Attention: The information contained in this page is subject to changes.
| Requirement | Required Readings | General/Recommended Readings | Reading Summary Posting |
There will be several background readings assigned each week. The readings will either be handed out a week before or listed on the Web page for required readings.
You are expected to read the material each week and write 2-3 paragraphs per reading giving your impressions and thoughts. The summaries should be informal and brief, and should consist of your own comments on the readings, NOT a rehash of the content.
You should email your summaries to TA: Gong Zhang (gzhang3 AT cc DOT gatech DOT edu), preferably before each class but no later than 11:59 pm on Friday each week (unless there is no reading assignments for the week). Late assignment will NOT be accepted unless approved in advance by the instructor.
The summary for each reading assignment is expected to consist of 1 paragraph on each of the following three aspects: (1) the positive aspect of the paper; (2) the negative aspect of the paper; and (3) a brief discussion on how the idea or method proposed or used in evaluation may be applied to your own project for the course.
You may want to keep these guidelines in mind when reading papers.
You may find the following short article helpful:
Efficient Reading of Papers in Science and Technology By Michael J. Hanson and updated by D. McNamee
2.
Spatial Indexing Techniques
7. Data Mining
8. Privacy Preserving Data Mining
You are expected to read papers in the required reading list, but only write
summary for one paper selected from the list of 2-3 required readings
associated with each lecture. Please use the Summary Template to write the reading
summaries.
NOTE: Most of the papers listed below are from ACM or IEEE conferences or Journals. Online proceedings can be accessible from the ACM /IEEE online library link provided by GT library. Your GT ID/Password are required to access the online library.
http://www.library.gatech.edu/research_help/subject/index.php?/computer_science/conferences
1.
Bugra Gedik
and Ling Liu. MobiEyes: A Distributed Location Monitoring Service Using Moving
Location Queries. IEEE Transactions on
2. Kipp Jones and Ling Liu. Map-matching: Towards Improving Wireless Positioning,
to appear in Proceedings of the 4th Annual International Conference on
3. Anand Murugappan and Ling Liu. A SpatioTemporal Placement Model
for Caching Location Dependent Queries, Proceedings of the 4th
Annual International Conference on Mobile and Ubiquitous Systems: Computing,
Networking and Services (Mobiquitous 2007). August
6-10, 2007,
4. Bugra Gedik, Ling Liu, Kun-Lung
Wu, Philip S. Yu. Lira: Lightweight, Region-aware Load Shedding in
Mobile CQ Systems. Proceedings of the IEEE 23rd International
Conference on Data Engineering.
5. Christian S. Jensen, Dan Lin, Beng Chin Ooi, Rui Zhang: Effective Density Queries on Continuously Moving Objects. ICDE 2006
6. Mindaugas Pelanis, Simonas Saltenis, Christian S. Jensen: Indexing the past, present, and anticipated future positions of moving objects. ACM Trans. Database Syst. 31(1): 255-298 (2006)
7. Hu, H., Lee, D.L., and Xu, J. Fast Nearest Neighbor Search on
Road Networks. Proceedings of the International Conference on
Extending Database Technology (EDBT 2006),
8.
Hu, H., Lee, D.L., and Lee,
V.C.S. Distance Indexing on Road Networks.
Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB
2006),
9. Reynold Cheng, Yuni Xia, Sunil Prabhakar, Rahul Shah: Change Tolerant Indexing for Constantly Evolving Data. ICDE 2005: 391-402
10. Reynold Cheng, Yuni Xia, Sunil Prabhakar, Rahul Shah, Jeffrey Scott Vitter: Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data. VLDB 2004 : 876-887
11. Fosca Giannotti, Mirco Nanni, Fabio Pinelli, Dino Pedreschi. Trajectory pattern mining, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining KDD '07.
12. Project lachesis: Parsing and
modeling location histories, in: GIScience,
2004Hariharan,
13. Extracting places from traces of locations.
In Proc. WMASH, pages 110--118,
14. Xu, Wu, Tang, Lee. Monitoring Top-k Query in Wireless Sensor Networks,
Proc. the 22nd IEEE Int. Conf. on Data Engineering (ICDE '06),
15. David Mark. Geographic Information Science: Defining the Field.
16. A bibliography of temporal, spatial and spatio-temporal data mining research, John F. Roddick , Myra Spiliopoulou , ACM SIGKDD Explorations Newsletter, v.1 n.1, p.34-38, June 1999
17. Modeling Transportation Routines using Hybrid Dynamic Mixed Networks , Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, James Marca and Craig Rindt, , In 21st Conference on Uncertainty in Artificial Intelligence (UAI), 2005.
18. Sui D.Z. Tobler's First Law of Geography: A Big Idea for a Small World? Annals of the Association of American Geographers 94 (2), 269b277.
20. John Heidemann. Nirupama Bulusu. Using Geospatial Information in Sensor Networks. USC/Information Sciences Institute. September 20, 2000.
21. Building Personal Maps from GPS Data. Lin Liao and Donald J. Patterson and Dieter Fox and Henry Kautz.
22. Y. Xu, W.-C. Lee, J. Xu, and G. Mitchel Processing Window Queries in Wireless Sensor Networks, Proc. the 22nd IEEE Int. Conf. on Data Engineering (ICDE '06), Atlanta, GA, April 2006.
23. L. Liao, D. Fox, and H. Kautz. Location-Based Activity Recognition using Relational Markov Networks. Proc. of the International Joint Conference on Artificial Intelligence (IJCAI-05).
24. D. Ashbrook and T. Starner, Using GPS to Learn Significant Locations and Predict Movement Across Multiple Users, Personal and Ubiquitous Computing, Vol. 7.5.
25. C. S. Jensen and R. T. Snodgrass. Temporal Data Management. IEEE TKDE, 11(1): 36--45 (1999).
26. Liao, Fox, Kautz, Learning and Inferring Transporation Routines, Artificial Intelligence 2007.
27. Patterson, Liao, Fox, Kautz, Inferring High-Level Behavior from Low-Level Sensors, UBICOMP 2003. ICS 280.
28. Fundamental Challenges in Mobile Computing, Satyanarayanan, M., Fifteenth ACM Symposium on Principles of Distributed Computing , May 1996, Philadelphia, PA, Revised version appeared as: "Mobile Computing: Where's the Tofu?", Proceedings of the ACM Sigmobile, April 1997, Vol. 1, No. 1.
29. Multi-Fidelity Algorithms for Interactive Mobile
Applications, Satyanarayanan,
M., Narayanan, D. Proceedings of the 3rd International Workshop on Discrete
Algorithms and Methods for Mobile Computing and Communications, August 1999,
30. Mobile Data Access, Noble, B.School of Computer Science,
31. Energy-aware adaptation for mobile applications, Flinn J., Satyanarayanan, M., Proceedings of the 17th ACM Symposium on Operating Systems Principles, December, 1999, Kiawah Island Resort, SC.
32. PowerScope: A Tool for Profiling the Energy Usage of Mobile Applications, Flinn J., Satyanarayanan, M., Proceedings of the Second IEEE Workshop on Mobile Computing Systems and Applications, February, 1999, New Orleans, LA
33. System Support for Mobile, Adaptive Applications, Noble, Brian, IEEE Personal Communications, Vol. 7, No. 1, February, 2000
34. Experience with adaptive mobile applications in Odyssey , Noble, B.D. and Satyanarayanan, M., Mobile Networks and Applications, Vol. 4, 1999
35. Agile Application-Aware Adaptation for Mobility, Noble, B., Satyanarayanan, M., Narayanan, D., Tilton, J.E., Flinn, J., Walker, K. Proceedings of the 16th ACM Symposium on Operating System Principles, October 1997, St. Malo, France
36. A Research Status Report on Adaptation for Mobile Data Access , Noble, B., Satyanarayanan, M. SIGMOD Record, Vol. 24, No. 4, December 1995
37. A Programming Interface for Application-Aware Adaptation in Mobile Computing , Noble, B., Price, M., Satyanarayanan, M., Proceedings of the Second USENIX Symposium on Mobile & Location-Independent Computing, Apr. 1995, Ann Arbor, MI
38. Application-Aware Adaptation for Mobile Computing
, Satyanarayanan, M., Noble, B., Kumar, P., Price, M.
Proceedings of the 6th ACM SIGOPS European Workshop,
Sep. 1994,
39. Mobile Information Access, Satyanarayanan, M. , IEEE Personal Communications, Vol. 3, No. 1, February 1996
40. Indexing Techniques for Power Management in Multi-Attribute Data Broadcast Qinglong Hu, Wang-Chien Lee, and Dik Lun Lee.
41. Power conserving And access Efficient Indexes For Wireless Computing Dik Lun Lee, and Qinglong Hu,
42. Power Conservative Multi-Attribute Queries on Data Broadcast, Qinglong Hu, Wang-Chien Lee, and Dik Lun Lee, ICDE 2000.
43. Effects of power conservation, wireless coverage and cooperation on data dissemination among mobile devices", Maria Papadopouli and Henning Schulzrinne, ACM SIGMOBILE Symposium on Mobile Ad Hoc Networking & Computing (MobiHoc) 2001, October 4-5, 2001, Long Beach, California. (Extension of the Sarnoff paper.)
44. Energy-aware Web Caching for Mobile Terminals.
Francoise Sailhan, Valrie Issarny. In Proceedings of the ICDCS Workshop on Web
Caching Systems. July 2002,
45. Power-Controlled Data Prefetching/Caching in Wireless Packet Networks, Savvas Gitzenis and Nicholas Bambos, IEEE Infocom 2002, New York.
46. Sleepers and Workaholics: Caching Strategies in Mobile Environments. Daniel Barbara, Tomasz Imielinski,VLDB Journal 4(4): 567-602(1995).
47. Indexing techniques for data broadcast on wireless channels. D.L. Lee, Q. Hu, and W. C. Lee,Proceedings of the Fifth International Conference on Foundations of Data Organization (FODO '98), Kobe, Japan, Nov 11-12, 1998, 175-182.
48. Indexing Techniques for Wireless Data Broadcast Under Data Clustering and Scheduling,Qinglong Hu, Wang-Chien Lee, and Dik Lun Lee, in Proceedings of ACM International Conference on Information and Knowledge Management (CIKM99), Kansas City, Missouri, Nov. 1999, pp. 351-358.
49. Location Privacy in Pervasive Computing, A. R. Beresford, F. Stajano. In Proc of IEEE Pervasive Computing 46-55, March 2003
50. A Customizable k-Anonymity Model for Protecting Location Privacy. B. Gedik, L. Liu, Proc of Intl Conf of Distributed Computing Systems ICDCS, 2005.
51. Framework for Security and Privacy in Automotive Telematics. S. Duri,
M. gruteser, X. Liu, P. Moskowitz,
R. Perez, M. Sing, J. M. TangProc of
Intl Workshop on
52. Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking. M. gruteser, D. GrunwaldProc of ACM/USENIX MobiSys, 2003.
2. Indexing the positions of continuously moving objects.
S. Saltenis, C. S. Jensen, S. T. Leutenegger,
and M. A.Lopez. In SIGMOD b00: Proceedings of the
2000 ACM SIGMOD international conference on Management of data, pages
331b342,
3.
Voronoi Diagram, Franz Aurenhammer, Rolf Klein1
4. S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu and C.T. Liu, Spatial Databases: Accomplishments and Research Needs, IEEE Transactions on Knowledge and Data Engineering, Jan.-Feb. 1999.
5.
S. Shekhar and Y. Huang, Discovering Spatial Co-location Patterns: a Summary of
Results, In Proc. of 7th International
Symposium on Spatial and Temporal Databases (SSTD01), July 2001.
6. S. Shekhar, C.T. Lu, P. Zhang, Detecting Graph-based Spatial Outliers: Algorithms and Applications, the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.
7.
S. Chawla, S. Shekhar, W. Wu and U. Ozesmi,
Extending Data Mining for Spatial Applications: A Case Study in Predicting Nest
Locations, Proc. Int. Confi. on 2000 ACM SIGMOD
Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD 2000),
8. S. Chawla, S. Shekhar, W. Wu and U. Ozesmi, Modeling Spatial Dependencies for Mining Geospatial Data, First SIAM International Conference on Data Mining, 2001.
9.
S. Shekhar, P.R. Schrater, R. R. Vatsavai, W. Wu,
and
10. The Quadtree and Related Hierarchical Data Structures. Finkel and Bentley, ACM Comput. Surv.1974
11. An introductory tutorial on kd-trees, A. Moore
12. Building of Trapezoidal Map from a set of non-intersecting lines, Jukka Kaartinen
13. Spatial data structures for version management of
engineering drawings in cad database. Y. Nakamura and H. Dekihara. In ICIAP b03: Proceedings of the 12th
International Conference on Image Analysis and Processing, page 219,
1. Data Clustering: A Review, A. K. Jain, M.N. Murthy and P.J. Flynn, ACM Computing Reviews, Nov 1999.
2. On Line Clustering, Athman Bouguettaya, IEEE Transaction on Knowledge and Data Engineering Volume 8, No. 2, April 1996.
3. Similarity Searching in Medical Image Databases, Euripides G.M. Petrakis and Christos Faloutsos, IEEE Transaction on Knowledge and Data Engineering Volume 9, No. 3, MAY/JUNE 1997.
4. Windows NT Clusters for Availability and Scalability, Rob Short, Rod Gamache, John Vert and Mike Massa ,Microsoft Online Research Papers, Microsoft Corporation.
5. Defining Data Mining, The Hows and Whys of Data Mining, and How It Differs From Other Analytical Techniques, Bruce Moxon, Online Addition of DBMS Data Warehouse Supplement, August 1996.
6. An Efficient Approach to Clustering in Large Multimedia Databases with Noise. Hinneburg A., Keim D.A. Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, AAAI Press, 1998. http://citeseer.ist.psu.edu/hinneburg98efficient.html
7. Data Clustering: Theory, Algorithms, and Applications, Guojun Gan , Chaoqun Ma , Jianhong Wu
8. Chameleon: A hierarchical Clustering Algorithms Using Dynamic Modeling IEEE Computer, George Karypis, Eui-Hong Han, and Vipin Kumar, Special Issue on Data Analysis and Mining. Vol. 32, No. 8, August 1999.
9. Keke Chen and Ling Liu. ``iVIRBRATE: Interactive Visualization Based Framework for Clustering Large Datasets", ACM Transactions on Information Systems.
10.
CURE: An efficient clustering
algorithm for large databases, S. Guha,
R. Rastogi, and K. Shim, In Proceedings of ACM SIGMOD
International Conference on Management of Data, pages 73--84,
11.
BIRCH: An Efficient Data
Clustering Method for Very Large Databases, Tian Zhang, Raghu Ramakrishnan, and Miron Livny, In Proceedings of the 1996 ACM SIGMOD International
Conference on Management of Data, pages 103--114,
12. Bipartite Graph Partitioning and Data Clustering. H. Zha and X. He and C. Ding and M. Gu and H. Simon. Proc. of {ACM} 10th Int'l Conf. Information and Knowledge Management, pp. 25--31, 2001.
13. Spectral biclustering of microarray data: coclustering genes and conditions. Y. Kluger and R. Basri and J.T. Chang and M. Gerstein. Genome Research. 13:703-716, 2003.
14. Automatic subspace clustering of high dimensional data for data mining applications. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data, Seattle, Washington, June 1998
15.
A divisive information-theoretic feature clustering
algorithm for text classification. I.S. Dhillon and
16.
Subspace clustering of high-dimensional binary data -- A
probabilistic approach. A. Patrikainen
and H. Mannila. Proc. Workshop on Clustering High
Dimensional Data in {
17. Segmentation using eigenvectors: a unifying view. Weiss Y. Proceedings IEEE International Conference on Computer Vision p. 975-982 (1999).
18. Coupled two-way clustering analysis of gene microarray data. G. Getz and E. Levine and E. Domany. Proceedings of the National Academy of Sciences of the United States of America, 94:12079-12084, 2000.
19. On clusterings - good, bad and spectral, S. Vempala R. Kannan and A. Vetta, in Proc. 41st Symposium on the Foundation of Computer Science, FOCS, 2000.
20. Co-clustering documents and words using bipartite spectral graph partitioning. I.S. Dhillon. Knowledge Discovery and Data Mining, pp. 269--274, 2001.
21.
Iterative Double Clustering for Unsupervised and
Semi-Supervised Learning, R. El-Yaniv
and O. Souroujon.NIPS 14, pp. 1025-1032, 2002.
1. Continuous Queries over Data Streams John S. Breese, David Heckerman, and Carl Kadie, S. Babu and J. Widom.In SIGMOD Record, September 2001.
2.
Towards Sensor Database Systems. Philippe
Bonnet, J. E. Gehrke, and Praveen Seshadri.
In
Proceedings of the Second International Conference on
3. Querying the Physical World. Philippe Bonnet, J. E. Gehrke, and Praveen Seshadri. IEEE Personal Communications, Vol. 7, No. 5, October 2000, pages 10-15. Special Issue on Smart Spaces and Environments.
4. Fjording the Stream: An Architecture for Queries over Streaming Sensor Data, Sam Madden and Michael J. Franklin,ICDE Conference, February, 2002, San Jose.
5.
Streaming Queries over Streaming Data Sirish Chandrasekaran, Michael J.
Franklin, VLDB Conference, August 2002,
6.
Monitoring Streams: A New Class of Data Management Applications.D. Carney,
U. Cetintemel, M. Cherniack,
C. Convey, S. Lee, G. Seidman, M. Stonebraker,
N. Tatbul, S. Zdonik. In
proceedings of the 28th International Conference on Very Large Data Bases
(VLDB'02), August 20-23,
7. Gigascope: a stream database for network applications , Chuck Cranor, Theodore Johnson, and Oliver Spatscheck,in Proceedings of SIGMOD 2003.
8. Query Processing, Approximation, and Resource Management in a Data Stream Management System. R. Motwani et al. CIDR, 2003.
9. Aurora: A New Model and Architecture for Data Stream Management. D. Abadi, D. Camey, U. Cetintemel, M. Chemiack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, and S. Zdonik. in VLDB Journal, 2003.
10. Issues in Data Stream Management. Golab, L. und Ozsu, M. T. ACM SIGMOD Record. 32(2). 2003.
Estimating Clustering Indexes in Data Streams, Luciana Buriol, Gereon Frahling, Stefano Leonardi, Christian Sohler, Proc. 15th European Symposium on Algorithms (ESA), 2007
1. Security and Privacy Issues in ePassport, Ari Juels, David Molnar and David Wagner, In Proceedings of Advances in Cryptology, 2005.
2. Privacy and Security Issues in Library RFID Issues, Practices, and Architectures, David Molnar and David Wagner, In Proceedings of ACM CCS, 2004.
3. High Power Proxies for Enhancing RFID Privacy and Utility, In Proceedings of PET, 2005.
4. RFID Security and Privacy: A Research Survey, Ari Juels, In Proceedings of IEEE Journal on Selected Areas in Communication, 2006.
5. A Platform for RFID Security and Privacy Administration. Melanie R. Rieback, Vrije Universiteit Amsterdam; Georgi N. Gaydadjiev, USENIX/SAGE Large Installation System Administration conference - LISA'06, December 2006
6. RFID Privacy: An Overview of Problems and Proposed Solutions, IEEE Security and Privacy. v3 i3. 34-43, Pages: 897-914, 2007
7. Protocols for RFID tag/reader authentication, Selwyn Piramuthu, Decision Support Systems, Volume 43, Issue 3, April 2007, Pages 897-914
1. Bigtable: A Distributed Storage System for Structured Data, Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows,Tushar Chandra, Andrew Fikes, Robert E. Gruber, 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2006
2. MapReduce: Simplified Data Processing on Large Clusters, Jeffrey Dean, Sanjay Ghemawat, OSDI'04: Sixth Symposium on Operating System Design and Implementation, 2004
3. Clustering Billions of Images with Large Scale Nearest Neighbor Search, Ting Liu, Charles Rosenberg, Henry A. Rowley, IEEE Workshop on Applications of Computer Vision, 2007
4. Scaling Up All Pairs Similarity Search, Roberto Bayardo, Yiming Ma, Ramakrishnan Srikant, Proc. of the 16th Int'l Conf. on the World Wide Web, 2007
5. Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping, Mikhail Bilenko, Sugato Basu, Mehran Sahami, Proceedings of the 5th IEEE International Conference on Data Mining, 2005
6. Evaluating similarity measures: a large-scale study in the orkut social network, Ellen Spertus, Mehran Sahami, Orkut Buyukkokten, Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2005), 2005
7. Unweaving a web of documents, R. Guha, Ravi Kumar, D. Sivakumar, Ravi Sundaram, KDD, 2005
8. Mining Optimized Gain Rules for Numeric Attributes,
Sergey
Brin,
9.