Data Analytics: 31st British International Conference on Databases, BICOD 2017, London, UK, July 10–12, 2017, Proceedings

175 Pages·2017·9.824 MB·English

by  Andrea Calì, Peter Wood, Nigel Martin, Alexandra Poulovassilis (eds.)

Andrea Calì
Peter Wood
Nigel Martin
Alexandra Poulovassilis (Eds.)
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Data Analytics
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31st British International Conference
on Databases, BICOD 2017
London, UK, July 10–12, 2017, Proceedings
123
Lecture Notes in Computer Science 10365
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Andrea Cal Peter Wood
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Nigel Martin Alexandra Poulovassilis (Eds.)
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Data Analytics
31st British International Conference
on Databases, BICOD 2017
–
London, UK, July 10 12, 2017
Proceedings
123
Editors
Andrea Calì NigelMartin
Birkbeck, University of London Birkbeck, University of London
London London
UK UK
PeterWood Alexandra Poulovassilis
Birkbeck, University of London Birkbeck, University of London
London London
UK UK
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Computer Science
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Preface
This volume contains research papers presented at BICOD 2017: the 31st British
InternationalConferenceonDatabases heldJuly10–12,2017,atBirkbeck,University
of London.
The BICOD Conference is an international venue with a long tradition of presen-
tation and discussion of research in the broad area of data management. The theme of
BICOD 2017 was “Data Analytics,” that is, the process of deriving higher-level
information from large sets of raw data.
The conference featured keynotes from three distinguished speakers: Sihem
Amer-Yahia, Laboratoire d’Informatique de Grenoble; Tim Furche, University of
Oxford and Wrapidity; and Elena Baralis, Politecnico di Torino. There were also two
invitedtutorials:byVasilikiKalavri,ETHZürich,ondistributedgraphprocessing;and
by Leopoldo Bertossi, Carleton University, on declarative approaches to data quality
assessment and cleaning.
BICOD 2017 attracted paper submissions from Australia, Austria, Canada,
Colombia, Germany, Ireland, Italy, Russia, Tunisia, and the UK. The Program Com-
mittee accepted 16 papers to appear in the proceedings covering topics such as data
cleansing, data integration, data wrangling, data mining and knowledge discovery,
graph data and knowledge graphs, intelligent data analysis, approximate and flexible
querying, data provenance, and ontology-based data access.
A prize was awarded for the best paper presented by a PhD student, which was
generously sponsored by Neo Technology. The authors of a small number of selected
bestpaperswereinvitedtosubmitextendedversionsoftheirworktoaspecialissueof
The Computer Journal.
We would like to thank all the authors for submitting their work to BICOD, the
Program Committee members for their contribution to selecting an inspiring set of
research papers, and the distinguished invited speakers for accepting our invitation to
presenttheirworkinLondon.Wealsogratefullythankeveryoneinvolvedinthelocal
organization, including Tara Orlanes-Angelopoulou, Phil Gregg, Lucy Tallentire,
Catherine Griffiths, and Matthew Jayes, for all their hard work and dedication in
making this conference possible.
May 2017 Andrea Calì
Peter Wood
Nigel Martin
Alexandra Poulovassilis
Organization
Program Committee
Andrea Calì Birkbeck, University of London, UK (PC Co-chair)
Alvaro Fernandes University of Manchester, UK
George Fletcher Eindhoven University of Technology, The Netherlands
Floris Geerts University of Antwerp, Belgium
Sven Helmer Free University of Bozen-Bolzano, Italy
Anne James Coventry University, UK
Mike Jackson Birmingham City University, UK
Sebastian Maneth University of Edinburgh, UK
Nigel Martin Birkbeck, University of London, UK
Werner Nutt Free University of Bozen-Bolzano, Italy
Dan Olteanu University of of Oxford, UK
Andreas Pieris University of Edinburgh, UK
Alessandro Provetti Birkbeck, University of London, UK
Alexandra Poulovassilis Birkbeck, University of London, UK
Mark Roantree Dublin City University, Ireland
Maria-Esther Vidal Universidad Simón Bolívar, Venezuela
John Wilson University of Strathclyde, UK
Peter Wood Birkbeck, University of London, UK (PC Co-chair)
Keynotes
Wrapping Millions of Documents Per Day -
and How That’s Just the Beginning
Tim Furche
Department of Computer Science, University of Oxford,Oxford, UK
CTOWrapidity Ltd.,Oxford, UK
[email protected]
Abstract.Companiesandresearchershavebeenpainfullymaintainingmanually
createdprogramsforwrapping(akascraping)websitesfordecades-employing
hundredsofengineerstowrapthousandsoreventensofthousandsofsources.
Wherewrappersbreak,engineersmustscrambletofixthewrappersmanuallyor
face the ire of their users. In DIADEM we demonstrated the effectiveness of
hybrid AIs for automatically generating wrapper programs in anacademic set-
tings. Our hybrid approach combined knowledge-based rule systems with
large-scaleanalyticsfoundedintechniquesfromtheNLPandMLcommunity.
Recently, we commercialized that technology into Wrapidity Ltd. and
quickly joined up with Meltwater, the leading global media-intelligence com-
pany.AtMeltwater,weareapplyingWrapidity’stechniquesandinsighttothe
wrapping of tens of thousands of news sources, processing over 10M unique
documents per day. But that’s just the beginning …. Together with Meltwater
engineers and researchers from San Francisco, Stockholm, Bangalore, and
Budapest, we are on the way to make the collection and analysis of “outside”
dataabreezethroughfairhair.ai,aplatformforaccessingandenrichingthevast
content stored andcollected byMeltwater every day.
Toward Interactive User Data Analytics
Sihem Amer-Yahia
CNRS, University Grenoble-Alps, Grenoble,France
Abstract. User data can be acquired from various domains. This data is char-
acterized by a combination of demographics such as age and occupation and
useractionssuchasratingamovie,reviewingarestaurantorbuyinggroceries.
User data is appealing to analysts in their role as data scientists who seek to
conductlarge-scalepopulationstudies,andgaininsightsonvariouspopulation
segments. It is also appealing to novice users in their role as information con-
sumerswhousethesocialWebforroutinetaskssuchasfindingabookclubor
choosingarestaurant.
User data exploration has been formulated as identifying group-level
behaviorsuchasAsianwomenwhopublishregularlyindatabases.Group-level
explorationenablesnewfindingsandaddressesissuesraisedbythepeculiarities
ofuserdatasuchasnoiseandsparsity.Iwillreviewourworkonone-shotand
interactive user data exploration. I will then describe the challenges of devel-
oping avisualanalytics tool for finding andconnectingusers andgroups.