[1] Pak Chung Wong, David Haglin, David Gillen, Daniel Chavarria, Vito Castellana, Cliff Joslyn, Alan Chappell, and Song Zhang. A visual analytics paradigm enabling trillion-edge graph exploration. In Large Data Analysis and Visualization (LDAV), 2015 IEEE 5th Symposium on, pages 57-64. IEEE, 2015. [ bib ]
[2] Alessandro Morari, Jesse Weaver, Oreste Villa, David Haglin, Antonino Tumeo, Vito Giovanni Castellana, and John Feo. High-performance, distributed dictionary encoding of rdf datasets. In Cluster Computing (CLUSTER), 2015 IEEE International Conference on, pages 250-253, Sept 2015. [ bib | DOI ]
Keywords: Benchmark testing;Data structures;Databases;Dictionaries;Encoding;Resource description framework;Throughput;GEMS;MPI;RDF;dictionary encoding;mapreduce;multithreading
[3] Eric L. Goodman, Edward Jimenez, Sinan al Saffar, Cliff Joslyn, David Haglin, and Dirk Grunwald. Optimizing graph queries with graph joins and sprinkle SPARQL. In 2014 Workshop on Complexity for Big Data, 2014. [ bib ]
[4] Alan Chappell, Sutanay Choudhury, John Feo, David Haglin, Alessandro Morari, Sumit Purohit, Karen Schuchardt, Antonino Tumeo, Jesse Weaver, and Oreste Villa. Toward a data scalable solution for facilitating discovery of scientific data resources. In 2013 Workshop on Data-Intensive Scalable Computing Systems (DISCS-2013), pages 55-60, November 2013. [ bib ]
[5] Alessandro Morari, Vito Giovanni Castellana, Oreste Villa, David Haglin, John Feo, Jesse Weaver, and Antonino Tumeo. Accelerating semantic graph databases on commodity clusters. In 2013 IEEE International Conference on Big Data (IEEE BigData 2013), pages 768-772, October 2013. [ bib ]
[6] Vito Giovanni Castellana, Antonino Tumeo, Oreste Villa, David Haglin, and John Feo. Composing data parallel code for a sparql graph engine. In 2013 ASE/IEEE International Conference on Big Data, pages 691-699, September 2013. [ bib ]
[7] Cliff Joslyn, Sutanay Choudhury, David Haglin, Bill Howe, Bill Nickless, and Bryan Olsen. Massive scale cyber traffic analysis: a driver for graph database research. In First International Workshop on Graph Data Management Experiences and Systems, GRADES '13, pages 3:1-3:6, New York, NY, USA, 2013. ACM. [ bib | DOI | http ]
[8] David J. Haglin, Robert D. Adolf, and Greg E. Mackey. Scalable, multithreaded, partially-in-place sorting. In IPDPS Workshops, pages 1656-1664, May 2013. [ bib ]
[9] David J. Haglin and Lawrence B. Holder. Combining structure and property values is essential for graph-based learning. In IPDPS Workshops, pages 1899-1904, May 2013. [ bib ]
[10] Chad Scherrer, Ambun Tewari, Mahantesh Halappanavar, and David Haglin. Feature clustering for accelerating parallel coordinate descent. In Neural information Processing Systems Foundation (NIPS), December 2012. [ bib ]
[11] Mahantesh Halappanavar, Yousu Chen, Robert Adolf, David Haglin, Zhenyu Huang, and Mark Rice. Towards efficient n-x contingency selection using group betweenness centrality. In Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC '12, pages 273-282, Washington, DC, USA, 2012. IEEE Computer Society. [ bib | DOI | http ]
[12] Tom Ferryman, David Haglin, Maria Vlachopoulou, Jian Yin, Chao Shen, Frank Tuffner, Guang Lin, Ning Zhou, and Jianzhong Tong. Net interchange schedule forecasting of electric power exchange for rto/isos. In IEEE-PES General Meeting, July 2012. [ bib ]
[13] Chad Scherrer, Mahantesh Halappanavar, Ambuj Tewari, and David Haglin. Scaling up coordinate descent algorithms for large l1 regularization problems. In International Conference on Machine Learning (ICML), June 2012. [ bib ]
[14] Nurul F. Zulkarnain, David J. Haglin, and John A. Keane. DisClose: Discovering colossal closed itemsets via a memory efficient compact row-tree. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2012), workshop DSDM'12, May 2012. [ bib ]
[15] Robert Adolf, David Haglin, Mahantesh Halappanavar, Yousu Chen, and Zhenyu Huang. Techniques for improving filters in power grid contingency analysis. In Petra Perner, editor, Machine Learning and Data Mining in Pattern Recognition - 7th International Conference, MLDM 2011, New York, NY, USA, August 30 - September 3, 2011. Proceedings, volume 6871 of Lecture Notes in Computer Science, pages 599-611. Springer, 2011. [ bib | http ]
[16] Cliff Joslyn, Sinan al Saffar, David Haglin, and Lawrence Holder. Combinatorial information theoretical measurement of the semantic significance of semantic graph motifs. In Mining Data Semantics Workshop (MDS 2011). ACM SIGKDD, August 2011. [ bib ]
[17] Eric L. Goodman, Edward Jimenez, David Mizell, Sinan Al-Saffar, Bob Adolf, and David J. Haglin. High-performance computing applied to semantic databases. In Grigoris Antoniou, Marko Grobelnik, Elena Simperl, Bijan Parsia, Dimitris Plexousakis, Pieter De Leenheer, and Jeff Pan, editors, The Semanic Web: Research and Applications, volume 6644 of Lecture Notes in Computer Science, pages 31-45. Springer Berlin / Heidelberg, 2011. 10.1007/978-3-642-21064-8_3. [ bib | http ]
[18] Jace Mogill and David J. Haglin. Toward parallel document clustering. In IPDPS Workshops, pages 1700-1709, 2011. [ bib ]
[19] Cliff Joslyn, Bob Adolf, Sinan al Saffar, John Feo, and David Haglin. Report on april, 2011, workshop on semantic graph database search patterns. In Jesse Weaver, Spyros Kotoulas, Jacopo Urbani, Eric Goodman, and David Mizell, editors, Workshop on High-Performance Computing for the Semantic Web (HPCSW2011), May 2011. [ bib | http ]
[20] Cliff Joslyn, Bob Adolf, Sinan al Saffar, John Feo, Eric Goodman, David Haglin, Greg Mackey, and David Mizell. High performance descriptive semantic analysis of semantic graph databases. In Jesse Weaver, Spyros Kotoulas, Jacopo Urbani, Eric Goodman, and David Mizell, editors, Workshop on High-Performance Computing for the Semantic Web (HPCSW2011), May 2011. [ bib | http ]
[21] Jace Mogill and David J. Haglin. A comparison of shared memory parallel programming models. In Cray Users Group (CUG2010), May 2010. [ bib ]
[22] Eric L. Goodman, David J. Haglin, Chad Scherrer, Daniel Chavarria-Miranda, Jace Mogill, and John Feo. Hashing strategies for the Cray XMT. In 2010 IEEE International Symposium on Parallel Distributed Processing, Workshops and Phd Forum (IPDPSW), April 2010. [ bib | DOI ]
[23] Paraskevas Yiapanis, David J. Haglin, Anna M. Manning, Ken Mayes, and John Keane. Variable-grain and dynamic work generation for minimal unique itemset mining. In 2008 IEEE International Conference on Cluster Computing, pages 33-41, September 2008. [ bib | DOI ]
[24] David J. Haglin and Anna M. Manning. On minimal infrequent itemset mining. In Robert Stahlbock, Sven F. Crone, and Stefan Lessmann, editors, Proceedings of the 2007 International Conference on Data Mining (DMIN 2007), pages 141-147. CSREA Press, June 2007. [ bib ]
[25] Dan Singer, David J. Haglin, and Anna M. Manning. Towards average case analysis of itemset mining. In Robert Stahlbock, Sven F. Crone, and Stefan Lessmann, editors, Proceedings of the 2007 International Conference on Data Mining (DMIN 2007), pages 127-133. CSREA Press, June 2007. [ bib ]
[26] KR Mayes, MJ Elliot, AM Manning, DJ Haglin, and JR Gurd. A distributed search infrastructure for Statistical Disclosure Control on a Grid. In Proceedings of the Second International Conference on e-Social Science, Manchester, 2006. [ bib ]
[27] A.M. Manning and D.J. Haglin. A new algorithm for finding minimal sample uniques for use in statistical disclosure assessment. In IEEE International Conference on Data Mining (ICDM05), pages 290-297. IEEE, November 2005. [Acceptance Rate = 69/630 = 11%]. [ bib ]
[28] T.W. Giblin, J. Hakkila, D.J. Haglin, and R.J. Roiger. The Gamma-Ray Burst ToolSHED is Open for Business. In AIP Conference Proceedings, volume 727, page 585, 2004. [ bib ]
[29] J. Hakkila, TW Giblin, RJ Roiger, DJ Haglin, WS Paciesas, and CA Meegan. The Dual Timescale Peak Flux and GRB Classes. In Astronomical Society of the Pacific Conference Series, volume 312, page 78, 2004. [ bib ]
[30] J. Hakkila, T.W. Giblin, W.S. Paciesas, R.J. Roiger, D.J. Haglin, and C.A. Meegan. Comments on Anisotropic Distributions of Faint BATSE GRBs. In AIP Conference Proceedings, volume 662, pages 144-146, 2003. [ bib ]
[31] J. Hakkila, T.W. Giblin, T.M. Freismuth, K.C. Young, A.J. Sprague, A.D. Stallworth, D.J. Haglin, R.J. Roiger, and W.S. Paciesas. The Internal Luminosity Functions of BATSE 5B Gamma-Ray Bursts. In AIP Conference Proceedings, volume 662, pages 147-149, 2003. [ bib ]
[32] J. Hakkila, R.J. Roiger, D.J. Haglin, T.W. Giblin, and W.S. Paciesas. A Comparison of Unsupervised Classifiers on BATSE Catalog Data. In AIP Conference Proceedings, volume 662, pages 179-182, 2003. [ bib ]
[33] J. Hakkila, D.J. Haglin, R.J. Roiger, T.W. Giblin, W.S. Paciesas, and C.A. Meegan. An update on the GRB ToolSHED project status. In AIP Conference Proceedings, volume 662, pages 556-559, 2003. [ bib ]
[34] D.J. Haglin, R.J. Roiger, J. Hakkila, and T. Giblin. Data mining from a web browser. In International Conference on Advances in Infrastructure for Electronic Business, Science, and Education, August 2002. [ bib ]
[35] J. Hakkila, T. Giblin, R.J. Roiger, D.J. Haglin, and W.S. Paciesas. How Data Mining Helps Expose Gamma-Ray Burst Properties and Instrumental Biases. In World Multiconference on Systemics, Cybernetics and Informatics, pages 479-484, July 2002. [ bib ]
[36] J. Hakkila, R. Mallozzi, R. Roiger, D. Haglin, G. Pendleton, and C. Meegan. Tools for Gamma-Ray Burst Data Mining. Gamma-Ray Bursts in the Afterglow Era, pages 60-62, 2001. [ bib ]
[37] J. Hakkila, R. Roiger, D. Haglin, R. Mallozzi, G. Pendleton, and C. Meegan. Mining Gamma-Ray Burst Data. Mining the Sky, pages 487-493, 2001. [ bib ]
[38] D.J. Haglin, R.J. Roiger, J. Hakkila, G. Pendleton, and R. Mallozzi. A GRB tool shed. In Gamma-Ray Bursts: 5th Huntsville Symposium, pages 877-881, 2000. [ bib ]
[39] J. Hakkila, C.A. Meegan, G.N. Pendleton, R.S. Mallozzi, D.J. Haglin, and R.J. Roiger. The fluence duration bias. Arxiv preprint astro-ph/0001338, pages 48-52, 2000. [ bib ]
[40] J. Hakkila, D.J. Haglin, R.J. Roiger, R.S. Mallozzi, G.N. Pendleton, and C.A. Meegan. Properties of gamma-ray burst classes. Arxiv preprint astro-ph/0001335, pages 33-37, 2000. [ bib ]
[41] J. Hakkila, D.J. Haglin, R.J. Roiger, R.S. Mallozzi, and G.N. Pendleton. Unsupervised induction and gamma-ray burst classification. Gamma-Ray Bursts, pages 38-42, 1999. [ bib ]
[42] Richard J. Roiger, Michael W. Geatz, David J. Haglin, and Jon Hakkila. ESX - a tool for knowledge discovery. In W.T. Price, editor, Proceedings of the Federal Data Mining Symposium & Exposition, pages 109-120, Fairfax VA, 1999. AFCEA International. [ bib ]
[43] J. Hakkila, D.J. Haglin, R.J. Roiger, R.S. Mallozzi, G.N. Pendleton, and C.A. Meegan. AI gamma-ray burst classification: Methodology/preliminary results. In Fourth Huntsville Gamma-Ray Burst Symposium, pages 77-81, 1997. [ bib ]
[44] David J. Haglin and John A. Kaliski. A massively parallel transportation solution. In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'95), pages 231-239, 1995. [ bib ]
[45] David J. Haglin. Bipartite expander matching is in NC. In Ninth IEEE International Symposium on Computer and Information Sciences, pages 148-155, 1994. [ bib ]
[46] DJ Haglin. On a fast deterministic parallel approximate matching algorithm. In Parallel and Distributed Processing, 1991. Proceedings of the Third IEEE Symposium on, pages 774-777, 1991. [ bib ]

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