My (fair) big data

Tiziana  Catarci; Monica Scannapieco; Marco Console; Camil Demetrescu

doi:10.1109/BigData.2017.8258267

My (fair) big data

Tiziana Catarci, Monica Scannapieco, Marco Console, Camil Demetrescu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Policy making has the strict requirement to rely on quantitative and high quality information. This paper will address the data quality issue for policy making by showing how to deal with Big Data quality in the different steps of a processing pipeline, with a focus on the integration of Big Data sources with traditional sources. In this respect, a relevant role is played by metadata and in particular by ontologies. Integration systems relying on ontologies enable indeed a formal quality evaluation of inaccuracy, inconsistency and incompleteness of integrated data. The paper will finally describe data confidentiality as a Big Data quality dimension, showing the main issues to be faced for its assurance.

Original language	English
Title of host publication	2017 IEEE International Conference on Big Data (BIGDATA)
Place of Publication	Boston, MA, USA
Publisher	Institute of Electrical and Electronics Engineers
Pages	2974-2979
Number of pages	6
ISBN (Electronic)	978-1-5386-2715-0
ISBN (Print)	978-1-5386-2716-7
DOIs	https://doi.org/10.1109/BigData.2017.8258267
Publication status	Published - 15 Jan 2018
Event	2017 IEEE International Conference on Big Data - Boston, United States Duration: 11 Dec 2017 → 14 Dec 2017 http://cci.drexel.edu/bigdata/bigdata2017/index.html

Conference

Conference	2017 IEEE International Conference on Big Data
Abbreviated title	Big Data 2017
Country/Territory	United States
City	Boston
Period	11/12/17 → 14/12/17
Internet address	http://cci.drexel.edu/bigdata/bigdata2017/index.html

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10.1109/BigData.2017.8258267

https://ieeexplore.ieee.org/document/8258267

Cite this

@inproceedings{4508c868cc1543d393e471a002dd6e8f,

title = "My (fair) big data",

abstract = "Policy making has the strict requirement to rely on quantitative and high quality information. This paper will address the data quality issue for policy making by showing how to deal with Big Data quality in the different steps of a processing pipeline, with a focus on the integration of Big Data sources with traditional sources. In this respect, a relevant role is played by metadata and in particular by ontologies. Integration systems relying on ontologies enable indeed a formal quality evaluation of inaccuracy, inconsistency and incompleteness of integrated data. The paper will finally describe data confidentiality as a Big Data quality dimension, showing the main issues to be faced for its assurance.",

author = "Tiziana Catarci and Monica Scannapieco and Marco Console and Camil Demetrescu",

year = "2018",

month = jan,

day = "15",

doi = "10.1109/BigData.2017.8258267",

language = "English",

isbn = "978-1-5386-2716-7",

pages = "2974--2979",

booktitle = "2017 IEEE International Conference on Big Data (BIGDATA)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2017 IEEE International Conference on Big Data, Big Data 2017 ; Conference date: 11-12-2017 Through 14-12-2017",

url = "http://cci.drexel.edu/bigdata/bigdata2017/index.html",

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TY - GEN

T1 - My (fair) big data

AU - Catarci, Tiziana

AU - Scannapieco, Monica

AU - Console, Marco

AU - Demetrescu , Camil

PY - 2018/1/15

Y1 - 2018/1/15

N2 - Policy making has the strict requirement to rely on quantitative and high quality information. This paper will address the data quality issue for policy making by showing how to deal with Big Data quality in the different steps of a processing pipeline, with a focus on the integration of Big Data sources with traditional sources. In this respect, a relevant role is played by metadata and in particular by ontologies. Integration systems relying on ontologies enable indeed a formal quality evaluation of inaccuracy, inconsistency and incompleteness of integrated data. The paper will finally describe data confidentiality as a Big Data quality dimension, showing the main issues to be faced for its assurance.

AB - Policy making has the strict requirement to rely on quantitative and high quality information. This paper will address the data quality issue for policy making by showing how to deal with Big Data quality in the different steps of a processing pipeline, with a focus on the integration of Big Data sources with traditional sources. In this respect, a relevant role is played by metadata and in particular by ontologies. Integration systems relying on ontologies enable indeed a formal quality evaluation of inaccuracy, inconsistency and incompleteness of integrated data. The paper will finally describe data confidentiality as a Big Data quality dimension, showing the main issues to be faced for its assurance.

U2 - 10.1109/BigData.2017.8258267

DO - 10.1109/BigData.2017.8258267

M3 - Conference contribution

SN - 978-1-5386-2716-7

SP - 2974

EP - 2979

BT - 2017 IEEE International Conference on Big Data (BIGDATA)

PB - Institute of Electrical and Electronics Engineers

CY - Boston, MA, USA

T2 - 2017 IEEE International Conference on Big Data

Y2 - 11 December 2017 through 14 December 2017

ER -

My (fair) big data

Abstract

Conference

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