Andrea Marrella

Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements.

WORKPAD: Process Management and Geo-Collaboration Help Disaster Response

In complex emergency/disaster scenarios, persons from teams from various emergency-response organizations collaborate to achieve a common goal. In these scenarios, the use of smart mobile devices and applications can improve the collaboration dynamically. The lack of basic interaction principles can be dangerous, as it could increase the level of disaster or can make the efforts ineffective. This paper examines the main results of the project WORKPAD finished in December 2009. WORKPAD worked on a two-level architecture to support rescue operators during emergency management. The use of a usercentered design methodology during the entire development cycle has guaranteed that the architecture and resulting system meet end-user requirements. The feasibility of its use in real emergencies is also proven by a demonstration showcased with real operators. The paper includes qualitative and quantitative results and presents guidelines that can be useful in developing emergency-management systems.

Planlets: Automatically Recovering Dynamic Processes in YAWL

Process Management Systems (PMSs) are currently more and more used as a supporting tool to coordinate the enactment of processes. YAWL, one of the best-known PMSs coming from academia, allows to define stable and well-understood processes and provides support for the handling of expected exceptions, which can be anticipated at design time. But in some real world scenarios, the environment may change in unexpected ways so as to prevent a process from being successfully carried out. In order to cope with these anomalous situations, a PMS should automatically recover the process at run-time, by considering the context of the specific case under execution. In this paper, we propose the approach of Planlets, self-contained YAWL specifications with recovery features, based on modeling of pre- and post-conditions of tasks and the use of planning techniques. We show the feasibility of the proposed approach by discussing its deployment on top of YAWL.

Continuous Planning for Solving Business Process Adaptivity

Process Management Systems (PMSs, aka Workflow Management Systems – WfMSs) are currently more and more used as a supporting tool to coordinate the enactment of processes. In real world scenarios, the environment may change in unexpected ways so as to prevent a process from being successfully carried out. In order to cope with these anomalous situations, a PMS should automatically adapt the process without completely replacing it. In this paper, we propose a technique, based on continuous planning, to automatically cope with unexpected changes, in order to modify only those parts of the process that need to be changed/adapted and keeping other parts stable. We also provide a running example that shows the practical applicability of the approach.

Synthesizing a Library of Process Templates through Partial-Order Planning Algorithms

The design time specification of dynamic processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To address these issues, we propose an approach that exploits partial-order planning algorithms for automatically synthesizing a library of process template definitions for different contextual cases. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially-known contextual environments.

Aligning Real Process Executions and Prescriptive Process Models through Automated Planning

We propose a planning-based approach to compute optimal alignments.The approach has been implemented with a state-of-the-art planning system.We performed an in-depth evaluation on real-life & synthetic process models and logs.The approach outperforms existing techniques when the size/noise of the inputs grows. Modern organizations execute processes to deliver product and services, whose enactment needs to adhere to laws, regulations and standards. Conformance checking is the problem of pinpointing where deviations are observed. This paper shows how instances of the conformance checking problem can be represented as planning problems in PDDL (Planning Domain Definition Language) for which planners can find a correct solution in a finite amount of time. If conformance checking problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with evident advantages in term of versatility and customization. The paper also reports on results of experiments conducted on two real-life case studies and on eight larger synthetic ones, mainly using the Fast-downward planner framework to solve the planning problems due to its performances. Some experiments were also repeated though other planners to concretely showcase the versatility of our approach. The results show that, when process models and event logs are of considerable size, our approach outperforms existing ones even by several orders of magnitude. Even more remarkably, when process models are extremely large and event log traces very long, the existing approaches are unable to terminate because they run out of memory, while our approach is able to properly complete the alignment task.

Intelligent Process Adaptation in the SmartPM System

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.

Coordinating Mobile Actors in Pervasive and Mobile Scenarios: An AI-Based Approach

Process management systems (PMSs) can be used not only in classical business scenarios, but also in highly dynamic and uncertain environments, for example, in supporting operators during emergency management for coordinating their activities. In such challenging situations, processes should be adapted in order to cope with anomalous situations, including connection anomalies and task faults. This requires the provision of intelligent support for the planning and enactment of complex processes, that allows to capture the knowledge about the dynamic context of a process. In this paper, we show how this knowledge, together with information about the capabilities of the available actors, may be specified and used to not only to support the selection of an appropriate set of agents to fill the roles in a given task, but also to solve the problem of adaptivity. The paper describes a first prototype of a PMS based on well-known artificial intelligence techniques and how it can be extended to tackle adaptation.

Towards a Goal-Oriented Framework for the Automatic Synthesis of Underspecified Activities in Dynamic Processes

It is difficult to produce a detailed model of a dynamic process ahead of time. Such processes may include some under specified activities whose exact definition is not yet known at design-time, and may not be known until the time that an instance of the process has started execution, due to their context-dependent nature. In this paper, we propose a goal-oriented framework to model and specify dynamic processes that allows us to dynamically select and/or synthesize automatically at run-time the content of under specified activities.

Multi-party Business Process Resilience By-Design: A Data-Centric Perspective

Nowadays every business organization operates in ecosystems and cooperation is mandatory. If, on the one side, this increases the opportunities for the involved organizations, on the other side, every actor is a potential source of failures with impacts on the entire ecosystem. For this reason, resilience is a feature that multi-party business processes today must enforce. As resilience concerns the ability to cope with unplanned situations, managing the critical issues is usually a run-time task.