apologies for cross-posting
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(meta)-data quality workshop (MDQual2107)
http://qualitics.org/mdqual2017
September 21, 2017, Thessaloniki, Greece
Part of the 21st International Conference on Theory and Practice of
Digital Libraries (TPDL 2017), September 18-21, 2017, Thessaloniki,
Greece, http://www.tpdl.eu/tpdl2017/
Submission Deadline: June 30, 2017
Description
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It is well known that we are rapidly moving towards a data driven world
where all aspects in our everyday lives are data driven. In all domains
from healthcare to retail and finance, data is collected, analysed and
used to make decisions, usually utilizing machine learning techniques.
Data Science involves collecting, cleansing and integrating data prior
of analysis. The quality of this data is critical and directly affects
the outcome of all data science related tasks. Moreover, metadata is
used to annotate data and facilitate data organization and retrieval.
Metadata quality also directly affects retrieval and other operations
(such as data integration) and workflows that are metadata driven.
Although various metrics have been proposed to measure metadata and data
quality, in most cases they are highly subjective and/or domain
specific. Moreover, they are directly related to the intented use of the
data, meaning that a dataset could be of high quality for one use and of
low quality for another. In all cases, (meta)data quality has a
tremendous impact on data science related tasks and ultimately in
everyday life.
The proposed workshop aims at exploring the various quality issues found
in people working with both data and metadata across domains. An
inter-‐disciplinary workshop where data scientists across different
domains will meet and:
share and exchange experiences regarding (meta)data quality
identify patterns in (meta)data quality
share methodologies and metrics that will help to measure (meta)-‐data
quality
share / propose tools that can be used effectively in improving
(automatically) (meta)-‐data quality.
This initiative aims at bringing together a community of data scientists
that have expertise in a diverse set of domains such as archives and
libraries, healthcare, biology, humanities, computer science and
engineering, environment, agriculture, economics, etc.
Apart from sharing metrics and methods to identify and resolve quality
issues and evaluate datasets, the proposed workshop aims at promoting
the use of tools and services for the automatic measurement and
improvement of (meta)data quality. Although few such tools are available
in the market, a good number of standalone micro-‐services are
available and can be used to automatically improve (meta)data quality.
Topics:
We welcome position papers expressing the data and metadata quality
needs from content providers (libraries, archives, museums, public and
private sector organizations that manage multimedia content). Moreover
we welcome research papers that describe, methods, metrics, services and
tools for measuring and ensuring quality. The workshop will provide a
session for demonstrating implemented systems and services in order to
trigger discussions on real world needs and running systems.
All papers submitted should be original and of high quality, addressing
issues in areas such as:
Data and metadata quality measurement methods
Data and metadata quality requirements for e-research, health, education
and digital humanities, etc
Metrics for data quality measurement in for e-research,
health, education and digital humanities, etc
Metrics for metadata quality measurement in for e-research,
health, education and digital humanities, etc
Tools and services for measuring quality
Tools and services for improving quality
Services for automatic data and metadata enrichment
Important dates
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Paper submissions: June 30 2017
Notification of Acceptance: July 14 2017
Camera ready: July 28 2017
Workshop: September 21 2017
Chairs and committees
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Dimitris Gavrilis, University of Patras, Greece, (Chair)
Christos Papatheodorou, Ionian University, Greece (Chair)
Trond Aalberg, NTNU, Norway
Amir Aryani, Australian National Data Service, Australia
Donatella Castelli, CNR, Italy
Valentine Charles, Europeana Foundation, The Netherlands
Peter Doorn, Data Archiving and Networked Services, The Netherlands
Pythagoras Karampiperis, Agroknow, Greece
Laurent Romary, French Institute for Research in Computer Science and
Automation, France
Timos Sellis, Swinburne University of Technology, Australia
Giannis Tsakonas, University of Patras, Greece
Webpage link
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http://qualitics.org/mdqual2017
