Title
DaProS: a data property specification tool to capture scientific sensor data properties
Abstract
Environmental scientists have begun to use advanced technologies such as wireless sensor networks and robotic trams equipped with sensors to collect data, such as spectral readings and carbon dioxide, which is leading to a rapid increase in the amount of data being stored. This has resulted in a need to evaluate promptly the accuracy of the data, the meaning of the data, and the correct operation of the instrumentation in order to not lose valuable time and information. Performing such evaluations requires scientists to rely on their knowledge and experience in the field. Field knowledge is rarely shared or reused by other scientists mostly because of the lack of a well-defined methodology for sharing information and appropriate tool support. This work presents the Data Property Specification (DaProS) tool that assists practitioners in specification and refinement of properties that can be used to check data quality. The tool can be used to capture scientific knowledge about data processes in remote sensing systems through the use of decision trees and questionnaires that guide practitioners in the specification process. In addition, the tool uses Disciplined Natural Language (DNL) property representations for scientists to validate that the specifications capture the intended meaning.
Year
DOI
Venue
2010
10.1007/978-3-642-16385-2_29
ER Workshops
Keywords
Field
DocType
natural language,intended meaning,appropriate tool support,specification process,scientific sensor data property,data property specification tool,advanced technology,scientific knowledge,field knowledge,data quality,data process,data property specification,sensor network,decision tree,remote sensing,domain engineering,wireless sensor network,carbon dioxide,data processing
Data science,Decision tree,Data mining,Data quality,Domain engineering,Data Property,Software engineering,Sociology of scientific knowledge,Computer science,Natural language,Wireless sensor network,Database
Conference
Volume
ISSN
ISBN
6413
0302-9743
3-642-16384-X
Citations 
PageRank 
References 
3
0.42
3
Authors
3
Name
Order
Citations
PageRank
Irbis Gallegos193.13
ann q gates212725.22
Craig E. Tweedie3154.61