Process mining techniques rely on event logs: the extraction of a process model (discovery) takes an event log as the input, the adequacy of a process model (conformance) is checked against an event log, and the enhancement of a process model is performed by using available data in the log. Several notations and formalisms for event log representation have been proposed in the recent years to enable efficient algorithms for the aforementioned process mining problems. In this paper we show how Conditional Partial Order Graphs (CPOGs), a recently introduced formalism for compact representation of families of partial orders, can be used in the process mining field, in particular for addressing the problem of compact and easy-to-comprehend representation of event logs with data. We present algorithms for extracting both the control flow as well as the relevant data parameters from a given event log and show how CPOGs can be used for efficient and effective visualisation of the obtained results. We demonstrate that the resulting representation can be used to reveal the hidden interplay between the control and data flows of a process, thereby opening way for new process mining techniques capable of exploiting this interplay. Finally, we present open-source software support and discuss current limitations of the proposed approach.

/pdf/mining-conditional-partial-order-graphs-from-event-logs-48xykht43x.pdf

Mining Conditional Partial Order Graphs from Event Logs

The process of developing a system for an Unnamed Aerial Vehicle (UAV) control is a complex task that we should be able to implement a set of elaborated algorithms with specific features as fast response for non-linear inputs, fault tolerance and easy maneuvering procedures. These features define the reachability and quality of the UAV overall control system. In this position paper, we propose a solution for the UAV control process using a Parallel Hierarchical Finite State Machine (PHFSM) that results in a high level system including all features required by an complex UAV control to search and rescue tasks. Following in this approach we can achieve many improvements than other usual implementations. Initials simulation experiments, using data from a simple UAV model, indicate in direction that many issues can be solved by this approach.

http://ieeexplore.ieee.org/iel7/7041512/7049711/07049801.pdf

A parallel hierarchical finite state machine approach to UAV control for search and rescue tasks

Despite significant advantages, wider usage of field-programmable gate arrays (FPGAs) has been limited by lengthy compilation and a lack of portability. Virtual-architecture overlays have partially addressed these problems, but previous work focuses mainly on heavily pipelined applications with minimal control requirements. We expand previous work by enabling more flexible control via overlay architectures for finite-state machines. Although not appropriate for control-intensive circuits, the presented architectures reduced compilation times of control changes in a convolution case study from 7 hours to less than 1 second, with no performance overhead and an area overhead of 0.2p.

Finite-State-Machine Overlay Architectures for Fast FPGA Compilation and Application Portability

In this paper, a novel intelligent modeling framework is introduced for modeling hybrid dynamical systems, which may contain both continuous-valued and discrete-valued dynamics and/or signals The proposed modeling framework consists of a fuzzy relational hybrid model (FRHM) structure and an online identification algorithm The FRHM structure has some useful properties, eg, the ability of modeling both hard and soft switchings (even simultaneously), model transparency (in view of knowledge extraction and knowledge embedment), and possibility of connecting disjoint models through mathematical operations (to construct larger model structures) In addition, the proposed FRHM structure can be used to model a wide variety of hybrid dynamical systems, including nonlinear dynamical systems, dynamical systems with switchings or logical functions, dynamical systems with discrete-valued or qualitative inputs and/or outputs, etc The fuzzy relational hybrid dynamical system is an important product of the proposed modeling structure, which, in this paper, has applied to modeling an asynchronous dc–dc buck converter as the case study

Introducing the Fuzzy Relational Hybrid Model as a Building Block for Intelligent Modeling of Hybrid Dynamical Systems

Process mining techniques rely on event logs: the extraction of a process model (discovery) takes an event log as the input, the adequacy of a process model (conformance) is checked against an event log, and the enhancement of a process model is performed by using available data in the log. Several notations and formalisms for event log representation have been proposed in the recent years to enable efficient algorithms for the aforementioned process mining problems. In this paper we show how Conditional Partial Order Graphs (CPOGs), a recently introduced formalism for compact representation of families of partial orders, can be used in the process mining field, in particular for addressing the problem of compact and
easy-to-comprehend visualisation of event logs with data. We present algorithms for extracting both the control flow as well as the relevant data parameters from a given event log and show how CPOGs can be used for efficient and effective visualisation of the obtained results. We
demonstrate that the resulting representation can be used to reveal the hidden interplay between the control and data flows of a process, thereby opening way for new process mining techniques capable of exploiting this interplay.

/pdf/event-log-visualisation-with-conditional-partial-order-3jpf9igp9m.pdf

Event log visualisation with conditional partial order graphs: from control flow to data

synthesis of finite state machines logic optimization is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the synthesis of finite state machines logic optimization is universally compatible with any devices to read.

Synthesis Of Finite State Machines Logic Optimization

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Synthesis Of Finite State Machines Logic Optimization