POD: A tool for process discovery using partial orders and independence information

Current process discovery techniques focus on the derivation of a process model on the basis of the activity relations extracted from an event log. However, there are situations where more knowledge can be provided to the discovery algorithm, thus alleviating the discovery challenge. In particular, the (partial) characterization of the independence or concurrency between a pair of activities may be well-known. In this paper we present POD, a tool for discovery of Petri nets that can incorporate this type of additional information. We believe requiring independence/concurrency information is very natural in many scenarios, e.g., when there is some knowledge of the underlying process. We show with one example how this extra information can effectively deal with problems such as log incompleteness.Peer ReviewedPostprint (author's final draft

Unfolding-Based Process Discovery

This paper presents a novel technique for process discovery. In contrast to the current trend, which only considers an event log for discovering a process model, we assume two additional inputs: an independence relation on the set of logged activities, and a collection of negative traces. After deriving an intermediate net unfolding from them, we perform a controlled folding giving rise to a Petri net which contains both the input log and all independence-equivalent traces arising from it. Remarkably, the derived Petri net cannot execute any trace from the negative collection. The entire chain of transformations is fully automated. A tool has been developed and experimental results are provided that witness the significance of the contribution of this paper.Comment: This is the unabridged version of a paper with the same title appearead at the proceedings of ATVA 201

Quality and inspiration. A study of the diversification of rhetoric of quality in relation to different conceptual domains in Zen and the Art of Motorcycle Maintenance: An Inquiry into Values by Robert M. Pirsig

This article discusses the basic types of concepts of quality occurring in Robert M. Pirsig’s Zen and the Art of Motorcycle Maintenance: An Inquiry into Values. These terms refer to the different conceptual domains, creating diversi ed types of rhetoric. All kinds of rhetoric refer to the discovery and awakening of individuality. The quality of education at university or on a motorbike must extend to all possible levels of the Great Chain of Being, it can not only be addressed with abstractedness. Conceptual diversi cation means diversifying rhetoric and style, which possibly corresponds to different levels of quality for Pirsig. In this sense, his proposals of a metaphysics of quality is part of a current dispute about the crisis within the humanities and the need to give it meaning, practicality and socially responsible utility. A metaphysics of quality uses the rhetoric of conceptual schemata which include: road, inclusion and container.Artykuł omawia podstawowe typy pojęcia jakości występujące w powieści Roberta M. Pirsiga pt. Zen i sztuka obsługi motocykla. Rozprawa o wartościach. Te pojęcia odnoszą się do różnych domen konceptualnych, tworząc tym samym zdywersyfkowane typy retoryki. Wszystkie typy retoryki odnoszą się do odkrywania i przebudzenia jednostkowości. Jakość kształcenia na uniwersytecie czy na motocyklu musi sięgać do wszystkich możliwych poziomów Wielkiego Łańcucha Bytu, nie może adresować jedynie abstrakcji. Dywersyfkacja konceptualna oznacza zdywersyfkowanie retoryki i stylu, co może odpowiadać także różnym poziomom jakości u Pirsiga. W tym sensie jego propozycja metafzyki jakości wpisuje się w aktualne spory o kryzysie humanistyki i konieczności jej usensownienia, upraktycznienia i użycia społecznie odpowiedzialnego. Metafzyka jakości posługuje się retoryką schematów konceptualnych drogi, inkluzji i pojemnika

Generalized alignment-based trace clustering of process behavior

Process mining techniques use event logs containing real process executions in order to mine, align and extend process models. The partition of an event log into trace variants facilitates the understanding and analysis of traces, so it is a common pre-processing in process mining environments. Trace clustering automates this partition; traditionally it has been applied without taking into consideration the availability of a process model. In this paper we extend our previous work on process model based trace clustering, by allowing cluster centroids to have a complex structure, that can range from a partial order, down to a subnet of the initial process model. This way, the new clustering framework presented in this paper is able to cluster together traces that are distant only due to concurrency or loop constructs in process models. We show the complexity analysis of the different instantiations of the trace clustering framework, and have implemented it in a prototype tool that has been tested on different datasets.Peer ReviewedPostprint (author's final draft

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

Artificial in its own right

Artificial Cells, , Artificial Ecologies, Artificial Intelligence, Bio-Inspired Hardware Systems, Computational Autopoiesis, Computational Biology, Computational Embryology, Computational Evolution, Morphogenesis, Cyborgization, Digital Evolution, Evolvable Hardware, Cyborgs, Mathematical Biology, Nanotechnology, Posthuman, Transhuman

LiCROM: Linear-Subspace Continuous Reduced Order Modeling with Neural Fields

Linear reduced-order modeling (ROM) simplifies complex simulations by approximating the behavior of a system using a simplified kinematic representation. Typically, ROM is trained on input simulations created with a specific spatial discretization, and then serves to accelerate simulations with the same discretization. This discretization-dependence is restrictive. Becoming independent of a specific discretization would provide flexibility to mix and match mesh resolutions, connectivity, and type (tetrahedral, hexahedral) in training data; to accelerate simulations with novel discretizations unseen during training; and to accelerate adaptive simulations that temporally or parametrically change the discretization. We present a flexible, discretization-independent approach to reduced-order modeling. Like traditional ROM, we represent the configuration as a linear combination of displacement fields. Unlike traditional ROM, our displacement fields are continuous maps from every point on the reference domain to a corresponding displacement vector; these maps are represented as implicit neural fields. With linear continuous ROM (LiCROM), our training set can include multiple geometries undergoing multiple loading conditions, independent of their discretization. This opens the door to novel applications of reduced order modeling. We can now accelerate simulations that modify the geometry at runtime, for instance via cutting, hole punching, and even swapping the entire mesh. We can also accelerate simulations of geometries unseen during training. We demonstrate one-shot generalization, training on a single geometry and subsequently simulating various unseen geometries

Partial-order-based process mining: a survey and outlook

The field of process mining focuses on distilling knowledge of the (historical) execution of a process based on the operational event data generated and stored during its execution. Most existing process mining techniques assume that the event data describe activity executions as degenerate time intervals, i.e., intervals of the form [t, t], yielding a strict total order on the observed activity instances. However, for various practical use cases, e.g., the logging of activity executions with a nonzero duration and uncertainty on the correctness of the recorded timestamps of the activity executions, assuming a partial order on the observed activity instances is more appropriate. Using partial orders to represent process executions, i.e., based on recorded event data, allows for new classes of process mining algorithms, i.e., aware of parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies consider using intermediate data abstractions that explicitly assume a partial order over a collection of observed activity instances. Considering recent developments in process mining, e.g., the prevalence of high-quality event data and techniques for event data abstraction, the need for algorithms designed to handle partially ordered event data is expected to grow in the upcoming years. Therefore, this paper presents a survey of process mining techniques that explicitly use partial orders to represent recorded process behavior. We performed a keyword search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field. We observe a recent uptake in works covering partial-order-based process mining, e.g., due to the current trend of process mining based on uncertain event data. Furthermore, we outline promising novel research directions for the use of partial orders in the context of process mining algorithms