2017 - ACM Distinguished Member
2013 - ACM Senior Member
His scientific interests lie mostly in Data mining, Scalability, Location-based service, Database and Database server. Mohamed F. Mokbel combines subjects such as Spatial analysis, Search engine indexing, High-level programming language and Taxonomy with his study of Data mining. The Scalability study combines topics in areas such as Database storage structures, Spatial database, Set, Temporal database and Big data.
His Set research integrates issues from Real-time computing and Theoretical computer science. His studies deal with areas such as Computer security and Collaborative filtering, Recommender system, World Wide Web as well as Location-based service. His Database study combines topics from a wide range of disciplines, such as Cloud computing, Undo and Anonymity.
Mohamed F. Mokbel spends much of his time researching Data mining, Database, Scalability, World Wide Web and Search engine indexing. The concepts of his Data mining study are interwoven with issues in Grid, Overhead and Set. The Database study combines topics in areas such as Information retrieval and Preference.
His studies in Scalability integrate themes in fields like Database server, Distributed computing, Location-based service, Temporal database and Server. In the field of World Wide Web, his study on Recommender system, Social media and Microblogging overlaps with subjects such as Popularity. His Search engine indexing research focuses on subjects like Visualization, which are linked to User interface.
The scientist’s investigation covers issues in Data mining, Big data, Search engine indexing, Database and Spatial analysis. His study in Data mining is interdisciplinary in nature, drawing from both Probabilistic logic, Scalability, Process and Set. His Big data study also includes fields such as
His research integrates issues of Visualization, Temporal database and Key in his study of Search engine indexing. He is involved in the study of Database that focuses on SQL in particular. His Spatial analysis research is multidisciplinary, incorporating elements of Range query, Data structure, Data science and Markov chain.
His primary scientific interests are in Data mining, Spatial analysis, Search engine indexing, Distributed File System and Database. His Data mining study frequently links to related topics such as Set. To a larger extent, Mohamed F. Mokbel studies World Wide Web with the aim of understanding Search engine indexing.
Mohamed F. Mokbel has included themes like Information retrieval and Database design in his World Wide Web study. The study incorporates disciplines such as Space partitioning, Block, Spatial database, Temporal database and Key in addition to Distributed File System. His work on Database engine as part of his general Database study is frequently connected to Database search engine, thereby bridging the divide between different branches of science.
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.
The new Casper: query processing for location services without compromising privacy
Mohamed F. Mokbel;Chi-Yin Chow;Walid G. Aref.
very large data bases (2006)
Location-based and preference-aware recommendation using sparse geo-social networking data
Jie Bao;Yu Zheng;Mohamed F. Mokbel.
advances in geographic information systems (2012)
A peer-to-peer spatial cloaking algorithm for anonymous location-based service
Chi-Yin Chow;Mohamed F. Mokbel;Xuan Liu.
advances in geographic information systems (2006)
SpatialHadoop: A MapReduce framework for spatial data
Ahmed Eldawy;Mohamed F. Mokbel.
international conference on data engineering (2015)
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
Mohamed F. Mokbel;Xiaopeing Xiong;Walid G. Aref.
international conference on management of data (2004)
Enabling private continuous queries for revealed user locations
Chi-Yin Chow;Mohamed F. Mokbel.
symposium on large spatial databases (2007)
Recommendations in location-based social networks: a survey
Jie Bao;Yu Zheng;David Wilkie;Mohamed Mokbel.
LARS: A Location-Aware Recommender System
Justin J. Levandoski;Mohamed Sarwat;Ahmed Eldawy;Mohamed F. Mokbel.
international conference on data engineering (2012)
SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases
X. Xiong;M.F. Mokbel;W.G. Aref.
international conference on data engineering (2005)
A demonstration of SpatialHadoop: an efficient mapreduce framework for spatial data
Ahmed Eldawy;Mohamed F. Mokbel.
very large data bases (2013)
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