Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

The increasing application of process-oriented approaches in new challenging dynamic domains beyond business computing (e.g., healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex knowledge-intensive processes in such domains. A knowledge-intensive process is influenced by user decision making and coupled with contextual data and knowledge production, and involves performing complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and knowledge-intensive processes must be robust to unexpected conditions and adaptable to unanticipated exceptions, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. To tackle this issue, in this paper we present SmartPM, a model and a prototype Process Management System featuring a set of techniques providing support for automated adaptation of knowledge-intensive processes at runtime. Such techniques are able to automatically adapt process instances when unanticipated exceptions occur, without explicitly defining policies to recover from exceptions and without the intervention of domain experts at runtime, aiming at reducing error-prone and costly manual ad-hoc changes, and thus at relieving users from complex adaptations tasks. To accomplish this, we make use of well-established techniques and frameworks from Artificial Intelligence, such as situation calculus, IndiGolog and classical planning. The approach, which is backed by a formal model, has been implemented and validated with a case study based on real knowledge-intensive processes coming from an emergency management domain.

In this paper, we discuss the application of concept of data quality to big data by highlighting how much complex is to define it in a general way. Already data quality is a multidimensional concept, difficult to characterize in precise definitions even in the case of well-structured data. Big data add two further dimensions of complexity: (i) being “ very ” source specific , and for this we adopt the interesting UNECE classification, and (ii) being highly unstructured and schema-less , often without golden standards to refer to or very difficult to access. After providing a tutorial on data quality in traditional contexts, we analyze big data by providing insights into the UNECE classification, and then, for each type of data source, we choose a specific instance of such a type (notably deep Web data, sensor-generated data, and Twitters/short texts) and discuss how quality dimensions can be defined in these cases. The overall aim of the paper is therefore to identify further research directions in the area of big data quality, by providing at the same time an up-to-date state of the art on data quality.

Declarative approaches are particularly suitable for modeling highly flexible processes. They especially apply to artful processes, i.e., rapid informal processes that are typically carried out by those people whose work is mental rather than physical (managers, professors, researchers, engineers, etc.), the so called “knowledge workers”. This paper describes MINERful++, a two-step algorithm for an efficient discovery of constraints that constitute declarative workflow models. As a first step, a knowledge base is built, with information about temporal statistics gathered from execution traces. Then, the statistical support of constraints is computed, by querying that knowledge base. MINERful++is fast, modular, independent of the specific formalism adopted for representing constraints, based on a probabilistic approach and capable of eliminating the redundancy of subsumed constraints.

With organizations increasingly depending on Web services to build complex applications, security and privacy concerns including the protection of access control policies are becoming a serious issue. Ideally, service providers would like to make sure that clients have knowledge of only portions of the access control policy relevant to their interactions to the extent to which they are entrusted by the Web service and without restricting the client’s choices in terms of which operations to execute. We propose ACConv , a novel model for access control in Web services that is suitable when interactions between the client and the Web service are conversational and long-running. The conversation-based access control model proposed in this article allows service providers to limit how much knowledge clients have about the credentials specified in their access policies. This is achieved while reducing the number of times credentials are asked from clients and minimizing the risk that clients drop out of a conversation with the Web service before reaching a final state due to the lack of necessary credentials. Clients are requested to provide credentials, and hence are entrusted with part of the Web service access control policies, only for some specific granted conversations which are decided based on: (1) a level of trust that the Web service provider has vis-à-vis the client, (2) the operation that the client is about to invoke, and (3) meaningful conversations which represent conversations that lead to a final state from the current one. We have implemented the proposed approach in a software prototype and conducted extensive experiments to show its effectiveness.

Service Oriented Computing is emerging as the main approach to build distributed enterprise applications on the Web. The widespread use of Web services is hindered by the lack of adequate security and privacy support. In this paper, we present a novel framework for enforcing access control in conversation-based Web services. Our approach takes into account the conversational nature of Web services. This is in contrast with existing approaches to access control enforcement that assume a Web service as a set of independent operations. Furthermore, our approach achieves a tradeoff between the need to protect Web service's access control policies and the need to disclose to clients the portion of access control policies related to the conversations they are interested in. This is important to avoid situations where the client cannot progress in the conversation due to the lack of required security requirements. We introduce the concept of k-trustworthiness that defines the conversations for which a client can provide credentials maximizing the likelihood that it will eventually hit a final state.

This paper addresses the issue of automatic service composition. We first develop a framework in which the exported behavior of a service is described in terms of a so-called execution tree, that is an abstraction for its possible executions. We then study the case in which such exported behavior (i.e. the execution tree of the service) can be represented by a finite state machine (i.e. finite state transition system). In this specific setting, we devise sound, complete and terminating techniques both to check for the existence of a composition, and to return a composition, if one exists. We also analyze the computational complexity of the proposed algorithms. Finally, we present an open source prototype tool, called [Formula: see text] (E-Service Composer), that implements our composition technique. To the best of our knowledge, our work is the first attempt to provide a provably correct technique for the automatic synthesis of service composition, in a framework where the behavior of services is explicitly specified.

Cooperative information systems (CISs) are often characterized by a high degree of data replication; as an example, in an e-government scenario, the personal data of citizens are stored by almost all administrations. In such scenarios, organizations typically provide the same information with distinct quality levels and this enables providing users with data of the highest available quality. Furthermore, the comparison of data values might be used to enforce a general improvement of data quality in all organizations. In the DaQuinCIS project (Aguilera et al., 1999), we propose an architecture for the management of data quality in CISs; this architecture allows the diffusion of data and related quality and exploits data replication to improve the overall quality of cooperative data. In this paper, we present an overview of a component of the DaQuinCIS architecture, namely the quality notification service (QNS), which is used to inform interested users when changes in quality values occur within the CIS. QNS can be used to control the quality of critical data, e.g. to keep track of quality changes and to be always aware when quality degrades under a certain threshold. The interaction between the QNS and its users follows the publish/subscribe paradigm: a user willing to be notified for quality changes subscribes to the QNS by submitting the features of the events to be notified for, through a specific subscription language. When a change in quality occurs, an event is published by the QNS i.e., all the users which have a consistent subscription receive a notification. However, as shown in the paper by Marchetti et al. (2003), currently available pub/sub infrastructures do not allow to meet all the requirements that a QNS implementation should satisfy, in particular scaling to a large number of users and coping with platform heterogeneity. QNS addresses both these problems through a layered architecture that: (i) encapsulates the technological infrastructure specific of each organization; (ii) adopts the standard Web-service technology to implement inter-organization communications; and (iii) embeds solutions and algorithms (namely, merge subscriptions and diffusion trees) to reduce the use of physical and computational resources. The remainder of this paper is organized as follows: we first introduce some preliminary concepts and the QNS specification; then we motivate and describe the internal architecture of the service. Due to the lack of space, explanations are given at a very high abstraction level. Interested readers can find technical and formal details, as well as running examples, in the paper by Scannapieco et al. (2003).