U
Umar Farooq Minhas
Researcher at Microsoft
Publications - 44
Citations - 1958
Umar Farooq Minhas is an academic researcher from Microsoft. The author has contributed to research in topics: Cloud computing & Computer science. The author has an hindex of 18, co-authored 39 publications receiving 1501 citations. Previous affiliations of Umar Farooq Minhas include University of Waterloo & IBM.
Papers
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Journal ArticleDOI
Clash of the titans: MapReduce vs. Spark for large scale data analytics
TL;DR: This paper evaluates the major architectural components in MapReduce and Spark frameworks including: shuffle, execution model, and caching, by using a set of important analytic workloads and shows that Map Reduce's execution model is more efficient for shuffling data than Spark, thus making Sort run faster on MapReduces.
Journal ArticleDOI
Automatic virtual machine configuration for database workloads
Ahmed A. Soror,Umar Farooq Minhas,Ashraf Aboulnaga,Kenneth Salem,Peter Kokosielis,Sunil Kamath +5 more
TL;DR: A virtualization design advisor is introduced that uses information about the anticipated workloads of each of the database systems to recommend workload-specific configurations offline and runtime information collected after the deployment of the recommended configurations can be used to refine the recommendation and to handle changes in the workload.
Proceedings ArticleDOI
ALEX: An Updatable Adaptive Learned Index
Jialin Ding,Umar Farooq Minhas,Jia Yu,Chi Wang,Jaeyoung Do,Yinan Li,Hantian Zhang,Badrish Chandramouli,Johannes Gehrke,Donald Kossmann,David B. Lomet,Tim Kraska +11 more
TL;DR: Alex as mentioned in this paper is a learned index for read-write workloads that contains a mix of point lookups, short range queries, inserts, updates, and deletes, but it is limited to static, read-only workloads.
Journal ArticleDOI
SQL-on-Hadoop: full circle back to shared-nothing database architectures
TL;DR: This paper compares the performance of Impala and Hive, the new emerging class of SQL-on-Hadoop systems that exploit a shared-nothing parallel database architecture over Hadoop, and examines the strengths and limitations of each system.
Journal ArticleDOI
ATHENA: an ontology-driven system for natural language querying over relational data stores
Diptikalyan Saha,Avrilia Floratou,Karthik Sankaranarayanan,Umar Farooq Minhas,Ashish Mittal,Fatma Ozcan +5 more
TL;DR: ATHENA is presented, an ontology-driven system for natural language querying of complex relational databases that uses domain specific ontologies, which describe the semantic entities, and their relationships in a domain, through a unique two-stage approach.