Spatiotemporal data mining in the era of big spatial data: Algorithms and applications

Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2012

Volumn 1, Issue , 2012, Pages 1-10

  Vatsavai, Ranga Raju ^a   Chandola, Varun ^a   Klasky, Scott ^a   Ganguly, Auroop ^b   Stefanidis, Anthony ^c   Shekhar, Shashi ^d  

^a OAK RIDGE NATIONAL LABORATORY   (United States)

^b Northeastern University   (United States)

^c GEORGE MASON UNIVERSITY   (United States)

^d University of Minnesota   (United States)

Author keywords

Big data; Computational and I O challenges; Large scale data mining; Spatiotemporal patterns

Indexed keywords

BIG DATUM; COMPUTATIONAL AND I/O CHALLENGES; COMPUTATIONALLY EFFICIENT; LARGE SCALE DATA; SPATIAL DATA MINING; SPATIO-TEMPORAL DATA; SPATIO-TEMPORAL DATA MINING; SPATIOTEMPORAL PATTERNS;

ALGORITHMS;

DATA MINING;

EID: 84876770463     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2447481.2447482     Document Type: Conference Paper

References (62)

1. D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz, and A. Ng. Discriminative learning of markov random fields for segmentation of 3d scan data. In CVPR '05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05) - Volume 2, pages 169-176, Washington, DC, USA, 2005. IEEE Computer Society.
2. L. Anselin. Spatial Econometrics: methods and models. Kluwer, Dordrecht, Netherlands, 1988.
3. J. Besag. Spatial interaction and the statistical analysis of lattice systems. Journal of Royal Statistical Society, 36:192-236, 1974.
4. J. Besag. On the statistical analysis of dirty pictures. J. Royal Statistical Soc, (48):259-302, 1986.
5. J. Bilmes. A gentle tutorial on the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. Technical Report, University of Berkeley, ICSI-TR-97-021, 1997., 1997.
6. Y. Boykov, O. Veksler, and R. Zabih. Fast Approximate Energy Minimization via Graph Cuts. International Conference on Computer Vision, September 1999.
7. R. Brittaine and N. Lutaladio. Jatropha: A samllholder bioenergy crop. the potential for pro-poor development. Integrated Crop Management, 8:1-114, 2010.
8. G. Capps, O. Franzese, B. Knee, M. Lascurain, and P. Otaduy. Class-8 heavy truck duty cycle project final report. ORNL/TM-2008/122, 2008.
9. M. Celik, B. Kazar, S. Shekhar, D. Boley, and D. Lilja. Northstar: A parameter estimation method for the spatial autoregression model. AHPCRC Technical Report No: 2005-001, 2007.
10. V. Chandola and R. R. Vatsavai. Scalable time series change detection for biomass monitoring using gaussian process. In NASA Conference on Intelligent Data Understanding (CIDU), pages 69-82, 2010.
11. V. Chandola and R. R. Vatsavai. A gaussian process based online change detection algorithm for monitoring periodic time series. In SIAM Data Mining (SDM), 2011.
12. V. Chandola and R. R. Vatsavai. A scalable gaussian process analysis algorithm for biomass monitoring. Statistical Analysis and Data Mining, 4(4):430-445, 2011.
13. S. Chatterjee, K. Steinhaeuser, A. Banerjee, S. Chatterjee, and A. R. Ganguly. Sparse group lasso: Consistency and climate applications. In SDM, pages 47-58, 2012.
14. P. Chou, P. Cooper, M. J. Swain, C. Brown, and L. Wixson. Probabilistic network inference for cooperative high and low levell vision. In In Markov Random Field, Theory and Applicaitons. Academic Press, New York, 1993.
15. N. Cressie. Statistics for Spatial Data (Revised Edition). Wiley, New York, 1993.
16. A. Crooks, A. Croitoru, A. Stefanidis, and J. Radzikowski. Earthquake: Twitter as a distributed sensor system. Transactions in GIS (in press), 0(0), 2012.
17. D. Das, E. Kodra, Z. Obradovic, and A. R. Ganguly. Mining extremes: Severe rainfall and climate change. In ECAI, pages 899-900, 2012.
18. H. Derin and H. Elliott. Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Transaction on Pattern Analysis and Machine Intelligence, (9):39-55, 1987.
19. C. Docan, M. Parashar, and S. Klasky. Dataspaces: an interaction and coordination framework for coupled simulation workflows. In HPDC, pages 25-36, 2010.
20. S. Fritz, I. McCallum, C. Schill, C. Perger, R. Grillmayer, F. Achard, F. Kraxner, and M. Obersteiner. Geo-wiki.org: The use of crowdsourcing to improve global land cover. Remote Sensing, 1(3):345-354, 2009.
21. A. R. Ganguly and K. Steinhaeuser. Data mining for climate change and impacts. In ICDM Workshops, pages 385-394, 2008.
22. A. R. Ganguly, K. Steinhaeuser, D. J. Erickson, M. Branstetter, E. S. Parish, N. Singh, J. B. Drake, and L. Buja. Higher trends but larger uncertainty and geographic variability in 21st century temperature and heat waves. Proceedings of the National Academy of Sciences, 106(37):15555-15559, 2009.
23. S. Geman and D. Geman. Stochastic relaxation, gibbs distributions and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, (6):721-741, 1984.
24. M. F. Goodchild. Citizens as Sensors: The World of Volunteered Geography, pages 370-378. John Wiley and Sons, Ltd, 2011.
25. J. Graesser, A. Cheriyadat, R. R. Vatsavai, V. Chandola, J. Long, and E. Bright. Image based characterization of formal and informal neighborhoods in an urban landscape. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 5(4):1164 - 1176, August 2012.
26. A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, WebKDD/SNA-KDD '07, pages 56-65, New York, NY, USA, 2007. ACM.
27. Y. Jhung and P. H. Swain. Bayesian Contextual Classification Based on Modified M-Estimates and Markov Random Fields. IEEE Transaction on Pattern Analysis and Machine Intelligence, 34(1):67-75, 1996.
28. G. Jun, R. R. Vatsavai, and J. Ghosh. Spatially adaptive classification and active learning of multispectral data with gaussian processes. In ICDM Workshops: Spatial and Spatiotemporal Data Mining (SSTDM), pages 597-603, 2009.
29. C. O. Justice, E. Vermote, J. R. Townshend, R. Defries, D. P. Roy, D. K. Hall, V. V. Salomonson, J. L. Privette, G. Riggs, A. Strahler, W. Lucht, R. B. Myneni, Y. Knyazikhin, S. W. Running, S. W. Steve W. Nemani, Z. Wan, A. R. Huete, W. van Leeuwen, R. E. Wolfe, L. Giglio, J.-P. Muller, P. Lewis, and M. J. Barnsley. The moderate resolution imagin spectrradiometer (modis): Land remote sensing for global chang research. IEEE Transactions on Geosciences and Remote Sensing, 36:1228-1249, 1998.
30. S.-C. Kao and A. R. Ganguly. Intensity, duration, and frequency of precipitation extremes under 21st-century warming scenarios. J. Geophys. Res., 116(D16119), 2011.
31. H. Kargupta, J. Gama, and W. Fan. The next generation of transportation systems, greenhouse emissions, and data mining. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pages 1209-1212, New York, NY, USA, 2010. ACM.
32. H. Kargupta, V. Puttagunta, M. Klein, and K. Sarkar. On-board vehicle data stream monitoring using mine-fleet and fast resource constrained monitoring of correlation matrices. New Gen. Comput., 25(1):5-32, Jan. 2007.
33. J. Kawale, S. Liess, A. Kumar, M. Steinbach, A. Ganguly, N. Samatova, F. Semazzi, P. Snyder, and V. Kumar. Data-guided discovery of climate dipoles in observations and models. In NASA Conference on Intelligent Data Understanding (CIDU), pages 1-15, 2011.
34. B. Kazar, S. Shekhar, D. Lilja, R. Vatsavai, and R. Pace. Comparing exact and approximate spatial auto-regression model solutions for spatial data analysis. In Third International Conference on Geographic Information Science (GIScience2004). LNCS, Springer, October 2004.
35. S. Khan, A. Ganguly, S. Bandyopadhyay, S. Saigal, D. Erickson, V. Protopopescu, and G. Ostrouchov. Non-linear statistics reveals stronger ties between enso and the tropical hydrological cycle. Geophysical Research Letters, 33(L24402):6, 2006.
36. S. Klasky and et. al. In situ data processing for extreme-scale computing. In SicDAC, page 16, 2011.
37. J. LeSage. Bayesian estimation of spatial autoregressive models. International Regional Science Review, (20):113-129, 1997.
38. J. LeSage. Regression Analysis of Spatial data. The Journal of Regional Analysis and Policy (Publisher: Mid-Continent Regional Science Association and UNL College of Business Administration), 27(2):83-94, 1997.
39. J. P. LeSage and R. Pace. Spatial dependence in data mining. In Geographic Data Mining and Knowledge Discovery. Taylor and Francis, forthcoming, 2001.
40. S. Z. Li. Markov random field modeling in image analysis. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2001.
41. J. Lovell. Left-hand-turn elimination. New York Times, http://goo.gl/3bkPb December 9, 2007.
42. C. Ma. Spatial autoregression and related spatio-temporal models. J. Multivarate Analysis, 88(1):152-162, 2004.
43. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers. Big data: The next frontier for innovation, competition and productivity. McKinsey Global Institute, 2011.
44. G. J. McLachlan and K. E. Basford. Mixture Models: Inference and Applications to Clustering. Marcel Dekker, 1988.
45. V. Norris, M. McCahill, and D. Wood. Editorial: The growth of CCTV: a global perspective on the international diffusion of video surveillance in publicly accessible space. Surveillance and Society, 2(2/3):110-135, 2004.
46. J. T. Overpeck, G. A. Meehl, S. Bony, and D. R. Easterling. Climate data challenges in the 21st century. Science, 331(6018):700-702, 2011.
47. R. Pace and R. Barry. Quick Computation of Regressions with a Spatially Autoregressive Dependent Variable. Geographic Analysis, 1997.
48. R. Pace and R. Barry. Sparse spatial autoregressions. Statistics and Probability Letters (Publisher: Elsevier Science), (33):291-297, 1997.
49. C. Rasmussen and C. Williams. Gaussian processes for machine learning. Adaptive computation and machine learning. MIT Press, 2006.
50. D. M. Romero, B. Meeder, and J. Kleinberg. Differences in the mechanics of information diffusion across topics: idioms, political hashtags and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web, pages 695-704, New York, NY, USA, 2011. ACM.
51. F. Sahito, A. Latif, and W. Slany. Weaving twitter stream into linked data a proof of concept framework. In 7th International Conference on Emerging Technologies (ICET), pages 1-6, 2011.
52. S. Shekhar, V. Gunturi, M. R. Evans, and K. Yang. Spatial big-data challenges intersecting mobility and cloud computing. In Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE '12, pages 1-6, New York, NY, USA, 2012. ACM.
53. S. Shekhar, P. Schrater, R. Vatsavai, W. Wu, and S. Chawla. Spatial contextual classification and prediction models for mining geospatial data. IEEE Transaction on Multimedia, 4(2):174-188, 2002.
54. A. H. Solberg, T. Taxt, and A. K. Jain. A Markov Random Field Model for Classification of Multisource Satellite Imagery. IEEE Transaction on Geoscience and Remote Sensing, 34(1):100-113, 1996.
55. A. Stefanidis, A. Crooks, and J. Radzikowski. Harvesting ambient geospatial information from social media feeds. GeoJournal, pages 1-20, 2011.
56. K. Steinhaeuser, A. Ganguly, and N. Chawla. Multivariate and multiscale dependence in the global climate system revealed through complex networks. Climate Dynamics, 39:889-895, 2012.
57. R. R. Vatsavai. Biomon: a google earth based continuous biomass monitoring system. In 17th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems (ACM-GIS), pages 536-537, New York, NY, USA, 2009. ACM.
58. R. R. Vatsavai. Stpminer: a highperformance spatiotemporal pattern mining toolbox. In Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities, PDAC '11, pages 29-34, New York, NY, USA, 2011. ACM.
59. R. R. Vatsavai, A. Cheriyadat, and S. S. Gleason. Unsupervised semantic labeling framework for identification of complex facilities in high-resolution remote sensing images. In ICDM Workshops, pages 273-280, 2010.
60. R. R. Vatsavai, S. Shekhar, and T. E. Burk. An efficient spatial semi-supervised learning algorithm. International Journal of Parallel, Emergent and Distributed Systems, 22(6):427-437, 2007.
61. C. E. Warrender and M. F. Augusteijn. Fusion of image classifications using Bayesian techniques with Markov rand fields. International Journal of Remote Sensing, 20(10):1987-2002, 1999.
62. N. Wayant, A. Crooks, A. Stefanidis, A. Croitoru, J. Radzikowski, J. Stahl, and J. Shine. Spatiotemporal clustering of twitter feeds for activity summarization. In GIScience (short paper), 2012.