Big data analysis using Apache Hadoop

Nandimath, Jyoti; Banerjee, Ekata; Patil, Ankur T.; Kakade, Pratima; Vaidya, Saumitra; Chaturvedi, Divyansh

doi:10.1109/iri.2013.6642536

Cited by 78 publications

(38 citation statements)

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“…Furthermore, it is a growing trend to carry out machine learning combined with the technology of big data. Since PULSim is developed in Java, it can be naturally integrated with Apache Hadoop [33], which is a Java-based software framework for managing and processing large amounts of data. The efficiency and the effectiveness of railway planning and operations will be continuously improved through integrating cutting-edge technologies with PULSim.…”

Section: Discussionmentioning

confidence: 99%

PULSim: User-Based Adaptable Simulation Tool for Railway Planning and Operations

Cui

Martin

Liang

2018

Journal of Advanced Transportation

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Simulation methods are widely used in the field of railway planning and operations. Currently, several commercial software tools are available that not only provide functionality for railway simulation but also enable further evaluation and optimisation of the network for scheduling, dispatching, and capacity research. However, the various tools are all lacking with respect to the standards they utilise as well as their published interfaces. For an end-user, the basic mechanism and the assumptions built into a simulation tool are unknown, which means that the true potential of these software tools is limited. One of the most critical issues is the lack of the ability of users to define a sophisticated workflow, integrated in several rounds of simulation with adjustable parameters and settings. This paper develops and describes a user-based, customisable platform. As the preconditions of the platform, the design aspects for modelling the components of a railway system and building the workflow of railway simulation are elaborated in detail. Based on the model and the workflow, an integrated simulation platform with open interfaces is developed. Users and researchers gain the ability to rapidly develop their own algorithms, supported by the tailored simulation process in a flexible manner. The productivity of using simulation tools for further evaluation and optimisation will be significantly improved through the useradaptable open interfaces.

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Section: Discussionmentioning

confidence: 99%

PULSim: User-Based Adaptable Simulation Tool for Railway Planning and Operations

Cui

Martin

Liang

2018

Journal of Advanced Transportation

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“…After processing, the segments are combined into a single result using the reducer function. The algorithm was initially implemented by Google to solve PageRank processing [32], but the most referenced implementation is Apache Hadoop [26] [25].…”

Section: Hadoop and Mapreducementioning

confidence: 99%

Data Warehouse and Big Data Integration

Salinas¹,

Lemus²

2017

IJCSIT

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“…In addition, the medical data contains a lot of unstructured and semi-structured data, which produces problems to traditional relational database, but Hadoop can well deal with this difficulty. However, Hadoop is not designed for replacing the existing relational database systems [4] .Hadoop and relational databases have both advantages and disadvantages in processing massive data, regarding to the situation and problems of its own. It is easier to research and analyze complex data using Hadoop, but relational database is good at dealing with intensive computing tasks.…”

Section: Infrastructure Layer Of Medical Big Datamentioning

confidence: 99%

Healthcare Information System Architecture Design Based on Big Data

Zhang¹

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract:The rapid development of medical information leads to the exponential increase of medical data. Medical big data has brought huge pressure to the existing hospital information systems. With the emerging of various kinds of unstructured data, the existing medical information system fails to meet the requirements of large data in storage space, storage speed, and storage structure, so it has to partially discard data, resulting in the loss of valuable medical data. However, the data integrity of the existing system is not enough, and the data processing speed is slow, hence it is obviously unable to meet the needs of users on data processing efficiency. Based on this, this paper puts forward the structure of intelligent medical system for large data, and analyzes the infrastructure layer, the information processing layer and the service provider layer of the three aspects.

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Big data analysis using Apache Hadoop

Cited by 78 publications

References 0 publications

PULSim: User-Based Adaptable Simulation Tool for Railway Planning and Operations

PULSim: User-Based Adaptable Simulation Tool for Railway Planning and Operations

Data Warehouse and Big Data Integration

Healthcare Information System Architecture Design Based on Big Data

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