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

This book constitutes the refereed proceedings of the 11^th 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.