Publications
2019
119. Nguyen, M. H., Li, J., Crawl, D., Block, J., and Altintas, I. Scaling Deep Learning-Based Analysis of High-Resolution Satellite Imagery with Distributed Processing, in Workshop on Machine Learning for Big Data Analytics in Remote Sensing at the 2019 IEEE International Conference on Big Data, 2019.
118. Tang, Y., Wang, J., Nguyen, M., & Altintas, I. (2019). PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data. Sensors, 19(20), 4400. https://www.mdpi.com/1424-8220/19/20/4400
117. Schram, M., Tallent, N., Friese, R., Singh, A., Altintas, I., Application of Deep Learning on Integrating Prediction, Provenance, and Optimization, in 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018), 214(2019). https://doi.org/10.1051/epjconf/201921406007
116. Sellars, S., Graham, J., Mishin, D., Marcus, K., Altintas, I., DeFanti, T., Smarr, L., Crittenden, C., Wuerthwein, F., Tatar, J., Nguyen, P., Shearer, E., Sorooshian, S., Martin Ralph, F., The Evolution Of Bits And Bottlenecks In A Scientific Workflow Trying To Keep Up With Technology: Accelerating 4D Image Segmentation Applied to NASA data, in 15th eScience Conference, 2019.
115. Benz, S. A., Park, H., Li, J., Crawl, D., Block, J., Nguyen, M. H., Altintas, I., Understanding a Rapidly Expanding Refugee Camp Using Convolutional Neural Networks and Satellite Imagery, in 15th eScience Conference, 2019.
114. Rule, A., Birmingham, A., Zuniga, C., Altintas, I., Huang, S., Knight, R., Moshiri, N., Nguyen, M. H., Rosenthal, S. B., Pérez, F., Rose, P. W., Ten Simple Rules for Writing and Sharing Computational Analyses in Jupyter Notebooks, accepted for publication in PLOS Computational Biology, 2019.
113. Yang P-C, Purawat S, Ieong PU, Jeng M-T, DeMarco KR, Vorobyov I, et al. (2019) A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows. PLoS Comput Biol 15(3): e1006856.https://doi.org/10.1371/journal.pcbi.1006856
112. I. Altintas, S. Purawat, D. Crawl, A. Singh, K. Marcus. Towards A Methodology and Framework for Workflow-Driven Team Science, IEEE Computing in Science & Engineering, 2019. https://doi.ieeecomputersociety.org/10.1109/MCSE.2019.2919688
111. I. Altintas, K. Marcus, I. Nealey, S. Sellars, J. Graham, D. Mishin, J. Polizzi, D. Crawl, T. DeFanti, and L. Smarr. Workflow-Driven Distributed Machine Learning in CHASE-CI: A Cognitive Hardware and Software Ecosystem Community Infrastructure, Workshop on Scalable Networks for Advanced Computing Systems, 2019. https://arxiv.org/abs/1903.06802
110. Garcia-Silva, A., Gomez-Perez, J. M., Palma, R., Krystek, M., Mantovani, S., Foglini, F., Grande, V., De Leo, F., Salvi, S., Trasatti, E., Romaniello, V., Albani, M., Silvagni, C., Leone, R., Marelli, F., Albani, S., Lazzarini, M., Napier, H. J., Glaves, H. M., Aldridge, T., Meertens, C., Boler, F., Loescher, H. W., Laney, C., Genazzio, M. A., Crawl, D., and Altintas, I. Enabling FAIR research in Earth Science through research objects, Future Generation Computer Systems, 2019, ISSN 0167-739X, https://doi.org/10.1016/j.future.2019.03.046.
2018
109. Nguyen, M. H., Block, J., Crawl, D., Siu, V., Bhatnagar, A., Rodriguez, F., Kwan, A., Baru, N., and Altintas, I. “Land Cover Classification at the Wildland Urban Interface using High-Resolution Satellite Imagery and Deep Learning,” in the 2018 IEEE International Conference on Big Data.
108. Nguyen, M. H., Abdelmaguid, E., Huang, J., Kenchareddy, S., Singla, D., Wilke, L., Bobar, M., Carruth, E. D., Uys, D., Altintas, I., Muse, E. D., Quer, G., and Steinhubl, S. “Analytics Pipeline for Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning,” in the Data Handling and Analytics for Health focused session at the 2018 IEEE eScience Conference.
107. Singh, A., Schram, M., Tallent, N., and Altintas I., "Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows" in IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications, at the IEEE Big Data 2018 Conference, Seattle, WA
106. Yazdani, M., Nguyen, M., Block, J., Crawl, D., Zurutuza, N., Kim, D., Hanson, G., and Altintas, I., Scalable Detection of Rural Schools in Africa using Convolutional Neural Networks and Satellite Imagery, In the fifth international workshop on Smart City Clouds: Technologies, Systems and Applications (SCCTSA) at the IEEE/ACM International Conference on Utility and Cloud Computing (UCC), 2018.
2017
105. Nguyen, M., Crawl, D., Li, J., Uys, D., Altintas, I., Automated Scalable Detection of Location-Specific Santa Ana Conditions from Weather Data using Unsupervised Learning, In Proceedings of the 2017 IEEE International Conference on Big Data. https://ieeexplore.ieee.org/abstract/document/8258046
104. Block, J., Yazdani, M., Nguyen, M., Crawl, D., Jankowska, M., Graham, J., DeFanti, T., Altintas, I., An Unsupervised Deep Learning Approach for Satellite Image Analysis with Applications in Demographic Analysis, In the thirteenth IEEE eScience conference, 2017. https://ieeexplore.ieee.org/abstract/document/8109118
103. A. Singh, E. Stephan, M. Schram, and I. Altintas, “Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows,” in 2017 IEEE 13th International Conference on e-Science (e-Science), 2017, pp. 586–591. https://ieeexplore.ieee.org/document/8109199/
102. A. Singh, A. Rao, S. Purawat, and I. Altintas, “A Machine Learning Approach for Modular Workflow Performance Prediction,” in Proceedings of the 12th Workshop on Workflows in Support of Large-Scale Science, New York, NY, USA, 2017, p. 7:1–7:11. https://doi.org/10.1145/3150994.3150998
101. Deelman, E., Peterka, T., Altintas, I., Carothers, C.D., Kleese van Dam, K., Moreland, K., Parashar, M., Ramakrishnan, L., Taufer, M., Vetter, J., 2017. The future of scientific workflows. Int’l Jnl of High Perf Comp Applictns 1094342017704893. doi:10.1177/1094342017704893
100. S. Purawat, P. Ieong, R. Malmstrom, G. Chan, R. Walker, I. Altintas, and R. Amaro, “A Kepler Workflow Tool for Reproducible Molecular Dynamics”, Biophysical Journal, 2017
99. S. Purawat, C. Cowart, R. E. Amaro, and I. Altintas, “Biomedical Big Data Training Collaborative (BBDTC): An effort to bridge the talent gap in biomedical science and research”, Journal of Computational Science, March 2017. http://dx.doi.org/10.1016/j.jocs.2017.03.010
98. Srivas, T., de Callafon, R., Crawl, D., Altintas, I., Data Assimilation of Wildfires with Fuel Adjustment Factors in FARSITE using Ensemble Kalman Filtering, In Proceedings of the Workshop on Data-Driven Computational Sciences (DDCS) at the 17th International Conference on Computational Science (ICCS 2017), 2017.
97. Crawl, D., Block, J., Lin, K., Altintas, I., Firemap: A Dynamic Data-Driven Predictive Wildfire Modeling and Visualization Environment, In Proceedings of the Workshop on Urgent Computing (UC) at the 17th International Conference on Computational Sciences (ICCS 2017), 2017.
96. Block, J., Vural, V., Chun, S., Katsis, Y., Crawl, D., Altintas, I., Huang, J. S., Evironmental Exposures and Inflammatory Bowel Disease in Childhood. In Pediatric Academic Societies 2017 Meeting.
2016
95. Singh, A., Stephan, E., Elsethagen, T., MacDuff, M., Raju, B., Schram, M., Kleese van Dam, K., J Kerbyson, D., Altintas I., Leveraging Large Sensor Streams for Robust Cloud Control, In Proceedings of the Big Data for Cloud Operations Management: Problems, Approaches, Tools, and Best Practices Workshop at IEEE International Conference on Big Data (IEEE BigData 2016) http://ieeexplore.ieee.org/document/7840839/
94. Nguyen, M., Uys, D., Crawl, D., Cowart, C., and Altintas, I.. A Scalable Approach for Location-Specific Detection of Santa Ana Conditions. In Proceedings of the 2016 IEEE International Conference on Big Data. https://ieeexplore.ieee.org/abstract/document/7840740
93. Hedayatnia, B., Yazdani, M., Nguyen, M., Block, J., and Altintas, I. Determining Feature Extractors for Unsupervised Learning on Satellite Images. In Proceedings of the First IEEE International Workshop on Big Spatial Data at the 2016 IEEE International Conference on Big Data. https://ieeexplore.ieee.org/document/7840908
92. Elsethagen, T., E. Stephan, B. Raju, M. Schram, M. MacDuff, D. Kerbyson, K. K. van Dam, A. Singh, and I. Altintas. 2016. “Data Provenance Hybridization Supporting Extreme-Scale Scientific Workflow Applications.” In 2016 New York Scientific Data Summit (NYSDS), 1–10. doi:10.1109/NYSDS.2016.7747819
91. Nguyen, M., Crawl, D. Masoumi, T., and Altintas, I. Integrated Machine Learning in the Kepler Scientific Workflow System. In Proceedings of the Third International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 16th International Conference on Computational Science (ICCS 2016). http://dx.doi.org/10.1016/j.procs.2016.05.545
90. Purawat, S., Cowart, C., Amaro, R., Altintas, I., Biomedical Big Data Training Collaborative (BBDTC): An Effort to Bridge the Talent Gap in Biomedical Science and Research, In the Third Workshop on Bridging the Talent Gap with Computational Science Methods at the 16th International Conference on Computational Science (ICCS 2016). doi:10.1016/j.procs.2016.05.454
89. Srivas, T., Artés, T., de Callafon, R., Altintas, I., Wildfire Spread Prediction and Assimilation for FARSITE Using Ensemble Kalman Filtering, In the Data-Driven Computational Sciences Workshop at the 16th International Conference on Computational Science (ICCS 2016). doi:10.1016/j.procs.2016.05.328
88. Wang, J., AbdelBaky, M., Diaz-Montes, J., Purawat, S., Parashar, M., Altintas, I., Kepler + CometCloud: Dynamic Scientific Workflow Execution on Federated Cloud Resources, In Proceedings of the Third International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 16th International Conference on Computational Science (ICCS 2016). doi:10.1016/j.procs.2016.05.363
87. Goyal, A., Singh, A., Bhargava, S., Crawl, D., Altintas, I., Hsu, CN., Natural Language Processing using Kepler Workflow System: First Steps, In Proceedings of the Third International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 16th International Conference on Computational Science (ICCS 2016). doi:10.1016/j.procs.2016.05.358
86. Crawl, D., Singh, A., Altintas, I., Kepler WebView: A Lightweight, Portable Framework for Constructing Real-time Web Interfaces of Scientific Workflows, In Proceedings of the Third International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 16th International Conference on Computational Science (ICCS 2016). doi:10.1016/j.procs.2016.05.361
2015
85. Gollner, M., Trouve, A., Altintas, I., Block, J., de Callafon, R., Clements, C., Cortes, A., Ellicott, E., Filippi, Jean Baptiste, Finney, M., Ide, K., Jenkins, Mary A., Jimenez, D., Lautenberger, C., Mandel, J., Rochoux, M., and Simeoni, A., “Towards Data-Driven Operational Wildfire Spread Modeling: A Report of the NSF-Funded WIFIRE Workshop,” 2015.
84. Qian Y., Kim H., Stanton R., Lee A., Scheuermann R., Purawat S., Wang J., Altintas I., Sinkovits R. and XuW.: FlowGate: Towards Extensible and Scalable Web-Based Flow Cytometry Data Analysis. In Proceedings of XSEDE 2015, July, 2015.
83. Singh A., Nguyen M., Purawat S., Crawl D., Altintas I., Modular Resource Centric Learning for Workflow Performance Prediction, in the 6th Workshop on Big Data Analytics: Challenges, and Opportunities (BDAC) at the 27th IEEE/ACM International Conference for High Performance Computing, Networking, Storage, and Analysis (SC15) http://arxiv.org/abs/1711.05429
82. Wang J., Crawl D., Purawat S., Nguyen M., Altintas I., Big Data Provenance: Challenges, State of the Art and Opportunities, in 2nd Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH), 2015. https://ieeexplore.ieee.org/document/7364047
81. Altintas I., Block J., de Callafon R., Crawl D., Cowart C., Gupta A., Nguyen M., Braun H.W., Schulze J., Gollner M., Trouve A., Smarr L., Towards an Integrated Cyberinfrastructure for Scalable Data-Driven Monitoring, Dynamic Prediction and Resilience of Wildfires. In Proceeedings of the Workshop on Dynamic Data-Driven Application Systems (DDDAS) at the 15th International Conference on Computational Science (ICCS 2015). Best Workshop Paper Award.
80. Davis J., Edgar T., Graybill R., Korambath P., Schott B., Swink D., Wang J., Wetzel J., Smart Manufacturing Technology. Accepted by Annual Review of Chemical and Biomolecular Engineering.
2014
79. Wang J., Tang Y., Nguyen M., Altintas I., A Scalable Data Science Workflow Approach for Big Data Bayesian Network Learning. Accepted by the International Symposium on Big Data Computing (BDC 2014), Acceptance rate: 22%. https://ieeexplore.ieee.org/document/7321725
78. Yang X., Wallom D., Waddington S., Wang J., Shaon A., Matthews B., Wilson M. Guo Y., Guo L., Blower J., Vasilakos A. V., Liu K., Kershaw P. Cloud Computing in e-Science: Research Challenges and Opportunities, Accepted by Journal of Supercomputing, Springer.
77. Chen W., Altintas I., Wang J., and Li J. Enhancing Smart Re-run of Kepler Scientific Workflows based on Near Optimum Provenance Caching in Cloud, accepted by IEEE 2014 Eighth International Symposium on Scientific Workflows and Big Data Science (SWF 2014), at 2014 Congress on Services (SERVICES 2014).
76. Zhao Z., Ding, W., and Wang, J. A Spatio-temporal Parallel Processing Platform for Traffic Sensor Data Streams. In Proceedings of the 2014 Asia-Pacific Services Computing Conference (APSCC 2014).
75. Zhao Z., Fang, J., Ding, W., and Wang, J. An Integrated Processing Platform for Traffic Sensor Data and Its Applications in Intelligent Transportation Systems, accepted by IEEE 2014 Second International Workshop on Service and Cloud Based Data Integration (SCDI 2014), at 2014 Congress on Services (SERVICES 2014).
74. Wang J., Crawl D., Altintas I., Li W., Big Data Applications using Workflows for Data Parallel Computing. Accepted by Computing in Science & Engineering, IEEE.
73. Wang J., Korambath P., Altintas I., Davis J., Crawl D., Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms. In Proceedings of the 14th International Conference on Computational Science (ICCS 2014).
72. Gan Z., Wang J., Salomonis N., Stowe J. C., Haddad G. G., McCulloch A. D., Altintas I., and Zambon A. C., MAAMD: A Workflow to Standardize Meta-Analyses and Comparison of Affymetrix Microarray Data, BMC Bioinformatics Journal. 15(1), 69, 2014.
71. Liu C., Wang J., Han. Y: Mashroom+: An Interactive Data Mashup Approach with Uncertainty Handling. Accepted by Journal of Grid Computing, Springer. DOI: 10.1007/s10723-013-9280-5.
70. Korambath P., Wang J., Kumar A., Hochstein L., Schott B., Graybill R., Baldea M., Davis J., Deploying Kepler Workflows as Services on a Cloud Infrastructure for Smart Manufacturing. In Proceedings of the Second International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 14th International Conference on Computational Science (ICCS 2014).
69. Chen R., Wan X., Lawrence A., Wang J., Crawl D., Phan S., Altintas. I, Ellisman M., EPiK - a Workflow for Electron Tomography in Kepler. In Proceedings of the Second International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 14th International Conference on Computational Science (ICCS 2014).
68. Gan Z., Stowe J. C., Altintas I., McCulloch A. D., and Zambon A. C., Using Kepler for Tool Integration in Microarray Analysis Workflows. In Proceedings of the Second International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 14th International Conference on Computational Science (ICCS 2014).
67. Leong P. U., Sorensen J., Vemu P. L., Wong C. W., Demir O, Williams N. P., Wang J., Crawl D., Swift R. V., Malmstrom R. D., Altintas I., Amaro R. E., Progress towards automated Kepler scientific workflows for computer-aided drug discovery and molecular simulations. In Proceedings of the Second International Workshop on Advances in the Kepler Scientific Workflow System and Its Applications at the 14th International Conference on Computational Science (ICCS 2014).
2013
66. Wang J., Crawl D., Ilkay Altintas, Kostas Tzoumas, Volker Markl. Comparison of Distributed Data-Parallelization Patterns for Big Data Analysis: A Bioinformatics Case Study. In Proceedings of the Fourth International Workshop on Data Intensive Computing in the Clouds (DataCloud 2013) at International Conference for High Performance Computing, Networking, Storage and Analysis (SC'13).
65. Plociennik M., Zok T., Altintas I., Wang J., Crawl D., Abramson D., Imbeaux F., Guillerminet B., Frauel Y., Lopez-Caniego M., Campos Plasencia, I., Pych W., Ciecielag P., Palak B., Owsiak M., Frauel Y., and ITM-TF contributors.: Approaches to Distributed Execution of ScientificWorkflows in Kepler. In Fundamenta Informaticae, 128 (3), 2013.
64. Liu C., Wang J., Han Y.: Situation-Aware Data Service Composition Based on Service Hyperlinks. In Proceedings of the Sixth International Workshop on Personalization in Cloud and Service Computing (PCS 2013) at the 14th International Conference on Web Information System Engineering (WISE 2013).
63. Altintas I., Workflow-driven programming paradigms for distributed analysis of biological big data, The First International Workshop on Big Data in Life Sciences In conjunction with ICCABS 2013 3rd IEEE International Conference on Computational Advances in Bio and Medical Sciences, New Orleans Airport, LA, June, 2013.
2012
62. Missier P., Ludaescher B., Dey S., Wang M., McPhillips T., Bowers ., Agun M., Altintas I.: Golden trail: Retrieving the data history that matters from a comprehensive provenance repository. International Journal of Digital Curation 7(1), 139-150, 2012.
61. Zhang C., Wang J., Zhao X., Han Y.: An Item-Targeted User Similarity Method for Data Service Recommendation. In Proceedings of the First International Workshop on Service and Cloud Based Data Integration (SCDI 2012).
60. Liu C., Wang J., Yan Wen, and Han Y.: A Unified Data and Service Integration Approach for Dynamic Business Collaboration. In Proceedings of the IEEE First International Conference on Services Economics (SE 2012).
59. Wang J., Altintas I.: Early Cloud Experiences with the Kepler Scientific Workflow System. Proceedia CS 9: p. 1630-1634, 2012.
58. Wang J., Crawl D., Altintas I.: A Framework for Distributed Data-Parallel Execution in the Kepler Scientific Workflow System. Procedia CS 9: p. 1620-1629, 2012.
57. Altintas I., Wang J., Crawl D., and Li W.: Challenges and Approaches for Distributed Workflow-Driven Analysis of Large-Scale Biological Data. In: Proceedings of the 2012 Joint EDBT/ICDT Workshops (EDBT-ICDT '12), Divesh Srivastava and Ismail Ari (Eds.), p. 73-78, 2012. DOI=10.1145/2320765.2320791 http://doi.acm.org/10.1145/2320765.232079100
56. Gan Z., Wang J., Salomonis N., Altintas I., McCulloch A.D., Zambon A.C.: MAAMD: A Workflow to Standardize Meta-Analyses of Affymetrix Microarray Data. HISB 2012: p. 120, 2012.
2011
55. Altintas I.: Collaborative provenance for workflow-driven science and engineering. FNWI, University of Amsterdam, The Netherlands, 2011. ISBN: 978-90-9025979-6
54. Sun S., Chen J., Li W., Altintas I., Lin AW., Peltier S., Stocks K., Allen E.E., Ellisman M.H., Grethe J.S. Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource. Nucleic Acids Research, Vol. 39, p.546-551, 2011
53. Altintas I., Anand M.K., Vuong T.N., Bowers S., Ludaescher B., Sloot P.M.A. A Data Model for Analyzing User Collaborations in Workflow-Driven eScience. The International Journal of Computers and Their Applications (IJCA), 2011. Vol. 18, No. 3, p.160-180, Dec, 2011
52. Wang J., Korambath P., Kim S., Johnson S., Jin K., Crawl D., Altintas I., Smallen S., Labate B., Houk K.N. Facilitating e-Science Discovery Using Scientific Workflows on the Grid. In: Guide to e-Science (Yang X, Wang L, Jie W eds.). 2011, Springer. p. 353-382. Available from: http://dx.doi.org/10.1007/978-0-85729-439-5_13
51. Altintas I., Crawl D., Crosby C.J., Cornillon P. Scientific workflows for the geosciences: An emerging approach to building integrated data analysis systems. In: Geoinformatics: Cyberinfrastructure for the Solid Earth Sciences (Keller RG, Baru C eds.) 2011, Cambridge Press. p. 237-250
50. Lin A.W., Altintas I., Churas C., Gujral M., Grethe J., Ellisman M. Case Study on the Use of REST Architectural Principles for Scientific Analysis CAMERA - Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis. In: REST: From Research to Practice (Wilde E., Pautasso C. Eds.). 2011, Springer. ISBN: 978-1-4419-8302-2. DOI: http://dx.doi.org/10.1007/978-1-4419-8303-9
49. Barseghian D., Crawl D., Jones M. B., Altintas I., Tao J., Riddle S., Sensor lifecycle management using scientific workflows, 33-38. In Proceedings of the Environmental Information Management Conference (EIM 2011), 2011.
48. Wang J., Korambath P., Altintas I, A Physical and Virtual Compute Cluster Resource Load Balancing Approach to Data-Parallel Scientific Workflow Scheduling. Services (2011 IEEE World Congress on Services), p. 212-215, 2011.
47. Crawl D., Wang J., Altintas I.: Provenance for MapReduce-based Data-Intensive Workflows. In Proceedings of the 6th Workshop on Workflows in Support of Large-Scale Science (WORKS11) at Supercomputing 2011 (SC2011) Conference. ACM 2011, pages 21-29.
46. Altintas I.: 2011. Distributed Workflow-Driven Analysis of Large-Scale Biological Data using bioKepler. In: Proceedings of the 2nd International Workshop on Petascale Data Analytics: Challenges and Opportunities (PDAC '11). ACM, p. 41-42, 2011.
45. Klasky S., Abbasi H., Logan J., Parashar M., Schwan K., Shoshani A., Wolf M., Ahern S., Altintas I., Bethel W., Chacon L., Chang C.S., Chen J., Childs H., Cummings J., Ethier S., Grout R., Lin Z., Liu Q., Ma X., Moreland K., Pascucci V., Podhorszki N., Samatova N., Schroeder W., Tchoua R., Wu J., Yu W., In-situ data processing for extreme-scale computing, Conference: Proceedings of the Scientific Discovery through Advanced Computing Conference (SciDAC 2011), July 10-14, 2011, Denver, Colorado.
44. Shoshani A., Altintas I., Chen J., Chin G., Choudhary A., Crawl D., Critchlow T.J., Gao K., Grimm B., Iyer H. et al., The Scientific Data Management Center: Available Technologies and Highlights, Conference: Proceedings of the Scientific Discovery through Advanced Computing Conference (SciDAC 2011), July 10-14, 2011, Denver, Colorado.
2010
43. Altintas I., Anand M., Crawl D., Bowers S., Belloum A., Missier P., Ludaescher B., Goble C.A., Sloot P.M.A, Understanding Collaborative Studies Through Interoperable Workflow Provenance. Provenance and Annotation of Data and Processes (IPAW 2010, McGuinness D., Michaelis J., Moreau L. eds.), Vol. 6378, p. 42-58, 2010.
42. Barseghian D., Altintas I., Jones M.B., Crawl D., Potter N., Gallagher J., Cornillon P., Schildhauer M., Borer E.T., Seabloom E.W. Workflows and extensions to the Kepler scientific workflow system to support environmental sensor data access and analysis. Ecological Informatics, Vol. 5, p. 42-50, 2010
41. Wang J., Korambath P., Kim S., Johnson S., Jin K., Crawl D., Altintas I., Smallen S., Labate B., Houk K.N, Theoretical enzyme design using the Kepler scientific workflows on the Grid. Proceedia Computer Science, The 10th International Conference on Computational Science (ICCS 2010), Vol. 1, p. 1169-1178, 2010.
40. Na'im A., Crawl D., Indrawan M., Altintas I., Sun S, Monitoring data quality in Kepler. The 19th International ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC), p. 560-564, 2010.
39. Missier P., Goble C., Dey S., Sarkar A., Shresta B., Ludaescher B., Bowers S., Altintas I., Anand M.K, Linking Multiple Workflow Provenance Traces for Interoperable Collaborative Science. Proceedings of the 5th Workshop on Workflows in Support of Large-Scale Science at SC10, Portland, OR, USA, p. 1-8, 2010.
38. Yildiz U., Mouallem P., Vouk M.A., Crawl D., Altintas I, Fault-Tolerance in Dataflow-Based Scientific Workflow Management. Services (2010 IEEE Congress on Services), p. 336-343, 2010.
37. Mouallem P., Crawl D., Altintas I., Vouk M.A., Yildiz U, A Fault-Tolerance Architecture for Kepler-Based Distributed Scientific Workflows. Proceedings of 22nd International Conference on Scientific and Statistical Database Management (SSDBM 2010), Berlin, Heidelberg, Vol. 6187, p. 452-460, 2010.
36. Altintas I., Chen J., Sedova M., Gupta A., Sun S., Lin A.W., Gujral M., Anand M.K., Li W., Grethe J.S, Extending the Data Model for Data-Centric Metagenomics Analysis using Scientific Workflows in CAMERA. Sixth IEEE International Conference on e-Science Workshops, p. 49-56, 2010.
35. Altintas I., Lin A.W., Chen J., Churas C., Gujral M., Sun S., Li W., Manansala R., Sedova M., Grethe J.S, -CAMERA 2.0: A Data-Centric Metagenomics Community Infrastructure Driven by Scientific Workflows. Services (SWF 2010 in conjunction with 2010 IEEE Congress on Services), p. 352-359, 2010.
34. Anand M., Bowers S., Altintas I., Ludaescher B, Approaches for Exploring and Querying Scientific Workflow Provenance Graphs. Provenance and Annotation of Data and Processes (IPAW 2010, McGuinness D., Michaelis J., Moreau L. eds.), Vol. 6378, p. 17-26, 2010.
2009
33. Ludaescher B., Altintas I., Bowers S., Cummings J., Critchlow T., Deelman E., De Roure D., Freire J., Goble C., Jones M. Chapter Scientific Process Automation and Workflow Management. In: Scientific Data Management: Challenges, Technology, and Deployment. (Shoshani A., Rotem D. eds.), Chapman & Hall/CRC Computational Science Series, 2009
32. Goderis A., Brooks C., Altintas I., Lee E.A., Goble C. Heterogeneous composition of models of computation Future Generation Computer Systems, Vol. 25, p. 552-560, 2009
31. Wang J., Crawl D., Altintas I, Kepler + Hadoop: a general architecture facilitating data-intensive applications in scientific workflow systems. Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science (WORKS '09) at SC09, Portland, OR, p.12:1-12:8, 2009.
30. Wang J., Altintas I., Hosseini P.R., Barseghian D., Crawl D., Berkley C., Jones M.B, Accelerating Parameter Sweep Workflows by Utilizing Ad-Hoc Network Computing Resources: An Ecological Example Services (2009 IEEE Congress on. Services), p. 267-274, 2009.
2008
29. Altintas I, Lifecycle of Scientific Workflows and their Provenance: A Usage Perspective. Proceedings of the 2008 IEEE Congress on Services (Services08), Part I, p.474-475, 2008.
28. Moreau L., Ludaescher B., Altintas I., Barga R.S., Bowers S., Callahan S.P., Chin Jr. G., Clifford B., Cohen S., Cohen Boulakia S., Davidson S.B., Deelman E., Digiampietri L.A., Foster I.T., Freire J., Frew J., Futrelle J., Gibson T., Gil Y., Goble C.A., Golbeck J., Groth P.T., Holland D.A., Jiang S., Kim J., Koop D., Krenek A., McPhillips T.M., Mehta G., Miles S., Metzger D., Munroe S., Myers J., Plale B., Podhorszki N., Ratnakar V., Santos E., Scheidegger C.E., Schuchardt K., Seltzer M.I., Simmhan Y.L., Silva C.T., Slaughter P., Stephan E.G., Stevens R., Turi D., Vo H.T., Wilde M., Zhao J., Zhao Y. Special Issue: The First Provenance Challenge. Concurrency and Computation: Practice and Experience, Vol. 20, p. 409-418, 2008
27. Moreau L., Ludaescher B., Altintas I., Barga R.S., Bowers S., Callahan S.P., Chin Jr. G., Clifford B., Cohen S., Cohen Boulakia S., Davidson S.B., Deelman E., Digiampietri L.A., Foster I.T., Freire J., Frew J., Futrelle J., Gibson T., Gil Y., Goble C.A., Golbeck J., Groth P.T., Holland D.A., Jiang S., Kim J., Koop D., Krenek A., McPhillips T.M., Mehta G., Miles S., Metzger D., Munroe S., Myers J., Plale B., Podhorszki N., Ratnakar V., Santos E., Scheidegger C.E., Schuchardt K., Seltzer M.I., Simmhan Y.L., Silva C.T., Slaughter P., Stephan E.G., Stevens R., Turi D., Vo H.T., Wilde M., Zhao J., Zhao Y. Special Issue: The First Provenance Challenge. Concurrency and Computation: Practice and Experience, Vol. 20, p. 409-418, 2008.
26. Ludaescher B., Podhorszki N., Altintas I., Bowers S., McPhillips T.M. From computation models to models of provenance: the RWS approach. Concurrency and Computation: Practice and Experience, Vol. 20, p.507-518, 2008
25. Crawl D., Altintas I, A Provenance-Based Fault Tolerance Mechanism for Scientific Workflows. Provenance and Annotation of Data and Processes (IPAW 2008, Revised Selected Papers, Freire J., Koop D., Moreau L. eds.), LNCS Vol. 5272, p. 152-159, 2008.
24. Abramson D., Enticott C., Altintas I, Nimrod/K: towards massively parallel dynamic grid workflows. Proceedings of the 2008 ACM/IEEE conference on Supercomputing (SC '08), p. 1-11, 2008.
23. Wang J., Altintas I., Berkley C., Gilbert L., Jones M.B, A High-Level Distributed Execution Framework for Scientific Workflows. IEEE International Conference on e-Science, p. 634-639, 2008.
22. Wang J., Yu J., Falcarin P., Han Y., Morisio M.: An Approach to Domain-Specific Reuse in Service-Oriented Environments. In Proceedings of the Tenth International Conference on Software Reuse (ICSR 2008), pages 221-232.
2007
21. Goderis A., Brooks C., Altintas I., Lee E., Goble C. Composing Different Models of Computation in Kepler and Ptolemy II. Computational Science - ICCS 2007 (Shi Y, van Albada G, Dongarra J, Sloot P), LNCS Vol. 4489, p. 182-190, 2007.
20. Vouk M., Altintas I., Barreto R., Blondin J., Cheng Z., Critchlow T., Khan A., Klasky S., Ligon J., Ludaescher B., Mouallem P., Parker S., Podhorszki N., Shoshani A., Silva C. Automation of Network-Based Scientific Workflows. In Grid-Based Problem Solving Environments (Gaffney P, Pool J eds.), Vol. 239, p. 35-61, 2007
2006
19. Jaeger-Frank E., Crosby C.J., Memon A., Nandigam V., Conner J., Arrowsmith J.R., Altintas I., Baru C. A Three Tier Architecture Applied to LiDAR Processing and Monitoring. Scientific Programming, Vol.14, p.185-194, 2006
18. Ludaescher B., Altintas I., Berkley C., Higgins D., Jaeger, E., Jones M., Lee E.A., Tao J., Zhao Y. Scientific Workflow Management and the Kepler System. Concurrency Computation: Practice and Experience, Vol.18, p. 1039-1065, 2006
17. Sloot P.M.A., Tirado-Ramos A., Altintas I., Bubak M., Boucher C. From Molecule to Man: Decision Support in Individualized E-Health. IEEE Computer, Vol.39, p.40-46, 2006.
16. Jaeger-Frank E., Crosby C.J., Memon A., Nandigam V., Arrowsmith J.R., Conner J., Altintas I., Baru C, Three Tier Architecture for LiDAR Interpolation and Analysis. Computational Science - ICCS 2006 (Alexandrov V., van Albada G., Sloot P., Dongarra J. eds.), LNCS Vol. 3993, p. 920-927, 2006.
15. Hou C.-Y., Altintas I., Jaeger-Frank E., Gilbert L., Moore R., Rajasekar A., Marciano R, A scientific workflow solution to the archiving of digital media. Workshop on Workflows in Support of Large-Scale Science, p.1-10, 2006.
14. Sudholt W., Altintas I., Baldridge K, Scientific Workflow Infrastructure for Computational Chemistry on the Grid. Computational Science - ICCS 2006 (Alexandrov V., van Albada G., Sloot P., Dongarra J. eds.), LNCS Vol. 3993, p. 69-76, 2006.
13. Altintas I., Barney O., Jaeger-Frank E. Provenance Collection Support in the Kepler Scientific Workflow System. Provenance and Annotation of Data (IPAW 2006, Revised Selected Papers, Moreau L., Foster I. eds.), LNCS Vol. 4145, p. 118-132, 2006.
12. Zhang J., Altintas I., Tao J., Liu X., Pennington D., Michener W, Integrating Data Grid and Web Services for E-Science Applications. A Case Study of Exploring Species Distributions. E-Science06, p. 31, 2006.
11. Altintas I., Barney O., Cheng Z., Critchlow T., Ludaescher B., Parker S., Shoshani A., Vouk M. Accelerating the scientific exploration process with scientific workflows. Tang DWM SciDAC 2006, SCIENTIFIC DISCOVERY THROUGH ADVANCED COMPUTING, Vol. 46 p. 468-478, 2006.
2005
10. Altintas I., Birnbaum A., Baldridge K.K., Sudholt W., Miller M., Amoreira C., Potier Y., Ludaescher B. A Framework for the Design and Reuse of Grid Workflows. Scientific Applications of Grid Computing: First International Workshop (Herrero P., Perez M.S., Robles V. eds.), LNCS, p. 120-133, 2005.
9. Jaeger E., Altintas I., Zhang J., Ludaescher B., Pennington D., Michener, A scientific workflow approach to distributed geospatial data processing using web services. Proceedings of the 17th international conference on Scientific and statistical database management (SSDBM05), p. 87-90, 2005.
8. Abramson D., Kommineni J., Altintas I, Flexible IO Services in the Kepler Grid Workflow System. Proceedings of the First International Conference on e-Science and Grid Computing (e-Science'05), p. 255-262, 2005.
7. Baldridge K.K., Sudholt W., Greenberg J.P., Amoreira C., Potier Y., Altintas I., Birnbaum A., Abramson D., Enticott C., Garic S. Cluster and Grid Infrastructure for Computational Chemistry and Biochemistry. In: Parallel Computing for Bioinformatics (Zomaya A.Y. ed.), John Wiley & Sons, 2005
6. Baldridge K.K., Greenberg J.P., Sudholt W., Mock S., Altintas I., Amoreira C., Potier Y., Birnbaum A., Bhatia K., Taufer M. The Computational Chemistry Prototyping Environment. In: Proceedings of the IEEE, Vol. 93, p. 510-521, 2005
2004
5. Altintas I., Jaeger E., Lin K., Ludaescher B., Memon A. A Web Service Composition and Deployment Framework for Scientific Workflows. ICWS '04: Proceedings of the IEEE International Conference on Web Services, p. 814, 2004.
4. Altintas I., Berkley C., Jaeger E., Jones M.B., Ludaescher B., Mock S. Kepler An Extensible System for Design and Execution of Scientific Workflows. Proceedings of the 16th International Conference on Scientific and Statistical Database Management (SSDBM 2004), p. 423-424, 2004.
2003
3. Altintas I., Bhagwanani S., Buttler D., Chandra S., Cheng Z., Coleman M., Critchlow T., Gupta A., Han W., Liu L., Ludaescher B., Pu C., Moore R., Shoshani A., Vouk M. A Modeling and Execution Environment for Distributed Scientific Workflows Proceedings of the 15th International Conference on Scientific and Statistical Database Management (SSDBM 2003), p. 247-250, 2003.
2. Ludaescher B., Altintas I., Gupta, Compiling abstract scientific workflows into web service workflows. Proceedings of the 15th International Conference on Scientific and Statistical Database Management (SSDBM 2003), p. 251-254, 2003.
2002
1. Ludaescher B., Altintas I., Gupta A. Time to Leave the Trees: From Syntactic to Conceptual Querying of XML. XML-Based Data Management and Multimedia Engineering - EDBT 2002 Workshops (Chaudhri A., Unland R., Djeraba C., Lindner W. eds.), Vol. 2490, p. 774-778, 2002.
Gollner, M., Trouve, A., Altintas, I., Block, J., de Callafon, R., Clements, C., Cortes, A., Ellicott, E., Filippi, Jean Baptiste, Finney, M., Ide, K., Jenkins, Mary A., Jimenez, D., Lautenberger, C., Mandel, J., Rochoux, M., and Simeoni, A., “Towards Data-Driven Operational Wildfire Spread Modeling: A Report of the NSF-Funded WIFIRE Workshop,” 2015.