Data Systems Lab at Maryland

DSLAM Directors

Picture of Daniel Abadi
Daniel Abadi
Daniel Abadi is the Darnell-Kanal Professor of Computer Science at UMD. He is best-known for the development of the storage and query execution engines of the C-Store (column-oriented database) prototype, which was commercialized by Vertica and eventually acquired by Hewlett-Packard and for his HadoopDB research on fault tolerant scalable analytical database systems which was commercialized by Hadapt and acquired by Teradata in 2014. Abadi is an ACM Fellow and has been a recipient of a Churchill Scholarship, a NSF CAREER Award, a Sloan Research Fellowship, a VLDB Best Paper Award, two VLDB Test of Time Awards (for the work on C-Store and HadoopDB), the 2008 SIGMOD Jim Gray Doctoral Dissertation Award, the 2013-2014 Yale Provost's Teaching Prize, and the 2013 VLDB Early Career Researcher Award. He was the PhD dissertation advisor of Alexander Thomson's and Jose Falerio's PhD dissertations, both of which won SIGMOD Jim Gray Doctoral Dissertation Awards (in 2015 and 2020 respectively). He received his PhD in 2008 from MIT. He blogs at DBMS Musings and tweets at @daniel_abadi. Picture of Amol Desphande
Amol Deshpande
Amol Deshpande is a Professor Computer Science at UMD. He received his Ph.D. from University of California at Berkeley in 2004, and his B.Tech. degree from Indian Institute of Technology, Bombay in 1998. His current research efforts focus on the challenges in managing and querying the inherently imprecise, incomplete, and uncertain data generated in environments like sensor networks, data streams, data integration, information extraction, and social networks. He has received best paper awards at the VLDB 2004, EWSN 2008, and VLDB 2009 conferences. He received an NSF CAREER award in 2006.
Research interests: Distributed systems, Parallel database systems, Scalable transactions Research interests: Graph database systems, Query optimization, Sensor network data management, Scalable statistical modeling of data, Uncertain data management, Energy-efficient data management

SLOG: Strictly serializable, low-latency, geopgraphically replicated database system

PI: Daniel Abadi

For decades, applications deployed on a world-wide scale have been forced to give up at least one of (1) strict serializability (2) low latency writes (3) high transactional throughput. SLOG uses a combinations of determinism, physical region locality in data access, and multi-versioning to eliminate this tradeoff. Determinism enables replicas that are physically far from each other to avoid state divergence without expensive network communication. Physical region locality allows low-latency linearizable reads from locations close to a data item's home. Multi-versioning enables arbitrary snapshot reads. SLOG makes the same programmer-friendly correctness guarantees as Google Spanner except with lower latency and much higher throughput under data contention. The deterministic database research component of SLOG is supported by NSF IIS-1763797 and the multi-versioning component is supported by NSF IIS-1718581.

SLOG Publications -->