Amol Deshpande
Professor
5154 Iribe Center
(301) 405-2703
Education:
Ph.D., University of California at Berkeley
Special Awards/Honors:
National Science Foundation (NSF) CAREER Award
Biography:
Amol Deshpande is a professor of computer science with an appointment in the University of Maryland Institute for Advanced Computer Studies.
Deshpande's research spans a spectrum of data management topics including query optimization, adaptive query processing, sensor network data management, scalable statistical modeling of data, uncertain data management, and graph databases. His 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.
Go here to view Deshpande's academic publications on Google Scholar.
Publications
2012
2012. A Temporal Pattern Search Algorithm for Personal History Event Visualization. Knowledge and Data Engineering, IEEE Transactions on. 24(5):799-812.
2011
2011. Energy efficient monitoring in sensor networks. Algorithmica. 59(1):94-114.
2011. Declarative analysis of noisy information networks. 2011 IEEE 27th International Conference on Data Engineering Workshops (ICDEW). :106-111.
2011. Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions. Proceedings of the VLDB Endowment. 4(7)
2011. Maximizing Expected Utility for Stochastic Combinatorial Optimization Problems. 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science (FOCS). :797-806.
2011. A unified approach to ranking in probabilistic databases. The VLDB Journal. 20(2):249-275.
2011. Sensitivity analysis and explanations for robust query evaluation in probabilistic databases. Proceedings of the 2011 international conference on Management of data. :841-852.
2010
2010. Increasing representational power and scaling reasoning in probabilistic databases. Proceedings of the 13th International Conference on Database Theory. :1-1.
2010. On Computing Compression Trees for Data Collection in Wireless Sensor Networks. 2010 Proceedings IEEE INFOCOM. :1-9.
2010. Lineage processing over correlated probabilistic databases. Proceedings of the 2010 international conference on Management of data. :675-686.
2010. Ranking continuous probabilistic datasets. Proc. VLDB Endow.. 3(1-2):638-649.
2010. Read-once functions and query evaluation in probabilistic databases. Proc. VLDB Endow.. 3(1-2):1068-1079.
2010. Sharing-aware horizontal partitioning for exploiting correlations during query processing. Proc. VLDB Endow.. 3(1-2):542-553.
2009
2009. Algorithms for distributional and adversarial pipelined filter ordering problems. ACM Trans. Algorithms. 5(2):24:1–24:34-24:1–24:34.
2009. On Computing Compression Trees for Data Collection in Sensor Networks. arXiv:0907.5442.
2009. P r DB: managing and exploiting rich correlations in probabilistic databases. The VLDB Journal. 18(5):1065-1090.
2009. Bisimulation-based approximate lifted inference. Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. :496-505.
2009. Indexing correlated probabilistic databases. Proceedings of the 35th SIGMOD international conference on Management of data. :455-468.
2009. Consensus answers for queries over probabilistic databases. Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. :259-268.
2009. PrDB: managing and exploiting rich correlations in probabilistic databases. The VLDB Journal. 18(5):1065-1090.
2009. PrDB: Managing Large-Scale Correlated Probabilistic Databases (Abstract). Scalable Uncertainty ManagementScalable Uncertainty Management. 5785:1-1.
2009. Ef?cient Query Evaluation over Temporally Correlated Probabilistic Streams IEEE 25th International Conference on Data Engineering, 2009. ICDE '09. :1315-1318.
2009. A unified approach to ranking in probabilistic databases. Proceedings of the VLDB Endowment. 2(1):502-513.
2009. Minimizing Communication Cost in Distributed Multi-query Processing. IEEE 25th International Conference on Data Engineering, 2009. ICDE '09. :772-783.
2009. Graphical models for uncertain data. Managing and Mining Uncertain Data. :77-77.
2008
2008. Network-Aware Join Processing in Global-Scale Database Federations. IEEE 24th International Conference on Data Engineering, 2008. ICDE 2008. :586-595.
2008. Online Filtering, Smoothing and Probabilistic Modeling of Streaming data. IEEE 24th International Conference on Data Engineering, 2008. ICDE 2008. :1160-1169.
2008. Predictive Modeling-Based Data Collection in Wireless Sensor Networks. Wireless Sensor NetworksWireless Sensor Networks. 4913:34-51.
2008. Energy Efficient Monitoring in Sensor Networks. LATIN 2008: Theoretical InformaticsLATIN 2008: Theoretical Informatics. 4957:436-448.
2008. Exploiting shared correlations in probabilistic databases. Proceedings of the VLDB Endowment. 1(1):809-820.
2008. Flow Algorithms for Parallel Query Optimization. IEEE 24th International Conference on Data Engineering, 2008. ICDE 2008. :754-763.
2007
2007. Data Management in the Worldwide Sensor Web. IEEE Pervasive Computing. 6(2):30-40.
2007. Probabilistic graphical models and their role in databases. Proceedings of the 33rd international conference on Very large data bases. :1435-1436.
2007. A graph-based approach to vehicle tracking in traffic camera video streams. Proceedings of the 4th workshop on Data management for sensor networks: in conjunction with 33rd International Conference on Very Large Data Bases. :19-24.
2007. Report on the Fourth International Workshop on Data Management for Sensor Networks (DMSN 2007). SIGMOD Rec.. 36(4):53-55.
2007. Representing and Querying Correlated Tuples in Probabilistic Databases. Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on. :596-605.
2007. Adaptive query processing. Foundations and Trends in Databases. 1(1):1-140.
2007. Representing Tuple and Attribute Uncertainty in Probabilistic Databases. Seventh IEEE International Conference on Data Mining Workshops, 2007. ICDM Workshops 2007. :507-512.
2007. Adaptive query processing: why, how, when, what next? Proceedings of the 33rd international conference on Very large data bases. :1426-1427.
2006
2006. Approximate Data Collection in Sensor Networks using Probabilistic Models. Proceedings of the 22nd International Conference on Data Engineering, 2006. ICDE '06. :48-48.
2006. MauveDB: supporting model-based user views in database systems. Proceedings of the 2006 ACM SIGMOD international conference on Management of data. :73-84.
2006. Flow algorithms for two pipelined filter ordering problems. Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. :193-202.
2005
2005. Toward on-line schema evolution for non-stop systems. 11th High Performance Transaction Systems Workshop.
2005. Using probabilistic models for data management in acquisitional environments. Proc. CIDR. :317-328.
2005. Resource-aware wireless sensor-actuator networks. Data Engineering. 1001:40-40.
2005. Exploiting Correlated Attributes in Acquisitional Query Processing. 21st International Conference on Data Engineering, 2005. ICDE 2005. Proceedings. :143-154.
2005. Model-based approximate querying in sensor networks. The VLDB Journal. 14(4):417-443.
2004
2004. An initial study of overheads of eddies. SIGMOD Rec.. 33(1):44-49.
2004. Lifting the burden of history from adaptive query processing. Proceedings of the Thirtieth international conference on Very large data bases - Volume 30. :948-959.
2004. Model-driven data acquisition in sensor networks. Proceedings of the Thirtieth international conference on Very large data bases - Volume 30. :588-599.
2003
2003. TelegraphCQ: continuous dataflow processing. Proceedings of the 2003 ACM SIGMOD international conference on Management of data. :668-668.
2003. Using state modules for adaptive query processing. 19th International Conference on Data Engineering, 2003. Proceedings. :353-364.
2003. Cache-and-query for wide area sensor databases. Proceedings of the 2003 ACM SIGMOD international conference on Management of data. :503-514.
2003. IRIS: Internet-scale Resource-Intensive Sensor Services. Intel Research, UC Berkeley, Carnegie Mellon University.
2003. TelegraphCQ: An architectural status report. IEEE Data Engineering Bulletin. 26(1):11-18.
2003. IrisNet: an architecture for internet-scale sensing services. Proceedings of the 29th international conference on Very large data bases - Volume 29. :1137-1140.
2003. MobiCom poster: mining a world of smart sensors. SIGMOBILE Mob. Comput. Commun. Rev.. 7(1):34-36.
2002
2002. On using correlation-based synopses during query optimization. Computer Science Division (EECS), University of California Berkeley.
2002. Irisnet: An architecture for compute-intensive wide-area sensor network services. Intel Corporation, Pittsburgh IRPTR-02. 10
2002. Decoupled query optimization for federated database systems. 18th International Conference on Data Engineering, 2002. Proceedings. :716-727.
2001
2001. Independence is good: dependency-based histogram synopses for high-dimensional data. SIGMOD Rec.. 30(2):199-210.
2001. Efficient stepwise selection in decomposable models. Proc. UAI. :128-135.
2000
2000. Adaptive query processing: Technology in evolution. IEEE Data Engineering Bulletin. 23(2):7-18.
1999
1999. A Study of the Structure of the Web. University of California, Berkeley.