Table Of ContentWanming Chu
Shinji Kikuchi
Subhash Bhalla (Eds.)
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Databases in Networked
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C Information Systems
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10th International Workshop, DNIS 2015
Aizu-Wakamatsu, Japan, March 23–25, 2015
Proceedings
123
Lecture Notes in Computer Science 8999
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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
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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
