Naughton's Wisconsin Bibliography: A Brief Guide
aa r X i v : . [ c s . G L ] M a y Naughton’s Wisconsin Bibliography: A Brief Guide
Joseph M. HellersteinNaughton Ph.D. StudentMay 19, 2016
He used to tell me, “Do what you like to do.It’ll probably turn out to be what you do best.”—Wallace Stegner,
Crossing to Safety
Over nearly three decades at the University of Wis-consin, Jeff Naughton has left an indelible mark oncomputer science. He has been a global leader of thedatabase research field, deepening its core and push-ing its boundaries. Many of Naughton’s ideas weretranslated directly into practice in commercial and open-source systems. But software comes and goes. In theend, it is the ideas themselves that have had impact, ideaswritten down in papers.Naughton has been a prolific scholar over the lastthirty years, with over 175 publications in his bibliog-raphy, covering a wide range of topics. This documentdoes not attempt to enumerate or even summarize thewealth of ideas that Naughton has published over thecourse of his academic career—the task is too daunting.Instead, the best this short note aims to do is to serve asa rough map of the territory: something to help other re-searchers navigate the wide spaces of Naughton’s work.
Jeffrey F. Naughton received his bachelor’s degree fromthe University of Wisconsin in 1982, and completed hisPh.D. at Stanford in 1987 under the direction of JeffreyUllman. After a two-year stint as an Assistant Professorat Princeton, he returned to the University of Wiscon-sin where he served on the faculty for 26 years. Duringhis years at Wisconsin, Naughton supervised numerousresearch students, served as Chair of the Computer Sci-ences Department, and was a five-time repeat recipientof the venerable “Cow Award” for excellence in class-room teaching.His research promise was recognized early with thePresidential Young Investigator award in 1991. Hisresearch success was honored in its fullness multi- ple times, including the ACM-SIGMOD Test of TimeAward in 2004 for Shore object database system [41],and the 2008 ACM Software Systems Award as a mem-ber of the Gamma Parallel Database team. As overar-ching recognition of both his contributions and ongoingpromise, Naughton received the University of WisconsinVilas Associate award for excellence in research in 2000,and was inducted as a Fellow of the ACM in 2002.As of this writing, Naughton has supervised at least 43Ph.D. students (Table 1). Including the five students heis currently supervising, Naughton has at least 92 Ph.D.descendants [188].This bibliography marks a checkpoint in Naughton’scareer, not an endpoint. In 2016, Naughton left the Uni-versity of Wisconsin for a senior post in research and de-velopment at Google. There will undoubtedly be manyyears of Naughton innovations in his new environment,and hopefully some of those ideas will appear in thescholarly literature as well.
Naughton’s PhD thesis focused on the optimization ofrecursive queries in Datalog [6], a topic of significanttheoretical interest at the time. Naughton contributedmultiple results in this area at Stanford, Princeton andWisconsin. Highlights include techniques begun in hisPhD thesis to rewrite queries to avoid recursion entirelywhen possible (bounded recursions) [1, 7, 28], and tech-niques to rewrite queries for efficient bottom-up evalua-tion [15]. Sadly, many of Naughton’s results in this areaare difficult to find online at present; interested scholarsare directed to their nearest university library for details.
Sampling and Estimation are recurring themes ofNaughton’s work throughout his career, with papers asecently as 2016 [189]. The heyday of Naughton’s sam-pling work occured in the first half of the 1990’s, in thedomain of selectivity estimation for query optimization.Naughton’s initial publications on the topic arose dur-ing his stint at Princeton, where he worked with cel-ebrated theoretician Richard Lipton. The first paperarose in the domain of recursive queries: estimatingthe size of transitive closures in order to optimize re-cursive queries in a cost-based manner [11]. This ledto subsquent papers on relational database sampling,often with Lipton or Peter J. Haas of IBM Research,covering traditional select/join query selectivity estima-tion [17, 37, 44, 52, 61] as well as distinct value (projec-tion) estimation [50].Naughton’s expertise on sampling and estimation in-formed many topics later in his career, discussed below.
Naughton’s work in the database group at Wisconsin ischaracterized by deep, abiding collaborations on sys-tems projects with his colleagues, notably David DeWittand Michael Carey.First among these efforts was work on Object-Oriented Databases. Particularly influential efforts in-cluded the Shore system [41] and the OO7 [35, 35] andBucky [64] benchmarks. The OO7 and Shore work areamong Naughton’s most-cited papers [187], attesting tothe broad interest in Naughton’s projects in this area. Ata more technical level, Naughton and his PhD studentsdid deep work in this context on clustering objects insecond storage [31, 34], database loading for intercon-nected objects [48, 58], and garbage collection in sec-ondary storage [49].
Naughton was a participant in Wisconsin’s Gamma Par-allel Database effort, which is considered one of thelandmark research systems in 20th century computinghistory. He was also on the team that did follow-on workon Parallel Geo-Spatial data management in the Paradiseproject [66]. Parallelism is another recurring theme inNaughton’s work throughout various topics below.Naughton’s contributions in parallel databases fo-cused on improving join processing, including non-equijoins (“band” joins) [21], and joins that have tocope with data skew [32]—the latter being a very com-mon problem in modern Big Data settings. In these pa-pers, Naughton brought his expertise in database sam-pling to bear on the runtime execution of queries [33].Naughton also worked on parallel execution of objecttraversal [43], as part of a body of work that presaged thepopularity of MapReduce-style parallel computation.
In addition to joins, Naughton did extensive work on ag-gregate query processing. This included early work onadaptive aggregation using sampling [55] in the contextof parallel databases. But some of Naughton’s most ex-tensive and well-cited aggregation work was in the areaof multidimensional data cubes, including his work oncomputing the cube [59, 68, 74, 75], and on workingwith materialized views of the cube [70, 72, 83, 107,120]. Multidimensional data cubes have become a fix-ture in the practical landscape of data management—they are a standard user interface metaphor in modernBusiness Intelligence tools and spreadsheets, and are anongoing area of focus for database vendors as well asopen-source database systems. As of the time of thiswriting, Naughton’s initial work on computing the cubewas both his third-most-cited [59] and seventh-most-cited [68] papers, with 775 and 527 citations respec-tively [187].
As the millenium drew to a close, the world becameconnected via the Internet and the World-Wide Web,and Naughton’s work turned to issues in web data man-agement. These included topics in managing semi-structured document data, and in serving database dataonline.Naughton and his students were among the leadersin bridging XML document management and traditionaldatabase ideas from the relational era. The work isbroadly applicable to any data model with nested and/orvariant structure, including the JSON model used inmany currently-popular document databases.Naughton’s work on XML and relational databaseswas extensive in its scope [76, 89, 101, 105, 111, 113,114, 118, 145]. It was also extremely influential in bothindustry and academia. Naughton’s single most widely-cited paper is his 1999 work that opened up this space,laying out the connections and differences betweenXML and relational databases [76]; as of this writing ithad over 1500 citations in the literature [187]. Anothertopic in this area that has attracted enormous attention isthe problem of answering containment queries [81, 90];the second of these papers is Naughton’s second-most-cited result, with over 1000 citations as of the time ofwriting [187].Naughton also returned to his roots in this domain, re-visiting problems such as selectivity estimation [84, 99]and recursive query processing [112] in the context ofXML. In later years, Naughton returned to the topicf document data in the guise of “sparse” relationaldatasets, which can be viewed either as relations withmany nulls, or key-value maps [123, 131]A related topic at the turn of the millenium was the in-tegration of databases with web servers. In this domain,Naughton worked on a series of papers regarding webcaching for database-backed websites [79, 87, 96, 141].
Naughton was an ongoing contributor to improving theuser experience for long-running queries—an abiding is-sue in large-scale analytics. Naughton’s work includedan ongoing effort into progress indicators for long-running queries [115, 119, 128, 165, 167, 168, 180], aswell as providing online results for those queries whilethey are in progress [94, 95, 176]. In both cases, progressand answers often need to be estimated, again exercisingNaughton’s expertise in database sampling and estima-tion.In a related vein, Naughton contributed fundamen-tal work on processing continuous queries over DataStreams [97, 103, 122, 166]—here too, results have tobe produced before data is fully consumed. The settingfor much of his work was the Niagara Internet QuerySystem [88], a vision of streaming XML documents thatcombined challenges in XML document processing withchallenges in stream processing, adaptive query execu-tion, data integration and text search.
In his last decade at Wisconsin, Naughton became in-terested in the topic of data privacy, with a particu-lar focus on anonymization methods for query process-ing. In many of Naughton’s papers in this area therewere connections to topics where he had done pioneer-ing prior work previously, including connections be-tween anonymization and spatial indexing [133], as wellas anonymization of set-valued attributes [146], stream-ing events [156], range predicates [169] and recursivequeries [170]. Naughton’s privacy work also includingdynamic anonymization [155] and anonymization of fre-quent itemset algorithms for data mining [161].
Another topic that Naughton explored in his final decadeat Wisconsin was that of extracting data from source sys-tems, and reasoning about the provenance (lineage) ofdata. This includes work both in the context of logs fromjob scheduling systems like Wisconsin’s long-running Condor project [129, 148], as well as automated in-formation extraction from text [132, 138, 159] whereNaughton also did core research [102, 130, 135, 137,139].
Related to his work on XML as well as InformationExtraction, Naughton and his students worked on vari-ous problems in searching and combining textual data indatabases. This includes work on combining keywordsearch results with forms [144, 149], approximate stringmembership [157, 160], and debugging of “why not”provenance in keyword search over databases [182].
A cross-cutting topic in Naughton’s work is the devel-opment and use of index structures, from generalizedsearch trees [51] to document store indexes [92, 93] totext queries [111, 178].
Jeff Naughton is sui generis : beyond category. So it isnot surprising that the categories above do not cover hiswork. Given his devotion to his many Ph.D. students,the best overview of Naughton’s work may be the topicsof his students’ dissertations in Table 1. To both Jeff andhis students, I apologize both for the work I misclassifiedabove, and the work I neglected to classify entirely.
Jeff Naughton’s bibliography and papers, impressive asthey are, present only a narrow picture of the man. I con-sider myself lucky to have studied with him at Wiscon-sin. As Jeff’s student both during and after my Ph.D., Ilearned many things beyond computer science. I learnedhow to shake off disappointment and failure, and turnthem into research results; I will be forever grateful forhis confidence in me and his gentle guidance throughdifficult times. I saw how humor can smooth the ups anddowns of learning. I was given patient lessons in balanc-ing ambition and grace, from a role model who coupleda characteristically midwestern humility to deep insight,steady confidence and a wicked sense of humor. Per-haps most significantly I got to see—first with puzzle-ment and later with admiration—how a first-rate scholarcan protect his time, put family first, and raise delightfulchildren. As the years have passed, I’ve had to find myown path through similar issues, and I’ve been gratefulto have seen the trails Jeff blazed. I don’t try to followeff directly; he is unique. But he has been a guide-post for me in many issues at the juncture of scholarship,drive, and fulfillment. For all that I am grateful.Like Jeff, I have lifelong ties to Madison; probablythis makes me more wistful about his departure from theUW than I might be otherwise. To me, Jeff has many ofthe qualities that represent the best of Wisconsin char-acter: wit, wisdom and friendly modesty. His departurewill leave a hole at the heart of the computer sciences de-partment. I know Jeff will bring all those qualities andmore to his new career in industry. I hope the peopleof both Wisconsin and the data management communitycontinue to benefit from Jeff’s brilliance and characterfor many years to come.And so, by circuitous and unpredictableroutes, we converge toward midcontinentand meet in Madison, and are at oncedrawn together.—Wallace Stegner,
Crossing to Safety
References [1] J. F. Naughton. Data Independent Recursion inDeductive Databases. In A. Silberschatz, editor,
Proceedings of the Fifth ACM SIGACT-SIGMODSymposium on Principles of Database Systems,March 24-26, 1986, Cambridge, Massachusetts,USA , pages 267–279. ACM, 1986.[2] J. F. Naughton. One-Sided Recursions. In H. F.Korth, editor,
XP / 7.52 Workshop on DatabaseTheory, University of Texas at Austin, TX, USA,August 13-15, 1986 , 1986.[3] J. F. Naughton. Redundancy in Function-Free Re-cursive Rules. In
Proceedings of the 1986 Sym-posium on Logic Programming, Salt Lake City,Utah, USA, September 22-25, 1986 , pages 236–245. IEEE-CS, 1986.[4] K. A. Morris, J. F. Naughton, Y. P. Saraiya, J. D.Ullman, and A. V. Gelder. YAWN! (Yet An-other Window on NAIL!).
IEEE Data Eng. Bull. ,10(4):28–43, 1987.[5] J. F. Naughton. One-Sided Recursions. InM. Y. Vardi, editor,
Proceedings of the SixthACM SIGACT-SIGMOD-SIGART Symposium onPrinciples of Database Systems, March 23-25,1987, San Diego, California, USA , pages 340–348. ACM, 1987.[6] J. F. Naughton.
Optimization of RecursiveDatabase Query Languages . PhD thesis, Stan- ford University, Stanford, CA, USA, 1987.AAI8801000.[7] J. F. Naughton and Y. Sagiv. A Decidable Class ofBounded Recursions. In M. Y. Vardi, editor,
Pro-ceedings of the Sixth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of DatabaseSystems, March 23-25, 1987, San Diego, Califor-nia, USA , pages 227–236. ACM, 1987.[8] R. W. Haddad and J. F. Naughton. CountingMethods for Cyclic Relations. In C. Edmondson-Yurkanan and M. Yannakakis, editors,
Proceed-ings of the Seventh ACM SIGACT-SIGMOD-SIGART Symposium on Principles of DatabaseSystems, March 21-23, 1988, Austin, Texas, USA ,pages 333–340. ACM, 1988.[9] K. Li and J. F. Naughton. Multiprocessor MainMemory Transaction Processing. In
DPDS , pages177–187, 1988.[10] J. F. Naughton. Compiling Separable Recursions.In H. Boral and P.- \ . Larson, editors, Proceedingsof the 1988 ACM SIGMOD International Con-ference on Management of Data, Chicago, Illi-nois, June 1-3, 1988 , pages 312–319. ACM Press,1988.[11] R. J. Lipton and J. F. Naughton. Estimatingthe Size of Generalized Transitive Closures. InP. M. G. Apers and G. Wiederhold, editors,
Pro-ceedings of the Fifteenth International Confer-ence on Very Large Data Bases, August 22-25,1989, Amsterdam, The Netherlands , pages 165–171. Morgan Kaufmann, 1989.[12] J. F. Naughton. Data Independent Recursionin Deductive Databases.
J. Comput. Syst. Sci. ,38(2):259–289, 1989.[13] J. F. Naughton. Minimizing function-free recur-sive inference rules.
J. ACM , 36(1):69–91, 1989.[14] J. F. Naughton, R. Ramakrishnan, Y. Sagiv, andJ. D. Ullman. Argument Reduction by Factoring.In P. M. G. Apers and G. Wiederhold, editors,
Proceedings of the Fifteenth International Con-ference on Very Large Data Bases, August 22-25,1989, Amsterdam, The Netherlands , pages 173–182. Morgan Kaufmann, 1989.[15] J. F. Naughton, R. Ramakrishnan, Y. Sagiv, andJ. D. Ullman. Efficient Evaluation of Right-, Left-, and Mult-Lineare Rules. In J. Clifford, B. G.Lindsay, and D. Maier, editors,
Proceedings of the1989 ACM SIGMOD International Conference onanagement of Data, Portland, Oregon, May 31- June 2, 1989 , pages 235–242. ACM Press, 1989.[16] K. Li, J. F. Naughton, and J. S. Plank. Real-Time,Concurrent Checkpoint for Parallel Programs. InD. A. Padua, editor,
Proceedings of the SecondACM SIGPLAN Symposium on Princiles & Prac-tice of Parallel Programming (PPOPP), Seattle,Washington, USA, March 14-16, 1990 , pages 79–88. ACM, 1990.[17] R. J. Lipton and J. F. Naughton. Query SizeEstimation by Adaptive Sampling. In D. J.Rosenkrantz and Y. Sagiv, editors,
Proceedings ofthe Ninth ACM SIGACT-SIGMOD-SIGART Sym-posium on Principles of Database Systems, April2-4, 1990, Nashville, Tennessee, USA , pages 40–46. ACM Press, 1990.[18] R. J. Lipton, J. F. Naughton, and D. A. Schneider.Practical Selectivity Estimation through AdaptiveSampling. In H. Garcia-Molina and H. V. Ja-gadish, editors,
Proceedings of the 1990 ACMSIGMOD International Conference on Manage-ment of Data, Atlantic City, NJ, May 23-25, 1990 ,pages 1–11. ACM Press, 1990.[19] J. F. Naughton and R. Ramakrishnan. Howto Forget the Past Without Repeating It. InD. McLeod, R. Sacks-Davis, and H.-J. Schek,editors, , pages 278–289. Morgan Kaufmann, 1990.[20] J. F. Naughton and S. Seshadri. On Estimatingthe Size of Projections. In S. Abiteboul and P. C.Kanellakis, editors,
ICDT’90, Third InternationalConference on Database Theory, Paris, France,December 12-14, 1990, Proceedings , volume 470of
Lecture Notes in Computer Science , pages499–513. Springer, 1990.[21] D. J. DeWitt, J. F. Naughton, and D. A. Schneider.An Evaluation of Non-Equijoin Algorithms. InG. M. Lohman, A. Sernadas, and R. Camps, edi-tors, , pages 443–452.Morgan Kaufmann, 1991.[22] D. J. DeWitt, J. F. Naughton, and D. A. Schnei-der. Parallel Sorting on a Shared-Nothing Archi-tecture using Probabilistic Splitting. In
Proceed-ings of the First International Conference on Par-allel and Distributed Information Systems (PDIS1991), Fontainebleu Hilton Resort, Miami Beach, Florida, December 4-6, 1991 , pages 280–291.IEEE Computer Society, 1991.[23] R. W. Haddad and J. F. Naughton. A CountingAlgorithm for a Cyclic Binary Query.
J. Comput.Syst. Sci. , 43(1):145–169, 1991.[24] K. Li, J. F. Naughton, and J. S. Plank. Check-pointing Multicomputer Applications. In
TenthSymposium on Reliable Distributed Systems,SRDS 1991, Pisa, Italy, September 30 - October2, 1991, Proceedings , pages 2–11. IEEE Com-puter Society, 1991.[25] K. Li, J. F. Naughton, and J. S. Plank. An ef-ficient checkpointing method for multicomputerswith wormhole routing.
International Journal ofParallel Programming , 20(3):159–180, 1991.[26] J. F. Naughton. One-Sided Recursions.
J. Com-put. Syst. Sci. , 42(2):199–236, 1991.[27] J. F. Naughton and R. Ramakrishnan. Bottom-UpEvaluation of Logic Programs. In J.-L. Lassezand G. D. Plotkin, editors,
Computational Logic- Essays in Honor of Alan Robinson , pages 640–700. The MIT Press, 1991.[28] J. F. Naughton and Y. Sagiv. A Simple Charac-terization of Uniform Boundedness for a Class ofRecursions.
J. Log. Program. , 10(3&4):233–252,1991.[29] S. Seshadri and J. F. Naughton. On the Ex-pected Size of Recursive Datalog Queries. InD. J. Rosenkrantz, editor,
Proceedings of theTenth ACM SIGACT-SIGMOD-SIGART Sympo-sium on Principles of Database Systems, May 29-31, 1991, Denver, Colorado, USA , pages 268–279. ACM Press, 1991.[30] S. Sudarshan, D. Srivastava, R. Ramakrishnan,and J. F. Naughton. Space Optimization in theBottom-Up Evaluation of Logic Programs. InJ. Clifford and R. King, editors,
Proceedings ofthe 1991 ACM SIGMOD International Confer-ence on Management of Data, Denver, Colorado,May 29-31, 1991 , pages 68–77. ACM Press,1991.[31] M. M. Tsangaris and J. F. Naughton. A Stochas-tic Approach for Clustering in Object Bases. InJ. Clifford and R. King, editors,
Proceedings ofthe 1991 ACM SIGMOD International Confer-ence on Management of Data, Denver, Colorado,May 29-31, 1991 , pages 12–21. ACM Press,1991.32] D. J. DeWitt, J. F. Naughton, D. A. Schneider,and S. Seshadri. Practical Skew Handling in Par-allel Joins. In L.-Y. Yuan, editor, , pages 27–40. Morgan Kaufmann, 1992.[33] S. Seshadri and J. F. Naughton. Sampling Is-sues in Parallel Database Systems. In A. Pirotte,C. Delobel, and G. Gottlob, editors,
Advances inDatabase Technology - EDBT’92, 3rd Interna-tional Conference on Extending Database Tech-nology, Vienna, Austria, March 23-27, 1992, Pro-ceedings , volume 580 of
Lecture Notes in Com-puter Science , pages 328–343. Springer, 1992.[34] M. M. Tsangaris and J. F. Naughton. On thePerformance of Object Clustering Techniques.In M. Stonebraker, editor,
Proceedings of the1992 ACM SIGMOD International Conferenceon Management of Data, San Diego, Califor-nia, June 2-5, 1992 , pages 144–153. ACM Press,1992.[35] M. J. Carey, D. J. DeWitt, and J. F. Naughton.The oo7 Benchmark. In P. Buneman and S. Jajo-dia, editors,
Proceedings of the 1993 ACM SIG-MOD International Conference on Managementof Data, Washington, D.C., May 26-28, 1993 ,pages 12–21. ACM Press, 1993.[36] D. J. DeWitt, J. F. Naughton, and J. Burger.Nested Loops Revisited. In
Proceedings of the2nd International Conference on Parallel andDistributed Information Systems (PDIS 1993), Is-sues, Architectures, and Algorithms, San Diego,CA, USA, January 20-23, 1993 , pages 230–242.IEEE Computer Society, 1993.[37] P. J. Haas, J. F. Naughton, S. Seshadri, and A. N.Swami. Fixed-Precision Estimation of Join Se-lectivity. In C. Beeri, editor,
Proceedings of theTwelfth ACM SIGACT-SIGMOD-SIGART Sym-posium on Principles of Database Systems, May25-28, 1993, Washington, DC, USA , pages 190–201. ACM Press, 1993.[38] R. J. Lipton and J. F. Naughton. Clocked Adver-saries for Hashing.
Algorithmica , 9(3):239–252,1993.[39] R. J. Lipton, J. F. Naughton, D. A. Schneider,and S. Seshadri. Efficient Sampling Strategies forRelational Database Operations.
Theor. Comput.Sci. , 116(1):195–226, 1993. [40] A. Shatdal and J. F. Naughton. Using SharedVirtual Memory for Parallel Join Processing. InP. Buneman and S. Jajodia, editors,
Proceedingsof the 1993 ACM SIGMOD International Confer-ence on Management of Data, Washington, D.C.,May 26-28, 1993 , pages 119–128. ACM Press,1993.[41] M. J. Carey, D. J. DeWitt, M. J. Franklin, N. E.Hall, M. L. McAuliffe, J. F. Naughton, D. T.Schuh, M. H. Solomon, C. K. Tan, O. G. Tsat-alos, S. J. White, and M. J. Zwilling. ShoringUp Persistent Applications. In R. T. Snodgrassand M. Winslett, editors,
Proceedings of the1994 ACM SIGMOD International Conference onManagement of Data, Minneapolis, Minnesota,May 24-27, 1994 , pages 383–394. ACM Press,1994.[42] M. J. Carey, D. J. DeWitt, C. Kant, and J. F.Naughton. A Status Report on the oo7 OODBMSBenchmarking Effort. In J. McKenna, J. E. B.Moss, and R. L. Wexelblat, editors,
OOP-SLA’94, Proceedings of the Ninth Annual Con-ference on Object-Oriented Programming Sys-tems, Languages, and Applications, Portland,Oregon, USA, October 23-27, 1994 , pages 414–426. ACM, 1994.[43] D. J. DeWitt, J. F. Naughton, J. C. Shafer,and S. Venkataraman. ParSets for ParallelizingOODBMS Traversals: Implementation and Per-formance. In
Proceedings of the Third Inter-national Conference on Parallel and DistributedInformation Systems (PDIS 94), Austin, Texas,September 28-30, 1994 , pages 111–120. IEEEComputer Society, 1994.[44] P. J. Haas, J. F. Naughton, and A. N. Swami. Onthe Relative Cost of Sampling for Join SelectivityEstimation. In V. Vianu, editor,
Proceedings ofthe Thirteenth ACM SIGACT-SIGMOD-SIGARTSymposium on Principles of Database Systems,May 24-26, 1994, Minneapolis, Minnesota, USA ,pages 14–24. ACM Press, 1994.[45] K. Li, J. F. Naughton, and J. S. Plank. Low-Latency, Concurrent Checkpointing for ParallelPrograms.
IEEE Trans. Parallel Distrib. Syst. ,5(8):874–879, 1994.[46] J. F. Naughton and R. Ramakrishnan. How toForget the Past Without Repeating It.
J. ACM ,41(6):1151–1177, 1994.[47] A. Shatdal, C. Kant, and J. F. Naughton. CacheConscious Algorithms for Relational Query Pro-essing. In J. B. Bocca, M. Jarke, and C. Zaniolo,editors,
VLDB’94, Proceedings of 20th Interna-tional Conference on Very Large Data Bases,September 12-15, 1994, Santiago de Chile, Chile ,pages 510–521. Morgan Kaufmann, 1994.[48] J. L. Wiener and J. F. Naughton. Bulk Load-ing into an OODB: A Performance Study. InJ. B. Bocca, M. Jarke, and C. Zaniolo, edi-tors,
VLDB’94, Proceedings of 20th InternationalConference on Very Large Data Bases, Septem-ber 12-15, 1994, Santiago de Chile, Chile , pages120–131. Morgan Kaufmann, 1994.[49] V.-F. Yong, J. F. Naughton, and J.-B. Yu. Stor-age Reclamation and Reorganization in Client-Server Persistent Object Stores. In
Proceedings ofthe Tenth International Conference on Data Engi-neering, February 14-18, 1994, Houston, Texas,USA , pages 120–131. IEEE Computer Society,1994.[50] P. J. Haas, J. F. Naughton, S. Seshadri, andL. Stokes. Sampling-Based Estimation of theNumber of Distinct Values of an Attribute. InU. Dayal, P. M. D. Gray, and S. Nishio, edi-tors,
VLDB’95, Proceedings of 21th InternationalConference on Very Large Data Bases, September11-15, 1995, Zurich, Switzerland , pages 311–322.Morgan Kaufmann, 1995.[51] J. M. Hellerstein, J. F. Naughton, and A. Pfeffer.Generalized Search Trees for Database Systems.In U. Dayal, P. M. D. Gray, and S. Nishio, edi-tors,
VLDB’95, Proceedings of 21th InternationalConference on Very Large Data Bases, September11-15, 1995, Zurich, Switzerland , pages 562–573.Morgan Kaufmann, 1995.[52] R. J. Lipton and J. F. Naughton. Query Size Es-timation by Adaptive Sampling.
J. Comput. Syst.Sci. , 51(1):18–25, 1995.[53] J. F. Naughton, R. Ramakrishnan, Y. Sagiv, andJ. D. Ullman. Argument Reduction by Factoring.
Theor. Comput. Sci. , 146(1&2):269–310, 1995.[54] S. Seshadri and J. F. Naughton. On the ExpectedSize of Recursive Datalog Queries.
J. Comput.Syst. Sci. , 51(2):137–148, 1995.[55] A. Shatdal and J. F. Naughton. Adaptive ParallelAggregation Algorithms. In M. J. Carey and D. A.Schneider, editors,
Proceedings of the 1995 ACMSIGMOD International Conference on Manage-ment of Data, San Jose, California, May 22-25,1995 , pages 104–114. ACM Press, 1995. [56] D. Srivastava, S. Sudarshan, R. Ramakrishnan,and J. F. Naughton. Space Optimization in De-ductive Databases.
ACM Trans. Database Syst. ,20(4):472–516, 1995.[57] S. Venkataraman, M. Livny, and J. F. Naughton.The Impact of Data Placement on Memory Man-agement for Multi-Server OODBMS. In P. S. Yuand A. L. P. Chen, editors,
Proceedings of theEleventh International Conference on Data Engi-neering, March 6-10, 1995, Taipei, Taiwan , pages355–364. IEEE Computer Society, 1995.[58] J. L. Wiener and J. F. Naughton. OODBBulk Loading Revisited: The Partitioned-ListApproach. In U. Dayal, P. M. D. Gray, andS. Nishio, editors,
VLDB’95, Proceedings of 21thInternational Conference on Very Large DataBases, September 11-15, 1995, Zurich, Switzer-land , pages 30–41. Morgan Kaufmann, 1995.[59] S. Agarwal, R. Agrawal, P. Deshpande,A. Gupta, J. F. Naughton, R. Ramakrishnan,and S. Sarawagi. On the Computation of Multi-dimensional Aggregates. In T. M. Vijayaraman,A. P. Buchmann, C. Mohan, and N. L. Sarda,editors,
VLDB’96, Proceedings of 22th Interna-tional Conference on Very Large Data Bases,September 3-6, 1996, Mumbai (Bombay), India ,pages 506–521. Morgan Kaufmann, 1996.[60] D. J. DeWitt, J. F. Naughton, J. C. Shafer,and S. Venkataraman. Parallelising OODBMSTraversals: A Performance Evaluation.
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SIGMOD1997, Proceedings ACM SIGMOD InternationalConference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA , pages 135–146.ACM Press, 1997.[65] P. Deshpande, J. F. Naughton, K. Ramasamy,A. Shukla, K. Tufte, and Y. Zhao. Cubing Algo-rithms, Storage Estimation, and Storage and Pro-cessing Alternatives for OLAP.
IEEE Data Eng.Bull. , 20(1):3–11, 1997.[66] J. M. Patel, J.-B. Yu, N. Kabra, K. Tufte, B. Nag,J. Burger, N. E. Hall, K. Ramasamy, R. Lueder,C. J. Ellmann, J. Kupsch, S. Guo, D. J. DeWitt,and J. F. Naughton. Building a Scaleable Geo-Spatial DBMS: Technology, Implementation, andEvaluation. In J. Peckham, editor,
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Distributed and Parallel Databases ,6(1):5–6, 1998.[72] A. Shukla, P. Deshpande, and J. F. Naughton. Ma-terialized View Selection for MultidimensionalDatasets. In A. Gupta, O. Shmueli, and J. Widom,editors,
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VLDB 2001, Proceedings of 27th Inter-national Conference on Very Large Data Bases,September 11-14, 2001, Roma, Italy , pages 591–600. Morgan Kaufmann, 2001. [85] A. Aboulnaga, J. F. Naughton, and C. Zhang.Generating Synthetic Complex-Structured XMLData. In
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SIGMOD Record , 30(3):20–26, 2001.[90] C. Zhang, J. F. Naughton, D. J. DeWitt, Q. Luo,and G. M. Lohman. On Supporting ContainmentQueries in Relational Database Management Sys-tems. In S. Mehrotra and T. K. Sellis, editors,
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Proceedings of the ACM SIG-MOD International Conference on Managementof Data, Beijing, China, June 12-14, 2007 , pages821–832. ACM, 2007.[132] J. Huang and J. F. Naughton. K-relevance: a spec-trum of relevance for data sources impacting aquery. In C. Y. Chan, B. C. Ooi, and A. Zhou, ed-itors,
Proceedings of the ACM SIGMOD Interna-tional Conference on Management of Data, Bei-ing, China, June 12-14, 2007 , pages 163–174.ACM, 2007.[133] T. Iwuchukwu and J. F. Naughton. K-Anonymization as Spatial Indexing: Toward Scal-able and Incremental Anonymization. In C. Koch,J. Gehrke, M. N. Garofalakis, D. Srivastava,K. Aberer, A. Deshpande, D. Florescu, C. Y.Chan, V. Ganti, C.-C. Kanne, W. Klas, and E. J.Neuhold, editors,
Proceedings of the 33rd Inter-national Conference on Very Large Data Bases,University of Vienna, Austria, September 23-27,2007 , pages 746–757. ACM, 2007.[134] A. Kini and J. F. Naughton. Database Sup-port for Weighted Match Joins. In , page 20.IEEE Computer Society, 2007.[135] W. Shen, A. Doan, J. F. Naughton, and R. Ra-makrishnan. Declarative Information ExtractionUsing Datalog with Embedded Extraction Pred-icates. In C. Koch, J. Gehrke, M. N. Garo-falakis, D. Srivastava, K. Aberer, A. Deshpande,D. Florescu, C. Y. Chan, V. Ganti, C.-C. Kanne,W. Klas, and E. J. Neuhold, editors,
Proceed-ings of the 33rd International Conference on VeryLarge Data Bases, University of Vienna, Austria,September 23-27, 2007 , pages 1033–1044. ACM,2007.[136] D. J. DeWitt, E. Paulson, E. Robinson, J. F.Naughton, J. Royalty, S. Shankar, and A. Kri-oukov. Clustera: an integrated computation anddata management system.
PVLDB , 1(1):28–41,2008.[137] A. Doan, J. F. Naughton, R. Ramakrishnan,A. Baid, X. Chai, F. Chen, T. Chen, E. Chu,P. DeRose, B. J. Gao, C. Gokhale, J. Huang,W. Shen, and B.-Q. Vuong. Information extrac-tion challenges in managing unstructured data.
SIGMOD Record , 37(4):14–20, 2008.[138] J. Huang, T. Chen, A. Doan, and J. F. Naughton.On the provenance of non-answers to queries overextracted data.
PVLDB , 1(1):736–747, 2008.[139] J. Kang and J. F. Naughton. Schema MatchingUsing Interattribute Dependencies.
IEEE Trans.Knowl. Data Eng. , 20(10):1393–1407, 2008.[140] G. Luo, J. F. Naughton, C. J. Ellmann, andM. Watzke. Transaction reordering with applica-tion to synchronized scans. In J. G. Shanahan, S. Amer-Yahia, I. Manolescu, Y. Zhang, D. A.Evans, A. Kolcz, K.-S. Choi, and A. Chowd-hury, editors,
Proceedings of the 17th ACM Con-ference on Information and Knowledge Manage-ment, CIKM 2008, Napa Valley, California, USA,October 26-30, 2008 , pages 1335–1336. ACM,2008.[141] Q. Luo, J. F. Naughton, and W. Xue. Form-basedproxy caching for database-backed web sites:keywords and functions.
VLDB J. , 17(3):489–513, 2008.[142] L. Shrinivas and J. F. Naughton. Issues in ap-plying data mining to grid job failure detectionand diagnosis. In M. Parashar, K. Schwan,J. B. Weissman, and D. Laforenza, editors,
Proceedings of the 17th International Sympo-sium on High-Performance Distributed Comput-ing (HPDC-17 2008), 23-27 June 2008, Boston,MA, USA , pages 239–240. ACM, 2008.[143] X. Chai, B.-Q. Vuong, A. Doan, and J. F.Naughton. Efficiently incorporating user feed-back into information extraction and integrationprograms. In U. etintemel, S. B. Zdonik, D. Koss-mann, and N. Tatbul, editors,
Proceedings ofthe ACM SIGMOD International Conference onManagement of Data, SIGMOD 2009, Provi-dence, Rhode Island, USA, June 29 - July 2, 2009 ,pages 87–100. ACM, 2009.[144] E. Chu, A. Baid, X. Chai, A. Doan, and J. F.Naughton. Combining keyword search and formsfor ad hoc querying of databases. In U. etintemel,S. B. Zdonik, D. Kossmann, and N. Tatbul, edi-tors,
Proceedings of the ACM SIGMOD Interna-tional Conference on Management of Data, SIG-MOD 2009, Providence, Rhode Island, USA, June29 - July 2, 2009 , pages 349–360. ACM, 2009.[145] A. Doan, J. F. Naughton, A. Baid, X. Chai,F. Chen, T. Chen, E. Chu, P. DeRose, B. J. Gao,C. Gokhale, J. Huang, W. Shen, and B.-Q. Vuong.The Case for a Structured Approach to ManagingUnstructured Data. In
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PVLDB , 2(1):934–945, 2009.[147] W. Lang, J. M. Patel, and J. F. Naughton. On en-ergy management, load balancing and replication.
SIGMOD Record , 38(4):35–42, 2009.148] C. F. Reilly and J. F. Naughton. TransparentlyGathering Provenance with Provenance AwareCondor. In J. Cheney, editor,
First Workshop onthe Theory and Practice of Provenance, TaPP’09,San Francisco, CA, USA, February 23, 2009, Pro-ceedings . USENIX, 2009.[149] A. Baid, I. Rae, J. Li, A. Doan, and J. F.Naughton. Toward Scalable Keyword Search overRelational Data.
PVLDB , 3(1):140–149, 2010.[150] G. Luo, J. F. Naughton, C. J. Ellmann, andM. Watzke. Transaction reordering.
Data Knowl.Eng. , 69(1):29–49, 2010.[151] J. F. Naughton. DBMS: Lessons from the first50 years, speculations for the next 50. In F. Li,M. M. Moro, S. Ghandeharizadeh, J. R. Haritsa,G. Weikum, M. J. Carey, F. Casati, E. Y. Chang,I. Manolescu, S. Mehrotra, U. Dayal, and V. J.Tsotras, editors,
Proceedings of the 26th Inter-national Conference on Data Engineering, ICDE2010, March 1-6, 2010, Long Beach, California,USA , page 2. IEEE, 2010.[152] S. Subramanian, Y. Zhang, R. Vaidyanathan, H. S.Gunawi, A. C. Arpaci-Dusseau, R. H. Arpaci-Dusseau, and J. F. Naughton. Impact of diskcorruption on open-source DBMS. In F. Li,M. M. Moro, S. Ghandeharizadeh, J. R. Haritsa,G. Weikum, M. J. Carey, F. Casati, E. Y. Chang,I. Manolescu, S. Mehrotra, U. Dayal, and V. J.Tsotras, editors,
Proceedings of the 26th Inter-national Conference on Data Engineering, ICDE2010, March 1-6, 2010, Long Beach, California,USA , pages 509–520. IEEE, 2010.[153] K. Q. Tran, S. Blanas, and J. F. Naughton. OnTransactional Memory, Spinlocks, and DatabaseTransactions. In R. Bordawekar and C. A. Lang,editors,
International Workshop on AcceleratingData Management Systems Using Modern Pro-cessor and Storage Architectures - ADMS 2010,Singapore, September 13, 2010 , pages 43–50,2010.[154] J. Do, D. Zhang, J. M. Patel, D. J. DeWitt, J. F.Naughton, and A. Halverson. TurbochargingDBMS buffer pool using SSDs. In T. K. Sel-lis, R. J. Miller, A. Kementsietsidis, and Y. Vele-grakis, editors,
Proceedings of the ACM SIG-MOD International Conference on Managementof Data, SIGMOD 2011, Athens, Greece, June12-16, 2011 , pages 1113–1124. ACM, 2011.[155] Y. He, S. Barman, and J. F. Naughton. Prevent-ing equivalence attacks in updated, anonymized data. In S. Abiteboul, K. Bhm, C. Koch, andK.-L. Tan, editors,
Proceedings of the 27th Inter-national Conference on Data Engineering, ICDE2011, April 11-16, 2011, Hannover, Germany ,pages 529–540. IEEE Computer Society, 2011.[156] Y. He, S. Barman, D. Wang, and J. F. Naughton.On the complexity of privacy-preserving com-plex event processing. In M. Lenzerini andT. Schwentick, editors,
Proceedings of the 30thACM SIGMOD-SIGACT-SIGART Symposium onPrinciples of Database Systems, PODS 2011,June 12-16, 2011, Athens, Greece , pages 165–174. ACM, 2011.[157] C. Sun and J. F. Naughton. The Token Distribu-tion Filter for Approximate String Membership.In A. Marian and V. Vassalos, editors,
Proceed-ings of the 14th International Workshop on theWeb and Databases 2011, WebDB 2011, Athens,Greece, June 12, 2011 , 2011.[158] J. Li, R. V. Nehme, and J. F. Naughton. GSLPI: ACost-Based Query Progress Indicator. In A. Ke-mentsietsidis and M. A. V. Salles, editors,
IEEE28th International Conference on Data Engineer-ing (ICDE 2012), Washington, DC, USA (Arling-ton, Virginia), 1-5 April, 2012 , pages 678–689.IEEE Computer Society, 2012.[159] C. F. Reilly, Y.-H. Chiang, and J. F. Naughton.Instrumenting a logic programming language togather provenance from an information extrac-tion application. In A. Mille, F. L. Gandon,J. Misselis, M. Rabinovich, and S. Staab, edi-tors,
Proceedings of the 21st World Wide WebConference, WWW 2012, Lyon, France, April 16-20, 2012 (Companion Volume) , pages 589–590.ACM, 2012.[160] C. Sun, J. F. Naughton, and S. Barman. Approx-imate String Membership Checking: A MultipleFilter, Optimization-Based Approach. In A. Ke-mentsietsidis and M. A. V. Salles, editors,
IEEE28th International Conference on Data Engineer-ing (ICDE 2012), Washington, DC, USA (Arling-ton, Virginia), 1-5 April, 2012 , pages 882–893.IEEE Computer Society, 2012.[161] C. Zeng, J. F. Naughton, and J.-Y. Cai. On differ-entially private frequent itemset mining.
PVLDB ,6(1):25–36, 2012.[162] Y. Chi, H. Hacigms, W.-P. Hsiung, and J. F.Naughton. Distribution-Based Query Scheduling.
PVLDB , 6(9):673–684, 2013.163] D. J. DeWitt, I. F. Ilyas, J. F. Naughton, andM. Stonebraker. We are drowning in a sea of leastpublishable units (LPUs). In K. A. Ross, D. Sri-vastava, and D. Papadias, editors,
Proceedingsof the ACM SIGMOD International Conferenceon Management of Data, SIGMOD 2013, NewYork, NY, USA, June 22-27, 2013 , pages 921–922.ACM, 2013.[164] Y. He, S. Barman, and J. F. Naughton. On LoadShedding in Complex Event Processing.
CoRR ,abs/1312.4283, 2013.[165] J. Li, R. V. Nehme, and J. F. Naughton. TowardProgress Indicators on Steroids for Big Data Sys-tems. In
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Proceedings of the ACM SIG-MOD International Conference on Managementof Data, SIGMOD 2013, New York, NY, USA,June 22-27, 2013 , pages 589–600. ACM, 2013.[167] W. Wu, Y. Chi, H. Hacigms, and J. F. Naughton.Towards Predicting Query Execution Time forConcurrent and Dynamic Database Workloads.
PVLDB , 6(10):925–936, 2013.[168] W. Wu, Y. Chi, S. Zhu, J. Tatemura, H. Hacigms,and J. F. Naughton. Predicting query executiontime: Are optimizer cost models really unusable?In C. S. Jensen, C. M. Jermaine, and X. Zhou,editors, , pages 1081–1092. IEEEComputer Society, 2013.[169] C. Zeng, J.-Y. Cai, P. Lu, and J. F. Naughton.On optimal differentially private mechanisms forcount-range queries. In W.-C. Tan, G. Guer-rini, B. Catania, and A. Gounaris, editors,
Joint2013 EDBT/ICDT Conferences, ICDT ’13 Pro-ceedings, Genoa, Italy, March 18-22, 2013 , pages261–271. ACM, 2013.[170] C. Zeng, E. Lantz, J. F. Naughton, and D. Page.On Differentially Private Inductive Logic Pro-gramming. In G. Zaverucha, V. S. Costa, andA. Paes, editors,
Inductive Logic Programming -23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Se-lected Papers , volume 8812 of
Lecture Notes inComputer Science , pages 18–30. Springer, 2013.[171] D. J. Abadi, R. Agrawal, A. Ailamaki, M. Bal-azinska, P. A. Bernstein, M. J. Carey, S. Chaud-huri, J. Dean, A. Doan, M. J. Franklin, J. Gehrke,L. M. Haas, A. Y. Halevy, J. M. Hellerstein,Y. E. Ioannidis, H. V. Jagadish, D. Kossmann,S. Madden, S. Mehrotra, T. Milo, J. F. Naughton,R. Ramakrishnan, V. Markl, C. Olston, B. C. Ooi,C. R, D. Suciu, M. Stonebraker, T. Walter, andJ. Widom. The Beckman Report on Database Re-search.
SIGMOD Record , 43(3):61–70, 2014.[172] Y.-H. Chiang, A. Doan, and J. F. Naughton. Mod-eling entity evolution for temporal record match-ing. In C. E. Dyreson, F. Li, and M. T. zsu, edi-tors,
International Conference on Management ofData, SIGMOD 2014, Snowbird, UT, USA, June22-27, 2014 , pages 1175–1186. ACM, 2014.[173] Y.-H. Chiang, A. Doan, and J. F. Naughton.Tracking Entities in the Dynamic World: AFast Algorithm for Matching Temporal Records.
PVLDB , 7(6):469–480, 2014.[174] C. Gokhale, S. Das, A. Doan, J. F. Naughton,N. Rampalli, J. W. Shavlik, and X. Zhu. Corleone:hands-off crowdsourcing for entity matching. InC. E. Dyreson, F. Li, and M. T. zsu, editors,
In-ternational Conference on Management of Data,SIGMOD 2014, Snowbird, UT, USA, June 22-27,2014 , pages 601–612. ACM, 2014.[175] Y. He, S. Barman, and J. F. Naughton. OnLoad Shedding in Complex Event Processing. InN. Schweikardt, V. Christophides, and V. Leroy,editors,
Proc. 17th International Conferenceon Database Theory (ICDT), Athens, Greece,March 24-28, 2014 , pages 213–224. OpenPro-ceedings.org, 2014.[176] W. Lang, R. V. Nehme, E. Robinson, and J. F.Naughton. Partial results in database systems. InC. E. Dyreson, F. Li, and M. T. zsu, editors,
In-ternational Conference on Management of Data,SIGMOD 2014, Snowbird, UT, USA, June 22-27,2014 , pages 1275–1286. ACM, 2014.[177] J. Li, J. F. Naughton, and R. V. Nehme. ResourceBricolage for Parallel Database Systems.
PVLDB ,8(1):25–36, 2014.[178] I. Rae, A. Halverson, and J. F. Naughton. In-RDBMS inverted indexes revisited. In I. F.ruz, E. Ferrari, Y. Tao, E. Bertino, and G. Tra-jcevski, editors,
IEEE 30th International Confer-ence on Data Engineering, Chicago, ICDE 2014,IL, USA, March 31 - April 4, 2014 , pages 352–363. IEEE, 2014.[179] K. Q. Tran, J. F. Naughton, B. Sundarmurthy, andD. Tsirogiannis. JECB: a join-extension, code-based approach to OLTP data partitioning. InC. E. Dyreson, F. Li, and M. T. zsu, editors,
In-ternational Conference on Management of Data,SIGMOD 2014, Snowbird, UT, USA, June 22-27,2014 , pages 39–50. ACM, 2014.[180] W. Wu, X. Wu, H. Hacigms, and J. F. Naughton.Uncertainty Aware Query Execution Time Predic-tion.
PVLDB , 7(14):1857–1868, 2014.[181] P. Xiong, H. Hacigms, and J. F. Naughton. Asoftware-defined networking based approach forperformance management of analytical queries ondistributed data stores. In C. E. Dyreson, F. Li,and M. T. zsu, editors,
International Conferenceon Management of Data, SIGMOD 2014, Snow-bird, UT, USA, June 22-27, 2014 , pages 955–966.ACM, 2014.[182] A. Baid, W. Wu, C. Sun, A. Doan, and J. F.Naughton. On Debugging Non-Answers in Key-word Search Systems. In G. Alonso, F. Geerts,L. Popa, P. Barcel, J. Teubner, M. Ugarte, J. V. d.Bussche, and J. Paredaens, editors,
Proceedingsof the 18th International Conference on Extend-ing Database Technology, EDBT 2015, Brussels,Belgium, March 23-27, 2015 , pages 37–48. Open-Proceedings.org, 2015.[183] A. Kumar, M. Jalal, B. Yan, J. F. Naughton, andJ. M. Patel. Demonstration of Santoku: Opti-mizing Machine Learning over Normalized Data.
PVLDB , 8(12):1864–1875, 2015.[184] A. Kumar, J. F. Naughton, and J. M. Patel. Learn-ing Generalized Linear Models Over NormalizedData. In T. K. Sellis, S. B. Davidson, and Z. G.Ives, editors,
Proceedings of the 2015 ACM SIG-MOD International Conference on Managementof Data, Melbourne, Victoria, Australia, May 31- June 4, 2015 , pages 1969–1984. ACM, 2015.[185] X. Wu, M. Fredrikson, W. Wu, S. Jha, andJ. F. Naughton. Revisiting Differentially Pri-vate Regression: Lessons From Learning Theoryand their Consequences.
CoRR , abs/1512.06388,2015. [186] D. Abadi, R. Agrawal, A. Ailamaki, M. Balazin-ska, P. A. Bernstein, M. J. Carey, S. Chaudhuri,J. Dean, A. Doan, M. J. Franklin, J. Gehrke,L. M. Haas, A. Y. Halevy, J. M. Hellerstein,Y. E. Ioannidis, H. V. Jagadish, D. Kossmann,S. Madden, S. Mehrotra, T. Milo, J. F. Naughton,R. Ramakrishnan, V. Markl, C. Olston, B. C. Ooi,C. R, D. Suciu, M. Stonebraker, T. Walter, andJ. Widom. The Beckman report on database re-search.
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CoRR ,abs/1601.05748, 2016.ame Ph.D. Dissertation TitleS. Seshadri 1992 Probabilistic Methods in Query ProcessingEmmanuel Tsangaris 1992 Principles of Static Clustering for Object Oriented DatabasesJoseph M. Hellerstein 1995 Optimization and Execution Techniques for Queries with ExpensiveMethodsBradley Rubin 1995 Information Retrieval Using a Combined Object-Oriented Database/FileSystem ParadigmJanet Wiener 1995 Algorithms for Loading Object-Oriented DatabasesSrikant Ramakrishnan 1996 Fast Algorithms for Mining Association Rules and Sequential PatternsAmbuj Shatdal 1996 Architectural Considerations for Parallel Query Evaluation AlgorithmsShivakumar Venkataraman 1996 Global Memory Management for Multi-Server Database SystemsYihong Zhao 1998 Performance Issues of Multi-Dimensional Data AnalysisPrasad Deshpande 1999 Efficient Database Support for OLAP QueriesAmit Shukla 1999 Materialized View Selection for Multidimensional DatasetsKarthikeyan Ramasamy 2001 Efficient Storage and Query Processing of Set-Valued AttributesJayavel Shanmugasundaram 2001 Bridging Relational Technology and XMLAshraf Aboulnaga 2002 Cost Estimation Techniques for Database SystemsQiong Luo 2002 Caching for Web-Based Database ApplicationsChun Zhang 2002 Relational Databases for XML IndexingJaewoo Kang 2003 Toward the Scalable Integration of Internet Information SourcesRaghav Kaushik 2003 Graph Summarization for Path Indexing in Graph-Structured DataStratis Viglas 2003 Novel Query Optimization and Evaluation TechniquesRajasekar Krishnamurthy 2004 XML-to-SQL Query TranslationGang Luo 2004 Techniques for Operational Data WarehousingAhmed Ayad 2006 Optimization and Approximation Techniques for Data StreamingQueriesJennifer Beckmann 2006 Relational Database Management System Support for Sparse Data SetsAlan Halverson 2006 Storage and Query Processing Optimizations for Hierarchically-Organized DataTochukwu Iwuchukwu 2007 Anonymization Techniques for Large and Dynamic Data SetsAmeet Kini 2007 Supporting Match Joins in Relational Database Management SystemsEric Chu 2008 Sparse Relational Data Sets: Issues and an ApplicationJiansheng Huang 2008 On Interpreting and Debugging Results of Database Queries over Impre-cise DataLakshmikant Shrinivas 2008 Applications of Data Mining to Cluster Scheduling and Failure Diagno-sisChristine Reilly 2010 Transparent Gathering of Provenance During Program ExecutionAkanksha Baid 2011 Toward Scalable Keyword Search over Relational DataYeye He 2012 Privacy Preserving Data Publishing and AnalysisChong Sun 2012 Multi-Filter String Matching and Human-Centric Entity Matching forInformation ExtractionKhai Tran 2013 Realizing Parallelism in OLTP WorkloadsChen Zeng 2013 On Differentially Private Mechanisms for Count-Range Queries and theirApplicationsYueh-Hsuan Chiang 2014 Towards Large-Scale Temporal Entity MatchingJiexing Li 2014 Performance Prediction and Resource Bricolage for Database SystemsIan Rae 2014 From Index Nested Loops to ZigZag Merge: An Experimental Analysisof Skipping Join AlgorithmsWentao Wu *Arun Kumar *Fatemah Panahi *Xi Wu *Bruhathi Sundarmurthy **
Ph.D. expected.