{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T09:17:48Z","timestamp":1780737468837,"version":"3.54.1"},"reference-count":86,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"publisher","award":["#1029711"],"award-info":[{"award-number":["#1029711"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2017,10,1]]},"DOI":"10.1109\/tkde.2017.2720168","type":"journal-article","created":{"date-parts":[[2017,6,29]],"date-time":"2017-06-29T19:43:22Z","timestamp":1498765402000},"page":"2318-2331","source":"Crossref","is-referenced-by-count":1307,"title":["Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data"],"prefix":"10.1109","volume":"29","author":[{"given":"Anuj","family":"Karpatne","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gowtham","family":"Atluri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James H.","family":"Faghmous","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Steinbach","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arindam","family":"Banerjee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Auroop","family":"Ganguly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shashi","family":"Shekhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nagiza","family":"Samatova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vipin","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2015.11.012"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00351"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1029\/98WR02577"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.20"},{"key":"ref76","first-page":"2249","article-title":"An empirical evaluation of thompson sampling","author":"chapelle","year":"2011","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772758"},{"key":"ref74","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-03711-5","author":"evensen","year":"2009","journal-title":"Data Assimilation The Ensemble Kalman Filter"},{"key":"ref39","volume":"1","author":"friedman","year":"2001","journal-title":"The Elements of Statistical Learning"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1002\/hyp.3360060305"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/2015WR017173"},{"key":"ref78","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"jordan","year":"2015","journal-title":"Science"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1137\/S0363012992237273"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5194\/npg-21-777-2014"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2014.335"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3389\/fmats.2016.00028"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2017.05.039"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1002\/2015WR017780"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s12665-013-2329-8"},{"key":"ref35","year":"2016"},{"key":"ref34","year":"2016","journal-title":"Physics Informed Machine Learning Conference"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asp013"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00532.x"},{"key":"ref61","first-page":"1934","article-title":"Generalized dantzig selector: Application to the k-support norm","author":"chatterjee","year":"2014","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553431"},{"key":"ref28","article-title":"Accounting for linkage disequilibrium in genome-wide association studies: A penalized regression method","volume":"6","author":"liu","year":"2013","journal-title":"Statist Interface"},{"key":"ref64","first-page":"543","article-title":"Tree-guided group Lasso for multi-task regression with structured sparsity","author":"kim","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24571-3_62"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.2012.681250"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972825.5"},{"key":"ref29","first-page":"799","article-title":"Post classification label refinement using implicit ordering constraint among data instances","author":"khandelwal","year":"2015","journal-title":"Proc IEEE Int Conf Data Mining"},{"key":"ref67","first-page":"253","article-title":"Predictive learning in the presence of heterogeneity and limited training data","author":"karpatne","year":"2014","journal-title":"Proc SIAM Int Conf Data Mining"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972832.72"},{"key":"ref69","article-title":"Automated plantation mapping in southeast asia using remote sensing data","author":"jia","year":"2016"},{"key":"ref2","article-title":"The data deluge","year":"2010","journal-title":"Special Supplements"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1126\/science.1170411","article-title":"Beyond the data deluge","volume":"323","author":"bell","year":"2009","journal-title":"Science"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.2514\/1.J055595"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1021\/cm100795d"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevFluids.2.034603"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/nmat3568"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1038\/nmat1691"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01932-6_25"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/qua.25040"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxm045"},{"key":"ref51","first-page":"316","article-title":"Multi-scale graphical models for spatio-temporal processes","author":"denli","year":"2014","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2528038"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-31858-5_7"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRev.136.B864"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.3934\/dcdsb.2012.17.1333"},{"key":"ref55","article-title":"Physics constrained nonlinear regression models for time series","volume":"26","author":"majda","year":"2012","journal-title":"Nonlinearity"},{"key":"ref54","doi-asserted-by":"crossref","DOI":"10.1201\/9781584889977","author":"basu","year":"2008","journal-title":"Constrained Clustering Advances in Algorithms Theory and Applications"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/568574.568580"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-25465-X_18"},{"key":"ref10","first-page":"8","article-title":"Big data: Science in the petabyte era","volume":"455","author":"graham-rowe","year":"2008","journal-title":"Nature"},{"key":"ref11","first-page":"639","article-title":"Special issue: Dealing with data","volume":"331","author":"jonathan","year":"2011","journal-title":"Science"},{"key":"ref40","author":"tan","year":"2005","journal-title":"Introduction to Data Mining"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1038\/nn.3839"},{"key":"ref13","article-title":"The end of theory: The data deluge makes the scientific method obsolete","author":"sterling","year":"2008","journal-title":"Wired Mag"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1002\/2014GL059205"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1126\/science.1248506","article-title":"The parable of Google flu: Traps in big data analysis","volume":"343","author":"lazer","year":"2014","journal-title":"Science"},{"key":"ref82","first-page":"410","article-title":"Spatio-temporal consistency as a means to identify unlabeled objects in a continuous data field","author":"faghmous","year":"2014","journal-title":"Proc 28th AAAI Conf Artif Intell"},{"key":"ref16","article-title":"Eight (no, nine!) problems with big data","volume":"6","author":"marcus","year":"2014","journal-title":"New York Times"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CIDU.2012.6382189"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/nature07634"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2007.421"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11181"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1111\/ropr.12080"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2015.28"},{"key":"ref80","first-page":"697","article-title":"Nearly tight bounds for the continuum-armed bandit problem","author":"kleinberg","year":"2004","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2009.36"},{"key":"ref3","author":"james","year":"2011","journal-title":"Big Data The Next Frontier for Innovation Competition and Productivity"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/528018a"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2004.12.018"},{"key":"ref85","article-title":"CaloGAN: Simulating 3D high energy particle showers in multi-layer electromagnetic calorimeters with generative adversarial networks","author":"paganini","year":"0","journal-title":"arXiv 1705 02355"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1089\/big.2014.0026"},{"key":"ref86","volume":"1","author":"hey","year":"2009","journal-title":"The Fourth Paradigm Data-Intensive Scientific Discovery"},{"key":"ref7","author":"baldi","year":"2001","journal-title":"Bioinformatics the Machine Learning Approach"},{"key":"ref49","first-page":"829","article-title":"Recommender systems","author":"melville","year":"2011","journal-title":"Encyclopedia of Machine Learning"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2015.114"},{"key":"ref46","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref45","first-page":"338","article-title":"Long short-term memory recurrent neural network architectures for large scale acoustic modeling","author":"sak","year":"2014","journal-title":"Proc Annu Conf Int Speech Commun Assoc"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2486.2011.02451.x"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1111\/geb.12335"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1097\/01.opx.0000192350.01045.6f"},{"key":"ref41","volume":"1","author":"vapnik","year":"1998","journal-title":"Statistical Learning Theory"},{"key":"ref44","article-title":"Recurrent neural network based language model","volume":"2","author":"mikolov","year":"2010","journal-title":"Proc 11th Annu Conf Int Speech Commun Assoc"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2016.10.015"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/ieeexplore.ieee.org\/ielaam\/69\/8031002\/7959606-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/8031002\/07959606.pdf?arnumber=7959606","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T06:37:21Z","timestamp":1692859041000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7959606\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,1]]},"references-count":86,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2017.2720168","relation":{},"ISSN":["1041-4347"],"issn-type":[{"value":"1041-4347","type":"print"}],"subject":[],"published":{"date-parts":[[2017,10,1]]}}}