List of Papers

Introduction, Motivation (IM)
1. IT Roadmap to a Geospatial Future, Computer Science and Telecommunications Board (National Academies), 2003 (ISBN: 0-309-08738-4).
2. GIS in Homeland Defense: An ESRI Whitepaper (Nov. 2001),
  Defense Science Panel report on NIMA (2000) , and
  NIMA/NGA Geospatial Intelligence Capstone Concept (2003)
3. The NASA Digital Earth Testbed.,Jeff de La Beaujardiere, Horace Mitchell, Robert Raskin, and Ananth Rao, 2000 Conference on Information and Knowledge Management McLean, VA 8th ACM Symposium on Advances in Geographic Information Systems, pg. 47-53.
4. Spatial Databases: Accomplishments and Research Needs, S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu and C.T. Liu, IEEE Transactions on Knowledge and Data Engineering, Jan.-Feb. 1999.
5. Spatial Pattern Recognition in Environmental and Health Sciences: A Perspective, G. M. Jacques, D. Greiling, A. Kaufmann, BioMedware and TerraSeer Inc.
6. Spatial albums , A Project at Microsoft Research.
7. Hess RL, Rubin RS, and West LA, Geographic information systems as a marketing information system technology, Decision Support Systems. 38(2):197-212, 2004 Nov.
Research and Creativity,
1. Daniel Saunders, Paul Thagard, Creativity in Computer Science, In J. C. Kaufman & J. Baer (Eds.), Creativity across domains: Faces of the muse. Mahwah, NJ: Lawrence Erlbaum Associates (forthcoming). (http://cogsci.uwaterloo.ca/Articles/creativity.pdf)
2. W. C. Booth, et al., The Craft of Research , U of Chicago Press, 2003, 2nd Ed., ISBN 0-226-06568-5.
3. Bland, Carole J., Anne Marie Weber-Main, Sharon Marie Lund, and Deborah A. Finstad. The Research Productive Department. Bolton, MA: Anker Publishing, Inc., 2005.
4. Phillip G. Amrmour, The Five Orders of Ignorance, Communications of the ACM, 43(10), October 2000, pp. 19-22 ( pdf )
5. D. K. Smith, T. Moores and J. Chang, Prepare Your Mind For Learning, Communications of the ACM, 48(9), September 2005, pp. 115-118.
6. Evaluating Computer Scientists and Engineers for Promotion and Tenure, ( http://www.cra.org/reports/tenure_review.html)
7. Computing Research Association, Why choose a Ph.D. in CS? (http://www.cra.org/reports/why.cs.phd.pdf)
Models for Spatial Data (MSD)
1. Max Egenhofer et al., Nine-intersection model for topological relationship among spatial objects. was published in " Egenhofer M.J. and Herring,
  ( Paper MSD 1 - Class presentation by Group G8 )
  1. Categorizing binary topological relationships between regions, lines, and points in geographic databases, Tech. Report., Department of Surveying Engineering, University of Maine, Orono, ME 1991.
  2. Modeling spatial relations between lines and regions: combining formal mathematical models and human subject testing, Egenhofer et al, Cartography and GIS, 21(3), 1994.
  3. Describing topological relationships with vornoi based 9-intersection model, J. Chen et al., IAPRS, 32(4).
2. Alternative framework from AI using logic (ask Alex), OGC Abstract Specifications such as Geometry specification ; spatial ontology .
3. Modelling Geospatial Application Databases Using UML-based Repositories aligned with International Standards in Geomatics,Jean Brodeur, Yvan Bedard and Marie-Josee Proulx, 2000 Conference on Information and Knowledge Management McLean, VA 8th ACM Symposium on Advances in Geographic Information Systems, pg. 39-46.
4. Spatial Pictorgram Enhanced Conceptual Data Models and Their Translation to Logical Data Models, S. Shekhar, R. Vatsavai, S.Chawla, T. Burk, Integrated Spatial Databases: Digital Images and GIS. Lecture Notes in Computer Science, Vol. 1737, Springer Verlag, Dec. 1999. Editors: P. Agouris and A. Stefanidis.
Query Languages (QL)
1. SQL/SDA: A Query Language for Supporting Spatial Data Analysis and Its Web-Based Implementation H. Lin and B. Huang, IEEE Transaction of Knowledge and Data Engineering(TKDE), July/August, 2001. ( Paper QL 1 - Class presentation by Group G2 )
2. An Object Model of Direction and Its Implications, S. Shekhar, X. Liu, S. Chawla, GeoInformatica 3:4, 357-379 (1999), Kluwer Academic Publishers; A summary of results was among 5 best papers in Sixth International Symposium on Advances in Geographic Information System(1998).
Storage and access methods (SA)
1. Indexing Medium-Dimensionality Data in Oracle, K.V. Ravi Kanth, Siva Ravada, Jayant Sharma, and Jay Banerjee, 1999 ACM SIGMOD Philadelphia, PA, pg. 521-522.
2. Analysis of the Clustering Properties of the Hilbert Space-Filling Curve, Bongki Moon, H.v. Jagadish, Christos Faloutsos, Joel H. Saltz, IEEE Transactions on Knowledge and Data Eng., January/February 2001 (Vol. 13, No. 1), pp. 124-141
3. Efficient Concurrency Control in Multidimensional Access Methods, Kaushik Chakrabarti and Sharad Mehrota, 1999 ACM SIGMOD Philadelphia, PA pg. 25-36.
Query Processing Algorithms (QP)
1. Scalable Sweeping-Based Spatial Join, Lars Arge, Octavian Procopiuc, Sridhar Ramaswamy, Torsten Suel, Jeffrey Scott Vitter, VLDB 1998: New York City, USA Subject- Spatial Data, pg. 570-581.
2. Optimal Multi-Step K-Nearest Neighbor Search, Thomas Seidl and Hans-Peter Kriegel, 1998 ACM SIGMOD Seattle, WA, pg. 154-165.
3. The Effect of Buffering on the Performance of R-Trees, Scott T. Leutenegger, Mario A. López, IEEE Transactions on Knowledge and Data Eng., January/February 2000 (Vol. 12, No. 1), pp. 33-44.
4. Processing Object-orientation-based Direction Queries, X. Liu, S. Shekhar, S. Chawla, Technical Report TR00-030, University of Minnesota, Minneapolis, MN. Submitted to IEEE Transaction of Knowledge and Data Engineering(TKDE). A summary of results was accepted in ACMGIS 2000.
Query Optimization (QO)
1. Selectivity Estimation in Spatial Databases, Swarup Acharya, Viswanath Poosala, and Sridhar Ramaswamy, 1999 ACM SIGMOD Philadelphia, PA, pg. 13-24.
2. Efficient Cost Models for Spatial Queries Using R-Trees, Yannis Theodoridis, Emmanuel Stefanakis, Timos Sellis ,IEEE Transactions on Knowledge and Data Eng., January/February 2000 (Vol. 12, No. 1).
Spatial Networks (SN)
1. Hierarchical Encoded Path Views for Path Query Processing: An Optimal Model and Its Performance Evaluation, Ning Jing, Yun-Wu Huang, Elke A. Rundensteiner, IEEE Transactions on Knowledge and Data Eng., May/June 1998 (Vol. 10, No. 3). ( Paper SN 1 - Class presentation by Group G10 )
2. Finding Shortest Paths in Large Network Systems, , Edward P.F. Chan, Ning Zhang, Proc. ACM Conf. on GIS, 2001.
3. Materialization Trade-Offs in Hierarchical Shortest Path Algorithms, S. Shekhar, A. Fetterer, and B. Goyal, Proc. Intl. Symp. on Large Spatial Databases, Springer Verlag (Lecture Notes in Computer Science), (1997).
4. CCAM: A Connectivity-Clustered Access Method for Networks and Network Computations, S. Shekhar and D. R. Liu, IEEE Trans. on Knowledge and Data Engineering, Vol. 9, No. 1, Jan. 1997. Csci TR 93-78.
5. Horner MW, and Murray AT, Spatial representation and scale impacts in transit service assessment Environment & Planning B-Planning & Design. 31(5):785-797, 2004 Sep.
Spatial Data Mining (SDM)
1. Spatial Pattern Recognition in Environmental and Health Sciences: A Perspective, G. M. Jacquez et al.
2. Discovering Spatial Co-location Patterns: A Summary of Results , S. Shekhar and Y. Huang, Proc. in 7th International Symposium on Spatial and Temporal Databases(SSTD01), L.A., CA, July 2001. ( Paper SDM 2 - Class presentation by Group G9 )
3. A Unified Approach to Detecting Spatial Outliers , S. Shekhar, C. T. Lu, and P. Zhang, GeoInformatica, 2003 (A shorter version appeared in SIGKDD 2001).
4. Spatial Contextual Classification and Prediction Models for Mining Geospatial Data, S. Shekhar, V. R. Raju, P. Schrater, W. Wu, IEEE Trans. on Multimedia Systems, vol4. No.2, June 2002.
Rastor Data, Data Warehouses (RDDW)
1. Microsoft TerraServer: A Spatial Data Warehouse, Tom Barclay, Jim Gray, and Don Slutz, 2000 ACM SIGMOD Dallas, TX, pg. 307-318
2. WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases, Aidong Zhang et. al., VLDB 1998: New York City, USA, pg. 428-439.
Mobile objects, and Spatio-temporal databases (MOSTD)
1. Selected Chapters from "Manolis Koubarakis, The CHOROCHRONOS Approach, 2003",
  1. Chapter1
  2. Chapter2 ( Paper MOSTD.1.2 - Class presentation by Group G11 )
  3. Chapter3
  4. Chapter4
  5. Chapter5
  6. Chapter6 ( Paper MOSTD 1.6 - Class presentation by Group G4 )
  7. Chapter7
  8. Chapter8
  9. Chapter9
  10. The link to the whole book.
2. Walid Aref, ACMGIS 2003, A paper on location based services:
  1. Nearest Neighbor Queries in Road Networks, Jensen et. al.
  2. Processing In-Route Nearest Neighbor Queries: A Comparison of Alternative Approaches, Shekhar and Yoo.
  3. Towards Scalable Location-aware Services: Requirements and Resarch Issues, Mokbel et. al. ( Paper MOSTD 4 -Class presentation by Group G3 )
  4. A Predictive Location Model for Location-Based Services, Karimi and Liu
  5. Computational Data Modeling for Network Constrained Moving Objects, Speicys et. al.
  6. Indexing of Network Constrained Moving Objects, Proser and Jensen
3. A tutourial on location based services, Graham Chen
Security, Cyber-infra-structure, Next Genration Systems (SCN)
1. Selected talks at congressional breakfast in Feb. 2004 on GIS and Homeland Defense : ucgis page , Direction magazine article ,
3. National Science Foundation Blue Ribbon Advisory Panel on Cyberinfrastructure, Revolutionizing Science and Engineering through Cyberinfrastructure, 2003; and
  What is Cyberinfrastructure? (Appendix A),
  Cyberinfrastructure and homeland defense, ( Paper SCN 1.3 - Class presentation by Group G1 )
  GEON: A Cyberinfrastructure for Geosciences,
4. A Uniform Indexing Scheme for Geo-spatial Data and Authorizations, V. Atluri, P. Mazzoleni.
5. Spying with Maps: Surveillance Technologies and the Future of Privacy, Mark Monmonier, U of Chicago Press, 2002 (ISBN 0226534278).
6. Efficient Geometry-Based Similarity search of 3D spatial databases,Daniel A. Keim, 1999 ACM SIGMOD Philadelphia, PA, pg. 419-430. ( Paper SCN3 - Class presentation by Group G5 )
7. Declustering and Load-Balancing Methods for Parallelizing Spatial Databases, S. Shekhar, S. Ravada, V. Kumar, D. Chubb, and G. Turner, IEEE Transactions on Knowledge and Data Engineering. ( Paper SCN4 - Class presentation by Group G6 )
Vision and Politics

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