Guildlines of Data Quality Issues for Data Integration in the Context of the TPC-DI Benchmark

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00096406" target="_blank" >RIV/00216224:14330/17:00096406 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5220/0006334301350144" target="_blank" >http://dx.doi.org/10.5220/0006334301350144</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0006334301350144" target="_blank" >10.5220/0006334301350144</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Guildlines of Data Quality Issues for Data Integration in the Context of the TPC-DI Benchmark

Popis výsledku v původním jazyce

Nowadays, many business intelligence or master data management initiatives are based on regular data integration, since data integration intends to extract and combine a variety of data sources, it is thus considered as a prerequisite for data analytics and management. More recently, TPC-DI is proposed as an industry benchmark for data integration. It is designed to benchmark the data integration and serve as a standardisation to evaluate the ETL performance. There are a variety of data quality problems such as multi-meaning attributes and inconsistent data schemas in source data, which will not only cause problems for the data integration process but also affect further data mining or data analytics. This paper has summarised typical data quality problems in the data integration and adapted the traditional data quality dimensions to classify those data quality problems. We found that data completeness, timeliness and consistency are critical for data quality management in data integration, and data consistency should be further defined in the pragmatic level. In order to prevent typical data quality problems and proactively manage data quality in ETL, we proposed a set of practical guidelines for researchers and practitioners to conduct data quality management in data integration.

Název v anglickém jazyce

Guildlines of Data Quality Issues for Data Integration in the Context of the TPC-DI Benchmark

Popis výsledku anglicky

Nowadays, many business intelligence or master data management initiatives are based on regular data integration, since data integration intends to extract and combine a variety of data sources, it is thus considered as a prerequisite for data analytics and management. More recently, TPC-DI is proposed as an industry benchmark for data integration. It is designed to benchmark the data integration and serve as a standardisation to evaluate the ETL performance. There are a variety of data quality problems such as multi-meaning attributes and inconsistent data schemas in source data, which will not only cause problems for the data integration process but also affect further data mining or data analytics. This paper has summarised typical data quality problems in the data integration and adapted the traditional data quality dimensions to classify those data quality problems. We found that data completeness, timeliness and consistency are critical for data quality management in data integration, and data consistency should be further defined in the pragmatic level. In order to prevent typical data quality problems and proactively manage data quality in ETL, we proposed a set of practical guidelines for researchers and practitioners to conduct data quality management in data integration.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 19th International Conference on Enterprise Information Systems

ISBN

9789897582479

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

135-144

Název nakladatele

SciTePress

Místo vydání

Porto, Portugal

Místo konání akce

Porto, Portugal

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

000697605900013