Authors:
Marko Junkkari
1
and
Antti Sirkka
2
Affiliations:
1
University of Tampere, Finland
;
2
Tieto Finland, Finland
Keyword(s):
Data-Centric Workflow, Complex Objects, Physical Assembly, Data Model, Lifecycle Data Management, Traceability Graph.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Business and Social Applications
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Management and Quality
;
Data Structures and Data Management Algorithms
;
Databases and Data Security
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
e-Business
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Symbolic Systems
;
Workflow Management and Databases
Abstract:
Data-centric workflows focus on how the data is transferred between processes and how it is logically stored. In addition to traditional workflow analysis, these can be applied to monitoring, tracing, and analyzing data in processes and their mutual relationships. In many applications, e.g. manufacturing, the tracing of products thorough entire lifecycle is becoming more and more important. In the present paper we define the traceability graph that involves a framework for data that adapts to different levels of precision of tracing. Advanced analyzing requires modeling of data in processes and methods for accumulating resources and emissions thorough the lifecycle of products. This, in turns, requires explicit modeling and presentation how objects are divided and/or composed and how information is cumulated via these tasks. The traceability graph focuses on these issues. The traceability graph is formally defined by set theory that is an established and exact specification method.