List of Papers and Books

• Introduction, Motivation (IM)

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  1. IT Roadmap to a Geospatial Future, Computer Science and Telecommunications Board (National Academies), 2003 (ISBN: 0-309-08738-4).
  2. 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.
  3. 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.
  4. Spatial Pattern Recognition in Environmental and Health Sciences: A Perspective, G. M. Jacques, D. Greiling, A. Kaufmann, BioMedware and TerraSeer Inc.
  5. Google Earth , A Project at Google.
  6. 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.
  7. Spatial Databases: Technologies, Techniques and Trends by Y. Manalopoulos, A. Papadopoulos, M. Gr. Vassilakopoulos; published by IDEA GROUP INC., 2005.
  
  
• Models for Spatial Data (MSD)

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  1. Max Egenhofer et al., Nine-intersection model for topological relationship among spatial objects. was published in " Egenhofer M.J. and Herring,
     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.
  5. Penninga, F., 3D Topographic data modeling: Why rigidity is preferable to pragmatism. In: Spatial Information Theory, Cosit 2005, Lecture Notes in Computer Science, Vol. 3693, pp.409-425, 2005
  
  
• Query Languages (QL)

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  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.
  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).
• Computing and Managing Cardinal Direction Relations, S. Skiadopoulos, C. Giannoukos, N. Sarkas, P. Vassiliadis, T. Sellis, M. Koubarakis, IEEE Transactions on Knowledge and Data Engineering, Volume 17, Issue 12, pg. 1610 - 1623, 2005
  
• Storage and access methods (SA)

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  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.
  4. 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.
  5. Yufei Tao, Dimitris Papadias. "Performance Analysis of R*-Trees with Arbitrary Node Extents," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 6, pp. 653-668, June, 2004.
  
  
• Query Processing Algorithms (QP)

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  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. 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.
  4. Shashi Shekhar, Chang-Tien Lu, Sanjay Chawla, Sivakumar Ravada: Efficient Join-Index-Based Spatial-Join Processing: A Clustering Approach. IEEE Trans. Knowl. Data Eng. 14(6): 1400-1421 (2002)
  5. Nikos Mamoulis, Dimitris Papadias: Slot Index Spatial Join. IEEE Trans. Knowl. Data Eng. 15(1): 211-231 (2003)
  6. Jun-Wong Song, Kyu-Young Whang, Young-Koo Lee, Min-Jae Lee, Sang-Wook Kim: Spatial Join Processing Using Corner Transformation. IEEE Trans. Knowl. Data Eng. 11(4): 688-695 (1999)
  7. Yufei Tao, Dimitris Papadias. "Range Aggregate Processing in Spatial Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1555-1570, December, 2004.
  8. Haibo Hu, Dik Lun Lee. "Range Nearest-Neighbor Query," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 78-91, January, 2006.
  
  
• Query Optimization (QO)

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  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)

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  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).
  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. Qingsong Lu, Betsy George, and Shashi Shekhar, Capacity Constrained Routing Algorithms for Evacuation Planning: A Summary of Results, Advances in Spatial and Temporal Databases, Proceeding of 9th International Symposium on Spatial and Temporal Databases (SSTD'05), Angra dos Reis, Brazil, August 22-24, 2005.
  6. Jochen Schiller, et al., Location-Based Services, The Morgan Kaufmann Series in Data Management Systems, 2004, ISBN 1558609296.
  7. M. L. Yiu, N. Mamoulis, and D. Papadias, Aggregate Nearest Neighbor Queries in Road Networks, IEEE Transactions on Knowledge and Data Engineering (TKDE), 17(6): 820-833, June 2005.
  8. Erik G. Hoel, Wee-Liang Heng, Dale Honeycutt, High Performance Multimodal Networks. SSTD 2005: 308-327
  9. Alminas Civilis, Christian S. Jensen, Stardas Pakalnis: Techniques for Efficient Road-Network-Based Tracking of Moving Objects. IEEE Trans. Knowl. Data Eng. 17(5): 698-712 (2005)
  
  
• Spatial Data Mining (SDM)

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  1. Spatial Pattern Recognition in Environmental and Health Sciences: A Perspective, G. M. Jacquez et al.
  2. Yan Huang, Shashi Shekhar, Hui Xiong: Discovering Colocation Patterns from Spatial Data Sets: A General Approach. IEEE Trans. Knowl. Data Eng. 16(12): 1472-1485 (2004)
  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.
  5. Statistics for Spatial Data (Wiley Series in Probability and Statistics), Noel A. C. Cressie, Wiley-Interscience, 1993 (ISBN 0471002550).
  6. S. Shekhar, P. Zhang, Y. Huang, R. Vatsavai, Trend in Spatial Data Mining, as a chapter to appear in Data Mining: Next Generation Challenges and Future Directions, H. Kargupta, A. Joshi, K. Sivakumar, and Y. Yesha(eds.), AAAI/MIT Press, 2003
  7. Jin Soung Yoo, Shashi Shekhar and Mete Celik, A Join-less Approach for Co-location Pattern Mining: A Summary of Results, IEEE International Conference on Data Mining(ICDM), 2005
  
  
• Rastor Data, Data Warehouses (RDDW)

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  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.
  3. Hanan Samet, Aya Soffer, Image Database Systems and Techniques: A Symbolic Approach, Morgan Kaufmann Publishers, ISBN: 1558605495, December, 2006
  4. H. Samet and A. Soffer. MARCO: MAp Retrieval by COntent. IEEE Transactions on PAMI, 18(8):783--798, Aug. 1996.
  5. Po-Whei Huang, Chu-Hui Lee. "Image Database Design Based on 9D-SPA Representation for Spatial Relations", IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1486-1496, December, 2004.
  
  
• Spatio-temporal databases (STD)

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  1. Selected Chapters from "Manolis Koubarakis, The CHOROCHRONOS Approach, 2003",
     
     1. Chapter1
     2. Chapter2
     3. Chapter3
     4. Chapter4
     5. Chapter5
     6. Chapter6
     7. Chapter7
     8. Chapter8
     9. Chapter9
        
     10. The link to the whole book.
  2. Vijay Khatri, Sudha Ram, Richard T. Snodgrass. "Augmenting a Conceptual Model with Geospatiotemporal Annotations," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 11, pp. 1324-1338, November, 2004.
  3. Ines Vega Lopez, Richard T. Snodgrass, and Bongki Moon, "Spatiotemporal Aggregate Computation: A Survey," IEEE Transactions on Knowledge and Data Engineering 17(2):271-286, February 2005.
  
  
• Mobile objects (MO)

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  1. 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.
     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
  2. A tutourial on location based services, Graham Chen
  3. "Exploiting Spatial Autocorrelation to Efficiently Process Correlation-Based Similarity Queries", the 8th Symp. on Spatial and Temporal Databases, 2003
  4. Ralf Hartmut Guting, Markus Schneider, Moving Objects Databases (The Morgan Kaufmann Series in Data Management Systems) Morgan Kaufmann, 2005
  
  
• Security, Cyber-infra-structure (SC)

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  1. Selected talks at congressional breakfast in Feb. 2004 on GIS and Homeland Defense : UCGIS page , Direction magazine article
  2. Spying with Maps: Surveillance Technologies and the Future of Privacy, Mark Monmonier, U of Chicago Press, 2002 (ISBN 0226534278).
  3. National Science Foundation Blue Ribbon Advisory Panel on Cyberinfrastructure, Revolutionizing Science and Engineering through Cyberinfrastructure, 2003; and
     What is Cyberinfrastructure? (Appendix A)
  4. V. Atluri and P. Mazzoleni, A Uniform Indexing Scheme for Geo-spatial Data and Authorizations, 16th IFIP WG11.3 Working Conference on Database Security, July 2002.
  
  
• Next Generation Application: Sensor N/W, Geo Info. Retrieval, Ontology, Parallel GIS, etc. (NGA)

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  1. Qishi Wu, Nageswara S.V. Rao, Jacob Barhen, S. Sitharama Iyengar, Vijay K. Vaishnavi, Hairong Qi, Krishnendu Chakrabarty. On Computing Mobile Agent Routes for Data Fusion in Distributed Sensor Networks, IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 6, pp. 740-753, June, 2004.
  2. Manli Zhu, Dimitris Papadias, Jun Zhang, Dik Lun Lee, Top-k Spatial Joins, IEEE Trans. Knowl. Data Eng. 17(4): 567-579 (2005)
  3. F. Fonseca and M. Egenhofer,(1999) Ontology-Driven Geographic Information Systems. in: C. B. Medeiros (Ed.), 7th ACM Symposium on Advances in Geographic Information Systems, Kansas City, MO, pp. 14-19.
  4. Efficient Geometry-Based Similarity search of 3D spatial databases, Daniel A. Keim, 1999 ACM SIGMOD Philadelphia, PA, pg. 419-430.
  5. 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.
  
  
• Vision and Politics

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  1. CRA Grand Challenges in Computer Science
  2. The Lowell Database Research Self Assessment: A Report on DB research challenges and needs, June, 2003
  3. John F. Roddick, Max J. Egenhofer, Erik G. Hoel, Dimitris Papadias, Betty Salzberg: Spatial, Temporal and Spatio-Temporal Databases - Hot Issues and Directions for PhD Research. SIGMOD Record 33(2): 126-131 (2004)
  4. Related links
  
  
• Research and Creativity

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  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.
  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. Armour, The Five Orders of Ignorance, Communications of the ACM, 43(10), October 2000, pp. 19-22
  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
  7. Computing Research Association, Why choose a Ph.D. in CS?
  8. Ronald T. Azuma, So Long, and thanks for the Ph.D.!