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2018 | Buch

Modeling of Service Time in Public Organization Based on Business Processes Kapitel lesen Erstes Kapitel lesen

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

herausgegeben von: A Min Tjoa, Prof. Li-Rong Zheng, Zhuo Zou, Maria Raffai, Li Da Xu, Niina Maarit Novak

Verlag: Springer International Publishing

Buchreihe : Lecture Notes in Business Information Processing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

EIS concepts, Theory and Methods

Frontmatter
Modeling of Service Time in Public Organization Based on Business Processes
Abstract
Time is essence in all processes involved while providing customer service. Very often the duration of a specific service is regulated by the law. However, it is also affected by other external and internal factors. Quantitative approaches to model the service time and creating systems that support the workflow management process are vital. This paper proposes a model of business process analysis on the operational level for various service-oriented organizations (including governmental agencies). The model estimates time of rendering services for heavily regulated public organizations. The analysis of the obtained results is discussed, potential applications are identified, and future research directions are formulated.
Larisa Bulysheva, Michael Kataev, Natalia Loseva
A Behavior Analysis Method Towards Product Quality Management
Abstract
Quality management is the basic activity in industrial production. Assuring the authenticity of testing datasets is extremely important for the quality of products. Many visual tools or association analysis methods are used to judge the authenticity of testing data, but it could not precisely capture behavior pattern and time consuming. In this paper, we propose a complete framework to excavate the features of testing datasets and analyze the testing behavior. This framework uses min-max normalization method to pre-process datasets and optimized k-means algorithm to label the training datasets, then SVM algorithm is applied to verify the accuracy of our framework. Using this framework, we can get the features of dataset and homologous behavior model to distinguish the quality of datasets. Some experiments are carried to measure the complete framework and we use various visual formats to show these results and to verify our method.
Congcong Ye, Chun Li, Guoqiang Li, Lihong Jiang, Hongming Cai
Method of Domain Specific Code Generation Based on Knowledge Graph for Quantitative Trading
Abstract
Quantitative methods have been adopted by more and more individual investors for investment activities. Many third party platforms have been developed to help users complete the process of backtesting, which fills the gap between the trading strategy code and the trading strategy model. However, using a quantitative platform for backtesting has a high threshold for users who do not have programming experience. There is still a huge gap between the description and the code of trading strategy. Code generation allows developers to focus more on business related design and implementation, thereby increasing the efficiency of software development. The import of domain knowledge can improve the accuracy of requirement parsing to improve the quality of constructed code model. The general knowledge base is often incomplete in terms of domain specific terms and relationships, and the construction of domain knowledge graphs requires more domain related data. In this paper, encyclopedia data and the financial report data are used to extract domain terms and relations. And then a domain knowledge graph for quantitative trading is constructed to realize the automatic generation of quantitative trading strategy code.
Jianshui Bi, Hongming Cai, Bo Zhou, Lihong Jiang
Image Database Management Architecture: Logical Structure and Indexing Methods
Abstract
Visual information is an important type of information in modern life. However, it is still not used by organizations in a full capacity. The major reason for that is the lack of internal structure of visual information. The existence of this structure in numerical data allows to build very effective tools for classification, storage, and retrieval of numerical information, such as a relational data management system. In case of visual information, each value of the picture is basically meaningless, but the set of pixels starts carry meaningful information. In this paper, we aim to classify different types of images based on the areas of origination and application. We also suggest the possible structure of the database management system with images as elements of it. Another objective is to propose the indexing methods, which allow to avoid the direct comparison of visual query consequently to entire database. We also introduce the idea of applying multi frame super-resolution method to development of store-retrieval procedures for a database with dynamical visual information.
Larisa Bulysheva, Alexander Bulyshev, Michael Kataev

IoT and Emerging Paradigm

Frontmatter
Internet of Things or Surveillance of Things?
Abstract
The paper deals with digital surveillance in the postmodern world. We define a new term ‘Surveillance of Things’ in the context of the study of the surveillance, and try to determine, whether and how the surveillance of people is connected with surveillance of things. We pay particular attention to the Internet of things and analyze in detail the principles of Sigfox network.
We work on the presumption that information about people obtained through surveillance of things are interpreted incorrectly and can have a direct impact on groups of people and also individuals.
Petr Doucek, Antonin Pavlicek, Ladislav Luc
The Economic Value of an Emergency Call System
Abstract
eCall is a complex solution, aimed at supporting drivers and car passengers in the event of an accident in Europe. This automatic emergency call system for motor vehicles, planned by the European Union, is installed in all new models of passenger cars and light commercial vehicles. In this context, the contribution analyses the monetary value of the eCall system implementation.
Tomas Lego, Andreas Mladenow, Niina Maarit Novak, Christine Strauss
An IoT-Big Data Based Machine Learning Technique for Forecasting Water Requirement in Irrigation Field
Abstract
Efficient water management is a major concern in rice cropping. Controlling the use of excessive water in irrigation field is essential for the protection of underground water that will also be the part of climate change adaptation. The sustainable use of water resources is the prior task in Bangladesh. Imbalances between demand and supply are the main region for degradation of surface and groundwater. The human readability of checking the water level on irrigation field is considerable for these circumstances. In this paper I discussed the procedure for monitoring of surface water level in irrigation field, continuous monitoring of weather condition like temperature, air pressure, sunlight, rainfall etc. by using sensor network. The aim is to create a machine learning mechanism for farmers that can be given a forecast of water demand of irrigation field by the collection of IoT based data. In turn, this will help the farmer to prepare them to give water and on the other hand it will be helpful to use appropriate ground water and also it can be used for predict energy utilization. In this research Multiple linear regression algorithm is used for this prediction. Data from the irrigation field of North-West part in Bangladesh is used here to find the result of prediction.
Fizar Ahmed

EIS for Industry 4.0

Frontmatter
Penetration of Industry 4.0 Principles into ERP Vendors’ Products and Services – A Central European Study
Abstract
The paper deals with aspects of EIS (Enterprise Information Systems) innovation based on the development of the internet of things. The article presents the main results of a central European study dealing with the penetration of the Industry 4.0 principles into the offers of a representative sample of ERP (Enterprise Resource Planning) vendors. The results show the current strategies of ERP vendors, the integration of the new principles of Industry 4.0 into ERP applications and the position of ERP systems in the roadmap of Industry 4.0 implementation.
Josef Basl
Systematic Analysis of Future Competences Affected by Industry 4.0
Abstract
Digital transformations boosted by new technological innovations entail restructured industrial processes and requalified skilled workers. Educational institutions must provide qualifications with learning outcomes fitting to these requirements. Nowadays skill gap analysis between both sides of labor market is a crucial research topic, but researchers mostly draw consequences from experts’ visions, trends in past data and not from systematic analysis. Educational institutions must gather information about competences required in the future to start transferring them these relevant knowledge in time. This paper presents an information system dedicated to estimate the importance of actual competences in the future based on different business scenarios.
András Gábor, Ildikó Szabó, Fizar Ahmed
Process-Based Analysis of Digitally Transforming Skills
Abstract
In Industry 4.0 a lot of jobs will be replaced by machines due to the technological revolution. Digital transformation entails new skills required to possess by people. This paper presents a solution to create data warehouse to assess future job skills based on the actual industrial business processes. The solution collects time series data from job portals and transforms them into the data warehouse to analyse skill sets. The structure of the data warehouse and the algorithm of extracting data from job vacancies have been introduced.
Ildikó Szabó, Katalin Ternai

Big Data Analytics

Frontmatter
Big Data Analytics – Geolocation from the Perspective of Mobile Network Operator
Abstract
The article demonstrates the possibilities of big data analysis of geolocation data of mobile network operator in the Czech Republic. Covers theoretical background of geolocation and then presents case studies conducted in the last four years: National Park visitors’ distribution analysis, mountain ski resort usage, use of mobility data for the preparation of city territorial and development plan and use of mobility data for efficient tourism management at Vaclav Havel Airport.
Antonin Pavlicek, Petr Doucek, Richard Novák, Vlasta Strizova
Pattern Discovery from Big Data of Food Sampling Inspections Based on Extreme Learning Machine
Abstract
Food sampling programs are implemented from time to time in local areas or throughout the country in order to guarantee food safety and to improve food quality. The hidden patterns in the accumulated huge amount of data and their potential values are worthy to research. In this paper, Extreme learning machine (ELM) is employed on real data sets collected from the food safety inspections of China in recent two years, in order to mine the relationship between food quality and food category, manufacturing site and season, inspection site and season, and many other attributes. Experimental results indicate that the ELM approach has better prediction precision and generalization ability than Logistic regression that was adopted in preceding work. The patterns obtained are helpful for making more effective food sampling plans and for more targeted food safety tracing.
Yi Liu, Xin Li, Jianxin Wang, Feng Chen, Junyu Wang, Yiwei Shi, Lirong Zheng
Big Data Analytics Using SQL: Quo Vadis?
Abstract
Big Data processing and analytics are dominated by tools other than SQL based relational databases, which have lost their numero uno status. In a world deluged by data, the general perception is that SQL databases play a marginal role even for analyzing structured Big Data despite their inherent strengths in processing such data. Focusing on the most important aspect of Big Data processing, namely analytics for data mining, we examine the validity of this perception through a study of competing technologies, published results on SQL implementations of data mining algorithms, the impact of cloud platforms and the raging debate on SQL vs NoSQL vs NewSQL. Contrary to the general belief, it appears that SQL databases in their parallel, columnar deployments on cloud with UDF support do solve some, if not all, Big Data problems and are not likely to become dinosaurs in Big Data era.
K. T. Sridhar

Intelligent Electronics and Systems for Industrial IoT

Frontmatter
Rethinking ‘Things’ - Fog Layer Interplay in IoT: A Mobile Code Approach
Abstract
A client-server architecture style is one of the common approaches enabling separation of concerns in distributed systems. In the Internet of Things architecture, this approach exists in different configuration of sensors, actuators, gateways in the Fog layer and servers in the Cloud. This configuration affects the degree of interoperability, scalability and other functional and non-functional system requirements. In this paper, we reflect on best practices in the web and REST style to address IoT challenges; one of the constraints in REST, Code on Demand, is used for IoT to enhance the flexibility and interoperability of resource constrained clients at the perception layer. Scripts written in a domain specific language, DoS-IL, are organized and stored at the Fog layer for sensor and actuators nodes to request and execute the incoming script. A generic application layer protocol and RESTful server are presented along with experimental results.
Behailu Negash, Tomi Westerlund, Pasi Liljeberg, Hannu Tenhunen
A Security Framework for Fog Networks Based on Role-Based Access Control and Trust Models
Abstract
Fog networks have been introduced as a new intermediate computational layer between the cloud layer and the consumer layer in a typical cloud computing model. The fog layer takes advantage of distributed computing through tiny smart devices and access points. To enhance the performance of the fog layer we propose utilization of unused computational resources of surrounding smart devices in the fog layer. However, this will raise security concerns. To tackle this problem, we propose in this paper a novel method using a trust model and Role Based Access Control System to manage dynamically joining mobile fog nodes in a fog computing system. In our approach, the new dynamic nodes are assigned non-critical computing tasks. Their trust level is then evaluated based on the satisfaction rate of assigned tasks which is obtained through different computing parameters. As the result of this evaluation, untrusted nodes are dropped by the fog system and nodes with a higher trust level are given a new role and privileges to access and process categorized data.
Farhoud Hosseinpour, Ali Shuja Siddiqui, Juha Plosila, Hannu Tenhunen
IoT Platform for Real-Time Multichannel ECG Monitoring and Classification with Neural Networks
Abstract
Internet of Things (IoT) platforms applied to health promise to offer solutions to the challenges in healthcare systems by providing tools for lowering costs while increasing efficiency in diagnostics and treatment. Many of the works on this topic focus on explaining the concepts and interfaces between different parts of an IoT platform, including the generation of knowledge based on smart sensors gathering bio-signals from the human body which are processed by data mining and more recently, deep neural networks hosted on cloud computing infrastructure. These techniques are designed to serve as useful intelligent companions to healthcare professionals in their practice. In this work we present details about the implementation of an IoT Platform for real-time analysis and management of a network of bio-sensors and gateways, as well as the use of a cloud deep neural network architecture for the classification of ECG data into multiple cardiovascular conditions.
Jose Granados, Tomi Westerlund, Lirong Zheng, Zhuo Zou
Deep Ensemble Effectively and Efficiently for Vehicle Instance Retrieval
Abstract
This paper aims to highlight instance retrieval tasks centered around ‘vehicle’, due to its wide range of applications in surveillance scenario. Recently, image representations based on the convolutional neural network (CNN) have achieved significant success for visual recognition, including instance retrieval. However, many previous retrieval methods have not exploit the ensemble abilities of different models, which achieve limited accuracy since a certain kind of visual representation is not comprehensive. So we propose a Deep Ensemble Efficiently and Effectively (DEEE) framework, to preserve the impressive performance of deep representations and combine various deep architectures in a complementary way. It is demonstrated that a large improvement can be acquired with slight increase on computation. Finally, we evaluate the performance on two public vehicle datasets, VehicleID and VeRi, both outperforming state-of-the-art methods by a large margin.
Zhengyan Ding, Xiaoteng Zhang, Shaoxi Xu, Lei Song, Na Duan
Backmatter
Metadaten
Titel
Research and Practical Issues of Enterprise Information Systems
herausgegeben von
A Min Tjoa
Prof. Li-Rong Zheng
Zhuo Zou
Maria Raffai
Li Da Xu
Niina Maarit Novak
Copyright-Jahr
2018
Electronic ISBN
978-3-319-94845-4
Print ISBN
978-3-319-94844-7
DOI
https://doi.org/10.1007/978-3-319-94845-4

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