2017 - ACM Senior Member
Sherif Sakr spends much of his time researching Cloud computing, Data science, Database, Big data and RDF query language. His Cloud computing study combines topics from a wide range of disciplines, such as Software and Provisioning. His work on Porting as part of general Software research is frequently linked to Economies of scale, thereby connecting diverse disciplines of science.
His work on Data science is being expanded to include thematically relevant topics such as Scalability. In his work, Cloud testing, NoSQL and Cloud computing security is strongly intertwined with Server, which is a subfield of Database. His Big data research includes themes of Graph database, Graph, Data processing, Exploit and Analytics.
His primary areas of study are Database, Cloud computing, Big data, Data science and Business process. The various areas that Sherif Sakr examines in his Database study include RDF Schema, RDF and Cloud database. His work in Cloud computing addresses subjects such as Distributed computing, which are connected to disciplines such as Scheduling.
His Big data research incorporates themes from Scalability, Analytics, Software engineering and SQL. His work deals with themes such as World Wide Web and Data processing, which intersect with Data science. His work on Business process management, Artifact-centric business process model, Business process modeling and Business process discovery is typically connected to Process management as part of general Business process study, connecting several disciplines of science.
Sherif Sakr mainly investigates Artificial intelligence, Big data, Machine learning, Data science and Analytics. His work carried out in the field of Artificial intelligence brings together such families of science as Dyslipidemia and Blood pressure. His Big data research includes elements of Data modeling, Scalability, SQL and Stream processing.
The study incorporates disciplines such as Software and Software engineering in addition to Scalability. While the research belongs to areas of Data science, Sherif Sakr spends his time largely on the problem of Graph, intersecting his research to questions surrounding Graph and Information retrieval. Sherif Sakr works mostly in the field of Analytics, limiting it down to topics relating to Cloud computing and, in certain cases, Distributed computing, as a part of the same area of interest.
Sherif Sakr mostly deals with Machine learning, Artificial intelligence, Data science, Big data and Scalability. His specific area of interest is Data science, where he studies Analytics. His work on NoSQL as part of his general Big data study is frequently connected to Benchmarking, thereby bridging the divide between different branches of science.
His studies in Scalability integrate themes in fields like Service, Set, Exploit, Software and Stream processing. His study on Exploit is mostly dedicated to connecting different topics, such as Cloud computing. His Software research incorporates elements of Domain and Database.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A Survey of Large Scale Data Management Approaches in Cloud Environments
S. Sakr;A. Liu;D. M. Batista;M. Alomari.
IEEE Communications Surveys and Tutorials (2011)
The family of mapreduce and large-scale data processing systems
Sherif Sakr;Anna Liu;Ayman G. Fayoumi.
ACM Computing Surveys (2013)
Graph query processing using plurality of engines
Sameh Elnikety;Yuxiong He;Sherif Sakr.
XQuery on SQL hosts
Torsten Grust;Sherif Sakr;Jens Teubner.
very large data bases (2004)
Relational processing of RDF queries: a survey
Sherif Sakr;Ghazi Al-Naymat.
international conference on management of data (2010)
Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project
Manal Alghamdi;Mouaz H Al-Mallah;Mouaz H Al-Mallah;Steven J Keteyian;Clinton Brawner.
PLOS ONE (2017)
XML compression techniques: A survey and comparison
Journal of Computer and System Sciences (2009)
Towards a Comprehensive Data Analytics Framework for Smart Healthcare Services
Sherif Sakr;Amal Elgammal.
Big Data Research (2016)
DREAM: distributed RDF engine with adaptive query planner and minimal communication
Mohammad Hammoud;Dania Abed Rabbou;Reza Nouri;Seyed-Mehdi-Reza Beheshti.
very large data bases (2015)
On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure
Basem Suleiman;Basem Suleiman;Sherif Sakr;Sherif Sakr;D. Ross Jeffery;D. Ross Jeffery;Anna Liu;Anna Liu.
Journal of Internet Services and Applications (2012)
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