Big Data Risk Analysis – linking wider business and safety information systems for improved safety management

2016 ◽  
Vol 36 (3) ◽  
pp. 131-133
Author(s):  
Coen van Gulijk ◽  
Colin Dennis
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Risk Analysis ◽  
Safety Management ◽  
Safety Information

scholarly journals BIG DATA FOR RISK ANALYSIS: THE FUTURE OF SAFE RAILWAYS

2016 ◽  
Author(s):  
Miguel Figueres Esteban
Keyword(s):  
Big Data ◽  
Risk Analysis ◽  
Safety Management ◽  
New Technology ◽  
Data Sources ◽  
Transport Systems ◽  
Text Documents ◽  
Railway Systems ◽  
Wide Range ◽  
Three Components

New technology brings ever more data to support decision-making for intelligent transport systems. Big Data is no longer a futuristic challenge, it is happening right now: modern railway systems have countless sources of data providing a massive quantity of diverse information on every aspect of operations such as train position and speed, brake applications, passenger numbers, status of the signaling system or reported incidents.The traditional approaches to safety management on the railways have relied on static data sources to populate traditional safety tools such as bow-tie models and fault trees. The Big Data Risk Analysis (BDRA) program for Railways at the University of Huddersfield is investigating how the many Big Data sources from the railway can be combined in a meaningful way to provide a better understanding about the GB railway systems and the environment within which they operate.Moving to BDRA is not simply a matter of scaling-up existing analysis techniques. BDRA has to coordinate and combine a wide range of sources with different types of data and accuracy, and that is not straight-forward. BDRA is structured around three components: data, ontology and visualisation. Each of these components is critical to support the overall framework. This paper describes how these three components are used to get safety knowledge from two data sources by means of ontologies from text documents. This is a part of the ongoing BDRA research that is looking at integrating many large and varied data sources to support railway safety and decision-makers.DOI: http://dx.doi.org/10.4995/CIT2016.2016.1825

The Correlation Analysis of the Big Data for Pipeline Defect

2017 ◽  
Author(s):  
Hewei Zhang ◽  
Shaohua Dong ◽  
Laibin Zhang
Keyword(s):  
Big Data ◽  
Correlation Analysis ◽  
Oil And Gas ◽  
Safety Management ◽  
Safety Information ◽  
Analysis Model ◽  
Long Distance ◽  
Correlation Relationship ◽  
Operation Pressure ◽  
The Impact

With the increasing of pipe diameter and operation pressure, the severity of the accident consequences has been increased, especially for the impact on the high consequence area. The safety of oil and gas pipeline is very important. At the same time, a lot of data were produced during the process and the amount of detection data signal has also reached the TB level. However, because the relationship between these data sets has not been established, most part of the “Big Data” in which the safety information of pipeline hidden was ignored and discarded. In order to effectively use the relevant pipeline data of the defects, the mutual information method was adopted to establish a correlation analysis model. Its main purpose was extracting all the factors that lead to pipeline defects from the “Big Data” and determined the crucial factors from them. A pipe segment on a long-distance pipeline with the length of 100km was taken as a case. Based on the correlation analysis model, the crucial factors which had great correlation relationship with pipeline defect were extracted, so as to provide reference to accident prevention. It is a new way of pipeline safety management.

Big Data Based Design of Food Safety Cloud Platform

2014 ◽  
Vol 536-537 ◽  
pp. 583-587 ◽  
Author(s):  
Kai Chen ◽  
Hong Tan ◽  
Jie Gao ◽  
Yang Lu
Keyword(s):  
Big Data ◽  
Food Safety ◽  
Management System ◽  
Safety Management ◽  
Safety Issue ◽  
Safety Information ◽  
Information Service ◽  
Guizhou Province ◽  
Cloud Platform ◽  
Inspection Data

Food safety has been in the spotlight of the global attention. In 2013, Chinas food safety supervision policy was undertaken a major reform. China Food and Drug Administration (CFDA) takes over the responsibility of food safety management, which in the past was conducted by different government sectors. Under this new management system, how to carry out the practical work has become a new issue. This paper introduces an innovative food safety management mode adopted by Guizhou province. On the basis of latest information technology, food production enterprises, government, testing organizations and consumers are integrated into a unified food safety information service cloud platform. The core technology of cloud platform is composed of food safety knowledge system, testing management system, food safety information publicity system as well as mobile application. The food factory inspection data, government inspection data, testing organizations testing data and consumers purchasing information are integrated into food safety and nutrient test big data. Utilizing the data to explore the information that is needed by all the parties, this paper tries to provide a solution to the risk exchange problem faced by Chinas food safety issue. At the same time, food safety problem can be solved through the contribution of different stakeholders.

scholarly journals The Concept of Safety Management in the Electromobility Development Strategy

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2482
Author(s):  
Katarzyna Chruzik ◽  
Marzena Graboń-Chałupczak
Keyword(s):  
Development Strategy ◽  
Safety Management ◽  
Service Providers ◽  
Safety Information ◽  
Road Transport ◽  
The European Union ◽  
Safety Standards ◽  
System A ◽  
Management Processes ◽  
Market Needs

Safety monitoring provides the detection of changes in systems or operations that may suggest any case of approaching a point close to exceeding the acceptable safety standards and indicates whether corrective/prevention actions have been taken. Safety information should be maintained within the scope of transport undertakings to ensure safety and be communicated to all responsible staff, depending on each person’s function in the processes. Regulatory authorities should continuously monitor the implementation of safety management processes and the processes performed by road transport service providers. Safety management, therefore, requires investment in development and modernisation to meet market needs resulting from the mobility of residents, the growth of transport, and the obligations of countries resulting from the transport and environmental policy pursued by the European Union. Along with changes in the transport system, a need to assess their significance for the transport system’s safety arises. Depending on the transport mode (rail, air, water, road), the scope of standardised requirements is quite different each time. The paper analyses the legal requirements and acceptable practices for assessing the significance of the change in all transport modes and develops a standard method for assessing the significance of the change that meets all the requirements of electromobility safety management systems.

An introduction to the industrial town’s safety management (ITSM) system

Work ◽  
2021 ◽  
pp. 1-10
Author(s):  
Hossein Ebrahimi ◽  
Seyedeh Melika Kharghani Moghadam
Keyword(s):  
Management System ◽  
Delphi Technique ◽  
Safety Management ◽  
Safety Information ◽  
Safety Training ◽  
Safety Level ◽  
Cross Sectional ◽  
Safety Management System ◽  
Industrial Town ◽  
System Objective

BACKGROUND: In industrial towns, the dangers of each industry also poses a threat to other industries due to the proximity of different industries to each other. So there is a need for a safety management system. OBJECTIVE: This study was conducted to introduce a management system for managing the safety of industrial towns. METHODOLOGY: This cross-sectional and qualitative study was conducted in three main phases: (1) Identify the elements of the safety management systems using literature review, (2) Screening and determining useful elements using Delphi technique and (3) Determining the structure of safety management system. RESULTS: Participation of the industries and their compliance with the standards were considered as the system foundation. The networks of safety information of the industries, accident’s database, safety training, contractors, emergency management and management of the changes were placed on the foundation as the system columns. The Industrial Town’s Safety Management (ITSM) system as the system roof was placed on the columns. This structure was placed within a two-line framework including the trade secrets and program audit. CONCLUSIONS: The ITSM system consists of a set of factors that can help manage the safety of the industrial towns. This system will increase the safety level of industrial towns by incorporating some safety principles. However, the safety management of an industrial town is very complex and requires a great deal of efforts.

scholarly journals Hydrological Layered Dialysis Research on Supply Chain Financial Risk Prediction under Big Data Scenario

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Jia Liu ◽  
Shiyong Li ◽  
Xiaoxia Zhu
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Risk Analysis ◽  
Risk Prediction ◽  
Financial Institutions ◽  
Surface Runoff ◽  
Financial Risk ◽  
Supply Chain Finance ◽  
Runoff Model

In recent years, internet development provides new channels and opportunities for small- and middle-sized enterprises’ (SMEs) financing. Supply chain finance is a hot topic in theoretical and practical circles. Financial institutions transform materialized capital flows into online data under big data scenario, which provides networked, precise, and computerized financial services for SMEs in the supply chain. By drawing on the risk management theory in economics and the distributed hydrological model in hydrology, this paper presents a supply chain financial risk prediction method under big data. First, we build a “hydrological database” used for the risk analysis of supply chain financing under big data. Second, we construct the risk identification models of “water circle model,” “surface runoff model,” and “underground runoff model” and carry on the risk prediction from the overall level (water circle). Finally, we launch the supply chain financial risk analysis from breadth level (surface runoff) and depth level (underground runoff); moreover, we integrate the analysis results and make financial decisions. The results can enrich the research on risk management of supply chain finance and provide feasible and effective risk prediction methods and suggestions for financial institutions.

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