Interactions with big data analytics

Fisher, Danyel; DeLine, Rob; Czerwinski, Mary; Drucker, Steven M.

doi:10.1145/2168931.2168943

Cited by 282 publications

(188 citation statements)

References 8 publications

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“…Big data is hence also considered an important tool in this scenario where it can be treated as fundamental input to decision-making and competitive advantage. Fisher et al (2012) argue that many decision makers, from company executives to government agencies to researchers and scientists, would like to base their decisions and actions on information. Therefore, big data analytics as a new discipline is a workflow that distills terabytes of low-value data, transforming them, in some cases, into a single bit of high-value data.…”

Section: People Process and Technology For Managing Big Datamentioning

confidence: 99%

“…According to Fisher et al (2012), there are a number of challenges involving big data and one of them concerns the analysis that must be performed from a vast mass of data that possibly have different structures. For this relevant challenge, the authors present a pipeline that considers a five-step set, representing a data management process, to provide the best results from an analytical visualization.…”

Section: People Process and Technology For Managing Big Datamentioning

confidence: 99%

“…It has been created as the software development waterfall model. The big data pipeline proposed by Fisher et al (2012) considers the steps that are shown in Figure 2. • Acquire data, determines where data are extracted.…”

Section: People Process and Technology For Managing Big Datamentioning

confidence: 99%

“…It can be seen that several areas currently have data volumes from dozens of terabytes to multiple petabytes (thousands of terabytes). Fisher, DeLine, Czerwinski & Drucker (2012), however, consider that most often big data refer to the conception that the volume of data cannot be treated, processed and analyzed in a simplified way, requiring much more robust technologies, techniques and people with new skills for managing these large data sets.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Big Data Management

Rossi

Hirama

2015

IISIT

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Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dimension that is related to storage, analytics and visualization of big data; the human aspects of big data; and, in addition, the process management dimension that involves in a technological and business approach the aspects of big data management.

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Section: People Process and Technology For Managing Big Datamentioning

confidence: 99%

Section: People Process and Technology For Managing Big Datamentioning

confidence: 99%

Section: People Process and Technology For Managing Big Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterizing Big Data Management

Rossi

Hirama

2015

IISIT

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“…Big data analytics is a workflow that distils terabytes of low-value data (e.g., every tweet) down to, in some cases, a single bit of high-value data (e.g., should Company X acquire Company Y?) [5].…”

Section: Introductionmentioning

confidence: 99%

Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective

Lau

Yang-Turner

Karacapilidis

2014

Studies in Big Data

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Big data analytics requires technologies to efficiently process large quantities of data. Moreover, especially in decision making, it not only requires individual intellectual capabilities in the analytical activities but also collective knowledge. Very often, people with diverse expert knowledge need to work together towards a meaningful interpretation of the associated results for new insight. Thus, a big data analysis infrastructure must both support technical innovation and effectively accommodate input from multiple human experts. In this chapter, we aim to advance our understanding on the synergy between human and machine intelligence in tackling big data analysis. Sensemaking models for big data analysis were explored and used to inform the development of a generic conceptual architecture as a means to frame the requirements of such an analysis and to position the role of both technology and human in this synergetic relationship. Two contrasting real-world use case studies were undertaken to test the applicability of the proposed architecture for the development of a supporting platform for big data analysis. Reflection on this outcome has further advanced our understanding on the complexity and the potential of individual and collaborative sensemaking models for big data analytics.

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Optimal provisioning and scheduling of analytics as a service in cloud computing

Moorthy

Pabitha

2019

Trans Emerging Tel Tech

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We propose an analytic request‐centric multiobjective provisioning and scheduling method based on hybrid particle swarm optimization, called analytics‐as‐a‐service (AaaS) provisioning and resource scheduling (APARS) system, to optimally provision analytics service and schedule cloud resources by taking into account of user specified constraints. Analytics‐as‐a‐service provisioning and resource scheduling is employed to maximize accuracy of the analytic service and minimize the execution time and cost of the analytic request by combining multiobjectives with the constraint of minimal deadline and budget. Provisioning AaaS and scheduling cloud resources for analytic request is challenging as the analytic request requires scaling up and down of cloud resources dynamically. Thus, this domain invites a rigid system that can provide optimal analytics using cloud services for specified quality of service parameters. Our proposed system can handle diverse set of analytic requests with the goal of minimizing execution time, cost, critical performance, load balancing level and maximizing resource utilization, AaaS utilization, and success rate. The proposed APARS system is tested and evaluated under different use cases and compared with other metaheuristic algorithms, and we found that the results to be convincing our claims. The experimentation is carried out for various scenarios and the proposed APARS is evaluated with other metaheuristic algorithm. Simulation results validate the APARS system and show that the proposed method performs better than other existing methods with various use cases.

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Interactions with big data analytics

Cited by 282 publications

References 8 publications

Characterizing Big Data Management

Characterizing Big Data Management

Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective

Optimal provisioning and scheduling of analytics as a service in cloud computing

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