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Mining for Strategic Competitive Intelligence
The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitive intelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an application scenario, namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations. To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the gestalt of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book. Each of those use cases is backed by a number of research papers, some of which are contained in its largely original version in the second part of the monograph.
Computational Trust Models and Machine Learning
Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: Explains how reputation-based systems are used to determine trust in diverse online communities Describes how machine learning techniques are employed to build robust reputation systems Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly Shows how decision support can be facilitated by computational trust models Discusses collaborative filtering-based trust aware recommendation systems Defines a framework for translating a trust modeling problem into a learning problem Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.
Web Engineering
This book constitutes the refereed proceedings of the 13th International Conference on Web Engineering, ICWE 2013, held in Aalborg, Denmark, in July 2013. The 21 full research papers, 4 industry papers, and 11 short papers presented were carefully reviewed and selected from 92 submissions. The scientific program was completed with 7 workshops, 6 demonstrations and posters. The papers cover a wide spectrum of topics, such as, among others: web mining and knowledge extraction, semantic and linked data management, crawling and web research, model-driven web engineering, component-based web engineering, Rich Internet Applications (RIAs) and client-side programming, web services, and end-user development.
Trust Management
This book constitutes the refereed proceedings of the Second International Conference on Trust Management, iTrust 2004, held in Oxford, UK, in March/April 2004. The 21 revised full papers and 6 revised short papers presented together with 3 invited contributions were carefully reviewed and selected from 48 submissions. Besides technical topics in distributed and open systems, issues from law, social sciences, business, and philosophy are addressed in order to develop a deeper and more fundamental understanding of the issues and challenges in the area of trust management in dynamic open systems.
Computing with Social Trust
This book has evolved out of roughly ve years of working on computing with social trust. In the beginning, getting people to accept that social networks and the relationships in them could be the basis for interesting, relevant, and exciting c- puter science was a struggle. Today, social networking and social computing have become hot topics, and those of us doing research in this space are nally nding a wealth of opportunities to share our work and to collaborate with others. This book is a collection of chapters that cover all the major areas of research in this space. I hope it will serve as a guide to students and researchers who want a strong introduction to work in the eld, and as enco...
Applied Artificial Intelligence
About This Book Step into the amazing world of Artificial Intelligence and Machine Learning using this compact and easy to understand book. Dive into Neural Networks and Deep Learning and create your own production ready AI models by using TensorFlow and Keras. Work through simple yet insightful examples that will get you up and running with Artificial Intelligence, TensorFlow and Keras in no time. Who This Book Is For This book is for Python developers who want to understand Neural Networks from ground up and build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. What You ...
On the Move to Meaningful Internet Systems 2004: CoopIS, DOA, and ODBASE
A special mention for 2004 is in order for the new Doctoral Symposium Workshop where three young postdoc researchers organized an original setup and formula to bring PhD students together and allow them to submit their research proposals for selection. A limited number of the submissions and their approaches were independently evaluated by a panel of senior experts at the conference, and presented by the students in front of a wider audience. These students also got free access to all other parts of the OTM program, and only paid a heavily discounted fee for the Doctoral Symposium itself. (In fact their attendance was largely sponsored by the other participants!) If evaluated as successful, ...
Current Trends in Database Technology - EDBT 2004 Workshops
This book constitutes the thoroughly refereed joint post-proceedings of five workshops held as part of the 9th International Conference on Extending Database Technology, EDBT 2004, held in Heraklion, Crete, Greece, in March 2004. The 55 revised full papers presented together with 2 invited papers and the summaries of 2 panels were selected from numerous submissions during two rounds of reviewing and revision. In accordance with the topical focus of the respective workshops, the papers are organized in sections on database technology in general (PhD Workshop), database technologies for handling XML information on the Web, pervasive information management, peer-to-peer computing and databases, and clustering information over the Web.
Hadoop MapReduce v2 Cookbook - Second Edition
If you are a Big Data enthusiast and wish to use Hadoop v2 to solve your problems, then this book is for you. This book is for Java programmers with little to moderate knowledge of Hadoop MapReduce. This is also a one-stop reference for developers and system admins who want to quickly get up to speed with using Hadoop v2. It would be helpful to have a basic knowledge of software development using Java and a basic working knowledge of Linux.
Propagation Phenomena in Real World Networks
“Propagation, which looks at spreading in complex networks, can be seen from many viewpoints; it is undesirable, or desirable, controllable, the mechanisms generating that propagation can be the topic of interest, but in the end all depends on the setting. This book covers leading research on a wide spectrum of propagation phenomenon and the techniques currently used in its modelling, prediction, analysis and control. Fourteen papers range over topics including epidemic models, models for trust inference, coverage strategies for networks, vehicle flow propagation, bio-inspired routing algorithms, P2P botnet attacks and defences, fault propagation in gene-cellular networks, malware propagat...
