More Web Proxy on the site http://driver.im/

short-paper

Ontology Based Rewriting Data Cleaning Operations

Authors:

Ricardo Almeida,

Paulo Oliveira,

João BarrosoAuthors Info & Claims

C3S2E '16: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

Pages 85 - 88

https://doi.org/10.1145/2948992.2949007

Published: 20 July 2016 Publication History

Abstract

Dealing with increasing amounts of data creates the need to deal with redundant, inconsistent and/or complementary repositories which may be different in their data models and/or in their schema. Current data cleaning techniques developed to tackle data quality problems are just suitable for scenarios were all repositories share the same model and schema. Recently, an ontology-based methodology was proposed to overcome this limitation. In this paper, this methodology is briefly described and applied to a real scenario in the health domain with data quality problems.

References

[1]

T. C. Redman, Data Quality: The Field Guide. Digital Press, 2001.

Digital Library

[2]

L. Atzori, A. Iera, and G. Morabito, 'The internet of things: A survey', Comput. Netw., vol. 54, no. 15, pp. 2787--2805, 2010.

Digital Library

[3]

S. Singh and N. Singh, 'Big Data analytics', in 2012 International Conference on Communication, Information Computing Technology (ICCICT), 2012, pp. 1--4.

[4]

C. Snijders, U. Matzat, and U.-D. Reips, 'Big data: Big gaps of knowledge in the field of internet science', Int. J. Internet Sci., vol. 7, no. 1, pp. 1--5, 2012.

[5]

E. F. Codd, 'A relational model of data for large shared data banks', Commun. ACM, vol. 13, no. 6, pp. 377--387, 1970.

Digital Library

[6]

G. Booch, Object-Oriented Analysis and Design with Applications, 2 edition. Redwood City, Calif: Addison-Wesley Professional, 1993.

Digital Library

[7]

J. Han, E. Haihong, G. Le, and J. Du, 'Survey on NoSQL database', in Pervasive computing and applications (ICPCA), 2011 6th international conference on, 2011, pp. 363--366.

[8]

P. Gupta, A. Goel, J. Lin, A. Sharma, D. Wang, and R. Zadeh, 'WTF: The Who to Follow Service at Twitter', in Proceedings of the 22Nd International Conference on World Wide Web, Republic and Canton of Geneva, Switzerland, 2013, pp. 505--514.

Digital Library

[9]

P. Oliveira, F. Rodrigues, P. Henriques, and H. Galhardas, 'A taxonomy of data quality problems', in 2nd Int. Workshop on Data and Information Quality, 2005, pp. 219--233.

[10]

D. Milano, M. Scannapieco, and T. Catarci, 'Using ontologies for xml data cleaning', in On the Move to Meaningful Internet Systems 2005: OTM 2005 Workshops, 2005, pp. 562--571.

Digital Library

[11]

T. Dasu, G. T. Vesonder, and J. R. Wright, 'Data quality through knowledge engineering', in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003, pp. 705--710.

Digital Library

[12]

C. Fürber and M. Hepp, 'Towards a vocabulary for data quality management in semantic web architectures', in Proceedings of the 1st International Workshop on Linked Web Data Management, 2011, pp. 1--8.

Digital Library

[13]

M. Knuth and H. Sack, 'Data cleansing consolidation with PatchR', in The Semantic Web: ESWC 2014 Satellite Events, Springer, 2014, pp. 231--235.

[14]

W3C, 'SPARQL 1.1 Query Language', 2013. {Online}. Available: https://www.w3.org/TR/sparql11-query/. {Accessed: 11-Apr-2016}.

[15]

P. Oliveira, F. Rodrigues, and P. Henriques, 'SmartClean: An Incremental Data Cleaning Tool', in Quality Software, 2009. QSIC'09. 9th International Conference on, 2009, pp. 452--457.

Digital Library

[16]

R. Almeida, P. Maio, P. Oliveira, and J. Barroso, 'An Ontology-based Methodology for Reusing Data Cleaning Knowledge':, 2015, pp. 202--211.

[17]

W3C, 'OWL Web Ontology Language Reference', 2004. {Online}. Available: https://www.w3.org/TR/owl-ref/. {Accessed: 12-Apr-2016}.

[18]

R. Almeida, P. Maio, P. Oliveira, and J. Barroso, 'A Methodology For Rewritting Data Cleaning Operations':, 2016, ICIQ.

Cited By

Barba-González CCaballero IVarela-Vaca ÁCruz-Lemus JGómez-López MNavas-Delgado I(2024)BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoningInformation and Software Technology10.1016/j.infsof.2023.107378167(107378)Online publication date: Mar-2024
https://doi.org/10.1016/j.infsof.2023.107378
Nayak ABožić BLongo L(2022)Linked Data Quality Assessment: A SurveyWeb Services – ICWS 202110.1007/978-3-030-96140-4_5(63-76)Online publication date: 18-Feb-2022
https://doi.org/10.1007/978-3-030-96140-4_5

Ontology Based Rewriting Data Cleaning Operations
1. Information systems
  1. Data management systems

Recommendations

An Ontology-based Methodology for Reusing Data Cleaning Knowledge
IC3K 2015: Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

The organizations' demand to integrate several heterogeneous data sources and an ever-increasing volume of data is revealing the presence of quality problems in data. Currently, most of the data cleaning approaches (for detection and correction of data ...
Ontology-Based Data Cleaning
NLDB '02: Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers

Multi-source information systems, such as data warehouses, are composed of a set of heterogeneous and distributed data sources. The relevant information is extracted from these sources, cleaned, transformed and then integrated. The confrontation of two ...
Foundational Challenges in Automated Semantic Web Data and Ontology Cleaning

Applying automated reasoning systems to data cleaning in the Semantic Web raises many foundational challenges regarding cleaning-agent design. The authors discuss these challenges and then argue that we can achieve logic trust in the Semantic Web only ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

C3S2E '16: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

July 2016

152 pages

ISBN:9781450340755

DOI:10.1145/2948992

Editor:
Evan Desai
ConfSys.org
,
General Chair:
Bipin C. Desai
Concordia University, Canada
,
Program Chairs:
Ana Alameida
ISEP
,
Jorge Bernardino
ISEC

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

BytePress
ISEP

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

C3S2E '16

C3S2E '16: Ninth International C* Conference on Computer Science & Software Engineering

July 20 - 22, 2016

Porto, Portugal

Acceptance Rates

Overall Acceptance Rate 12 of 42 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
118
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Barba-González CCaballero IVarela-Vaca ÁCruz-Lemus JGómez-López MNavas-Delgado I(2024)BIGOWL4DQ: Ontology-driven approach for Big Data quality meta-modelling, selection and reasoningInformation and Software Technology10.1016/j.infsof.2023.107378167(107378)Online publication date: Mar-2024
https://doi.org/10.1016/j.infsof.2023.107378
Nayak ABožić BLongo L(2022)Linked Data Quality Assessment: A SurveyWeb Services – ICWS 202110.1007/978-3-030-96140-4_5(63-76)Online publication date: 18-Feb-2022
https://doi.org/10.1007/978-3-030-96140-4_5

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents