6,127 research outputs found
Static Analysis of Deterministic Negotiations
Negotiation diagrams are a model of concurrent computation akin to workflow
Petri nets. Deterministic negotiation diagrams, equivalent to the much studied
and used free-choice workflow Petri nets, are surprisingly amenable to
verification. Soundness (a property close to deadlock-freedom) can be decided
in PTIME. Further, other fundamental questions like computing summaries or the
expected cost, can also be solved in PTIME for sound deterministic negotiation
diagrams, while they are PSPACE-complete in the general case.
In this paper we generalize and explain these results. We extend the
classical "meet-over-all-paths" (MOP) formulation of static analysis problems
to our concurrent setting, and introduce Mazurkiewicz-invariant analysis
problems, which encompass the questions above and new ones. We show that any
Mazurkiewicz-invariant analysis problem can be solved in PTIME for sound
deterministic negotiations whenever it is in PTIME for sequential
flow-graphs---even though the flow-graph of a deterministic negotiation diagram
can be exponentially larger than the diagram itself. This gives a common
explanation to the low-complexity of all the analysis questions studied so far.
Finally, we show that classical gen/kill analyses are also an instance of our
framework, and obtain a PTIME algorithm for detecting anti-patterns in
free-choice workflow Petri nets.
Our result is based on a novel decomposition theorem, of independent
interest, showing that sound deterministic negotiation diagrams can be
hierarchically decomposed into (possibly overlapping) smaller sound diagrams.Comment: To appear in the Proceedings of LICS 2017, IEEE Computer Societ
Discovering duplicate tasks in transition systems for the simplification of process models
This work presents a set of methods to improve the understandability of process models. Traditionally, simplification methods trade off quality metrics, such as fitness or precision. Conversely, the methods proposed in this paper produce simplified models while preserving or even increasing fidelity metrics. The first problem addressed in the
paper is the discovery of duplicate tasks. A new method is proposed that avoids overfitting by working on the transition system generated by the log. The method is able to discover duplicate tasks even in the presence of concurrency and choice. The second problem is the structural simplification of the model by identifying optional and repetitive tasks. The tasks are substituted by annotated events that allow the removal of silent tasks and reduce the complexity of the
model. An important feature of the methods proposed in this paper is that they are independent from the actual miner used for process discovery.Peer ReviewedPostprint (author's final draft
Adaptable processes
We propose the concept of adaptable processes as a way of overcoming the
limitations that process calculi have for describing patterns of dynamic
process evolution. Such patterns rely on direct ways of controlling the
behavior and location of running processes, and so they are at the heart of the
adaptation capabilities present in many modern concurrent systems. Adaptable
processes have a location and are sensible to actions of dynamic update at
runtime; this allows to express a wide range of evolvability patterns for
concurrent processes. We introduce a core calculus of adaptable processes and
propose two verification problems for them: bounded and eventual adaptation.
While the former ensures that the number of consecutive erroneous states that
can be traversed during a computation is bound by some given number k, the
latter ensures that if the system enters into a state with errors then a state
without errors will be eventually reached. We study the (un)decidability of
these two problems in several variants of the calculus, which result from
considering dynamic and static topologies of adaptable processes as well as
different evolvability patterns. Rather than a specification language, our
calculus intends to be a basis for investigating the fundamental properties of
evolvable processes and for developing richer languages with evolvability
capabilities
Learning About Meetings
Most people participate in meetings almost every day, multiple times a day.
The study of meetings is important, but also challenging, as it requires an
understanding of social signals and complex interpersonal dynamics. Our aim
this work is to use a data-driven approach to the science of meetings. We
provide tentative evidence that: i) it is possible to automatically detect when
during the meeting a key decision is taking place, from analyzing only the
local dialogue acts, ii) there are common patterns in the way social dialogue
acts are interspersed throughout a meeting, iii) at the time key decisions are
made, the amount of time left in the meeting can be predicted from the amount
of time that has passed, iv) it is often possible to predict whether a proposal
during a meeting will be accepted or rejected based entirely on the language
(the set of persuasive words) used by the speaker
Verifying Real-Time Systems using Explicit-time Description Methods
Timed model checking has been extensively researched in recent years. Many
new formalisms with time extensions and tools based on them have been
presented. On the other hand, Explicit-Time Description Methods aim to verify
real-time systems with general untimed model checkers. Lamport presented an
explicit-time description method using a clock-ticking process (Tick) to
simulate the passage of time together with a group of global variables for time
requirements. This paper proposes a new explicit-time description method with
no reliance on global variables. Instead, it uses rendezvous synchronization
steps between the Tick process and each system process to simulate time. This
new method achieves better modularity and facilitates usage of more complex
timing constraints. The two explicit-time description methods are implemented
in DIVINE, a well-known distributed-memory model checker. Preliminary
experiment results show that our new method, with better modularity, is
comparable to Lamport's method with respect to time and memory efficiency
Implementing a Decision-Aware System for Loan Contracting Decision Process
The paper introduces our work related to the design and implementation of a decision-aware system focused on the loan contracting decision process. A decision-aware system is a software that enables the user to make a decision in a simulated environment and logs all the actions of the decision maker while interacting with the software. By using a mining algorithm on the logs, it creates a model of the decision process and presents it to the user. The main design issue introduced in the paper is the possibility to log the mental actions of the user. The main implementation issues are: user activity logging programming and technologies used. The first section of the paper introduces the state-of-the-art research in process mining and the framework of our research; the second section argues the design of the system; the third section introduces the actual implementation and the fourth section shows a running example.Decision-Aware Systems, Decision Activity Logs, Decision Mining, Codeigniter, JSON
Automated repair of process models with non-local constraints using state-based region theory
State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.This work was partly supported by the Australian Research Council Discovery Project DP180102839.
This work was supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.Peer ReviewedPostprint (author's final draft
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