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E-grāmata: Research and Practical Issues of Enterprise Information Systems: 11th IFIP WG 8.9 Working Conference, CONFENIS 2017, Shanghai, China, October 18-20, 2017, Revised Selected Papers

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This book constitutes the refereed proceedings of the 11th IFIP WG 8.9 Working Conference on Research and Practical Issues of Enterprise Information Systems, CONFENIS 2017, held in Shanghai, China, in October 2017.

The 17 full papers presented in this volume were carefully reviewed and selected from 39 submissions. They were organized in topical sections named: EIS concepts, theory and methods; IoT and emerging paradigm; EIS for industry 4.0; big data analytics; and intelligent electronics and systems for industrial IoT.


EIS concepts, Theory and Methods.- Modeling of Service Time in Public
Organization Based on Business Processes.- A Behavior Analysis Method towards
Product Quality Management.- Method of Domain Specific Code Generation Based
on Knowledge Graph for Quantitative Trading.- Image Database Management
Architecture: Logical Structure and Indexing Methods.- IoT and Emerging
Paradigm.- Internet of Things or Surveillance of Things.- The Economic Value
of An Emergency Call System.- An IoT-Big Data Based Machine Learning
Technique for Forecasting Water Requirement in Irrigation Field.- EIS for
Industry 4.0.- Penetration of Industry 4.0 Principles into ERP Products A
Central European Study.- Systematic Analysis of future competences affected
by Industry 4.0.- Process-Based Analysis of Digitally Transforming Skills.-
Big Data Analytics.- Big Data Analytics Geolocation from the Perspective of
Mobile Network Operator.- Pattern Discovery from Big Data of Food Sampling
Inspections Based on Extreme Learning Machine.- Big Data Analytics Using SQL:
Quo Vadis.- Intelligent Electronics and Systems for Industrial IoT.-
Rethinking Things - Fog Layer Interplay in IoT: A Mobile Code Approach.- A
Security Framework for Fog Networks based on Role-Based Access Control and
Trust Models.- IoT Platform for Real-time Multichannel ECG Monitoring and
Classification with Neural Networks.- Deep Ensemble Effectively and
Efficiently for Vehicle Instance Retrieval.