QDB 2016
International Workshop on
Quality in Databases
http://dbis.rwth-aachen.de/QDB2016/
in conjunction with VLDB 2016
(http://vldb2016.persistent.com/index.php)
New Delhi, India
Monday, September 5, 2016
*** NEWS ***
** Divesh Srivastava (AT&T Labs Research) will give the keynote on “Data Glitches = Constraint Violations – Empirical Explanations”
** Deadline extended to June 3, 2016. **
** Selected papers will be invited to a special issue in the
ACM Journal on Data and Information Quality **
Call for Papers
===============
Data quality problems arise frequently when data is integrated from disparate
sources. In the context of Big Data applications, data quality is becoming
more important because of the unprecedented volume, large variety, and high
velocity. The challenges caused by volume and velocity of Big Data have been
addressed by many research projects and commercial solutions and can be
partially solved by modern, scalable data management systems. However, variety
remains to be a daunting challenge for Big Data Integration and requires also
special methods for data quality management. Variety (or heterogeneity) exists
at several levels: at the instance level, the same entity might be described
with different attributes; at the schema level, the data is structured with
various schemas; but also at the level of the modeling language, different
data models can be used (e.g., relational, XML, or a document-oriented JSON
representation). This might lead to data quality issues such as consistency,
understandability, or completeness. The heterogeneity of data sources in the
Big Data Era requires new integration approaches which can handle the large
volume and speed of the generated data as well as the variety and quality of
the data. Thus, heterogeneity and data quality are seen as challenges for many
Big Data applications. While in some applications, a limited data quality for
individual data items does not cause serious problems when a huge amount of
data is aggregated, data quality problems in data sources are often revealed
by the integration of these sources with other information. Data quality has
been coined as ‘fitness for use’; thus, if data is used in another context
than originally planned, data quality might become an issue. Similar
observations have been also made for data warehouses which lead to a separate
research area about data warehouse quality.
The workshop QDB 2016 aims at discussing recent advances and challenges on
data quality management in database systems, and focuses especially on
problems related to Big Data Integration and Big Data Quality.
Research Topics
===============
Topics covered by the workshop include, but are not restricted to, the following
Big Data Quality
* Data quality in Big Data integration
* Data quality models
* Data quality in data streams
* Data quality management for Big Data systems
* Data cleaning, deduplication, record linkage
* Big Data Provenance, Auditing
Big Data Integration
* Big Data systems for data integration
* Real-time (On-the-fly) data integration
* Graph-based algorithms for Big Data integration
* Integration and analytics over large-scale data stores
* Data integration for data lakes
* Efficiency and optimization opportunities in Big Data Integration
* Data Stream Integration
Management of Heterogeneous Data
* Query processing, indexing and storage for heterogeneous data
* Information retrieval over semi-structured or unstructured data
* Efficient index structures for keyword queries
* Query processing of keyword queries
* Data visualization for heterogeneous data
* Management of heterogeneous graph structures
* Knowledge discovery, clustering, data mining for heterogeneous Data
Schema and Metadata Management
* Innovative algorithms and systems for “Schema-on-Read”
* Schema inference in semi-structured data
* Pay-as-you-go schema definition
* Schema & graph summarization techniques
* Metadata models for Big Data
* Schema matching for Big Data
Important Dates
===============
* Submission: June 3, 2016 ** EXTENDED **
* Notification: July 1, 2016
* Camera-Ready Version: July 15, 2016
* Workshop Date: September 5, 2016
Paper Submission
================
QDB welcomes full paper submission of original and previously unpublished
research. All submissions will be peer-reviewed, and once accepted will be
included in the workshop proceedings.
Submission Guidelines:
* Full-length papers are accepted through the online submission system of the
workshop. Full papers can be up to 8 pages in length including all figures,
tables and references. It should be submitted as a PDF according to the
VLDB format. Templates can be found at
http://vldb2016.persistent.com/formatting_guidelines.php
* We also encourage submission of short papers (up to 4 pages) reporting
work in progress.
* Submissions in PDF are to be uploaded to the workshop’s EasyChair submission site:
https://easychair.org/conferences/?conf=qdb16
Workshop Proceedings
====================
The proceedings of the workshop will be published online as a volume of the
CEUR Workshop Proceedings (http://www.ceur-ws.org, ISSN 1613-0073), a well-known
website for publishing workshop proceedings. It is indexed by the major
publication portals, such as Citeseer, DBLP and Google Scholar.
Furthermore, the best papers of the workshop will be invited to a special issue
to the ACM Journal of Data and Information Quality (http://jdiq.acm.org/) to
submit an extended version of their work.
Workshop Organizers
===================
Laure Berti, Qatar Computing Research Institute, Qatar
Verikat N. Gudivada, East Carolina University, Greenville, USA
Rihan Hai, RWTH Aachen University, Germany
Christoph Quix, Fraunhofer FIT & RWTH Aachen University, Germany
Hongzhi Wang, Harbin Institute of Technology, China
Website
=======
http://www.dbis.rwth-aachen.de/QDB2016/
Contact
=======
qdb2016@dbis.rwth-aachen.de