Analysis of Petri Nets and Transition Systems

This paper describes a stand-alone, no-frills tool supporting the analysis of (labelled) place/transition Petri nets and the synthesis of labelled transition systems into Petri nets. It is implemented as a collection of independent, dedicated algorithms which have been designed to operate modularly, portably, extensibly, and efficiently.Comment: In Proceedings ICE 2015, arXiv:1508.0459

From isomorphism to polymorphism: connecting interzeolite transformations to structural and graph similarity

Zeolites are nanoporous crystalline materials with abundant industrial applications. Despite sustained research, only 235 different zeolite frameworks have been realized out of millions of hypothetical ones predicted by computational enumeration. Structure-property relationships in zeolite synthesis are very complex and only marginally understood. Here, we apply structure and graph-based unsupervised machine learning to gain insight on zeolite frameworks and how they relate to experimentally observed polymorphism and phase transformations. We begin by describing zeolite structures using the Smooth Overlap of Atomic Positions method, which clusters crystals with similar cages and density in a way consistent with traditional hand-selected composite building units. To also account for topological differences, zeolite crystals are represented as multigraphs and compared by isomorphism tests. We find that fourteen different pairs and one trio of known frameworks are graph isomorphic. Based on experimental interzeolite conversions and occurrence of competing phases, we propose that the availability of kinetic-controlled transformations between metastable zeolite frameworks is related to their similarity in the graph space. When this description is applied to enumerated structures, over 3,400 hypothetical structures are found to be isomorphic to known frameworks, and thus might be realized from their experimental counterparts. Using a continuous similarity metric, the space of known zeolites shows additional overlaps with experimentally observed phase transformations. Hence, graph-based similarity approaches suggest a venue for realizing novel zeolites from existing ones by providing a relationship between pairwise structure similarity and experimental transformations.Comment: 11 pages, 6 figure

Membrane Systems and Petri Net Synthesis

Automated synthesis from behavioural specifications is an attractive and powerful way of constructing concurrent systems. Here we focus on the problem of synthesising a membrane system from a behavioural specification given in the form of a transition system which specifies the desired state space of the system to be constructed. We demonstrate how a Petri net solution to this problem, based on the notion of region of a transition system, yields a method of automated synthesis of membrane systems from state spaces.Comment: In Proceedings MeCBIC 2012, arXiv:1211.347

Mining structured Petri nets for the visualization of process behavior

Visualization is essential for understanding the models obtained by process mining. Clear and efficient visual representations make the embedded information more accessible and analyzable. This work presents a novel approach for generating process models with structural properties that induce visually friendly layouts. Rather than generating a single model that captures all behaviors, a set of Petri net models is delivered, each one covering a subset of traces of the log. The models are mined by extracting slices of labelled transition systems with specific properties from the complete state space produced by the process logs. In most cases, few Petri nets are sufficient to cover a significant part of the behavior produced by the log.Peer ReviewedPostprint (author's final draft

Superpositional Quantum Network Topologies

We introduce superposition-based quantum networks composed of (i) the classical perceptron model of multilayered, feedforward neural networks and (ii) the algebraic model of evolving reticular quantum structures as described in quantum gravity. The main feature of this model is moving from particular neural topologies to a quantum metastructure which embodies many differing topological patterns. Using quantum parallelism, training is possible on superpositions of different network topologies. As a result, not only classical transition functions, but also topology becomes a subject of training. The main feature of our model is that particular neural networks, with different topologies, are quantum states. We consider high-dimensional dissipative quantum structures as candidates for implementation of the model.Comment: 10 pages, LaTeX2

Higher-Order Process Modeling: Product-Lining, Variability Modeling and Beyond

We present a graphical and dynamic framework for binding and execution of business) process models. It is tailored to integrate 1) ad hoc processes modeled graphically, 2) third party services discovered in the (Inter)net, and 3) (dynamically) synthesized process chains that solve situation-specific tasks, with the synthesis taking place not only at design time, but also at runtime. Key to our approach is the introduction of type-safe stacked second-order execution contexts that allow for higher-order process modeling. Tamed by our underlying strict service-oriented notion of abstraction, this approach is tailored also to be used by application experts with little technical knowledge: users can select, modify, construct and then pass (component) processes during process execution as if they were data. We illustrate the impact and essence of our framework along a concrete, realistic (business) process modeling scenario: the development of Springer's browser-based Online Conference Service (OCS). The most advanced feature of our new framework allows one to combine online synthesis with the integration of the synthesized process into the running application. This ability leads to a particularly flexible way of implementing self-adaption, and to a particularly concise and powerful way of achieving variability not only at design time, but also at runtime.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455

A design representation model for high-level synthesis

Design tools share and exchange various types of information pertaining to the design. The identification of a uniform design representation to capture this information is essential for the development of a successful design environment. We have done an extensive study on the representation needs of existing database tools in the UCI CADLAB; examples of which are graph compilers for high-level hardware specifications, state schedulers, hardware allocators, and microarchitecture optimizers. The result of this study is the development of a design representation model that will serve as a common internal representation (DDM) for all system and behavioral synthesis tools. DDM thus builds the foundation for a CAD Framework in which design tools can communicate via operating on this common representation. The design information is composed of three separate graph models: the conceptual model, the behavioral model and the structural model. The conceptual model (represented by a Design Entity Graph) captures the overall organization of the design information, such as, versions and configurations. The behavioral model (represented by an Augmented Control/Data Flow Graph) describes the design behavior. The structural model (represented by an Annotated Component Graph) captures the hierarchical data path structure and its geometric information. In this paper, we define the last two graph models. They both capture the actual design data of the application domain. Since VHDL has gained increasing popularity as hardware description language for synthesis, we give numerous examples throughout this report that show how the proposed design representation model can be used to represent VHDL specifications

Process Mining of Programmable Logic Controllers: Input/Output Event Logs

This paper presents an approach to model an unknown Ladder Logic based Programmable Logic Controller (PLC) program consisting of Boolean logic and counters using Process Mining techniques. First, we tap the inputs and outputs of a PLC to create a data flow log. Second, we propose a method to translate the obtained data flow log to an event log suitable for Process Mining. In a third step, we propose a hybrid Petri net (PN) and neural network approach to approximate the logic of the actual underlying PLC program. We demonstrate the applicability of our proposed approach on a case study with three simulated scenarios