A Survey of Big Data Analytics

Honest, Nirali

doi:10.5121/ijist.2016.6204

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…At this stage, big data analytics may be divided as descriptive, diagnostic, predictive and prescriptive analytics with the purpose of application from information learning to insight and further to foresight (optimization) of the process. Technologies utilized for the querying and analysis include data mining, clustering, knowledge discovery, machine learning, MapReduce, Massively Parallel Processing (MPP), Multi-Dimensional On-Line Analytical Processing (MOLAP), visual analytics and statistical models [122]- [125]. Table 1 provides an exemplar of big data analytics technologies for reference.…”

Section: B Big Data Analytics Technologiesmentioning

confidence: 99%

Requirements for Big Data Adoption for Railway Asset Management

2020

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Nowadays, huge amounts of data have been captured along with the day-today operation of assets including railway systems. Hence, we have come to the era of big data. The utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. The vital information such as precursor information collected on failure modes and knowledge that may be available for analysis is hidden within the large extent of data. There are analysis tools incorporated with techniques such as multiple regression analysis and machine learning that are facilitated by the availability of big data. Therefore, the utilization of big data technologies for asset condition information management is becoming indispensable for improving asset management decision making. This paper provides a review of the requirements and challenges for big data analytics applications to railway asset management. The review focuses on railway asset data collection, data management, data applications with the implementation of Blockchain technology as well as big data analytics technologies. The need for, and the importance of big data analytics in railway asset management; and the requirement for the asset condition data collection in the railway industry are highlighted. Research challenges in railway asset management via application of big data analytics are identified and the future research directions are presented. INDEX TERMS Big data analytics, asset management, railway, data management, blockchain.

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Section: B Big Data Analytics Technologiesmentioning

confidence: 99%

Requirements for Big Data Adoption for Railway Asset Management

2020

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“…First was simple computational complexity, as the required computations grow as a higher order polynomial function of n for agglomerative CA and an exponential function of n for divisive CA (Rokach and Maimon ). This is possible to overcome with approximation methods or through investments in parallel computing methods such as MapReduce, which partitions data and automates distributed computation (Honest and Patel ). However, accuracy reductions from approximation methods may drive spurious cluster formations, and costly investments in parallel methods may still face limitations with some problem classes (see Greenlaw et al.…”

Section: Big Data Analysismentioning

confidence: 99%

Thirsty in an Ocean of Data? Pitfalls and Practical Strategies When Partnering With Industry on Big Data Supply Chain Research

Smyth

Croxton

Franklin

et al. 2018

J of Business Logistics

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I ncreased volume, velocity, and variety of data provides new opportunities for businesses to take advantage of data science techniques, predictive analytics, and big data. However, firms are struggling to make use of their disjointed and unintegrated data streams. Despite this, academics with the analytic tools and training to pursue such research often face difficulty gaining access to corporate data. We explore the divergent goals of practitioners and academics and how the gap that exists between the communities can be overcome to derive mutual value from big data. We describe a practical roadmap for collaboration between academics and practitioners pursuing big data research. Then we detail a case example of how, by following this roadmap, researchers can provide insight to a firm on a specific supply chain problem while developing a replicable template for effective analysis of big data. In our case study, we demonstrate the value of effectively pairing management theory with big data exploration, describe unique challenges involved in big data research, and develop a novel and replicable hierarchical regression-based process for analyzing big data.

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“…In [6] Nirali Honest and Atul Patel, 2016 described the limitation of traditional approach to manage the data and the components that are useful in handling big data and used Hadoop framework with the major components of the framework and working process within the framework. In [7] O. Liu and K.L. Man et.al, 2016 discussed the mechanism of preferential attachment during network evolution, which is considered one of the key factors in the formation of scale-free networks and tested the effectiveness of this model by a simulation using data of a real-world Chinese social network.…”

Section: Reviewmentioning

confidence: 99%

Secured File Storage System In Big Data With Cloud Access Using Security Algorithms

Prathibha¹,

Anitha²,

Ramyabharathi³

2018

IJTSRD

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Big data is a technology to huge data sets, have high Velocity, high Volume and high Variety and complex structure with the difficulties of analyzing, storing and processing. The paper focuses on extraction of data efficiently in big data and how to manage the data and the components that are useful in handling big data. Security in the era of big data and especially to the problem of reconciling security and privacy models by exploiting the map reduce framework. Data can be classified as public, confidential and sensitive This paper proposes the big data applications with the Hadoop Distributed Framework for storing huge data in cloud in a highly efficient manner In order to avoid the third party issues and produce the exact data to the user by implementing the encryption and decryption approach using SHA 512 algorithms to avoid the security issues in big data.

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A Survey of Big Data Analytics

Cited by 16 publications

References 8 publications

Requirements for Big Data Adoption for Railway Asset Management

Requirements for Big Data Adoption for Railway Asset Management

Thirsty in an Ocean of Data? Pitfalls and Practical Strategies When Partnering With Industry on Big Data Supply Chain Research

Secured File Storage System In Big Data With Cloud Access Using Security Algorithms

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