An incremental approach for real-time big data visual analytics

Authors: Garcia, I., Casado, R. and Bouchachia, A.

Journal: Proceedings - 2016 4th International Conference on Future Internet of Things and Cloud Workshops, W-FiCloud 2016

Pages: 177-182

DOI: 10.1109/W-FiCloud.2016.46

Abstract:

In the age of Big Data, the real-Time interactive visualization is a challenge due to latency of executing calculation over terabytes (even, petabytes) datasets. The execution of an operation has to finish before its outcome is displayed, which would be an issue in those scenarios where low-latency responses are required. To address such a requirement, this paper introduces a new approach for real-Time visualization of extremely large data-At-rest as well as data-in-motion by showing intermediate results as soon as they become available. This should allow the data analyst to take decisions in real-Time.

https://eprints.bournemouth.ac.uk/27237/

Source: Scopus

An incremental approach for real-time Big Data visual analytics

Authors: Garcia, I., Casado, R. and Bouchachia, A.

Journal: 2016 IEEE 4TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD WORKSHOPS (FICLOUDW)

Pages: 177-182

DOI: 10.1109/W-FiCloud.2016.46

https://eprints.bournemouth.ac.uk/27237/

Source: Web of Science (Lite)

An incremental approach for real-time Big Data visual analytics

Authors: García, I., Casado, R., García, V. and Bouchachia, A.

Conference: The IEEE 4th International Conference on Future Internet of Things and Cloud

Dates: 22-24 August 2016

https://eprints.bournemouth.ac.uk/27237/

Source: Manual

An incremental approach for real-time Big Data visual analytics

Authors: García, I., Casado, R., García, V. and Bouchachia, A.

Conference: IEEE 4th International Conference on Future Internet of Things and Cloud

Abstract:

In the age of Big Data, the real-time interactive visualization is a challenge due to latency of executing calculation over terabytes (even, petabytes) datasets. The execution of an operation has to finish before its outcome is displayed, which would be an issue in those scenarios where low-latency responses are required. To address such a requirement, this paper introduces a new approach for real-time visualization of extremely large data-at-rest as well as data-in-motion by showing intermediate results as soon as they become available. This should allow the data analyst to take decisions in real-time.

https://eprints.bournemouth.ac.uk/27237/

Source: BURO EPrints