Requirements for a Provenance Visualization Component

Abstract

Abstract. The need for interpretation of provenance data increases with the introduction of further provenance related IT-systems. The interpretation of data only becomes intuitively with providing good and efficient visualization possibilities. During the development of general provenance visualization techniques, provenance users are classified into groups regarding their view to provenance information. The end-user requirements are evaluated on an abstract level to have a basis for research. Different intentions of end-users regarding provenance are identified and put into relationship with standard visualization types. Examples for standard visualization types are given and a brief forecast to future achievements is made.

A provenance metadata model integrating ISO geospatial lineage and the OGC WPS: Conceptual model and implementation

“…In complex environments, scientists rely on visualization tools to help them understand large amounts of data that are generated from experiments (Salton, Allan, Buckley, & Singhal, 1994). Beyond the models used to capture and store provenance, an effective visualization of provenance is also necessary to understand and evaluate data and the processes involved (Kunde, Bergmeyer, & Schreiber, 2008). According to Steele and Iliinsky (2010), there are two categories of data visualization: Exploratory, designed to support researchers who are not certain about what is in the data; and Explanatory, when a researcher is trying to explain the data to someone else.…”

Visualization of network data provenance

“…Kunde et. al [12] derive abstract types of user requirements for provenance visualization, including: 1) process: the sequence of the process steps is in the center of inspection; 2) results: the intermediate or end results of interactions are in the center of the users view; 3) relationship: the relationship of interactions or actors is important; 4) timeline: the time is important to observe; 5) participation: the correctness of the participants is important; 6) compare: the comparison of two subjects shows the difference between them; 7) interpretation: an individual visualization view depending on the special question of the end-user.…”

Automatic Provenance Collection and Publishing in a Science Data Production Environment—Early Results

“…Unlike Digital Object Identifiers (DOIs) 4 , ARKs allow qualifying information (e.g., granule identifiers) to be appended to the dataset identifier and passed along by the identifier resolution system. Unlike Persistent URLs (PURLs) 5 , ARKs are self-identifying, and don't require the reservation of a portion of the HTTP URL namespace for their implementation.…”

“…We realize that different kinds of queries or user communities may require alternative provenance renderings [5]. We are therefore exploring connecting the ES3 database to a generic web-based graph browsing system [4].…”