Roberto Posenato

Controllability in Temporal Conceptual Workflow Schemata

Workflow technology has emerged as one of the leading technologies in modelling, redesigning, and executing business processes. Currently available workflow management systems (WfMS ) and research prototypes offer a very limited support for the definition, detection, and management of temporal constraints over business processes. In this paper, we propose a new advanced workflow conceptual model for expressing time constraints in business processes and, in particular, we introduce and discuss the concept of controllability for workflow schemata and its evaluation at process design time. Controllability refers to the capability of executing a workflow for any possible duration of tasks. Since in several situations durations of tasks cannot be decided by WfMSs , even tough the minimum and the maximum durations for each task are known, checking controllability is stronger than verifying the consistency of the workflow temporal constraints.

Conceptual modeling of flexible temporal workflows

Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing business processes. The management of temporal aspects in the definition of a workflow process has been considered only recently in the literature. Currently available Workflow Management Systems (WfMS) and research prototypes offer a very limited support for the definition, detection, and management of temporal constraints over business processes. In this article, we propose a new advanced workflow conceptual model for expressing time constraints in business processes and we present a general technique to check different levels of temporal consistency for workflow schemata at process design time: since a time constraint can be satisfied in different ways, we propose a classification of temporal workflows according to the way time constraints are satisfied. Such classification can be used to successfully manage flexible workflows at runtime.

Representing Business Processes Through a Temporal Data-Centric Workflow Modeling Language: An Application to the Management of Clinical Pathways

Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing business processes in several different application domains. Among them, the representation and management of health and clinical processes have been attracting a growing interest. Such processes are in general related to the way each health organization provides the required healthcare services. Health and clinical processes underlie the specification and application of clinical protocols, clinical guidelines, clinical pathways, and the most common clinical/administrative procedures. Current workflow systems are lacking in effective management of three general key aspects that are common (not only) in the clinical/health context: data dependencies, exception handling, and temporal constraints. For example, a laparoscopic intervention may need the results of the concurrent bioptic analysis to be properly concluded while exceptional recovery activities have to be performed in case of emergency evidence during standard treatment; however, the successful application of a fibrinolytic therapy requires a maximum delay of 30 min after the admission into the emergency department. In this paper, we propose TNest, a new advanced, structured, and highly modular workflow modeling language that allows one to easily express data dependencies and time constraints during process design, in addition to exception handling and compensation activities. As for temporal constraints, we focus here on temporal controllability which is the capability of executing a workflow for all possible durations of all tasks satisfying all temporal constraints. Moreover, we analyze the computational complexity of the temporal controllability problem in TNest, and we propose a general algorithm to check the controllability. All the features of TNest that have been considered to model clinical pathways from classical clinical guidelines, i.e., those features for the management of STEMI patients, published by the American College of Cardiology/American Heart Association, will be used throughout the paper as a motivating scenario.

Sound and Complete Algorithms for Checking the Dynamic Controllability of Temporal Networks with Uncertainty, Disjunction and Observation

Temporal networks are data structures for representing and reasoning about temporal constraints on activities. Many kinds of temporal networks have been defined in the literature, differing in their expressiveness. The simplest kinds of networks have polynomial algorithms for determining their consistency or controllability, but corresponding algorithms for more expressive networks (e.g., Those that include observation nodes or disjunctive constraints) have so far been unavailable. However, recent work has introduced a new approach to such algorithms based on translating temporal networks into Timed Game Automata (TGAs) and then using off-the-shelf software to synthesize execution strategies -- or determine that none exist. So far, that approach has only been used on Simple Temporal Networks with Uncertainty, for which polynomial algorithms already exist. This paper extends the temporal-network-to-TGA approach to accommodate observation nodes and disjunctive constraints. Insodoing the paper presents, for the first time, sound and complete algorithms for checking the dynamic controllability of these more expressive networks. The translations also highlight the theoretical relationships between various kinds of temporal networks and the TGA model. The new algorithms have immediate applications in the workflow models being developed to automate business processes, including in the health-care domain.

Controllability of Time-Aware Processes at Run Time

Companies increasingly adopt process-aware information systems (PAISs) to analyze, coordinate, and monitor their business processes. Although the proper handling of temporal constraints (e.g., deadlines, minimum time lags between activities) is crucial for many applications, contemporary PAISs lack a sophisticated support of the temporal perspective of business processes. In previous work, we introduced Conditional Simple Temporal Networks with Uncertainty (CSTNU) for checking controllability of time constraint networks with decision points. In particular, controllability refers to the ability of executing a time constraint network independent of the actual duration of its activities, while satisfying all temporal constraints. In this paper, we demonstrate how CSTNUs can be applied to time-aware business processes in order verify their controllability at design as well as at run time. In particular, we present an algorithm for ensuring the controllability of time-aware process instances during run time. Overall, proper run-time support of time-aware business processes will broaden the use of PAIS significantly.

An Algorithm for Checking the Dynamic Controllability of a Conditional Simple Temporal Network with Uncertainty - Revisited

A Simple Temporal Network with Uncertainty (STNU) is a framework for representing and reasoning about temporal problems involving actions whose durations are bounded but uncontrollable. A dynamically controllable STNU is one for which there exists a strategy for executing its time-points that guarantees that all of the temporal constraints in the network will be satisfied no matter how the uncontrollable durations turn out. A Conditional Simple Temporal Network with Uncertainty (CSTNU) augments an STNU to include observation nodes, the execution of which incrementally and dynamically determines the set of constraints that must be satisfied. Previously, we generalized the notion of dynamic controllability to cover CSTNUs and presented a sound algorithm for determining whether arbitrary CSTNUs are dynamically controllable. That algorithm extends edge-generation/constraint-propagation rules from an existing DC-checking algorithm for STNUs with new rules required to deal with the observation nodes. This paper revisits that algorithm, modifying some of its rules to cover more cases, while preserving the soundness of the algorithm.

A Sound-and-Complete Propagation-Based Algorithm for Checking the Dynamic Consistency of Conditional Simple Temporal Networks

A Conditional Simple Temporal Network (CSTN) is a data structure for representing and reasoning about time-points and temporal constraints, some of which may apply only in certain scenarios. The scenarios in a CSTN are represented by conjunctions of propositional literals whose truth values are not known in advance, but instead are observed in real time, during execution. The most important property of a CSTN is whether it is dynamically consistent (DC), that is, whether there exists a strategy for executing its time-points such that all relevant constraints are guaranteed to be satisfied no matter which scenario is incrementally revealed during execution. Prior approaches to determining the dynamic consistency of CSTNs (a.k.a., solving the Conditional Simple Temporal Problem) are primarily of theoretical interest, they have not been realized in practical algorithms. This paper presents a sound-and-complete DC-checking algorithm for CSTNs that is based on the propagation of constraints labeled by propositions. The paper also presents an empirical evaluation of the new algorithm that demonstrates that it may be practical for a variety of applications. This is the first empirical evaluation of any DC-checking algorithm for CSTNs ever reported in the literature.

Modelling temporal, data-centric medical processes

Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing medical processes and some interesting workflow management systems are available. Nevertheless, such systems are lacking in an effective management of two key aspects: data dependencies and temporal constraints. In clinical/health context these two aspects are of paramount importance. For example, a surgery intervention could need the results of the concurrent bioptic analysis to be properly concluded; on the other hand, to successfully apply a fibrinolytic therapy to patients with ST-segment Elevation Myocardial Infarction (STEMI) a maximum delay of 30 minutes must be considered w.r.t. the emergency department admission. In this paper, we propose TNest, a new advanced, structured and highly modular workflow modeling language that allows one to easily express data dependencies and time constraints during process design. All the features of TNest have been considered to model the process related to classical clinical guidelines, i.e. those for the management of STEMI patients, published by the American College of Cardiology/American Heart Association.

Simple Temporal Networks with Partially Shrinkable Uncertainty

The Simple Temporal Network with Uncertainty (STNU) model focuses on the representation and evaluation of temporal constraints on time-point variables (timepoints), of which some (i.e., contingent timepoints) cannot be assigned (i.e., executed by the system), but only be observed. Moreover, a temporal constraint is expressed as an admissible range of delays between two timepoints. Regarding the STNU model, it is interesting to determine whether it is possible to execute all the timepoints under the control of the system, while still satisfying all given constraints, no matter when the contingent timepoints happen within the given time ranges (controllability check). Existing approaches assume that the original contingent time range cannot be modified during execution. In real world, however, the allowed time range may change within certain boundaries, but cannot be completely shrunk. To represent such possibility more properly, we propose Simple Temporal Network with Partially Shrinkable Uncertainty (STNPSU) as an extension of STNU. In particular, STNPSUs allow representing a contingent range in a way that can be shrunk during run time as long as shrinking does not go beyond a given threshold. We further show that STNPSUs allow representing STNUs as a special case, while maintaining the same efficiency for both controllability checks and execution.

Hyper temporal networks

Simple Temporal Networks (STNs) provide a powerful and general tool for representing conjunctions of maximum delay constraints over ordered pairs of temporal variables. In this paper we introduce Hyper Temporal Networks (HyTNs), a strict generalization of STNs, to overcome the limitation of considering only conjunctions of constraints but maintaining a practical efficiency in the consistency check of the instances. In a Hyper Temporal Network a single temporal hyperarc constraint may be defined as a set of two or more maximum delay constraints which is satisfied when at least one of these delay constraints is satisfied. HyTNs are meant as a light generalization of STNs offering an interesting compromise. On one side, there exist practical pseudo-polynomial time algorithms for checking consistency and computing feasible schedules for HyTNs. On the other side, HyTNs offer a more powerful model accommodating natural constraints that cannot be expressed by STNs like “Trigger off exactlyδmin before (after) the occurrence of the first (last) event in a set.”, which are used to represent synchronization events in some process aware information systems/workflow models proposed in the literature.