Databases in Networked Information Systems: 10th International Workshop, DNIS 2015, Aizu-Wakamatsu, Japan, March 23-25, 2015. Proceedings PDF
Preview Databases in Networked Information Systems: 10th International Workshop, DNIS 2015, Aizu-Wakamatsu, Japan, March 23-25, 2015. Proceedings
Wanming Chu Shinji Kikuchi Subhash Bhalla (Eds.) 9 9 Databases in Networked 9 8 S C Information Systems N L 10th International Workshop, DNIS 2015 Aizu-Wakamatsu, Japan, March 23–25, 2015 Proceedings 123 Lecture Notes in Computer Science 8999 CommencedPublicationin1973 FoundingandFormerSeriesEditors: GerhardGoos,JurisHartmanis,andJanvanLeeuwen EditorialBoard DavidHutchison LancasterUniversity,UK TakeoKanade CarnegieMellonUniversity,Pittsburgh,PA,USA JosefKittler UniversityofSurrey,Guildford,UK JonM.Kleinberg CornellUniversity,Ithaca,NY,USA FriedemannMattern ETHZurich,Switzerland JohnC.Mitchell StanfordUniversity,CA,USA MoniNaor WeizmannInstituteofScience,Rehovot,Israel C.PanduRangan IndianInstituteofTechnology,Madras,India BernhardSteffen TUDortmundUniversity,Germany DemetriTerzopoulos UniversityofCalifornia,LosAngeles,CA,USA DougTygar UniversityofCalifornia,Berkeley,CA,USA GerhardWeikum MaxPlanckInstituteforInformatics,Saarbruecken,Germany Wanming Chu Shinji Kikuchi Subhash Bhalla (Eds.) Databases in Networked Information Systems 10th International Workshop, DNIS 2015 Aizu-Wakamatsu, Japan, March 23-25, 2015 Proceedings 1 3 VolumeEditors WanmingChu ShinjiKikuchi SubhashBhalla UniversityofAizu,DatabaseSystemsLaboratory Aizu-Wakamatsu,Fukushima965-8580,Japan E-mail:[email protected] [email protected] [email protected] ISSN0302-9743 e-ISSN1611-3349 ISBN978-3-319-16312-3 e-ISBN978-3-319-16313-0 DOI10.1007/978-3-319-16313-0 SpringerChamHeidelbergNewYorkDordrechtLondon LibraryofCongressControlNumber:2015932993 LNCSSublibrary:SL3–InformationSystemsandApplication,incl.Internet/Web andHCI ©SpringerInternationalPublishingSwitzerland2015 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof thematerialisconcerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation, broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionorinformation storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodology nowknownorhereafterdeveloped.Exemptedfromthislegalreservationarebriefexcerptsinconnection withreviewsorscholarlyanalysisormaterialsuppliedspecificallyforthepurposeofbeingenteredand executedonacomputersystem,forexclusiveusebythepurchaserofthework.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheCopyrightLawofthePublisher’slocation, inistcurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Permissionsforuse maybeobtainedthroughRightsLinkattheCopyrightClearanceCenter.Violationsareliabletoprosecution undertherespectiveCopyrightLaw. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Whiletheadviceandinformationinthisbookarebelievedtobetrueandaccurateatthedateofpublication, neithertheauthorsnortheeditorsnorthepublishercanacceptanylegalresponsibilityforanyerrorsor omissionsthatmaybemade.Thepublishermakesnowarranty,expressorimplied,withrespecttothe materialcontainedherein. Typesetting:Camera-readybyauthor,dataconversionbyScientificPublishingServices,Chennai,India Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) Preface Business data analytics in scientific domains depends on computing infrastruc- ture. A scientific exploration of data is beneficial for large-scale public utility services,eitherdirectlyorindirectly.Manyresearcheffortsarebeingmadeindi- verseareas,suchasbigdataanalyticsandcloudcomputing,sensornetworksand high-leveluserinterfacesforinformationaccessesbycommonusers.Government agenciesinmanycountriesplantolaunchfacilitiesineducation,health-care,and informationsupport as a partof e-governmentinitiatives. In this context, infor- mationinterchangemanagementhas become anactive researchfield. A number ofnewopportunitieshaveevolvedindesignandmodelingbasedonthenewcom- puting needs of the users. Database systems play a central role in supporting networked information systems for access and storage management aspects. The 10th International Workshop on Databases in Networked Information Systems (DNIS) 2015 was held during March 23–25, 2015 at University of Aizu in Japan. The workshop program included research contributions, and invited contributions. A view of research activity in information interchange manage- ment and related researchissues was provided by the sessions on related topics. The keynote address was contributed by Prof. Divyakant Agrawal. The session on“InformationandKnowledgeManagement”hadaninvitedcontributionfrom Dr. Paolo Bottoni. The following section on “Business Data Analytics and Vi- sualization,” had an invited contribution from Dr. Arnab Nandi. The session on“NetworkedInformationResources”includesdaninvitedcontributionbyDr. Shelly Sachdeav. The section on “Business Data Analytics in Astronomy and Sciences,” had an invited contribution by Dr. Naoki Yoshida. I would like to thankthemembersofthe ProgramCommittee fortheirsupportandallauthors who considered DNIS 2015 in making researchcontributions. The sponsoringorganizationsand the Steering Committee deserve praisefor thesupporttheyprovided.Anumberofindividualscontributedtothesuccessof the workshop. I thank Dr. Umeshwar Dayal, Prof. J. Biskup, Prof. D. Agrawal, Dr. CyrusShahabi,Prof.T. Nishida, andProf.Shrinivas Kulkarniforproviding continuous support and encouragement. The workshop received invaluable support from the University of Aizu. In this context, I thank Prof. Ryuichi Oka, President of University of Aizu. Many thanks also to the faculty members at the university for their cooperation and support. March 2015 Wanming Chu Shinji Kikuchi Subhash Bhalla Organization The DNIS 2015international workshopwas organizedby the Graduate Depart- ment of Information Technology and Project Management, University of Aizu, Aizu-Wakamatsu, Fukushima, Japan. Steering Committee Divyakant Agrawal University of California, USA HosagraharV. Jagadish University of Michigan, USA Masaru Kitsuregawa University of Tokyo, Japan Toyoaki Nishida Kyoto University, Japan Krithi Ramamritham Indian Institute of Technology, Bombay, India Cyrus Shahabi University of Southern California, USA Executive Chair N. Bianchi-Berthouze University College London, UK Program Chair S. Bhalla University of Aizu, Japan Publicity Committee Chair Shinji Kikuchi University of Aizu, Japan Publications Committee Co-chair Wanming Chu University of Aizu Program Committee D. Agrawal University of California, USA V. Bhatnagar University of Delhi, India P. Bottoni La Sapienza University of Rome, Italy L. Capretz University of Western Untario, Canada Richard Chbeir Bourgogne University, France G. Cong Nanyang Technological University, Singapore Pratul Dublish Microsoft Research, USA Fernando Ferri IRPPS - CNR, Rome , Italy W.I. Grosky University of Michigan-Dearborn, USA J. Herder Universityof Applied Sciences, Fachhochschule Du¨sseldorf, Germany VIII Organization H.V. Jagadish University of Michigan, USA Sushil Jajodia George Mason University, USA Q. Jin Waseda University, Japan A. Kumar Pennsylvania State University, USA A. Mondal Xerox Research, Bangaloru, India K. Myszkowski Max-Planck-Institut fu¨r Informatik, Germany Alexander Pasko Bournemouth University, UK L. Pichl International Christian University, Tokyo, Japan P.K. Reddy International Institute of Information Technology, Hyderabad, India C. Shahabi University of Southern California, USA M. Sifer Sydney University, Australia F. Wang Microsoft Research, USA Sponsoring Institution Center for Strategy of International Programs,University of Aizu, Aizu-Wakamatsu City, Fukushima, Japan. Table of Contents Big Data Analysis The Big Data Landscape: Hurdles and Opportunities................. 1 Divyakant Agrawal and Sanjay Chawla Discovering Chronic-Frequent Patterns in Transactional Databases ..... 12 R. Uday Kiran and Masaru Kitsuregawa High Utility Rare Itemset Mining over Transaction Databases ......... 27 Vikram Goyal, Siddharth Dawar, and Ashish Sureka Skyband-Set for Answering Top-k Set Queries of Any Users ........... 41 Md. Anisuzzaman Siddique, Asif Zaman, and Yasuhiko Morimoto Information and Knowledge Management Towards an Ontology-BasedGeneric Pipeline Editor.................. 56 Paolo Bottoni and Miguel Ceriani Synthetic Evidential Study as Primordial Soup of Conversation ........ 74 Toyoaki Nishida, Atsushi Nakazawa, Yoshimasa Ohmoto, Christian Nitschke, Yasser Mohammad, Sutasinee Thovuttikul, Divesh Lala, Masakazu Abe, and Takashi Ookaki Understanding Software Provisioning: An Ontological View ........... 84 Evgeny Pyshkin, Andrey Kuznetsov, and Vitaly Klyuev *AIDA: A Language of Big Information Resources ................... 112 Yutaka Watanobe and Nikolay Mirenkov Business Data Analytics and Visualization Interactive Tweaking of Text Analytics Dashboards .................. 122 Arnab Nandi, Ziqi Huang, Man Cao, Micha Elsner, Lilong Jiang, Srinivasan Parthasarathy, and Ramiya Venkatachalam Topic-Specific YouTube Crawling to Detect Online Radicalization...... 133 Swati Agarwal and Ashish Sureka ATSOT: Adaptive Traffic Signal Using mOTes....................... 152 Punam Bedi, Vinita Jindal, Heena Dhankani, and Ruchika Garg Covariance Structure and Systematic Risk of Market Index Portfolio ... 172 Luk´aˇs Pichl X Table of Contents Networked Information Resources I Moving from Relational Data Storage to Decentralized Structured Storage System .................................................. 180 Upaang Saxena, Shelly Sachdeva, and Shivani Batra Comparing Infrastructure Monitoring with CloudStack Compute Services for Cloud Computing Systems ............................. 195 Aparna Datt, Anita Goel, and Suresh Chand Gupta Efficiency of NoSQL Databases under a Moderate Application Load .... 213 Mohammad Shamsul Arefin, Khondoker Nazibul Hossain, and Yasuhiko Morimoto Business Data Analytics in Astronomy and Sciences A Large Sky Survey Project and the Related Big Data Analysis ....... 228 Naoki Yoshida A Photometric Machine-Learning Method to Infer Stellar Metallicity ... 231 Adam A. Miller Query Languages for Domain Specific Information from PTF Astronomical Repository.......................................... 237 Yilang Wu and Wanming Chu Networked Information Resources II Pariket: Mining Business Process Logs for Root Cause Analysis of Anomalous Incidents ............................................. 244 Nisha Gupta, Kritika Anand, and Ashish Sureka Modeling Personalized Recommendations of Unvisited Tourist Places Using Genetic Algorithms......................................... 264 Sunita Tiwari and Saroj Kaushik A Decentralised Approach to Computer Aided Teaching via Interactive Documents...................................................... 277 Lothar M. Schmitt Author Index.................................................. 289 The Big Data Landscape: Hurdles and Opportunities Divyakant Agrawal1,2 and Sanjay Chawla1,3 1 Qatar Computing Research Institute,Qatar 2 University of California SantaBarbara, USA 3 University of Sydney,Australia Abstract. BigDataprovidesanopportunitytointerrogatesomeofthe deepest scientific mysteries, e.g., how the brain works and develop new technologies, like driverless cars which, till very recently, were more in the realm of science fiction than reality. However Big Data as an entity in its own right creates several computational and statistical challenges in algorithm, systems and machine learning design that need to be ad- dressed.InthispaperwesurveytheBigDatalandscapeandmapoutthe hurdles that must be overcome and opportunities that can be exploited in thisparadigm shifting phenomenon. 1 Introduction Big data has emerged as one of the most promising technology paradigm in the past few years. Availability of large data arises in numerous application contexts: trillions of words in English and other languages, hundreds of billions oftext documents, a largenumber oftranslationsof documents in one language to other languages,billions ofimagesand videosalong withtextual annotations and summaries, thousands of hours of speech recordings, trillions of log records capturing human activity, and the list goes on. During the past decade, careful processing and analysis of different types of data has had transformative effect. Many applications that were buried in the pages of science fiction have become a reality, e.g., driverless cars, language agnostic conversation, automated image understanding, and most recently deep learning [6] to simulate a human brain. In the technology context, Big Data has resulted in significant research and development challenges. From a systems perspective, scalable storage, retrieval, processing,analysis,andmanagementofdata posesthe biggestchallenge.From an application perspective, leveraging large amounts of data to develop models of physical reality becomes a complex problem. The interesting dichotomy is that the bigness of data in the system context makes some of the known data processing solutions that were “acceptable” to “not acceptable.” For example, standardalgorithmsforcarryingoutjoin processingmay havetobe revisitedin theBigDatacontext.Incontrast,thebignessofdataallowsmanyapplicationsto movefrombeing“notpossible”to“possible.”Forexamplerealtime,automated, high quality and robust language translation seems entirely feasible. Thus, new W.Chuetal.(Eds.):DNIS2015,LNCS8999,pp.1–11,2015. (cid:2)c SpringerInternationalPublishingSwitzerland2015
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