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A typical problem in database theory is to verify whether there exists a relation (or database) instance satisfying a number of given dependency constraints. This problem has recently received a renewed deal of interest within the context of data exchange, but the issue of handling constraints on aggregate data has not been much investigated so far, notwithstanding the relevance of aggregate operations in exchange systems. This paper introduces count constraints that require the results of given count operations on a relation to be within a certain range. Count constraints are defined by a suitable extension of first order predicate calculus, based on set terms, and they are then used in a new decisional problem, the Inverse OLAP: given a star schema, does there exist a relation instance satisfying a set of given count constraints? The new problem turns out to be NEXP complete under various conditions: program complexity, data complexity and combined complexity. Count constraints can be also used into a data exchange system context, where data from the source database are transferred to the target database using aggregate operations.

Process mining techniques have been receiving great attention in the literature for their ability to automatically support process (re)design. The output of these techniques is a concrete workflow schema that models all the possible execution scenarios registered in the logs, and that can be profitably used to support further-coming enactments. In this paper, we face process mining in a slightly different perspective. Indeed, we propose an approach to process mining that combines novel discovery strategies with abstraction methods, with the aim of producing hierarchical views of the process that satisfactorily capture its behavior at different level of details. Therefore, at the highest level of detail, the mined model can support the design of concrete workflows; at lower levels of detail, the views can be used in advanced business process platforms to support monitoring and analysis. Our approach consists of several algorithms which have been integrated into a systems architecture whose description is accounted for in the paper as well.

Designing, analyzing and managing complex processes are recently become crucial issues in most application contexts, such as e-commerce, business process (re-)engineering, Web/grid computing. In this paper, we propose a framework that supports the designer in the definition and in the analysis of complex processes by means of several facilities for reusing, customizing and generalizing existent process components. To this aim we tightly integrate process models with a domain ontology and an activity ontology, so providing a semantic vision of the application context and of the processes themselves. Moreover, the framework is equipped with a set of techniques providing for advanced functionalities, which can be very useful when building and analyzing process models, such as consistency checking, interactive ontology navigation, automatic (re)discovering of process models. A software architecture fully supporting our framework is also presented and discussed.

This paper presents a new formalism for modelling workflows schemes which combines a control flow graph representation with simple (i.e., stratified), yet powerful DATALOG rules to express complex properties and constraints on executions. Both the graph representation and the DATALOG rules are mapped into a unique program in DATALOG ev!, that is a recent extension of DATALOG for handling events. This mapping enables the designer to simulate the actual behavior of the modeled scheme by fixing an initial state and an execution scenario (i.e., a sequence of executions for the same workflow) and querying the state after such executions. As the scenario includes a certain amount of non-determinism, the designer may also verify under which conditions a given (desirable or undesirable) goal can be eventually achieved.