In this article we introduce${{\sf STARDUST}}$(event STream Analysis for pRocess Discovery Using Sampling sTragies), a process discovery approach that analyses a trace stream, in order to discover a process model that may change over time. The basic idea is to adopt a sampling technique to select the most representative trace variants to be considered for the process discovery, then to alert a concept drift as the trace variants to be sampled change over time and, finally, to trigger the discovery of a new process model as a drift is alerted. We formulate the proposed approach under the assumption that the trace distribution commonly follows the Pareto's principle (i.e., a few trace variants covers the majority of cases) which is commonly satisfied in several business processes. Experimental results on various benchmark event logs handled as streams show the effectiveness of the proposed approach also compared to a state-of-the- art concept drift detection approach.

Incomplete information arises in many current database applications. Certain answers are a widely accepted semantics of query answering over incomplete databases. Since their computation is a coNP-hard problem, recent research has focused on developing polynomial time approximation algorithms computing a sound (but possibly incomplete) set of certain answers. In this demo we showcase ACID, a system to compute sound sets of certain answers. The central tools of its underlying algorithms are conditional tables and the conditional evaluation of relation algebra. Different evaluation strategies can be applied, with more accurate ones having higher complexity, but returning more certain answers. We show how to query incomplete databases using the ACID system, which offers a suite of approximation algorithms enabling users to choose the technique that best meets their needs in terms of balance between efficiency and quality of the result's approximation.