the Java Virtual Machine, or formalizing the ITIL change management process. This kind of improvement promotes more consistency in the interpretation of comprehensive models, as well as real exchangeability of models between different tools. In the outlook at the end of the book, the authors conclude with proposing extensions that address actor modeling (including an intuitive way to denote permissions and obligations), integration of user-centric views, a refined communication concept, and data integration.

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.

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 Turn analyzes the research of information seeking and retrieval (IS&R) and proposes a new direction of integrating research in these two areas: the fields should turn off their separate and narrow paths and construct a new avenue of research. An essential direction for this avenue is context as given in the subtitle Integration of Information Seeking and Retrieval in Context. Other essential themes in the book include:

IS&R research models, frameworks and theories; search and works tasks and situations in context; interaction between humans and machines; information acquisition, relevance and information use; research design and methodology based on a structured set of explicit variables - all set into the holistic cognitive approach. The present monograph invites the reader into a construction project - there is much research to do for a contextual understanding of IS&R.

The Turn represents a wide-ranging perspective of IS&R by providing a novel unique research framework, covering both individual and social aspects of information behavior, including the generation, searching, retrieval and use of information. Regarding traditional laboratory information retrieval research, the monograph proposes the extension of research toward actors, search and work tasks, IR interaction and utility of information. Regarding traditional information seeking research, it proposes the extension toward information access technology and work task contexts.

The Turn is the first synthesis of research in the broad area of IS&R ranging from systems oriented laboratory IR research to social science oriented information seeking studies.

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."

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."

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 consists of an anthology of writings. The aim is to honour Marco to celebrate the 35th year of his academic career . The book consists of a collection of selected opinions in the field of IS. Some themes are: IT and Information Systems organizational impacts, Systems development, Business process management, Business organization, e-government, social impact of IT.

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 shows how business process management (BPM), as a management discipline at the intersection of IT and Business, can help organizations to master digital innovations and transformations. At the same time, it discusses how BPM needs to be further developed to successfully act as a driver for innovation in a digital world. In recent decades, BPM has proven extremely successful in managing both continuous and radical improvements in many sectors and business areas. While the digital age brings tremendous new opportunities, it also brings the specific challenge of correctly positioning and scoping BPM in organizations. This book shows how to leverage BPM to drive business innovation in the digital age. It brings together the views of the world's leading experts on BPM and also presents a number of practical cases. It addresses mangers as well as academics who share an interest in digital innovation and business process management. The book covers topics such as BPM and big data, BPM and the Internet of Things, and BPM and social media. While these technological and methodological aspects are key to BPM, process experts are also aware that further nontechnical organizational capabilities are required for successful innovation. The ideas presented in this book have helped us a lot while implementing process innovations in our global Logistics Service Center. Joachim Gantner, Director IT Services, Swarovski AG Managing Processes - everyone talks about it, very few really know how to make it work in today's agile and competitive world. It is good to see so many leading experts taking on the challenge in this book. Cornelius Clauser, Chief Process Officer, SAP SE This book provides worthwhile readings on new developments in advanced process analytics and process modelling including practical applications - food for thought how to succeed in the digital age. Ralf Diekmann, Head of Business Excellence, Hilti AG This book is as an important step towards process innovation systems. I very much like to congratulate the editors and authors for presenting such an impressive scope of ideas for how to address the challenging, but very rewarding marriage of BPM and innovation. Professor Michael Rosemann, Queensland University of Technology

This proceedings volume introduces recent work on the storage, retrieval and visualization of spatial Big Data, data-intensive geospatial computing and related data quality issues. Further, it addresses traditional topics such as multi-scale spatial data representations, knowledge discovery, space-time modeling, and geological applications. Spatial analysis and data mining are increasingly facing the challenges of Big Data as more and more types of crowd sourcing spatial data are used in GIScience, such as movement trajectories, cellular phone calls, and social networks. In order to effectively manage these massive data collections, new methods and algorithms are called for. The book highlights state-of-the-art advances in the handling and application of spatial data, especially spatial Big Data, offering a cutting-edge reference guide for graduate students, researchers and practitioners in the field of GIScience.

This book provides an overview of current Intellectual Property (IP) based System-on-Chip (SoC) design methodology and highlights how security of IP can be compromised at various stages in the overall SoC design-fabrication-deployment cycle. Readers will gain a comprehensive understanding of the security vulnerabilities of different types of IPs. This book would enable readers to overcome these vulnerabilities through an efficient combination of proactive countermeasures and design-for-security solutions, as well as a wide variety of IP security and trust assessment and validation techniques. This book serves as a single-source of reference for system designers and practitioners for designing secure, reliable and trustworthy SoCs.

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.

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.

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).

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.

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.

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.

"Incomplete Information System and Rough Set Theory: Models and Attribute Reductions" covers theoretical study of generalizations of rough set model in various incomplete information systems. It discusses not only the regular attributes but also the criteria in the incomplete information systems. Based on different types of rough set models, the book presents the practical approaches to compute several reducts in terms of these models. The book is intended for researchers and postgraduate students in machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, and granular computing.

Dr. Xibei Yang is a lecturer at the School of Computer Science and Engineering, Jiangsu University of Science and Technology, China; Jingyu Yang is a professor at the School of Computer Science, Nanjing University of Science and Technology, China.