Temporal and Resource Controllability of Workflows Under Uncertainty

Workflow technology has long been employed for the modeling, validation and execution of business processes. A workflow is a formal description of a business process in which single atomic work units (tasks), organized in a partial order, are assigned to processing entities (agents) in order to achieve some business goal(s). Workflows can also employ workflow paths (projections with respect to a total truth value assignment to the Boolean variables associated to the conditional split connectors) in order (not) to execute a subset of tasks. A workflow management system coordinates the execution of tasks that are part of workflow instances such that all relevant constraints are eventually satisfied. Temporal workflows specify business processes subject to temporal constraints such as controllable or uncontrollable durations, delays and deadlines. The choice of a workflow path may be controllable or not, considered either in isolation or in combination with uncontrollable durations. Access controlled workflows specify workflows in which users are authorized for task executions and authorization constraints say which users remain authorized to execute which tasks depending on who did what. Access controlled workflows may consider workflow paths too other than the uncertain availability of resources (users, throughout this thesis). When either a task duration or the choice of the workflow path to take or the availability of a user is out of control, we need to verify that the workflow can be executed by verifying all constraints for any possible combination of behaviors arising from the uncontrollable parts. Indeed, users might be absent before starting the execution (static resiliency), they can also become so during execution (decremental resiliency) or they can come and go throughout the execution (dynamic resiliency). Temporal access controlled workflows merge the two previous formalisms by considering several kinds of uncontrollable parts simultaneously. Authorization constraints may be extended to support conditional and temporal features. A few years ago some proposals addressed the temporal controllability of workflows by encoding them into temporal networks to exploit "off-the-shelf" controllability checking algorithms available for them. However, those proposals fail to address temporal controllability where the controllable and uncontrollable choices of workflow paths may mutually influence one another. Furthermore, to the best of my knowledge, controllability of access controlled workflows subject to uncontrollable workflow paths and algorithms to validate and execute dynamically resilient workflows remain unexplored. To overcome these limitations, this thesis goes for exact algorithms by addressing temporal and resource controllability of workflows under uncertainty. I provide several new classes of (temporal) constraint networks and corresponding algorithms to check their controllability. After that, I encode workflows into these new formalisms. I also provide an encoding into instantaneous timed games to model static, decremental and dynamic resiliency and synthesize memoryless execution strategies. I developed a few tools with which I carried out some initial experimental evaluations

Conditional Simple Temporal Networks with Uncertainty and Decisions

A Conditional Simple Temporal Network with Uncertainty (CSTNU) is a formalism able to model temporal plans subject to both conditional constraints and uncertain durations. The combination of these two characteristics represents the uncontrollable part of the network. That is, before the network starts executing, we do not know completely which time points and constraints will be taken into consideration nor how long the uncertain durations will last. Dynamic Controllability (DC) implies the existence of a strategy scheduling the time points of the network in real time depending on how the uncontrollable part behaves. Despite all this, CSTNUs fail to model temporal plans in which a few conditional constraints are under control and may therefore influence (or be influenced by) the uncontrollable part. To bridge this gap, this paper proposes Conditional Simple Temporal Networks with Uncertainty and Decisions (CSTNUDs) which introduce decision time points into the specification in order to operate on this conditional part under control. We model the dynamic controllability checking (DC-checking) of a CSTNUD as a two-player game in which each player makes his moves in his turn at a specific time instant. We give an encoding into timed game automata for a sound and complete DC-checking. We also synthesize memoryless execution strategies for CSTNUDs proved to be DC and carry out an experimental evaluation with Esse, a tool that we have designed for CSTNUDs to make the approach fully automated

Consistency checking of STNs with decisions: Managing temporal and access-control constraints in a seamless way

A Simple Temporal Network (STN) consists of time points modeling temporal events and constraints modeling the minimal and maximal temporal distance between them. A Simple Temporal Network with Decisions (STND) extends an STN to model temporal plans with decisions. STNDs label time points and constraints by conjunctions of literals saying for which scenarios (i.e., complete truth value assignments to the propositions) they are relevant. In this paper, we deal with the use of STNDs for modeling and synthesizing execution strategies. We propose an incremental hybrid SAT-based consistency checking algorithm for STNDs that is faster than the one previously proposed and allows for the synthesis of all consistent scenarios and related early execution schedules (offline temporal planning). We carry out an experimental evaluation with Kappa, a tool that we developed for STNDs. We also show that any STND can be easily translated into a disjunctive temporal network and vice versa

Complexity of weak, strong and dynamic controllability of CNCUs

A Constraint Network Under Conditional Uncertainty (CNCU) is a formalism able to model a constraint satisfaction problem (CSP) where variables and constraints are labeled by a conjunction of Boolean variables, or booleans, whose truth value assignments are out of control and only discovered upon the execution of their related observation points (special kind of variables). At the start of the execution of the CNCU (i.e., the online assignment of values to variables), we do not know yet which constraints and variables will be taken into consideration nor in which order. Weak controllability implies the existence of a strategy to execute a CNCU whenever the whole uncontrollable part is known before executing. Strong controllability is the opposite case and implies the existence of a strategy to execute a CNCU always the same way no matter how the uncontrollable part will behave. Dynamic controllability implies the existence of an adaptive strategy to execute the CNCU taking into account how the uncontrollable part is behaving. In this paper we classify the computational complexity of weak, strong and dynamic controllability of CNCUs. We prove that weak controllability is Πp2-complete, strong controllability is NP-complete and dynamic controllability is PSPACE-complete

Complexity of Weak, Strong and Dynamic Controllability of CNCUs

A Constraint Network Under Conditional Uncertainty (CNCU) is a formalism able to model a constraint satisfaction problem (CSP) where variables and constraints are labeled by a conjunction of Boolean variables, or booleans, whose truth value assignments are out of control and only discovered upon the execution of their related observation points (special kind of variables). Before the execution of the CNCU starts (i.e., the online assignment of values to variables), we do not know completely which constraints and variables will be taken into consideration nor in which order. Weak controllability implies the existence of a strategy to execute a CNCU whenever the whole uncontrollable part is known before executing. Strong controllability is the opposite case and implies the existence of a strategy to execute a CNCU always the same way no matter how the uncontrollable part will behave. Dynamic controllability implies the existence of a strategy to execute a CNCU possibly differently depending on how the uncontrollable part is behaving. We prove that weak controllability is $Pi_2^p$-complete, strong controllability is NP-complete and dynamic controllability is PSPACE-complete