Adopting user interacted mobile node data to the Flexible Data Input Layer Architecture

Tavakoli, Siamak; Mousavi, Amir

doi:10.1109/issnip.2008.4762044

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…They concluded that an efficient big data infrastructure is a balancing speed of development, ease of analysis, flexibility and scalability. Tavakoli and Mousavi (2008) demonstrated their cloud infrastructure for scientific analysis. Using Hadoop map-reduce, they classified the scientific algorithms according to how easy they could be adapted to map-reduce.…”

Section: Big Data Transport Systemsmentioning

confidence: 99%

Cloud big data application for transport

Kemp

Vargas-Solar

Silva

et al. 2016

IJASM

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International audienceThis paper presents a cloud service oriented approach for managing and analysing big data required by transport applications. Big data analyticsbrings new insights and useful correlations of large data collections providing undiscovered knowledge. Applying it to transport systems brings betterunderstanding to the transport networks revealing unexpected choking points in cities. This facility is still largely inaccessible to small companies due to their limited access to computational resources. A cloud-oriented architecture opens new perspectives for providing efficient and personalised big data management and analytics services to (small) companies

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Section: Big Data Transport Systemsmentioning

confidence: 99%

Cloud big data application for transport

Kemp

Vargas-Solar

Silva

et al. 2016

IJASM

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“…Proposing a big data infrastructure on the cloud will make developing big data infrastructures more accessible to small businesses for several reasons: little initial investment, ease of development through Service-Oriented Architecture (SOA) and using services developed by specialist of each service. Satish Narayana Srirama and co [16] demonstrated their cloud infrastructure for scientific analysis. Using Hadoop mapreduce, they classified the scientific algorithms according to how easy they could be adapted to mapreduce.…”

Section: 2mentioning

confidence: 99%

Service Oriented Big Data Management for Transport

Kemp

Vargas-Solar

Silva

et al. 2015

Communications in Computer and Information Science

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International audienceThe increasing power of computer hardware and the sophistication of computer software have brought many new possibilities to information world. On one side the possibility to analyse massive data sets has brought new insight, knowledge and information. On the other, it has enabled to massively distribute computing and has opened to a new programming paradigm called Service Oriented Computing particularly well adapted to cloud computing. Applying these new technologies to the transport industry can bring new understanding to town transport infrastructures. The objective of our work is to manage and aggregate cloud services for managing big data and assist decision making for transport systems. Thus this paper presents our approach to propose a service oriented architecture for big data analytics for transport systems based on the cloud. Proposing big data management strategies for data produced by transport infra‐ structures, whilst maintaining cost effective systems deployed on the cloud, is a promising approach. We present the advancement for developing the Data acquisition service and Information extraction and cleaning service as well as the analysis for choosing a sharding strategy

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Adopting user interacted mobile node data to the Flexible Data Input Layer Architecture

Cited by 2 publications

References 14 publications

Cloud big data application for transport

Cloud big data application for transport

Service Oriented Big Data Management for Transport

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