Anomaly Detection In Streaming Data
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Anomaly Detection In Streaming Data
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Author : Pascal Weiß
language : en
Publisher:
Release Date : 2016
Anomaly Detection In Streaming Data written by Pascal Weiß and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
A Fully Online Approach For Anomaly Detection And Change Point Detection In Streaming Data Using Lstm
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Author : Memoona Khanam
language : en
Publisher:
Release Date : 2023
A Fully Online Approach For Anomaly Detection And Change Point Detection In Streaming Data Using Lstm written by Memoona Khanam and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Applied Data Science
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Author : Martin Braschler
language : en
Publisher: Springer
Release Date : 2019-06-13
Applied Data Science written by Martin Braschler and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-13 with Computers categories.
This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.
Machine Learning For Streaming Data With Python
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Author : Joos Korstanje
language : en
Publisher: Packt Publishing
Release Date : 2022-07-15
Machine Learning For Streaming Data With Python written by Joos Korstanje and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-15 with categories.
Apply machine learning to streaming data with the help of practical examples, and deal with challenges that surround streaming Key Features: Work on streaming use cases that are not taught in most data science courses Gain experience with state-of-the-art tools for streaming data Mitigate various challenges while handling streaming data Book Description: Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream data in your machine learning models. What You Will Learn: Understand the challenges and advantages of working with streaming data Develop real-time insights from streaming data Understand the implementation of streaming data with various use cases to boost your knowledge Develop a PCA alternative that can work on real-time data Explore best practices for handling streaming data that you absolutely need to remember Develop an API for real-time machine learning inference Who this book is for: This book is for data scientists and machine learning engineers who have a background in machine learning, are practice and technology-oriented, and want to learn how to apply machine learning to streaming data through practical examples with modern technologies. Although an understanding of basic Python and machine learning concepts is a must, no prior knowledge of streaming is required.
Change Point Detection For Streaming Data Using Support Vector Methods
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Author : Charles William Harrison
language : en
Publisher:
Release Date : 2022
Change Point Detection For Streaming Data Using Support Vector Methods written by Charles William Harrison and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Sequential multiple change point detection concerns the identification of multiple points in time where the systematic behavior of a statistical process changes. A special case of this problem, called online anomaly detection, occurs when the goal is to detect the first change and then signal an alert to an analyst for further investigation. This dissertation concerns the use of methods based on kernel functions and support vectors to detect changes. A variety of support vector-based methods are considered, but the primary focus concerns Least Squares Support Vector Data Description (LS-SVDD). LS-SVDD constructs a hypersphere in a kernel space to bound a set of multivariate vectors using a closed-form solution. The mathematical tractability of the LS-SVDD facilitates closed-form updates for the LS-SVDD Lagrange multipliers. The update formulae concern either adding or removing a block of observations from an existing LS-SVDD description, respectively, and thus LS-SVDD can be constructed or updated sequentially which makes it attractive for online problems with sequential data streams. LS-SVDD is applied to a variety of scenarios including online anomaly detection and sequential multiple change point detection.
Streaming Data
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Author : Andrew Psaltis
language : en
Publisher: Simon and Schuster
Release Date : 2017-05-31
Streaming Data written by Andrew Psaltis and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-31 with Computers categories.
Summary Streaming Data introduces the concepts and requirements of streaming and real-time data systems. The book is an idea-rich tutorial that teaches you to think about how to efficiently interact with fast-flowing data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology As humans, we're constantly filtering and deciphering the information streaming toward us. In the same way, streaming data applications can accomplish amazing tasks like reading live location data to recommend nearby services, tracking faults with machinery in real time, and sending digital receipts before your customers leave the shop. Recent advances in streaming data technology and techniques make it possible for any developer to build these applications if they have the right mindset. This book will let you join them. About the Book Streaming Data is an idea-rich tutorial that teaches you to think about efficiently interacting with fast-flowing data. Through relevant examples and illustrated use cases, you'll explore designs for applications that read, analyze, share, and store streaming data. Along the way, you'll discover the roles of key technologies like Spark, Storm, Kafka, Flink, RabbitMQ, and more. This book offers the perfect balance between big-picture thinking and implementation details. What's Inside The right way to collect real-time data Architecting a streaming pipeline Analyzing the data Which technologies to use and when About the Reader Written for developers familiar with relational database concepts. No experience with streaming or real-time applications required. About the Author Andrew Psaltis is a software engineer focused on massively scalable real-time analytics. Table of Contents PART 1 - A NEW HOLISTIC APPROACH Introducing streaming data Getting data from clients: data ingestion Transporting the data from collection tier: decoupling the data pipeline Analyzing streaming data Algorithms for data analysis Storing the analyzed or collected data Making the data available Consumer device capabilities and limitations accessing the data PART 2 - TAKING IT REAL WORLD Analyzing Meetup RSVPs in real time
Real Time Anomaly Detection With In Flight Data
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Author : Nicolas Aussel
language : en
Publisher:
Release Date : 2019
Real Time Anomaly Detection With In Flight Data written by Nicolas Aussel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
With the rise of the number of sensors and actuators in an aircraft and the development of reliable data links from the aircraft to the ground, it becomes possible to improve aircraft security and maintainability by applying real-time analysis techniques. However, given the limited availability of on-board computing and the high cost of the data links, current architectural solutions cannot fully leverage all the available resources limiting their accuracy.Our goal is to provide a distributed algorithm for failure prediction that could be executed both on-board of the aircraft and on a ground station and that would produce on-board failure predictions in near real-time under a communication budget. In this approach, the ground station would hold fast computation resources and historical data and the aircraft would hold limited computational resources and current flight's data.In this thesis, we will study the specificities of aeronautical data and what methods already exist to produce failure prediction from them and propose a solution to the problem stated. Our contribution will be detailed in three main parts.First, we will study the problem of rare event prediction created by the high reliability of aeronautical systems. Many learning methods for classifiers rely on balanced datasets. Several approaches exist to correct a dataset imbalance and we will study their efficiency on extremely imbalanced datasets.Second, we study the problem of log parsing as many aeronautical systems do not produce easy to classify labels or numerical values but log messages in full text. We will study existing methods based on a statistical approach and on Deep Learning to convert full text log messages into a form usable as an input by learning algorithms for classifiers. We will then propose our own method based on Natural Language Processing and show how it outperforms the other approaches on a public benchmark.Last, we offer a solution to the stated problem by proposing a new distributed learning algorithm that relies on two existing learning paradigms Active Learning and Federated Learning. We detail our algorithm, its implementation and provide a comparison of its performance with existing methods.
Real Time Sentiment Based Anomaly Detection In Twitter Data Streams
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Author : Khantil Ragnesh Patel
language : en
Publisher:
Release Date : 2016
Real Time Sentiment Based Anomaly Detection In Twitter Data Streams written by Khantil Ragnesh Patel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Twitter has over 316 million active users and the engagement of these Twitter users results in the rapid production of data, notably in the context of popular topics (such as news stories, politics, and sports). This data is available in the form of data streams, which has led many researchers to develop analysis techniques especially for Twitter data streams. Although anomaly detection in time series is a well established research area, its application to detect sentiment-based anomalies in large volumes of streaming data began recently. A sentiment-based anomaly is de ned as a sudden increase in the time series of tweets individually associated with a positive, neutral, or negative sentiment. The goal of this research is to develop and evaluate a technique to automatically detect sentiment-based anomalies, while avoiding the repeated detection of anomalies of similar types. Detecting anomalies in data streams is challenging due the requirement that anomalies be detected in real-time. We propose an approach for real-time sentiment-based anomaly detection (RSAD) in Twitter data streams. Sentiment classi cation is used to split the input data stream into three independent streams (positive, neutral, and negative), which are then analyzed separately for anomalous spikes in the number of tweets. Rare anomalies and the rst occurrence of repeated anomalies are distinguished from the repeated occurrence of similar anomalies. Six approaches for anomaly detection in data streams, including two baseline approaches, are described. These approaches were tested on two user-generated datasets. The rst dataset concerned an international sports event and was collected from Twitter and the second concerned a political party and was collected from multiple social media platforms. Results from these evaluations show that a probabilistic exponentially weighted moving average (PEWMA), coupled with a sliding window that uses a median absolute deviation (MAD) calculation, is effective at identifying sentiment-based anomalies. The PEWMA-MAD approach is consistently among the top two methods for all cases tested. The simple linear regression approach is slightly better in the case of the second dataset. Overall, the results suggest that the PEWMA-MAD approach may be robust su ciently to be applied to a wide variety of datasets from di erent social media platforms. ii.
Outlier Detection For Temporal Data
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Author : Manish Gupta
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Outlier Detection For Temporal Data written by Manish Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
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
Networking 2011
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Author : Jordi Domingo-Pascual
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-04-28
Networking 2011 written by Jordi Domingo-Pascual and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-28 with Business & Economics categories.
The two-volume set LNCS 6640 and 6641 constitutes the refereed proceedings of the 10th International IFIP TC 6 Networking Conference held in Valencia, Spain, in May 2011. The 64 revised full papers presented were carefully reviewed and selected from a total of 294 submissions. The papers feature innovative research in the areas of applications and services, next generation Internet, wireless and sensor networks, and network science. The first volume includes 36 papers and is organized in topical sections on anomaly detection, content management, DTN and sensor networks, energy efficiency, mobility modeling, network science, network topology configuration, next generation Internet, and path diversity.