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This book is for those Splunk developers who want to learn advanced strategies to deal with big data from an enterprise architectural perspective. You need to have good working knowledge of Splunk.

Data mining --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Computer programs.

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Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies.

Data mining. --- Statistics. --- Data Mining and Knowledge Discovery.

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Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. Twenty different real-world case studies illustrate various techniques in rapidly growing areas, including: RetailCrime and homeland securityStock mark

Data mining --- R (Computer program language) --- Industrial applications --- GNU-S (Computer program language) --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Domain-specific programming languages --- Database searching

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Wissen – ob sicher oder unsicher – muss in wissensbasierten Systemen adäquat dargestellt und maschinell verarbeitet werden. Den Autoren ist es gelungen, die unterschiedlichen Methoden anschaulich zu präsentieren, so dass dieses Werk zum Selbststudium wie auch als Begleittext für entsprechende Vorlesungen geeignet ist. Die fünfte Auflage wurde überarbeitet und um den aktuellen Bereich der Argumentation erweitert. Praxisnahe Selbsttestaufgaben mit online zur Verfügung gestellten ausführlichen Lösungen erleichtern das Lernen. Inhalt Logikbasierte Wissensrepräsentation - Maschinelles Lernen und Data Mining - Fallbasiertes Schließen - Default-Logiken - Logisches Programmieren und Antwortmengen - Argumentation - Aktionen und Planen - Agenten - Probabilistische Netze - Anwendungsbeispiele aus Medizin, Genetik und Wirtschaft Zielgruppe Studierende der Informatik und verwandter Gebiete Autoren Prof. Dr. Christoph Beierle ist Universitätsprofessor für Informatik/Wissensbasierte Systeme an der FernUniversität in Hagen. Prof. Dr. Gabriele Kern-Isberner ist Universitätsprofessorin für Informatik/Information Engineering an der Universität Dortmund.

Data mining. --- Coding theory. --- Information theory. --- Data Mining and Knowledge Discovery. --- Coding and Information Theory.

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This book constitutes the refereed proceedings of the 10th International Symposium on Bioinformatics Research and Applications, ISBRA 2014, held in Zhangjiajie, China, in June 2014. The 33 revised full papers and 31 one-page abstracts included in this volume were carefully reviewed and selected from 119 submissions. The papers cover a wide range of topics in bioinformatics and computational biology and their applications including the development of experimental or commercial systems.

Bioinformatics --- Bioinformatics. --- Data mining. --- Optical pattern recognition. --- Computational Biology/Bioinformatics. --- Data Mining and Knowledge Discovery. --- Pattern Recognition. --- Mathematical and Computational Biology. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Data processing --- Pattern recognition. --- Biomathematics. --- Mathematics --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception