| I am a Partner Development Manager at Bing Search Infrastructure Team. I manage a team to develop a cloud-scale distributed computation system, called SCOPE, targeted for massive data analysis over tens of thousands of machines at Microsoft Bing.
SCOPE combines benefits from
both traditional parallel databases and MapReduce execution engines
to allow easy programmability and deliver massive scalability and
high performance through advanced optimization. Similar to parallel
databases, the system has a SQL-like declarative scripting language
with no explicit parallelism, while being amenable to efficient
parallel execution on large clusters. An optimizer is responsible
for converting scripts into efficient execution plans for the
distributed computation engine. A physical execution plan consists
of a directed acyclic graph (DAG) of vertices. Execution of the plan
is orchestrated by a job manager that schedules execution on
available machines and provides fault tolerance and recovery, much
like MapReduce systems. SCOPE is
being used daily for a variety of data analysis and data mining
applications over tens of thousands of machines at Microsoft,
powering Bing and other online services.
I used to be a researcher in the Database Group at Microsoft Research, part of the Microsoft Corporation. My research is in the area of database, in particular query processing, query optimization, large scale distributed computing, and architecture-conscious database systems. Before joining Microsoft, I obtained my Ph.D. in Computer Science at Columbia University and B.S. at University of Science and Technology of China.
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| Recent Publications ([All Publications]) -
“SCOPE: Parallel Databases Meet MapReduce”
Jingren Zhou, Nicolas Bruno, Ming-chuan Wu, Paul Larson, Ronnie Chaiken,
Darren Shakib, The VLDB Journal, 2012. pdf
- “Spotting Code Optimizations in Data-Parallel Pipelines through PeriSCOPE” Zhenyu Guo, Xuepeng Fan, Rishan Chen, Jiaxing Zhang, Hucheng Zhou, Sean McDirmid, Chang Liu, Wei Lin, Jingren Zhou, and Lidong Zhou, in Proc. of the 2012 OSDI Conference (OSDI'12). pdf
- “Advanced Partitioning Techniques for Massively Distributed Computation” Jingren Zhou, Nicolas Bruno, and Wei Lin, in Proc. of the 2012 SIGMOD Conference (SIGMOD'12). pdf
- “Recurring Job Optimization in Scope” Nicolas Bruno, Sameer Agarwal, Srikanth Kandula, Bing Shi, Ming-Chuan Wu, and Jingren Zhou, in Proc. of the 2012 SIGMOD Conference (SIGMOD'12). pdf
- “SCOPE Playback: Self-Validation in the Cloud” Ming-Chuan Wu, Jingren Zhou, Nicolas Bruno, Yu Zhang, and Jon Fowler, in Proc. of the 2012 DBTest Workshop (DBTest'12). pdf
- “Exploiting Common Subexpressions for Cloud Query Processing” Yasin Silva, Paul Larson, and Jingren Zhou, in Proc. of the 2012 ICDE Conference (ICDE'12). pdf
- “Incorporating Partitioning and Parallel Plans into the SCOPE Optimizer” Jingren Zhou, Per-Åke Larson, and Ronnie Chaiken, in Proc. of the 2010 ICDE Conference (ICDE’10). pdf
- “SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets” Ronnie Chaiken, Bob Jenkins, Per-Åke Larson, Bill Ramsey, Darren Shakib, Simon Weaver, and Jingren Zhou, in Proc. of the 2008 VLDB Conference (VLDB'08). pdf
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