Big data with cognitive computing: A review for the future

Gupta, Shivam; Kar, Arpan Kumar; Baabdullah, Abdullah M.; Alkhowaiter, Wassan Abdullah

doi:10.1016/j.ijinfomgt.2018.06.005

Cited by 227 publications

(97 citation statements)

References 58 publications

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“…It can be argued that it is Big Data that has empowered AI for its current boom and the domain of cognitive computing will be incomplete without harnessing the benefits of Big Data analytics (Gupta, Kar, Baabdullah, & Al-Khowaiter, 2018). The Big Data era has added types of data that were not previously used in analysis, such as that from social media (Martínez-Rojas, Pardo-Ferreira, & Rubio-Romero, 2018;Ragini et al, 2018).…”

Section: Understanding the Synergy Of Ai And Big Datamentioning

confidence: 99%

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Duan

Edwards

Dwivedi

2019

International Journal of Information Management

1,181

554

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Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.

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Section: Understanding the Synergy Of Ai And Big Datamentioning

confidence: 99%

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Duan

Edwards

Dwivedi

2019

International Journal of Information Management

1,181

554

View full text Add to dashboard Cite

show abstract

“…Going deep in technical details, algorithms and methods (e.g., representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning) is out of the scope of this paper, however the interested readers can find a survey of how machine learning is used for big data processing in [12]. Other references can be found in [13], which showcases a large understanding of past, present and future directions in this domain, made through a mapping of the characteristics of cognitive computing, i.e., observation, interpretation, evaluation and decision, versus the so-called V's of big data, i.e., Volume, Variety, Veracity, Velocity and Value.…”

Section: The Impact Of Big Data On E-learningmentioning

confidence: 99%

Collaborative E-learning Environments with Cognitive Computing and Big Data

Coccoli¹,

Maresca²,

Molinari³

2019

JVLC

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The actual scenario of e-learning environments and techniques is fast-changing from both the technology side and the users' perspective. In this vein, applications and services as well as methodologies are evolving rapidly, running after the more recent innovations and thus adopting distributed cloud architectures to provide the most advanced solutions. In this situation, two influential technological factors emerge: the former is cognitive computing, which can provide learners and teachers with innovative services enhancing the whole learning process, also introducing improvements in human-machine interactions; the latter is a new wave of big data derived from heterogeneous sources, which impacts on educational tasks and acts as enabler for the development of new analytics-based models, for both management activities and education tasks. Concurrently, from the side of learning techniques, these phenomena are revamping collaborative models so that we should talk about communities rather than classrooms. In these circumstances, it seems that current Learning Management Systems (LMS) may need a redesign. In this respect, the paper outlines the evolutionary trends of Technology-Enhanced Learning (TEL) environments and presents the results achieved within two experiences carried on in two Italian universities.

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“…Going deep in technical details, algorithms and methods (e.g., representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning) is out of the scope of this paper, however the interested readers can find a survey of how machine learning is used for big data processing in a paper by Qiu et al [12]. Other references can be found in [13], where Gupta et al present a large understanding of past, present and future directions in this domain, made through a mapping of the characteristics of cognitive computing, i.e., observation, interpretation, evaluation and decision, versus the so-called V's of big data, i.e., Volume, Variety, Veracity, Velocity and Value.…”

Section: A the Impact Of Big Data On E-learningmentioning

confidence: 99%

When E-Learning Meets Big Data, Cognitive Computing, and Collaborative Environments

Coccoli¹,

Maresca²,

Molinari³

2019

International Conferences on Distributed Multimedia Systems

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The e-learning scenario is fast-changing in all of its main aspects from both the technology side and the users' perspective. In this vein, applications and services as well as methodologies are evolving rapidly running after the more recent innovations, through the adoption of novel infrastructures and distributed architectures, to provide the most advanced solutions. In this situation, we observe that the more influential technological factors are two. The former is the availability of a new wave of big data also inherent educational tasks, which can be considered a powerful source for all the people involved in the educational process and acts as enabler for the development of new models for both management and education, based on analytics. The latter is cognitive computing, which can provide learners and teachers with innovative services enhancing the whole learning process, also introducing new capabilities to improve human machine interactions. In these circumstances, it seems that Learning Management Systems (LMS) have reached their maturity stage and they may need a redesign. In this respect, the paper outlines the evolutionary trends of Technology Enhanced Learning (TEL) and presents the results achieved within two experiences carried on in two Italian universities.

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Big data with cognitive computing: A review for the future

Cited by 227 publications

References 58 publications

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

Collaborative E-learning Environments with Cognitive Computing and Big Data

When E-Learning Meets Big Data, Cognitive Computing, and Collaborative Environments

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