Novel Insights for Railroad Maintenance Using Big Data Analytics

2018 ◽  
Author(s):  
Naji Albakay ◽  
Michael Hempel ◽  
Hamid Sharif
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Descriptive Analysis ◽  
Big Data Analytics ◽  
Association Mining ◽  
Contributing Factors ◽  
The Past ◽  
Repair Costs ◽  
Input Variables ◽  
Railroad Safety

Train accidents can be attributed to human factors, equipment factors, track factors, signaling factors, and Miscellaneous factors. Not only have these accidents caused damages to railroad infrastructure and train equipment leading to excessive maintenance and repair costs, but some of these have also resulted in injuries and loss of lives. Big Data Analytics techniques can be utilized to provide insights into possible accident causes, thus resulting in improving railroad safety and reducing overall maintenance expenses as well as spotting trends and areas of operational improvements. We propose a comprehensive Big Data approach that provides novel insights into the causes of train accidents and find patterns that led to their occurrence. The approach utilizes a combination of Big Data algorithms to analyze a wide variety of data sources available to the railroads, and is being demonstrated using the FRA train accidents/incidents database to identify factors that highly contribute to accidents occurring over the past years. The most important contributing factors are then analyzed by means of association mining analysis to find relationships between the cause of accidents and other input variables. Applying our analysis approach to FRA accident report datasets we found that railroad accidents are correlating strongly with the track type, train type, and train area of operation. We utilize the proposed approach to identify patterns that would lead to occurrence of train accidents. The results obtained using the proposed algorithm are compatible with the ones obtained from manual descriptive analysis techniques.

scholarly journals Review on Big Data & Analytics – Concepts, Philosophy, Process and Applications

2017 ◽  
Vol 17 (2) ◽  
pp. 3-27 ◽  
Author(s):  
Kari Venkatram ◽  
Mary A. Geetha
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Review Article ◽  
Cutting Edge ◽  
Competitive Edge ◽  
The Past ◽  
Past Data ◽  
State Of Art

Abstract Big Data analytics has been the main focus in all the industries today. It is not overstating that if an enterprise is not using Big Data analytics, it will be a stray and incompetent in their businesses against their Big Data enabled competitors. Big Data analytics enables business to take proactive measure and create a competitive edge in their industry by highlighting the business insights from the past data and trends. The main aim of this review article is to quickly view the cutting-edge and state of art work being done in Big Data analytics area by different industries. Since there is an overwhelming interest from many of the academicians, researchers and practitioners, this review would quickly refresh and emphasize on how Big Data analytics can be adopted with available technologies, frameworks, methods and models to exploit the value of Big Data analytics.

scholarly journals A new theoretical understanding of big data analytics capabilities in organizations: a thematic analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Renu Sabharwal ◽  
Shah Jahan Miah
Keyword(s):  
Big Data ◽  
Thematic Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Development Theory ◽  
Research Model ◽  
Theoretical Understanding ◽  
The Past ◽  
Validity Assessment

AbstractBig Data Analytics (BDA) usage in the industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision-making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in the organizational domain. Big Data has been defined in various ways and, the past literature about the classification of BDA and its capabilities is explored in this research. We conducted a literature review using PRISMA methodology and integrated a thematic analysis using NVIVO12. By adopting five steps of the PRISMA framework—70 sample articles, we generate five themes, which are informed through organization development theory, and develop a novel empirical research model, which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.

scholarly journals General Psychology as Common Ground and Point of View: Enduring and Evolving Features

2020 ◽  
Vol 24 (1) ◽  
pp. 6-17 ◽  
Author(s):  
Lisa M. Osbeck
Keyword(s):  
Big Data ◽  
Philosophy Of Science ◽  
Data Analytics ◽  
Common Ground ◽  
Big Data Analytics ◽  
Point Of View ◽  
Critical Psychology ◽  
General Psychology ◽  
The Past

The article draws from historical and contemporary resources to articulate the enduring or persistent responsibilities of general psychology, suggesting “common ground” and “point of view” as useful concepts in line with these. It then explores three important developments in the discipline over the past several decades—big data analytics, methodological proliferation, and critical psychology—and considers the role of general psychology in relation to these developments. The point of the article is to claim and illustrate that general psychology includes a philosophy of science from within, and that it has lasting importance to the broader discipline, even as the discipline itself transforms.

An Analysis of Big Data Analytics

2021 ◽  
pp. 203-230
Author(s):  
Vijander Singh ◽  
Amit Kumar Bairwa ◽  
Deepak Sinwar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Past Century ◽  
Data Generation ◽  
Microsoft Excel ◽  
Web Based ◽  
The Past ◽  
Digital World ◽  
World Information

In the development of the advanced world, information has been created each second in numerous regions like astronomy, social locales, medical fields, transportation, web-based business, logical research, horticulture, video, and sound download. As per an overview, in 60 seconds, 600+ new clients on YouTube and 7 billion queries are executed on Google. In this way, we can say that the immense measure of organized, unstructured, and semi-organized information are produced each second around the cyber world, which should be managed efficiently. Big data conveys properties such as unpredictability, 'V' factor, multivariable information, and it must be put away, recovered, and dispersed. Logical arranged data may work as information in the field of digital world. In the past century, the sources of data as to size were very limited and could be managed using pen and paper. The next generation of data generation tools include Microsoft Excel, Access, and database tools like SQL, MySQL, and DB2.

Big Data Analytics in Developing Economies

2021 ◽  
pp. 149-166
Author(s):  
Forgor Lempogo ◽  
Ezer Osei Yeboah-Boateng ◽  
William Leslie Brown-Acquaye
Keyword(s):  
Developing Countries ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Developing World ◽  
Developing Economies ◽  
Big Data Analytics ◽  
It Infrastructure ◽  
Developed Economies ◽  
The Past

In a world increasingly driven by data, most developed economies are leveraging big data to achieve greater feats in various sectors of their economies. From advertisement, commerce, healthcare, and energy to defense, big data has given new insights into the huge volume of data accumulated over the past few decades that is helping reshape our knowledge and understanding of these sectors. Unfortunately, the same cannot be said about the state of big data in the developing world, where investments in IT infrastructure are dangerously low, keeping huge proportions of the population offline. This chapter discussed the challenges that exist in developing countries, which affect the smooth take-off of big data and data science as well as recommendations as to how countries and companies in the developing world can overcome these challenges to harness the benefits and opportunities presented by this technology.

Extending The Theoretical Understanding of Big Data Analytics Capabilities In Organizations: A Thematic Analysis

2021 ◽  
Author(s):  
Renu Sabharwal ◽  
Shah Jahan Miah
Keyword(s):  
Big Data ◽  
Thematic Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Development Theory ◽  
Research Model ◽  
Theoretical Understanding ◽  
The Past ◽  
Validity Assessment

Abstract Big Data Analytics (BDA) usage in industry has been increased markedly in recent years. As a data-driven tool to facilitate informed decision making, the need for BDA capability in organizations is recognized, but few studies have communicated an understanding of BDA capabilities in a way that can enhance our theoretical knowledge of using BDA in organizational domain. Big Data has been defined in various ways and , the past literature about classification of BDA and its capabilities is explored in this research . We conduct a literature review using PRISMA methodology, and integrate a thematic analysis using NVIVO12. By adopting five steps of PRISMA framework - , 70 sample articles we generate five themes, which informed through organization development theory, and develop a novel empirical research model which we submit for validity assessment. Our findings improve effectiveness and enhance the usage of BDA applications in various Organizations.

scholarly journals Big Data Characteristics (V’s) in Industry

2021 ◽  
Vol 8 (1) ◽  
pp. 1-9
Author(s):  
Nagham Saeed ◽  
Laden Husamaldin
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Daily Basis ◽  
Current Status ◽  
Multiple Channels ◽  
Industry Sector ◽  
The Past ◽  
General Scope ◽  
Over Time

In the new digital age, Data is the collection of the observation and facts in terms of events, thus data is continuously growing, getting denser and more varied by the minute across multiple channels. Nowadays, consumers generate mass amounts of data on a daily basis. Hence, Big Data (BD) emerged and is evolving rapidly, the various types of data being processed are huge, and ensuring that this data is being used efficiently is becoming increasingly more difficult. BD has been differentiated into several characteristics (the V’s) and many researchers have been developing more characteristics for new purposes over the past years. Therefore, it is shown from observation that there is a clear gap between researchers about the current status of the BD characteristics. Even after the introduction of newer characteristics, many papers are still proposing the use of 3 or 5 V’s, while some researchers are far more progressed and has reached up to 10V’s. This paper will provide an overview of the main characteristics that have been added over time and investigate the recent growth of Big Data Analytics (BDA) characteristics in each industry sector which will provide some detailed and general scope for most researchers to consider and learn from.

Challenges of using big data for humanitarian relief: lessons from the literature

2019 ◽  
Vol 10 (4) ◽  
pp. 423-446 ◽  
Author(s):  
Pankaj Sharma ◽  
Ashutosh Joshi
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Data Analytics ◽  
Descriptive Analysis ◽  
Big Data Analytics ◽  
Humanitarian Relief ◽  
Content Type ◽  
Digital World ◽  
Disaster Relief Operations ◽  
Relief Operations

Purpose Big data analytics has emerged as one of the most used keywords in the digital world. The hype surrounding the buzz has led everyone to believe that big data analytics is the panacea for all evils. As the insights into this new field are growing and the world is discovering novel ways to apply big data, the need for caution has become increasingly important. The purpose of this paper is to conduct a literature review in the field of big data application for humanitarian relief and highlight the challenges of using big data for humanitarian relief missions. Design/methodology/approach This paper conducts a review of the literature of the application of big data in disaster relief operations. The methodology of literature review adopted in the paper was proposed by Mayring (2004) and is conducted in four steps, namely, material collection, descriptive analysis, category selection and material evaluation. Findings This paper summarizes the challenges that can affect the humanitarian logistical missions in case of over dependence on the big data tools. The paper emphasizes the need to exercise caution in applying digital humanitarianism for relief operations. Originality/value Most published research is focused on the benefits of big data describing the ways it will change the humanitarian relief horizon. This is an original paper that puts together the wisdom of the numerous published works about the negative effects of big data in humanitarian missions.

scholarly journals Strategic determinants of big data analytics in the AEC sector: a multi-perspective framework

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Sushil S. Chaurasia ◽  
Surabhi Verma
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Construction Projects ◽  
Big Data Analytics ◽  
Large Data ◽  
Service Firms ◽  
Contributing Factors ◽  
Holistic Model ◽  
Construction Firms ◽  
Aec Industry

With constant flow of large data sets generated by different organisations, big data analytics promises to be a revolutionary game changer for Architecture, Engineering and Construction (AEC) industry. Despite the potential of Big Data, there has been little research conducted thus far to understand the Big Data phenomenon, specifically in the AEC industry. The objective of this research therefore is to understand the contributing factors for adopting big data in AEC firms. The investigation combined the perceived strategic value of BDA with the TOE framework (technology, organization, and environment), to develop and test a holistic model on big data adoption. A set of hypotheses derived from the extant literature was tested on data from structured surveys of about 365 firms, categorised as construction service firms (engineering and architecture) and construction firms (firms engaged in managing construction projects). The results indicated that the inhibitors and facilitators of BDA adoption are different in the construction services (architecture and engineering) and construction firms. For effective adoption of BDA solutions, the findings will guide the business managers to have realistic expectations of BDA integration challenges in AEC sector.

Biological Big Data Analysis and Visualization

2018 ◽  
pp. 244-259 ◽  
Author(s):  
Vignesh U ◽  
Parvathi R
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Health Informatics ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Biological Data ◽  
Normal Database ◽  
Association Mining ◽  
Public Health Informatics ◽  
Complexity Measures

The chapter deals with the big data in biology. The largest collection of biological data maintenance paves the way for big data analytics and big data mining due to its inefficiency in finding noisy and voluminous data from normal database management systems. This provides the domains such as bioinformatics, image informatics, clinical informatics, public health informatics, etc. for big data analytics to achieve better results with higher efficiency and accuracy in clustering, classification and association mining. The complexity measures of the health care data leads to EHR (Evidence-based HealthcaRe) technology for maintenance. EHR includes major challenges such as patient details in structured and unstructured format, medical image data mining, genome analysis and patient communications analysis through sensors – biomarkers, etc. The big biological data have many complications in their data management and maintenance especially after completing the latest genome sequencing technology, next generation sequencing which provides large data in zettabyte size.

Sign in / Sign up

Export Citation Format

Share Document