• 129 Citations
  • 6 h-Index
20082018
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Fingerprint Dive into the research topics where Radwa El Shawi is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Processing Engineering & Materials Science
Anisotropic media Engineering & Materials Science
Anisotropic Media Mathematics
Industry Engineering & Materials Science
Data structures Engineering & Materials Science
Exercise Medicine & Life Sciences
Path Mathematics
Monitoring Engineering & Materials Science

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Research Output 2008 2018

  • 129 Citations
  • 6 h-Index
  • 15 Article
  • 8 Conference contribution
  • 1 Conference article

Correction to: Runtime self-monitoring approach of business process compliance in cloud environments

Barnawi, A., Awad, A., Elgammal, A., Shawi, R. E., Almalaise, A. & Sakr, S., 28 Feb 2018, (Accepted/In press) In : Cluster Computing. 1 p.

Research output: Contribution to journalArticleResearchpeer-review

Monitoring
Industry
Compliance

Correction To: Large scale graph processing systems: survey and an experimental evaluation

Batarfi, O., El Shawi, R., Fayoumi, A. G., Nouri, R., Beheshti, S. M. R., Barnawi, A. & Sakr, S., 28 Feb 2018, (Accepted/In press) In : Cluster Computing. 1 p.

Research output: Contribution to journalArticleResearchpeer-review

Processing

Declarative stack for distributed graph processing

Elshawi, R., Aldhabaan, A. & Sakr, S., 28 Feb 2018, In : Journal of Theoretical and Applied Information Technology. 96, 4, p. 1083-1090 8 p.

Research output: Contribution to journalArticleResearchpeer-review

Graph in graph theory
Processing
Social sciences
Astronomy
Computational Biology

A framework of improving business prcoess execution using social artif acts

Al-Barakati, A., Barnawi, A., Bajaber, F., Almalais, A., Buhari, S. & El Shawi, R., 15 Aug 2017, In : Journal of Theoretical and Applied Information Technology. 95, 15, p. 3523-3533 11 p.

Research output: Contribution to journalArticleResearchpeer-review

Business Process
Navigation
Person
Industry
Web 2.0
5 Citations (Scopus)

Comparison of machine learning techniques to predict all-cause mortality using fitness data: The Henry Ford exercIse testing (FIT) project

Sakr, S., Elshawi, R., Ahmed, A. M., Qureshi, W. T., Brawner, C. A., Keteyian, S. J., Blaha, M. J. & Al-Mallah, M. H., 19 Dec 2017, In : BMC Medical Informatics and Decision Making. 17, 1, 174.

Research output: Contribution to journalArticleResearchpeer-review

Exercise
Mortality
Decision Trees
Health
Area Under Curve