| Abstract |
Provenance of a resource is a record that describes entities and processes involved
in producing and delivering or otherwise influencing that resource. Generally, the
above record can be considered as information that has great importance in the
scientific community regarding the experiments that are conducted as part of its
research. This information can be later used for the validation, interpretation or
the reproduction of scientific results and is commonly stored on metadata placed in
various Metadata Repositories (MRs) or Knowledge Bases (KBs).
However, in various settings it is prohibitive to store the complete provenance
information because of (a) the immense space requirements needed and (b) the
difficulty of controlling its quality due to the existence of possible errors.
We address the problem by introducing provenance-based inference rules as a
means to reduce the amount of provenance information that has to be stored, and
to ease quality control (e.g., corrections). Roughly, we show how information can be
propagated by identifying a number of basic inference rules over a core conceptual
model representing provenance. The propagation of provenance concerns fundamental
modeling concepts such as actors, activities, events, devices and information
objects and their associations.
However, since a KB is not static but changes over time due to several factors,
a rising question is how we can satisfy change requests while still supporting the
aforementioned inference rules. Towards this end, we elaborate on the specification
of the required add/delete operations, and consider two different semantics for
deletion of information. We describe the corresponding change algorithms, and we
report on comparative results for two repository strategies regarding the derivation
of new knowledge. The results allow us to understand the tradeoffs related to the
use of inference rules on storage space and performance of queries and updates.
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Reasoning and Evolution of Event-Based Provenance Information
