Anzeige
Erschienen in:
2018 | OriginalPaper | Buchkapitel
Data Provenance in Citizen Science Databases
verfasst von : Nikita Tiufiakov, Ajantha Dahanayake, Tatiana Zudilova
Erschienen in: New Trends in Databases and Information Systems
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Today, more and more scientific groups are developing citizen science applications. Citizen science is a relatively new domain of science that has already proved to be as beneficial as classical science. One of the major challenges citizen science face is the data quality assurance. It uses several techniques to verify the data quality based on expert evaluation, voting systems, etc. Data provenance is used in many scientific systems and provides reliable mechanism for tracking data history. It includes history of origin, changes, and all interactions between different parts of data. Data provenance by itself has many types such as “Why provenance”, “When provenance”, and “What provenance”. The purpose of this work is to build a prototype of a database with built-in data provenance. Several databases systems and models such as Relational databases, NoSQL databases are taken into consideration. Experiments are been conducted to test limitations of proposed prototype.