State of Big Data Analysis in the Cloud

Ahuja, Sanjay P.; Moore, Bryan

doi:10.5539/nct.v2n1p62

Cited by 22 publications

(11 citation statements)

References 16 publications

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“…The analysis relies on being sensitive to practical challenges (often domain-specific issues) and opportunities engineers may face when dealing with Big Data as a problem. Just as in other fields and industries, Big Data intervene and disrupt simply by posing challenges and opportunities for computer science and engineering, for example in terms of ‘volume, velocity, and variety’ (Beyer, 2011) – for ‘Big Data is [ sic ] data that either is too large, grows too fast, or does not fit into traditional architectures’ (Ahuja and Moore, 2013: 62, emphasis added; Krishnan, 2013) – including for companies like Facebook, Google, Twitter, and LinkedIn. Indeed, processing huge quantities of transactions – much of it ‘just’ moving data entities documenting user operations like posts, likes, or shares around – can involve significant financial costs and other valuable and limited resources.…”

Section: Engineering Big Data At Facebookmentioning

confidence: 99%

Accounting for the social: Investigating commensuration and Big Data practices at Facebook

Vlist

2016

Big Data & Society

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This study explores Big Data practices at Facebook through an investigation of the role of commensuration or 'the transformation of different qualities into a common metric' in the structuration of analysis and interaction with a major online social media platform. It proposes a conceptual framework and demonstrates the empirical potential of a pragmatic approach based on reading published materials and available documentation. Facebook's Data Warehousing and Analytics Infrastructure serves as an illustrative example to begin tracing out and describe data assemblages in more detail. In being attentive to the motivations, drivers and challenges engineers face when dealing with Big Data, it is argued that their solutions can enable and support but also constrain specific analytical and transactional capabilities or data flows between various devices and actors. The analysis thus moves beyond methodological critiques of the utility of Big Data that lack empirical support and specificity. It is further argued that analytics not just describe but also actively participate in the enactment of social worlds, thereby opening possibilities for new markets or market segments to arise. Online sociality accounts for a model of the social that makes it visible and measurable qua markets inviting data recontextualisation and the creation of value along multiple axes. Contra Facebook's claim to make the web more 'social', an investigation of commensuration brings to the fore the question how the social is accounted for in the first place.

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Section: Engineering Big Data At Facebookmentioning

confidence: 99%

Accounting for the social: Investigating commensuration and Big Data practices at Facebook

Vlist

2016

Big Data & Society

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“…The ways in which cloud computing responds to these needs for data collection, storage and management was discussed by Ahuja et al [14]. The availability of abundant data sources from intelligent devices and smart homes, the rapid progress made in IoT, and Big data technology, make it possible to apply appropriate algorithms to enable intelligent decisions in driving smart city activities.…”

Section: Introductionmentioning

confidence: 99%

Hadoop Oriented Smart Cities Architecture

Dıaconıța

Bologa

2018

Sensors

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A smart city implies a consistent use of technology for the benefit of the community. As the city develops over time, components and subsystems such as smart grids, smart water management, smart traffic and transportation systems, smart waste management systems, smart security systems, or e-governance are added. These components ingest and generate a multitude of structured, semi-structured or unstructured data that may be processed using a variety of algorithms in batches, micro batches or in real-time. The ICT architecture must be able to handle the increased storage and processing needs. When vertical scaling is no longer a viable solution, Hadoop can offer efficient linear horizontal scaling, solving storage, processing, and data analyses problems in many ways. This enables architects and developers to choose a stack according to their needs and skill-levels. In this paper, we propose a Hadoop-based architectural stack that can provide the ICT backbone for efficiently managing a smart city. On the one hand, Hadoop, together with Spark and the plethora of NoSQL databases and accompanying Apache projects, is a mature ecosystem. This is one of the reasons why it is an attractive option for a Smart City architecture. On the other hand, it is also very dynamic; things can change very quickly, and many new frameworks, products and options continue to emerge as others decline. To construct an optimized, modern architecture, we discuss and compare various products and engines based on a process that takes into consideration how the products perform and scale, as well as the reusability of the code, innovations, features, and support and interest in online communities.

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“…Increasingly, this type of interaction also links to the business objective since the interaction and the data it generates are becoming the assets for companies such as LinkedIn and Facebook. The big data generated on their platforms are used to offer their users a variety of BDA-based services [47].…”

Section: The Servicementioning

confidence: 99%