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Gautam Das

Name

[Das, Gautam]
  • Professor, Department of Computer Science & Engineering

Biography

Please see website at: http://ranger.uta.edu/~gdas/

Appointments

    • Jan 2010 to Jan 2013 Professor
      University of Texas at Arlington
    • Jan 2004 to Jan 2010 Assoc Prof
      University of Texas at Arlington
    • Jan 1999 to Jan 2004 Researcher
      Microsoft   Microsoft Research
    • Aug 1995 to Jan 1999 Associate Professor
      University of Memphis
    • Aug 1990 to Aug 1995 Assistant Professor
      University of Memphis

Research and Expertise

  • Databases and Information Retrieval
    The objective of this project seeks to explore and extend the overlap of the fields of databases and information retrieval. Current database techniques are unable cope with information overload in database storage and retrieval. Our work focuses on exploiting data mining for advanced data summarization and also enabling tighter coupling between database querying and data mining. For example, suppose a user is searching for homes for sale in a particular city in a realtor’s database. Instead of returning literally hundreds of properties for sale, it would be more appropriate to present the user with a summary, e.g. the top twenty homes ranked by price and/or location. Another extension of information retrieval techniques is keyword-based search in databases. Text documents as well as structured relational data are rich sources of information, and integrated querying and browsing of structured relational databases and of text are of vital importance. Yet querying of relational and text systems have important differences. We are investigating how relational querying can be enriched by borrowing ideas from the information retrieval. While I was at Microsoft Research, we experimented with several prototype systems (e.g., DBXplorer for supporting keyword-based querying over databases), and our results in this area have been published in several conferences, such as VLDB 2004, CIDR 2003 and ICDE 2002.
  • Approximate Query Processing
    In many OLAP and decision support environments, it is often desirable to answer complex long-running aggregate database queries approximately, provided some estimate of the error is also given. For example, when a sales manager asks “give me the aggregate sales of Product X, grouped by the US states”, she/he is probably not interested in getting answers to the nearest cent. We approach this difficult problem using statistical sampling-based techniques. In contrast to previous heuristic sampling-based approaches, we treat the problem as an optimization problem whose goal is to minimize the expected error in answering queries in a given workload. While I was at Microsoft Research, we prototyped the system as a SQL client and tested it on several databases as well as synthetic benchmarks such as TPC-H. We are also applying our AQP techniques in improving sampling-based histogram construction techniques. Our results appear very promising when compared to previous efforts in this area, and have been published in several recent papers (e.g. SIGMOD 2004, SIGMOD 2003, SIGMOD 2001, ICDE 2001), as well as have been the subject of several invited talks and tutorials. In the future I intend to investigate other approaches to AQP (e.g. the use of wavelet transformations), and attempt to extend approximate query processing beyond aggregate queries. One exciting new area of research is approximate query processing in sensor networks, where the distributed nature of the computation poses new challenges to the problems of sampling and communication.
  • Similarity in Categorical Databases
    Similarity between complex objects is a central notion in data mining, with applications in segmentation and clustering. However traditional similarity measures are often inadequate for many applications, especially for categorical data where there no natural numeric notion of distance. For example, in market-basket databases, two products (such as Coke and Pepsi) can be essentially similar, but how do we discover this similarity automatically? In our research, we use other attributes as external probes and compare the buying behavior of customers with respect to these probes. Thus, Coke and Pepsi may be inferred to be similar because their customers are perhaps equally likely to buy chips (where chips is a probe). We have also explored new notions of context- sensitive similarity between attributes, as well as between rows of the database (i.e. the customers), and various clustering and classification algorithms based on these measures. Our results have appeared in several data mining conferences, such as KDD 1998 and PKDD 2000. (Much of this is joint work with Professor Heikki Mannila of University of Helsinki).
  • Similarity in Time-Series Databases
    Similarity problems involving time-series data are equally interesting, and I have worked on time series similarity, as well as on indexing and rule discovery problems. We want to quickly answer questions such as “which stocks are similar to stock X over the last two weeks?”, and also automatically infer rules such as “if stock X goes up and stock Y remains the same, then stock Z will shortly go down”. We have developed similarity models that are more sophisticated than traditional Euclidean distance models. One of the models is an interesting combination of algorithmic ideas from two different areas of computer science: edit distances from pattern matching, and cutting theorems from computational geometry. We have also experimented with novel variations of several known indexing methods, such as discrete Fourier transforms and Latent Semantic Indexing. In the rule discovery problem, we search for episode- like rules after discretizing the sequences using vector-quantization techniques. Our data sets come from a variety of sources, such as economic indicators, telecommunication signals, and stock data. Our time- series results have appeared in several conferences, journals, and edited books. In addition, we have given tutorials on the subject at KDD 2000 and SIGMOD 2001. (Much of this is joint work with several colleagues such as Professor Heikki Mannila of University of Helsinki and Professor Dimitris Gunopulos of UC Riverside.)
  • Algorithms and Computational Geometry
    In addition to my research in databases and data mining, I have worked in computational geometry and combinatorial algorithms. One of my areas of interest is geometric network design, which is concerned with the design and analysis of geometric networks, such as Euclidean TSPs, graph spanners, shortest paths, and spanning trees. I started working on graph spanners as part of my Ph.D dissertation. During the years that followed, my colleagues and I obtained several significant results concerning spanners. For example, in two papers (SODA 1995 and STOC 1995) we develop and analyze an algorithm that efficiently constructs sparse graph spanners with low edge weight. This algorithm had been applied to develop an almost linear-time PTAS for the Euclidean Traveling Salesman Problem . Graph spanners have been a fertile area of research ever since, with new and exciting applications, such as in the areas of wireless ad-hoc networks and internet routing. An important aspect of my future plans is to employ combinatorial algorithms and computational geometry in database and data mining research. In data mining, objects are often represented in high dimensional geometric space. In fact, in the special case of spatial data mining, the geometric representation is even more direct. As another example, database and data mining problems that arise in sensor networks also appear to possess significant geometric and topological aspects. There is a wealth of results from algorithms and computational geometry that can be potentially used, such as range searching, proximity problems (k-nearest neighbor), clustering, stochastic geometry, and approximation theory.

Publications

      Journal Article 2015
      • Alexios Kotsifakos, Alexandra Stefan, Vassilis Athitsos, Gautam Das, and Panagiotis Papapetrou: DRESS: Dimensionality Reduction for Efficient Sequence Search. To appear in the Data Mining and Knowledge Discovery Journal (DAMI) (also to be presented at PKDD 2015)

        {Journal Article }
      2015
      • Senjuti Basu Roy, Saravanan Thirumuruganathan, Sihem Amer-Yahia, Gautam Das: Task-Assignment Optimization in Knowledge Intensive Crowdsourcing. To appear in VLDB Journal, 2015

        {Journal Article }

      Conference Proceeding 2015
      • Azade Nazi, Saravanan Thirumuruganathan, Vagelis Hristidis, Nan Zhang, and Gautam Das: Answering Complex Queries in an Online Community Network. Poster paper, accepted in ICWSM 2015

        {Conference Proceeding }
      2015
      • Mahashweta Das and Gautam Das: Structured Analytics in Social Media. Tutorial, to appear in PVLDB 2015

        {Conference Proceeding }

      Conference Paper 2015
      • Azade Nazi, Mahashweta Das, and Gautam Das: The TagAdvisor: Luring the Lurkers to Review Web Item. To appear in SIGMOD 2015

        {Conference Paper }
      2015
      • Azade Nazi, Zhuojie Zhou, Saravanan Thirumuruganathan, Nan Zhang, and Gautam Das: Walk, Not Wait: Faster Sampling Over Online Social Networks. To appear in PVLDB 2015

        {Conference Paper }
      2015
      • M. Farhad Rahman, Weimo Liu, Saravanan Thirumuruganathan, Nan Zhang, and Gautam Das: Privacy Implications of Database Ranking. To appear in PVLDB 2015

        {Conference Paper }
      2015
      • Saravanan Thirumuruganathan, Habibur Rahman, Sofiane Abbar, and Gautam Das: Beyond Itemsets: Mining FrequentFeaturesets over Structured Items. To appear in PVLDB 2015

        {Conference Paper }
      2015
      • Zhuojie Zhou, Nan Zhang, and Gautam Das: Leveraging History for Faster Sampling of Online Social Networks. To appear in PVLDB 2015

        {Conference Paper }
      2015
      • Weimo Liu, M. Farhad Rahman, Saravanan Thirumuruganathan, Nan Zhang, and Gautam Das: Aggregate Estimations over Location Based Services. To appear in PVLDB 2015

        {Conference Paper }
      2015
      • Habibur Rahman, Saravanan Thirumuruganathan, Senjuti Basu Roy, Sihem Amer-Yahia, and Gautam Das: Worker Skill Estimation in Team-Based Tasks. To appear in PVLDB 2015

        {Conference Paper }

      Conference Proceeding 2014
      • Senjuti Basu Roy, Saravanan T., Sihem Amer-Yahia, Gautam Das, and Cong Yu. Exploiting Group Recommendation Functions for Flexible Preferences. To appear in Proc. of ICDE 2014

        {Conference Proceeding }
      2014
      • Sofiane Abbar, Habibur Rahman, Saravanan T., Carlos Castillo, and Gautam Das. Ranking Item Features by Mining Online User-Item Interactions. To appear in Proc. of ICDE 2014

        {Conference Proceeding }
      2014
      • Gautam Das: Exploration and Mining of Web Repositories. Tutorial, at COMAD 2014

        {Conference Proceeding }
      2014
      • Nan Zhang and Gautam Das. Exploration and Mining of Web Repositories. Tutorial at WSDM 2014

        {Conference Proceeding }

      Conference Paper 2014
      • Weimo Liu, Saravanan Thirumuruganathan, Nan Zhang, and Gautam Das: Aggregate Estimation Over Dynamic Hidden Web Databases. To appear in PVLDB 2014

        {Conference Paper }
      2014
      • Weimo Liu, Saad Bin Suhaim, Saravanan Thirumuruganathan, Nan Zhang, Gautam Das, and Ali Jaoua: HDBTracker: Aggregate Tracking and Monitoring Over Dynamic Web Databases. Demo paper, to appear in PVLDB 2014

        {Conference Paper }
      2014
      • Saravanan Thirumuruganathan, Nan Zhang, Vagelis Hristidis, and Gautam Das: Aggregate Estimation Over a Microblog Platform. To appear in Proc. of SIGMOD 2014

        {Conference Paper }
      2014
      • Davide Mottin, Alice Marascu, Senjuti Basu Roy, Gautam Das, Themis Palpanas, Yannis Velegrakis: IQR: An Interactive Query Relaxation System for the Empty-Answer Problem. Demo paper, to appear at SIGMOD 2014.

        {Conference Paper }
      2014
      • Milad Eftekhar, Saravanan Thirumuruganathan, Gautam Das, and Nick Koudas: Price Trade-offs in Social Media Advertising. To appear in Proceeding of ACM Conference on Online Social Networks (COSN) 2014

        {Conference Paper }
      2014
      • Naeemul Hassan, Huadong Feng, Ramesh Venkataraman, Gautam Das, Chengkai Li, Nan Zhang: Anything You Can Do, I Can Do Better: Finding Expert Teams by CrewScout. Demo paper, to appear in CIKM 2014

        {Conference Paper }
      2014
      • Azade Nazi, Saravanan Thirumuruganathan, Vagelis Hristidis, Nan Zhang, Khaled Shaban, and Gautam Das: Query Hidden Attributes in Social Networks. Third International Workshop on Intelligent Data Processing (IDP 2014), collocated with ICDM 2014

        {Conference Paper }

      Journal Article 2014
      • Mahashweta Das, Saravanan T., Sihem Amer-Yahia, Gautam Das and Cong Yu. An Expressive Framework and Efficient Algorithms for the Analysis of Collaborative Tagging. To appear in VLDB Journal Special Issue 2014 onBest of VLDB 2012

        {Journal Article }

      Book Chapter 2013
      • G. Das. "Sampling Methods in Approximate Query Answering Systems," Encyclopedia of Data Warehousing and Mining, vol. 2005, J. Wang, Eds. Information Science Publishing.
        {Book Chapter }
      2013
      • C. Ann, J. L. Ratanamahatana, E. K. Gunopulos, M. Vlachos, and G. Das. "Mining Time Series Data," Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, vol. 2005, O. Maimon, Eds. Kluwer Academic Publishers.
        {Book Chapter }

      Conference Paper 2013
      • Saravanan Thirumuruganathan, Nan Zhang, and Gautam Das: Rank Discovery From Web Databases. In PVLDB 2013(to be presented at VLDB 2014)

        {Conference Paper }
      2013
      • Mingyang Zhang, Nan Zhang, and Gautam Das: Mining a Search Engine's Corpus Without a Query Pool. Full paper, CIKM 2013

        {Conference Paper }
      2013
      • Mahashweta Das, Habibur Rahman, Vagelis Hristidis, and Gautam Das: Generating Informative Snippet to Maximize Item Visibility. Short paper, CIKM 2013

        {Conference Paper }
      2013
      • Saravanan Thirumuruganathan, Nan Zhang, Gautam Das: Breaking the Top-k Barrier of Hidden Web Databases. In Proc. of ICDE 2013

        {Conference Paper }
      2013
      • Zhuojie Zhou, Nan Zhang, Zhiguo Gong, Gautam Das: Faster Random Walks By Rewiring Online Social Networks On-The-Fly. In Proc. of ICDE 2013

        {Conference Paper }
      2013
      • Senjuti Basu Roy, Ioanna Lykourentzou, Saravanan Thirumuruganathan, Sihem Amer-Yahia and Gautam Das:Crowds, not Drones: Modeling Human Factors in Interactive CrowdsourcingDBCrowd 2013 held in conjunction with VLDB 2013

        {Conference Paper }
      2013
      • Davide Mottin, Alice Marascu, Senjuti Basu Roy, Gautam Das, Themis Palpanas, and Yannis Velegrakis: A Probabilistic Optimization Framework for the Empty-Answer Problem. In PVLDB 2013 (to be presented at VLDB 2014)

        {Conference Paper }

      Journal Article 2013
      • Manos Papagelis, Gautam Das, Nick Koudas: Sampling Online Social Networks. To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2012

        {Journal Article }
      2013
      • Alexandra Stefan, Vassilis Athitsos, and Gautam Das: The Move-Split-Merge Metric for Time Series. To appear inIEEE Transactions on Knowledge and Data Engineering (TKDE), 2012

        {Journal Article }
      2013
      • Chengkai Li, Nan Zhang, Naeemul Hassan, Sundaresan Rajasekaran, and Gautam Das: On Skyline Groups. To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2013

        {Journal Article }

      Journal Article 2012
      • N. Zhang, L.O'Neill, G. Das, X. Cheng, and H. Heng: No Silver Bullet: Identifying Security Vulnerabilities In Anonymization Protocols for Hospital Databases. Published in International Journal of Healthcare Information Systems and Informatics

        {Journal Article }
      2012
      • Senjuti Basu Roy, Gautam Das, Sajal Das: Algorithms for Computing Best Coverage Path in the Presence of Obstacles in a Sensor Field. To appear in Journal of Discrete Algorithms (Elsevier), 2012

        {Journal Article }

      Conference Paper 2012
      • Mingyang Zhang, Nan Zhang, and Gautam Das: Aggregate Suppression for Enterprise Search Engines, Full paper, in Proc. of ACM SIGMOD 2012

        {Conference Paper }
      2012
      • Nan Zhang and Gautam Das: Mining Deep Web Repositories. Tutorial, in ECML-PKDD 2012

        {Conference Paper }
      2012
      • Mahashweta Das, Saravanan Thirumuruganathan, Sihem Amer-Yahia, Gautam Das and Cong Yu: MapRat: Meaningful Explanation, Interactive Exploration and Geographic Visualization of Collaborative Ratings. Demo paper, in PVLDB 2012.

        {Conference Paper }
      2012
      • Chengkai Li, Nan Zhang, Naeemul Hassan, Sundaresan Rajasekaran, and Gautam Das: On Skyline Groups. In Proc. of CIKM 2012

        {Conference Paper }
      2012
      • Mahashweta Das, Saravanan Thirumuruganathan, Sihem Amer-Yahia, Gautam Das and Cong Yu: Who Tags What? An Analysis Framework. In PVLDB 2012.(Also to appear in VLDBJ special issue on Best of VLDB 2012)

        {Conference Paper }

      Book Chapter 2012
      • Foto Afrati, Gautam Das, Aristides Gionis, Heikki Mannila, Taneli Mielikainen, and Panayiotis Tsaparas:Mining Chains of Relations. Book Chapter, Data Mining: Foundations and Intelligent Paradigms, Chapter 11, ISRL 24, Volume 2, 2012

        {Book Chapter }

      Journal Article 2011
      • Nan Zhang and Gautam Das: Exploration of Deep Web Repositories, Tutorial, In PVLDB 2011.
        {Journal Article }
      2011
      • Xin Jin, Aditya Mone, Nan Zhang, and Gautam Das: MOBIES: Mobile-Interface Enhancement Service for Hidden Web Databases, Demo paper, in Proc. of SIGMOD 2011.
        {Journal Article }
      2011
      • Mahashweta Das, Sihem Amer-Yahia, Gautam Das, and Cong Yu: MRI: Meaningful Interpretations of Collaborative Ratings. In PVLDB 2011.

        {Journal Article }
      2011
      • Xin Jin, Nan Zhang, and Gautam Das: Attribute Domain Discovery for Hidden Web Databases, In Proc. of SIGMOD 2011.

        {Journal Article }
      2011
      • Mingyang Zhang, Nan Zhang, and Gautam Das: Mining a Search Engine Corpus: Efficient Yet Unbiased Sampling and Aggregate Estimation, In Proc. of SIGMOD 2011.

        {Journal Article }
      2011
      • Senjuti Basu Roy, Sihem Amer-Yahia, Gautam Das and Cong Yu: Interactive Itinerary Planning, In Proc. of ICDE 2011 (Acceptance rate 19.8%).

        {Journal Article }
      2011
      • Mahashweta Das, Gautam Das, and Vagelis Hristidis: Leveraging Collaborative Tagging for Web Item Design. Full paper, in Proc. of ACM SIGKDD 2011 (Acceptance rate 7.8%)

        {Journal Article }
      2011
      • H. Howie Huang, Nan Zhang, Wei Wang, Gautam Das, and Alex Szalay: Just-In-Time Analytics on Large File Systems, In Proc. of USENIX Conference on File and Storage Technologies, FAST 2011.

        {Journal Article }
      2011
      • Xin Jin, Nan Zhang, and Gautam Das: ASAP: Eliminating Algorithm-based Disclosure in Privacy-Preserving Data Publishing, to appear in Information Systems (Elsevier), 2011

        {Journal Article }
      2011
      • Xin Jin, Aditya Mone, Nan Zhang, and Gautam Das: Randomized Generalization for Aggregate Suppression Over Hidden Web Databases. In PVLDB 2011.

        {Journal Article }

      Journal Article 2010
      • Benjamin Arai, Gautam Das, Dimitris Gunopulos, Vagelis Hristidis, Nick Koudas: An Access Cost Aware Approach for Object Retrieval over Multiple Sources. In PVLDB 2010.
        {Journal Article }
      2010
      • Arjun Dasgupta, Xin Jin, Bradley Jewell, Nan Zhang, and Gautam Das: Unbiased estimation of size and other aggregates over hidden web databases. In Proc. SIGMOD 2010.
        {Journal Article }
      2010
      • Senjuti Basu Roy, Sihem Amer-Yahia, Ashish Chawla, Gautam Das and Cong Yu. Space Efficiency in Group Recommendations, In VLDB Journal Special Issue on Data Management and Mining for Social Networks and Social Media, 2010.
        {Journal Article }
      2010
      • Ning Yan, Chengkai Li, Senjuti Basu Roy, Rakesh Ramegowda, Gautam Das, Facetedpedia: Enabling Query-Dependent Faceted Search for Wikipedia, Demo paper, in Proc. of CIKM 2010.
        {Journal Article }
      2010
      • Xin Jin, Mingyang Zhang, Nan Zhang, Gautam Das: Versatile Publishing for Privacy Preservation. Full paper, in Proc. ACM SIGKDD 2010 (Acceptance rate 13.3%).
        {Journal Article }
      2010
      • Feng Zhao, Gautam Das,  Kian-Lee Tan,  Anthony K. H. Tung: Call to Order: A Hierarchical Browsing Approach to Eliciting Users' Preference. In Proc. SIGMOD 2010.
        {Journal Article }
      2010
      • Xin Jin, Nan Zhang, and Gautam Das: Algorithm-safe Privacy Preserving Data Publishing, In Proc. EDBT 2010 (Acceptance rate 18%)
        {Journal Article }
      2010
      • Chengkai Li, Ning Yan, Senjuti Basu Roy, Lekhendro Lisham, and Gautam Das: Facetedpedia: Dynamic Generation of Query-Dependent Faceted Interfaces for Wikipedia. In Proc. WWW 2010 (Acceptance rate 14%)
        {Journal Article }
      2010
      • Arjun Dasgupta, Nan Zhang, and Gautam Das: Turbo-Charging Hidden Database Samplers with Overflowing Queries and Skew Reduction. In Proc. EDBT 2010 (Acceptance rate 18%)
        {Journal Article }
      2010
      • Sihem Amer-Yahia, Senjuti Basu Roy, Ashish Chawla, Gautam Das and Cong Yu: Constructing and Exploring Composite Items. In Proc. SIGMOD 2010.
        {Journal Article }

      Journal Article 2009
      • Sihem Amer-Yahia, Senjuti Basu Roy, Ashish Chawla, Gautam Das, Cong Yu: Group Recommendation: Semantics and Efficiency. Full paper, in VLDB 2009. (Acceptance rate 17.9%)
        {Journal Article }
      2009
      • Senjuti Basu Roy, Haidong Wang, Ullas Nambiar, Gautam Das, Mukesh Mohania: DynaCet: Building Dynamic Faceted Search Systems over Databases. Demo paper, in Proc. ICDE 2009. (Acceptance rate 28%)
        {Journal Article }
      2009
      • Albert Angel, Surajit Chaudhuri, Gautam Das, Nick Koudas: Ranking Objects Based on Relationships and Fixed Associations. In Proc. EDBT 2009. (Acceptance rate 33%)
        {Journal Article }
      2009
      • Surajit Chaudhuri, Gautam Das: Keyword Querying and Ranking in Databases. Tutorial at VLDB 2009.
        {Journal Article }
      2009
      • Nikos Sarkas, Gautam Das, Nick Koudas: Improved Search for Socially Annotated Data, In PVLDB 2009.
        {Journal Article }
      2009
      • Senjuti Basu Roy, Gautam Das: Top-k Implementation Techniques of Minimum Effort Driven Faceted Search for Databases. In Proc. of COMAD 2009. (Acceptance rate 38%)
        {Journal Article }
      2009
      • Muhammed Miah, Gautam Das, Vagelis Hristidis, Heikki Mannila: Determining Attributes to Maximize Visibility of Objects. To appear in IEEE Transactions on Data Engineering (TKDE), 2009.
        {Journal Article }
      2009
      • Arjun Dasgupta, Nan Zhang, Gautam Das, Surajit Chaudhuri: Privacy Preservation of Aggregates in Hidden Databases: Why and How? Full Paper, in Proc. of SIGMOD 2009. (Acceptance rate 15.9%)
        {Journal Article }
      2009
      • Anirban Maiti, Arjun Dasgupta, Nan Zhang, Gautam Das: HDSampler: Revealing Data behind Web Form Interfaces. Demo paper, in Proc. of SIGMOD 2009. (Acceptance rate 37%)
        {Journal Article }
      2009
      • Benjamin Arai, Gautam Das, Dimitrios Gunopulos, Nick Koudas: Anytime Measures for Top-k Algorithms on Exact and Fuzzy Data Sets.  Invited Paper, VLDB Journal 18(2): 407-427 (2009) on special issue of Best Papers of VLDB 2007.
        {Journal Article }
      2009
      • Gautam Das: Top-k Algorithms and Applications. Tutorial at DASFAA 2009.
        {Journal Article }
      2009
      • Gautam Das, Nan Zhang: Privacy Risks in Health Databases from Aggregate Disclosure. In Proc PSPAE/PETRA 2009.
        {Journal Article }
      2009
      • Gautam Das, Nan Zhang: Aggregates Disclosure in Hidden Web Databases: An Urgent Challenge, Position paper, NSF Workshop on Data and Applications Security, 2009.
        {Journal Article }
      2009
      • Arjun Dasgupta, Nan Zhang, Gautam Das: Leveraging COUNT Information in Sampling Hidden Databases. Full paper, in Proc. ICDE 2009. (Acceptance rate 17%)
        {Journal Article }
      2009
      • Nikos Sarkas, Nilesh Bansal, Gautam Das, Nick Koudas: Measure Driven Keyword Query Expansion. Full paper, in VLDB 2009. (Acceptance rate 16.7%)
        {Journal Article }

      Journal Article 2008
      • G. Das, N. Sarkas, N. Koudas: Categorical Skylines for Streaming Data. In Proc. of SIGMOD 2008. (Acceptance rate 18%)
        {Journal Article }
      2008
      • Muhammed Miah, Gautam Das, Vagelis Hristidis, Heikki Mannila: Standing Out in a Crowd: Selecting Attributes for Maximum Visibility. In Proc. ICDE 2008 (Acceptance rate 19%)
        {Journal Article }
      2008
      • Gautam Das, Nick Koudas, Manos Papagelis, Sushruth Puttaswamy: Efficient Sampling of Information in Social Networks. In Proc. CIKM/SSM 2008
        {Journal Article }
      2008
      • Z. Joseph, G. Das, L. Fegaras: Distinct Value Estimation in Unstructured P2P Databases. In Proc. PETRA 2008
        {Journal Article }
      2008
      • Song Lin, Benjamin Arai, Dimitrios Gunopulos, Gautam Das: Energy Efficient Adaptive Region Sampling in Sensor Networks. In Proc. ICDE 2008. (Acceptance rate 19%)
        {Journal Article }
      2008
      • Senjuti Basu Roy, Haidong Wang, Ullas Nambiar, Gautam Das and Mukesh Mohania: Minimum-Effort Driven Dynamic Faceted Search in Structured Databases. In Proc. CIKM 2008. (Acceptance rate 17%)
        {Journal Article }
      2008
      • P. Miettinen, T. Mielikainen, A. Gionis, G. Das, H. Mannila: The Discrete Basis Problem. In IEEE Transactions on Data Engineering (TKDE), 2008, pp. 1348-1362.
        {Journal Article }

      Journal Article 2007
      • Gautam Das, Dimitrios Gunopulos, Nick Koudas, Dimitris Tsirogiannis. Answering Top-k Queries Using Views. HDMS 2007
        {Journal Article }
      2007
      • G. Das: Random Sampling from Databases and Applications. Invited Tutorial at the Intl. Conference on Information Technology ICIT 2007.
        {Journal Article }
      2007
      • G. D. Arai and N. K. Gunopulos. "Anytime Measures for Top-k Algorithms," VLDB, pp. 914-925, 2007.
        {Journal Article }
      2007
      • D. G. Das and N. S. Koudas. "Ad-hoc Top-k Query Answering for Data Streams," VLDB, pp. 183-194, 2007.
        {Journal Article }
      2007
      • S. Basu, G. D. Roy, and S. Das. "Computing Best Coverage Path in the Presence of Obstacles in a Sensor Field," WADS, pp. 577-588, 2007.
        {Journal Article }
      2007
      • G. D. Dasgupta and H. Mannila. "A random walk approach to sampling hidden databases," SIGMOD Conference, pp. 629-640, 2007.
        {Journal Article }
      2007
      • G. D. Kapoor, S. S. Hristidis, and G. Weikum. "STAR: A System for Tuple and Attribute Ranking of Query Answers," ICDE, pp. 1483-1484, 2007.
        {Journal Article }
      2007
      • G. D. Arai and V. K. Gunopulos. "Efficient Approximate Query Processing in Peer-to-Peer Networks," IEEE Trans. Knowl. Data Eng., vol. 19, no. 7, pp. 919-933, 2007.
        {Journal Article }
      2007
      • G. D. Chaudhuri and V. Narasayya. "Optimized Stratified Sampling for Approximate Query Processing," ACM Transactions on Database Systems (TODS), vol. 32, no. 2, pp. 9, 2007.
        {Journal Article }

      Journal Article 2006
      • L. Fegaras, W. He, G. Das, and D. Levine. XML Query Routing in Structured P2P Systems. DBISP2P 2006 workshop in conjunction with VLDB 2006.
        {Journal Article }
      2006
      • D. G. Das and D. T. Koudas. "Answering Top-k Queries Using Views," VLDB, 2006.
        {Journal Article }
      2006
      • G. D. Arai, D. Gunopulos, and V. Kalogeraki. "Approximating Aggregations in Peer-to-Peer Databases," HDMS, 2006.
        {Journal Article }
      2006
      • V. H. Das, N. Kapoor, and S. Sudarshan. "Ordering the Attributes of Query Results," SIGMOD, 2006.
        {Journal Article }
      2006
      • G. D. Arai, D. Gunopulos, and V. Kalogeraki. "Approximating Aggregation Queries in Peer-to-Peer Networks," ICDE, 2006.
        {Journal Article }
      2006
      • G. D. Chaudhuri and G. W. Hristidis. "Probabilistic Information Retrieval Approach for Ranking of Database Query Results," ACM Transactions on Database Systems (TODS), vol. 31, no. 3, pp. 1134-1168, 2006.
        {Journal Article }
      2006
      • T. M. Miettinen, G. D. Gionis, and H. Mannila. "The Discrete Basis Problem," PKDD, 2006.
        {Journal Article }

      Journal Article 2005
      • Gautam Das: Sampling Methods in Approximate Query Answering Systems. Invited Book Chapter, Encyclopedia of Data Warehousing and Mining. Editor John Wang, Information Science Publishing, 2005.
        {Journal Article }
      2005
      • Chotirat Ann Ratanamahatana, Jessica Lin, Dimitrios Gunopulos, Eamonn Keogh, Michail Vlachos, and Gautam Das. Mining Time Series Data. In O. Maimon and Rokach (eds.), Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers, Kluwer Academic Publishers. 2005.
        {Journal Article }
      2005
      • G. Das. "Approximate Query Processing Techniques. Invited Tutorial at the 11th International Conference on Management of Data," COMAD, 2005.
        {Journal Article }
      2005
      • G. D. Afrati, H. M. Gionis, and P. T. Mielikainen. "Mining Chains of Relations," ICDM, 2005.
        {Journal Article }
      2005
      • G. Das. "Approximate Query Processing," Tutorial, SBBD, 2005.
        {Journal Article }

      Conference Paper 2004
      • Surajit Chaudhuri, Gautam Das, Utkarsh Srivastava: Effective Use of Block-Level Sampling in Statistics Estimation. SIGMOD Conference 2004.
        {Conference Paper }

      Journal Article 2004
      • Michalis Vlachos, Dimitrios Gunopulos, Gautam Das: Rotation Invariant Measures for Trajectories. KDD 2004.
        {Journal Article }
      2004
      • Surajit Chaudhuri, Gautam Das, Vagelis Hristidis, Gerhard Weikum: Probabilistic Ranking of Database Query Results. VLDB 2004.
        {Journal Article }
      2004
      • Yi-Min Wang, Lili Qiu, Chad Verbowski, Dimitris Achlioptas, Gautam Das, Paul Larson: Summary-based Routing for Content-based Event Distribution Networks. Computer Communication Review (CCR) Oct. 2004.
        {Journal Article }

      Conference Paper 2003
      • Brian Babcock, Surajit Chaudhuri, Gautam Das: Dynamic Sample Selection for Approximate Query Processing. SIGMOD Conference 2003.
        {Conference Paper }
      2003
      • Brian Babcock, Surajit Chaudhuri, Gautam Das: Dynamic Sample Selection for Approximate Query Processing. SIGMOD Conference 2003.
        {Conference Paper }

      Journal Article 2003
      • Gautam Das: Survey of Approximate Query Processing Techniques. (Invited Tutorial) SSDBM 2003.
        {Journal Article }

      Book Chapter 2003
      • Michail Vlachos, Dimitrios Gunopulos, Gautam Das: Indexing Time-Series Under Conditions of Noise, Invited Chapter in Data Mining in Time Series Data Bases, World Scientific Publishing, 2003.
        {Book Chapter }
      2003
      • Gautam Das, Dimitrios Gunopulos: Time Series Similarity and Indexing. Invited Chapter in Handbook on Data Mining, Lawrence Erlbaum Associates, 2003.
        {Book Chapter }

      Journal Article 2002
      • Binay K. Bhattacharya, Gautam Das, Asish Mukhopadhyay, Giri Narasimhan: Optimally Computing a Shortest Weakly Visible Line Segment Inside a Simple Polygon. Computational Geometry 23(1): 1-29 (2002).
        {Journal Article }
      2002
      • Sanjay Agrawal, Surajit Chaudhuri, Gautam Das: DBXplorer: A System For Keyword-Based Search Over Relational Databases. ICDE 2002.
        {Journal Article }

      Conference Paper 2002
      • Sanjay Agrawal, Surajit Chaudhuri, Gautam Das: DBXplorer: Enabling Keyword Search over Relational Databases. (Demo), SIGMOD Conference 2002: 627.
        {Conference Paper }

      Book Chapter 2002
      • Yi-Min Wang, Lili Qiu, Dimitris Achlioptas, Gautam Das, Paul Larson, Helen J. Wang. Subscription Partitioning and Routing in Content-based Publish/Subscribe Networks. 16th International Symposium on DIStributed Computing (DISC'02), 2002.
        {Book Chapter }

      Conference Paper 2001
      • Surajit Chaudhuri, Gautam Das, Vivek Narasayya: A Robust, Optimization-Based Approach for Approximate Answering of Aggregate Queries. SIGMOD Conference 2001.
        {Conference Paper }
      2001
      • Dimitrios Gunopulos, Gautam Das: Time Series Similarity Measures and Time Series Indexing. (Tutorial), SIGMOD Conference 2001.
        {Conference Paper }

      Journal Article 2001
      • Surajit Chaudhuri, Gautam Das, Mayur Datar, Rajeev Motwani, Vivek Narasayya: Overcoming Limitations of Sampling for Aggregation Queries. ICDE 2001.
        {Journal Article }
      2001
      • Béla Bollobás, Gautam Das, Dimitrios Gunopulos, Heikki Mannila: Time-Series Similarity Problems and Well-Separated Geometric Sets. Nordic Journal of Computing, 8(4):409-423, 2001.
        {Journal Article }
      2001
      • Danny Z. Chen, Gautam Das, Michiel H. M. Smid: Lower Bounds for Computing Geometric Spanners and Approximate Shortest Paths. Discrete Applied Mathematics 110(2-3): 151-167 (2001).
        {Journal Article }

      Journal Article 2000
      • Gautam Das, Heikki Mannila: Context-Based Similarity Measures for Categorical Databases. PKDD 2000: 201-210.
        {Journal Article }
      2000
      • Gautam Das, Michiel H. Smid: A Lower Bound for Approximating the Geometric Minimum Weight Matching. Information Processing Letters 74(5-6): 253-255 (2000).
        {Journal Article }
      2000
      • Dimitrios Gunopulos, Gautam Das: Time Series Similarity Measures. (Tutorial), KDD 2000.
        {Journal Article }

      Journal Article 1998
      • Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Renganathan, Padhraic Smyth: Rule Discovery from Time Series. KDD 1998: 16-22.
        {Journal Article }
      1998
      • Gautam Das, Heikki Mannila, Pirjo Ronkainen: Similarity of Attributes by External Probes. KDD 1998: 23-29
        {Journal Article }

Courses

      • CSE 5301-002 DESIGN AND ANALYSIS OF ALGORITHMS
        Techniques for analyzing upper bounds for algorithms and lower bounds for problems. Problem areas include: sorting, data structures, graphs, dynamic programming, combinatorial algorithms, introduction to parallel models.
        Spring - Regular Academic Session - 2011 Download Syllabus
      • CSE 6339-001 Web Search, Mining, and Integration
        Special topics in advanced database systems. May be repeated for credit when topics vary. For this semester, topics include Approximate Query Processing, Information Retrieval, and Social Networks
        Spring - Regular Academic Session - 2011 Download Syllabus

Other Service Activities

  • Uncategorized
    • Dec  National Science Foundation
      panelist and reviewer
    • Dec  Israel Science Foundation
      panelist and reviewer