DaQL 2.0: Measure Data Quality based on Entity Models

Christian Lettner; Reinhard Stumptner; Werner Fragner; Franz Rauchenzauner; Lisa Ehrlinger

doi:10.1016/j.procs.2021.01.327

DaQL 2.0: Measure Data Quality based on Entity Models

Christian Lettner, Reinhard Stumptner, Werner Fragner, Franz Rauchenzauner, Lisa Ehrlinger

Institute for Application-oriented Knowledge Processing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

In order to make good decisions, the data used for decision-making needs to be of high quality. As the volume of data continually increases, ensuring high data quality is a big challenge nowadays and needs to be automated with tools. The goal of the Data Quality Library (DaQL) is to provide a tool to continuously ensure and measure data quality as proposed in [5]. In this paper, we present the current status of the development of the new DaQL version 2.0. The main contribution of DaQL 2.0 is the possibility to define data quality rules for complex data objects (called entities), which represent business objects. In contrast to existing tools, a user does not require detailed knowledge about the database schema that is observed.

Original language	English
Title of host publication	Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)
Publisher	Elsevier B.V.
Pages	772-777
Number of pages	6
Volume	180
DOIs	https://doi.org/10.1016/j.procs.2021.01.327
Publication status	Published - 2021

Publication series

Name	Procedia Computer Science

Fields of science

102010 Database systems
102014 Information design
102015 Information systems
102019 Machine learning
102022 Software development
102028 Knowledge engineering
102033 Data mining
102035 Data science
509018 Knowledge management

JKU Focus areas

Digital Transformation

Access to Document

10.1016/j.procs.2021.01.327

Cite this

@inproceedings{304c7b83b87b450b9c37ea41f632d538,

title = "DaQL 2.0: Measure Data Quality based on Entity Models",

abstract = "In order to make good decisions, the data used for decision-making needs to be of high quality. As the volume of data continually increases, ensuring high data quality is a big challenge nowadays and needs to be automated with tools. The goal of the Data Quality Library (DaQL) is to provide a tool to continuously ensure and measure data quality as proposed in [5]. In this paper, we present the current status of the development of the new DaQL version 2.0. The main contribution of DaQL 2.0 is the possibility to define data quality rules for complex data objects (called entities), which represent business objects. In contrast to existing tools, a user does not require detailed knowledge about the database schema that is observed.",

author = "Christian Lettner and Reinhard Stumptner and Werner Fragner and Franz Rauchenzauner and Lisa Ehrlinger",

year = "2021",

doi = "10.1016/j.procs.2021.01.327",

language = "English",

volume = "180",

series = "Procedia Computer Science",

publisher = "Elsevier B.V.",

pages = "772--777",

booktitle = "Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)",

}

DaQL 2.0: Measure Data Quality based on Entity Models. / Lettner, Christian; Stumptner, Reinhard; Fragner, Werner et al.
Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020). Vol. 180 Elsevier B.V., 2021. p. 772-777 (Procedia Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - DaQL 2.0: Measure Data Quality based on Entity Models

AU - Lettner, Christian

AU - Stumptner, Reinhard

AU - Fragner, Werner

AU - Rauchenzauner, Franz

AU - Ehrlinger, Lisa

PY - 2021

Y1 - 2021

N2 - In order to make good decisions, the data used for decision-making needs to be of high quality. As the volume of data continually increases, ensuring high data quality is a big challenge nowadays and needs to be automated with tools. The goal of the Data Quality Library (DaQL) is to provide a tool to continuously ensure and measure data quality as proposed in [5]. In this paper, we present the current status of the development of the new DaQL version 2.0. The main contribution of DaQL 2.0 is the possibility to define data quality rules for complex data objects (called entities), which represent business objects. In contrast to existing tools, a user does not require detailed knowledge about the database schema that is observed.

AB - In order to make good decisions, the data used for decision-making needs to be of high quality. As the volume of data continually increases, ensuring high data quality is a big challenge nowadays and needs to be automated with tools. The goal of the Data Quality Library (DaQL) is to provide a tool to continuously ensure and measure data quality as proposed in [5]. In this paper, we present the current status of the development of the new DaQL version 2.0. The main contribution of DaQL 2.0 is the possibility to define data quality rules for complex data objects (called entities), which represent business objects. In contrast to existing tools, a user does not require detailed knowledge about the database schema that is observed.

UR - https://www.sciencedirect.com/science/article/pii/S1877050921003781

U2 - 10.1016/j.procs.2021.01.327

DO - 10.1016/j.procs.2021.01.327

M3 - Conference proceedings

VL - 180

T3 - Procedia Computer Science

SP - 772

EP - 777

BT - Proceedings of the 2nd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2020)

PB - Elsevier B.V.

ER -

DaQL 2.0: Measure Data Quality based on Entity Models

Abstract

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DasRes 2 - Data Analysis Systems

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DaQL 2.0: Measure Data Quality based on Entity Models

Abstract

Publication series

Fields of science

JKU Focus areas

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DasRes 2 - Data Analysis Systems

Cite this