Guide to Industrial Analytics (Record no. 184795)
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fixed length control field | 04967nam a22006495i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-030-79104-9 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | DE-He213 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240423130058.0 |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
fixed length control field | cr nn 008mamaa |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 210927s2021 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9783030791049 |
-- | 978-3-030-79104-9 |
024 7# - OTHER STANDARD IDENTIFIER | |
Standard number or code | 10.1007/978-3-030-79104-9 |
Source of number or code | doi |
050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
Classification number | QA76.9.D343 |
072 #7 - SUBJECT CATEGORY CODE | |
Subject category code | UNF |
Source | bicssc |
072 #7 - SUBJECT CATEGORY CODE | |
Subject category code | UYQE |
Source | bicssc |
072 #7 - SUBJECT CATEGORY CODE | |
Subject category code | COM021030 |
Source | bisacsh |
072 #7 - SUBJECT CATEGORY CODE | |
Subject category code | UNF |
Source | thema |
072 #7 - SUBJECT CATEGORY CODE | |
Subject category code | UYQE |
Source | thema |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.312 |
Edition number | 23 |
100 1# - MAIN ENTRY--PERSONAL NAME | |
Personal name | Hill, Richard. |
Relator term | author. |
Relator code | aut |
-- | http://id.loc.gov/vocabulary/relators/aut |
245 10 - TITLE STATEMENT | |
Title | Guide to Industrial Analytics |
Medium | [electronic resource] : |
Remainder of title | Solving Data Science Problems for Manufacturing and the Internet of Things / |
Statement of responsibility, etc | by Richard Hill, Stuart Berry. |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2021. |
264 #1 - | |
-- | Cham : |
-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2021. |
300 ## - PHYSICAL DESCRIPTION | |
Extent | XXV, 275 p. 172 illus., 108 illus. in color. |
Other physical details | online resource. |
336 ## - | |
-- | text |
-- | txt |
-- | rdacontent |
337 ## - | |
-- | computer |
-- | c |
-- | rdamedia |
338 ## - | |
-- | online resource |
-- | cr |
-- | rdacarrier |
347 ## - | |
-- | text file |
-- | |
-- | rda |
490 1# - SERIES STATEMENT | |
Series statement | Texts in Computer Science, |
International Standard Serial Number | 1868-095X |
505 0# - FORMATTED CONTENTS NOTE | |
Formatted contents note | 1. Introduction to Industrial Analytics -- 2. Measuring Performance -- 3. Modelling and Simulating Systems -- 4. Optimising Systems -- 5. Production Control and Scheduling -- 6. Simulating Demand Forecasts -- 7. Investigating Time Series Data -- 8. Determining the Minimum Information for Effective Control -- 9. Constructing Machine Learning Models for Prediction -- 10. Exploring Model Accuracy. |
520 ## - SUMMARY, ETC. | |
Summary, etc | Monitoring and managing operational performance is a crucial activity for industrial and business organisations. The emergence of low cost, accessible computing and storage through the Industrial Internet of Things (IIoT) has generated considerable interest in innovative approaches to doing more with data. Data Science, predictive analytics, machine learning, artificial intelligence and the more general approaches to modelling, simulating and visualizing industrial systems have often been considered topics only for research labs and academic departments. This book debunks the mystique around applied data science and shows readers, using tutorial-style explanations and real-life case studies, how practitioners can develop their own understanding of performance to achieve tangible business improvements. Topics and features: Describes hands-on application of data-science techniques to solve problems in manufacturing and the IIoT Presents relevant case study examples that make use of commonly available (and often free) software to solve real-world problems Enables readers to rapidly acquire a practical understanding of essential modelling and analytics skills for system-oriented problem solving Includes a schedule to organize content for semester-based university delivery, and end-of-chapter exercises to reinforce learning This unique textbook/guide outlines how to use tools to investigate, diagnose, propose and implement analytics solutions that will provide the evidence for business cases, or to deliver explainable results that demonstrate positive impact within an organisation. It will be invaluable to students, applications developers, researchers, technical consultants, and industrial managers and supervisors. Dr. Richard Hill is a professor of Intelligent Systems, head of the Department of Computer Science, and director of the Centre for Industrial Analytics at the University of Huddersfield, UK. His other Springer titles include Guide to Vulnerability Analysis for Computer Networks and Systems and Big-Data Analytics and Cloud Computing. Dr. Stuart Berry is Emeritus Fellow in the Department of Computing and Mathematics at the University of Derby, UK. He is a co-editor of the Springer title, Guide to Computational Modelling for Decision Processes. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Data mining. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Big data. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Manufactures. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Machine learning. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Computer networks . |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Computer science. |
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Data Mining and Knowledge Discovery. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Big Data. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Machines, Tools, Processes. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Machine Learning. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Computer Communication Networks. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Computer Science. |
700 1# - ADDED ENTRY--PERSONAL NAME | |
Personal name | Berry, Stuart. |
Relator term | author. |
Relator code | aut |
-- | http://id.loc.gov/vocabulary/relators/aut |
710 2# - ADDED ENTRY--CORPORATE NAME | |
Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
773 0# - HOST ITEM ENTRY | |
Title | Springer Nature eBook |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
Display text | Printed edition: |
International Standard Book Number | 9783030791032 |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
Display text | Printed edition: |
International Standard Book Number | 9783030791056 |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
Display text | Printed edition: |
International Standard Book Number | 9783030791063 |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
Uniform title | Texts in Computer Science, |
-- | 1868-095X |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-3-030-79104-9">https://doi.org/10.1007/978-3-030-79104-9</a> |
912 ## - | |
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912 ## - | |
-- | ZDB-2-SXCS |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | eBooks-CSE-Springer |
No items available.