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Inconsistency Tolerance in OWL 2 QL Knowledge and Action Bases Statement of Interest
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@MISC{Calvanese_inconsistencytolerance,
author = {Diego Calvanese and Evgeny Kharlamov and Marco Montali and Dmitriy Zheleznyakov},
title = {Inconsistency Tolerance in OWL 2 QL Knowledge and Action Bases Statement of Interest},
year = {}
}
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Abstract
A Business Process (BP) is constituted by (i) data that describes the state of affairs and (ii) a set of activities (to be performed) over this data. The activities, when combined in what is usually referred to as an execution flow, achieve some business goal. When analyzing BPs, one is usually interested in querying possible execution flows to extract useful information and verifying dynamic properties over them. 1 To this effect, BPs are typically modeled via high-level specifications of activities [17], which are later compiled into an executable code. Since the logics of business processes is captured by these specifications, tools for querying and analyzing possible execution flows are extremely valuable for companies [11,12,16]. Fully taking into account the presence of data significantly complicates the analysis of execution flows since it makes the system to be an infinite state one in general. On the other hand, it is often assumed that the data is simple enough and does not significantly impact on the analysis of possible execution flows. For these reasons, in the classical modeling paradigm of BPs, the data part is typically abstracted away, and the analysis is carried out under this simplification. In knowledge-intensive applications, however, where a crucial aspect of BPs is to
Keyphrases
inconsistency tolerance ql knowledge action base statement business process execution flow possible execution flow executable code data part business goal classical modeling paradigm infinite state possible execution useful information knowledge-intensive application dynamic property crucial aspect high-level specification
