Time Series Databases
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Data Mining In Time Series Databases
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Author : Mark Last
language : en
Publisher: World Scientific
Release Date : 2004
Data Mining In Time Series Databases written by Mark Last and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Mathematics categories.
Adding the time dimension to real-world databases produces Time Series Databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. This book covers the state-of-the-art methodology for mining time series databases. The novel data mining methods presented in the book include techniques for efficient segmentation, indexing, and classification of noisy and dynamic time series. A graph-based method for anomaly detection in time series is described and the book also studies the implications of a novel and potentially useful representation of time series as strings. The problem of detecting changes in data mining models that are induced from temporal databases is additionally discussed. Contents: A Survey of Recent Methods for Efficient Retrieval of Similar Time Sequences (H M Lie); Indexing of Compressed Time Series (E Fink & K Pratt); Boosting Interval-Based Literal: Variable Length and Early Classification (J J Rodriguez Diez); Segmenting Time Series: A Survey and Novel Approach (E Keogh et al.); Indexing Similar Time Series under Conditions of Noise (M Vlachos et al.); Classification of Events in Time Series of Graphs (H Bunke & M Kraetzl); Median Strings--A Review (X Jiang et al.); Change Detection in Classfication Models of Data Mining (G Zeira et al.). Readership: Graduate students, reseachers and practitioners in the fields of data mining, machine learning, databases and statistics.
Time Series Databases
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Author : Ted Dunning
language : en
Publisher: O'Reilly Media
Release Date : 2014
Time Series Databases written by Ted Dunning and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Computers categories.
Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis. You'll explore the theory behind time series databases and learn practical methods for implementing them. Authors Ted Dunning and Ellen Friedman provide a detailed examination of open source tools such as OpenTSDB and new modifications that greatly speed up data ingestion. You'll learn: A variety of time series use cases The advantages of NoSQL databases for large-scale time series data NoSQL table design for high-performance time series databases The benefits and limitations of OpenTSDB How to access data in OpenTSDB using R, Go, and Ruby How time series databases contribute to practical machine learning projects How to handle the added complexity of geo-temporal data For advice on analyzing time series data, check out Practical Machine Learning: A New Look at Anomaly Detection, also from Ted Dunning and Ellen Friedman.
Time Series Databases
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Author : Ted Dunning. Ellen Friedman
language : en
Publisher:
Release Date :
Time Series Databases written by Ted Dunning. Ellen Friedman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
A Comparison Of Nosql Time Series Databases
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Author : Kevin Rudolph
language : en
Publisher: GRIN Verlag
Release Date : 2015-05-21
A Comparison Of Nosql Time Series Databases written by Kevin Rudolph and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-21 with Technology & Engineering categories.
Research Paper (undergraduate) from the year 2015 in the subject Engineering - Industrial Engineering and Management, grade: 1,0, Technical University of Berlin (Wirtschaftsinformatik - Information Systems Engineering (ISE)), course: Seminar: Hot Topics in Information Systems Engineering, language: English, abstract: During the last years NoSQL databases have been developed to ad-dress the needs of tremendous performance, reliability and horizontal scalability. NoSQL time series databases (TSDBs) have risen to combine valuable NoSQL properties with characteristics of time series data encountering many use-cases. Solutions offer the efficient handling of data volume and frequency related to time series. Developers and decision makers struggle with the choice of a TSDB among a large variety of solutions. Up to now no comparison exists focusing on the specific features and qualities of those heterogeneous applications. This paper aims to deliver two frameworks for the comparison of TSDBs, firstly with a focus on features and secondly on quality. Furthermore, we apply and evaluate the frameworks on up to seven open-source TSDBs such as InfluxDB and OpenTSDB. We come to the result that the investigated TSDBs differ mainly in support- and extension related points. They share performance-enhancing techniques, time-related query capabilities and data schemas optimized for the handling of time-series data.
Maxdata
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Author : Wilhelm A. Hennerkes
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Maxdata written by Wilhelm A. Hennerkes 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 2012-12-06 with Business & Economics categories.
This handbook gives a detailed introduction to the Time Series Database System MAXDATA, which offers a very simple and convenient handling of voluminous numerical databases on a personal computer. It may be regarded as a special education and teaching instrument for the management and evaluation of empirical data and will teach the reader how to do empirical work without any effort. The handbook aims to give the reader a precise idea of the creation, management, documentation and evaluation of voluminous numerical databases on a microcomputer and gives some tips for managing individual numerical databases, but also for having direct access to official national and international economic offline databases. We believe that you will not regret your decision to use MAXDATA in your day-to-day work with statistical data, analyses, graphics, reports etc. Our aim was to design a software product which solves all the major problems associated with professional, decentralized data processing, whilst meeting the highest user requirements for user-friendliness and performance. We hope we have succeeded; positive user response appear to prove our point. Why MAXDATA was created MAXDATA was born of frustration at the multiplicity of computer programs flooding the software market, many of which offer extremely high performance (almost to the point of confusion) but which can generally only be used by computer specialists or those who have undergone a long period of training.
Data Mining In Time Series Databases
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Author :
language : en
Publisher:
Release Date : 2004
Data Mining In Time Series Databases written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Data mining categories.
On Line Management Of Time Series Databases
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Author : John Hamilton
language : en
Publisher:
Release Date : 1988
On Line Management Of Time Series Databases written by John Hamilton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Time-series analysis categories.
Traffic Monitoring And Analysis
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Author : Antonio Pescapè
language : en
Publisher: Springer
Release Date : 2012-03-07
Traffic Monitoring And Analysis written by Antonio Pescapè and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-07 with Computers categories.
This book constitutes the proceedings of the 4th International Workshop on Traffic Monitoring and Analysis, TMA 2012, held in Vienna, Austria, in March 2012. The thoroughly refereed 10 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 31 submissions. The contributions are organized in topical sections on traffic analysis and characterization: new results and improved measurement techniques; measurement for QoS, security and service level agreements; and tools for network measurement and experimentation.
Data Mining In Time Series And Streaming Databases
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Author : Last Mark
language : en
Publisher: World Scientific
Release Date : 2018-01-11
Data Mining In Time Series And Streaming Databases written by Last Mark and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-11 with Computers categories.
This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining. The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic. Contents: Streaming Data Mining with Massive Online Analytics (MOA) (Albert Bifet, Jesse Read, Geoff Holmes and Bernhard Pfahringer)Weightless Neural Modeling for Mining Data Streams (Douglas O Cardoso, João Gama and Felipe França)Ensemble Classifiers for Imbalanced and Evolving Data Streams (Dariusz Brzezinski and Jerzy Stefanowski)Consensus Learning for Sequence Data (Andreas Nienkötter and Xiaoyi Jiang)Clustering-Based Classification of Document Streams with Active Learning (Mark Last, Maxim Stoliar and Menahem Friedman)Supporting the Mining of Big Data by Means of Domain Knowledge During the Pre-mining Phases (Rémon Cornelisse and Sunil Choenni)Data Analytics: Industrial Perspective & Solutions for Streaming Data (Mohsin Munir, Sebastian Baumbach, Ying Gu, Andreas Dengel and Sheraz Ahmed) Readership: Researchers, academics, professionals and graduate students in artificial intelligence, machine learning, databases, and information science. Keywords: Time Series;Data Streams;Big Data;Internet of Things;Concept Drift;Sequence Mining;Episode Mining;Incremental Learning;Active LearningReview:0
Solving Business Problems With Informix Timeseries
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Author : Vaibhav S Dantale
language : en
Publisher: IBM Redbooks
Release Date : 2012-09-21
Solving Business Problems With Informix Timeseries written by Vaibhav S Dantale and has been published by IBM Redbooks this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-21 with Computers categories.
The world is becoming more and more instrumented, interconnected, and intelligent in what IBM® terms a smarter planet, with more and more data being collected for analysis. In trade magazines, this trend is called big data. As part of this trend, the following types of time-based information are collected: Large data centers support a corporation or provide cloud services. These data centers need to collect temperature, humidity, and other types of information over time to optimize energy usage. Utility meters (referred to as smart meters) allow utility companies to collect information over a wireless network and to collect more data than ever before. IBM Informix® TimeSeries is optimized for the processing of time-based data and can provide the following benefits: Storage savings: Storage can be optimized when you know the characteristics of your time-based data. Informix TimeSeries often uses one third of the storage space that is required by a standard relational database. Query performance: Informix TimeSeries takes into consideration the type of data to optimize its organization on disk and eliminates the need for some large indexes and additional sorting. For these reasons and more, some queries can easily have an order of magnitude performance improvement compared to standard relational. Simpler queries: Informix TimeSeries includes a large set of specialized functions that allow you to better express the processing that you want to execute. It even provides a toolkit so that you can add proprietary algoritms to the library. This IBM Redbooks® publication is for people who want to implement a solution that revolves around time-based data. It gives you the information that you need to get started and be productive with Informix TimeSeries.