From Databases to Big Data

Madden, Sam

doi:10.1109/mic.2012.50

Cited by 459 publications

(220 citation statements)

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“…According to Russom (2011), -Volume‖ is considered the primary attribute of Big Data. Organisations are dealing with terabytes and petabytes of data and this outstrips the traditional databases (Thusoo et al,2010;Madden, 2012;Shah et al, 2012;Provost and Fawcett, 2013;Chen et al, 2014).…”

Section: Big Data Technologiesmentioning

confidence: 99%

“…-Velocity‖ allows obtaining information in real time or nearly real time or process the data more quickly that helps organisations to move faster into the business context and make them more agile (Davenport et al;2012;McAfee and Brynjolfsson, 2012).…”

Section: Big Data Technologiesmentioning

confidence: 99%

“…Strategic information is not strictly represented in database formats but can be also located in sources hard to access, i.e. e-mails, internal companies' documents, mental databases (Forrester, 1994;Madden, 2012;Lin et al;2014, Kwon et al;.…”

Section: The Big Data Integration Into Bpm Process: the Frameworkmentioning

confidence: 99%

“…Several studies on contingency theory, applied to decisionmaking, have highlighted that the quality and significance of decisions is dependent on: (i) decision-makers' knowledge and intuition (Vroom and Jago, 1974;Beach and Mitchell, 1978;Shah et al;2012); (ii) characteristics of decision task (Beach and Mitchell, 1978) and; (iii) data collection (Blackwell, 1953;Galbraith, 1974). To react to external environment changes, decision-makers have a need for selective and well-timed but also relevant and accurate information (Miller and Mork, 2013;Bertei and Marchi, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Exploring Qualitative Data: the use of Big Data technology as support in strategic decision-making

Bertei¹,

Marchi²,

Buoncristiani³

2015

IJDAR

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Abstract. This paper is a preliminary study that investigates whether the Big Data technology contributes to making the strategic decision-making more objective. Decisions depend on the decision-makers´ knowledge and intuition, the characteristics of decision task as well as the quality of the data analysed. Focusing on the decision objective component, it appears that the contemporary business context is characterised by: (i) a vast amount of data, but not all of this is meaningful; and (ii) an unexplored kind of data, namely qualitative data. The aim of the paper is twofold. Firstly, to elaborate a theoretical framework integrating Big Data technology into strategic decision-making. And secondly, assessing the feasibility of the elaborated model on a strategic variable in the hi-tech field.

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Section: Big Data Technologiesmentioning

confidence: 99%

Section: Big Data Technologiesmentioning

confidence: 99%

Section: The Big Data Integration Into Bpm Process: the Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Qualitative Data: the use of Big Data technology as support in strategic decision-making

Bertei¹,

Marchi²,

Buoncristiani³

2015

IJDAR

View full text Add to dashboard Cite

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“…On the other hand, visual query formulation, still being a considerable challenge in itself, faces inevitable scalability issues both in terms of query answering and query formulation (aka query construction), mainly introduced by the ever increasing volume, complexity, velocity, and variety of data -the so-called Big Data phenomenon (cf. [10,11]). …”

Section: Introductionmentioning

confidence: 99%

A Preliminary Approach on Ontology-Based Visual Query Formulation for Big Data

Soylu

Skjæveland

Giese

et al. 2013

Communications in Computer and Information Science

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Abstract. Data access in an enterprise setting is a determining factor for the potential of value creation processes such as sense-making, decision making, and intelligence analysis. As such, providing friendly data access tools that directly engage domain experts (i.e., end-users) with data, as opposed to the situations where database/IT experts are required to extract data from databases, could substantially increase competitiveness and profitability. However, the ever increasing volume, complexity, velocity, and variety of data, known as the Big Data phenomenon, renders the end-user data access problem even more challenging. Optique, an ongoing European project with a strong industrial perspective, aims to countervail the Big Data effect, and to enable scalable end-user data access to traditional relational databases by using an ontology-based approach. In this paper, we specifically present the preliminary design and development of our ontology-based visual query system and discuss directions for addressing the Big Data effect.

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