Speaker: Mohammad Sadoghi, UC Davis
Abstract: Arguably data is a new natural resource in the enterprise world with an unprecedented degree of proliferation and heterogeneity. However, to derive real-time actionable insights from the data, it is important to bridge the gap between analyzing a large volume of data (i.e., OLAP) and managing the
data that is being updated at a high velocity (i.e., OLTP). Historically, there has been a divide where specialized engines were developed to support either OLAP or OLTP workloads but not both; thus, limiting the analysis to stale and possibly irrelevant data.
In this talk, we present our proposed architecture to combine the real-time processing of analytical and transactional workloads within a single unified engine. To support querying and retaining the current and historic data, we design a novel efficient index maintenance techniques paving the way to a novel optimistic concurrency control. From the concurrency perspective, we further pose a question: is it possible to have concurrent execution over shared data without having any concurrency control? To answer this question, we investigate a deterministic approach to transaction processing geared towards many-core hardware by proposing a novel queue-oriented, control-free concurrency architecture (QueCC) that exhibits minimal coordination during execution while offering serializable guarantees. From the storage perspective, we develop an update-friendly lineage-based storage architecture (LSA) that offers a contention-free and lazy staging of columnar data from a write-optimized form (OLTP) into a read-optimized form (OLAP) in a transactionally consistent approach. Finally, we share our vision to move from a centralized platform onto a secure democratic and decentralized computational model.
Bio: Mohammad Sadoghi is an Assistant Professor of Computer Science at the University of California, Davis. Formerly, he was an Assistant Professor at Purdue University and Research Staff Member at IBM T.J. Watson Research Center. He received his Ph.D. from the Computer Science Department at the University of Toronto in 2013. His research spans all facets of secure and massive-scale data management. At UC Davis, he leads the ExpoLab research group with the aim to pioneer a new exploratory data platform—referred to as ExpoDB—a distributed ledger that unifies secure transactional and real-time analytical processing, all centered around a democratic and decentralized computational model. Prof. Sadoghi has over 60 publications and has filed 34 U.S. patents. His SIGMOD'11 paper was awarded the EPTS Innovative Principles Award, his EDBT'11 paper was selected as one of the best EDBT papers in 2011, and his ESWC'16 paper won the Best In-Use Paper Award. He is serving as Workshop/Tutorial Co-Chair at Middleware'18, has served as the PC Chair (Industry Track) at ACM DEBS'17, co-chaired a new workshop series, entitled Active, at both ICDE and Middleware, and co-chaired the Doctoral Symposium at Middleware'17. He served as the Area Editor for Transaction Processing in the Encyclopedia of Big Data Technologies by Springer. He is co-authoring a book on "Transaction Processing on Modern Hardware" as part of Morgan & Claypool Synthesis Lectures on Data Management. He regularly serves on the program committee of SIGMOD, VLDB, ICDE, EDBT, Middleware, ICDCS, DEBS, and ICSOC.
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