The Sixth SIAM International Conference on Data Mining continues the tradition of presenting approaches, tools, and systems for data mining in fields such as science, engineering, industrial processes, healthcare, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance, and principled analysis techniques and algorithms, based on sound statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them.
The Seventh SIAM International Conference on Data Mining (SDM 2007) continues a series of conferences whose focus is the theory and application of data mining to complex datasets in science, engineering, biomedicine, and the social sciences. These datasets challenge our abilities to analyze them because they are large and often noisy. Sophisticated, highperformance, and principled analysis techniques and algorithms, based on sound statistical foundations, are required. Visualization is often critically important; tuning for performance is a significant challenge; and the appropriate levels of abstraction to allow end-users to exploit sophisticated techniques and understand clearly both the constraints and interpretation of results are still something of an open question.
The annual Workshop on Algorithm Engineering and Experiments (ALENEX) provides a forum for the presentation of original research in all aspects of algorithm engineering, including the implementation and experimental evaluation of algorithms and data structures. The workshop was sponsored by SIAM, the Society for Industrial and Applied Mathematics, and SIGACT, the ACM Special Interest Group on Algorithms and Computation Theory. The aim of ANALCO is to provide a forum for the presentation of original research in the analysis of algorithms and associated combinatorial structures.
What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions
Knowledge discovery in ubiquitous environments is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems. It aims to provide a unifying framework for systematically investigating the mutual dependencies of otherwise quite unrelated technologies employed in building next-generation intelligent systems: machine learning, data mining, sensor networks, grids, peer-to-peer networks, data stream mining, activity recognition, Web 2.0, privacy, user modelling and others. This state-of-the-art survey is the outcome of a large number of workshops, summer schools, tutorials and dissemination events organized by KDubiq (Knowledge Discovery in Ubiquitous Environments), a networking project funded by the European Commission to bring together researchers and practitioners of this emerging community. It provides in its first part a conceptual foundation for the new field of ubiquitous knowledge discovery - highlighting challenges and problems, and proposing future directions in the area of 'smart', 'adaptive', and 'intelligent' learning. The second part of this volume contains selected approaches to ubiquitous knowledge discovery and treats specific aspects in detail. The contributions have been carefully selected to provide illustrations and in-depth discussions for some of the major findings of Part I.
The papers in this volume are the refereed papers presented at AI-2015, the Thirty-fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2015 in both the technical and the application streams. They present new and innovative developments and applications, divided into technical stream sections on Knowledge Discovery and Data Mining, Machine Learning and Knowledge Acquisition, and AI in Action, followed by application stream sections on Applications of Genetic Algorithms, Applications of Intelligent Agents and Evolutionary Techniques, and AI Applications. The volume also includes the text of short papers presented as posters at the conference. This is the thirty-second volume in the Research and Development in Intelligent Systems series, which also incorporates the twenty-third volume in the Applications and Innovations in Intelligent Systems series. These series are essential reading for those who wish to keep up to date with developments in this important field.
Advances in web technology and the proliferation of sensors and mobile devices connected to the internet have resulted in the generation of immense data sets available on the web that need to be represented, saved, and exchanged. Massive data can be managed effectively and efficiently to support various problem-solving and decision-making techniques. Emerging Technologies and Applications in Data Processing and Management is a critical scholarly publication that examines the importance of data management strategies that coincide with advancements in web technologies. Highlighting topics such as geospatial coverages, data analysis, and keyword query, this book is ideal for professionals, researchers, academicians, data analysts, web developers, and web engineers.
