Model Creation Process Research Articles

Modeling of computer network with fault-tolerance gateway in OMNeT++

Subject of Research. The paper considers the issues of providing fault-tolerance gateways in computer networks with the use of First Hop Redundancy Protocols (FHRP). The description of FHRP family protocols and simulator OMNeT++ is given. The process of simulation model creation in OMNeT++ using ANSA-INET library is described. Detailed description of HSRP algorithm is presented and researching opportunities for simulation study by OMNeT++ simulator are considered. Work Objective. The aim of the work is the study of computer network models creation with fault-tolerance gateway in OMNeT++ simulator on the HSRP protocol example. Method. The proposed approach to the study of FHRP protocols in computer networks is based on carrying out simulation experiments in simulation environment. We show development potential for computer networks based on FHRP protocols in OMNeT++ using ANSA-INET framework which contains a large library of completed network models, components and protocol implementations. The paper describes in brief the process of computer network model development and ANSA-INET framework main abilities concerning the modeling of fault-tolerance gateways. Main Results. Computer network model with routers cluster is developed. Every router works with HSRP protocol. It gives the possibility to perform a router redundancy, that is a gateway in this computer network. Simulation model of computer network with fault-tolerance gateway is developed. We present the method of developing computer network models using FHRP protocols for fault-tolerance gateway implementation. The main stages of model development and completion are described. Practical Relevance. The presented results can be used in the design of high-reliable and fault-tolerance computer networks providing continuity of service at network gateway failure. The described tools and algorithm for development of computer network models give the possibility to carry out research on gateway fault-tolerance in computer networks and create computer-aided design systems for reliable computer networks.

Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

The process industries rely on various software systems and use a wide range of technologies. Predictive modeling techniques are often applied to data obtained from these systems to build the predictive functions used to optimize the production processes. Therefore, there is a need to provide a proper representation of knowledge and data and to improve the communication between the data scientists who develop the predictive functions and domain experts who possess the expert knowledge of the domain. This can be achieved by developing a semantic model that focuses on cross-sectorial aspects rather than concepts for specific industries, and that specifies the meta-classes for the formal description of these specific concepts. This model should cover the most important areas including modeling the production processes, data analysis methods, and evaluation using the performance indicators. In this paper, our primary objective was to introduce the specifications of the Cross-sectorial domain model and to present a set of tools that support data analysts and domain experts in the creation of process models and predictive functions. The model and the tools were used to design a knowledge base that could support the development of predictive functions in the green anode production in the aluminum production domain.

Learning process modeling phases from modeling interactions and eye tracking data

The creation of a process model is a process consisting of five distinct phases, i.e., problem understanding, method finding, modeling, reconciliation, and validation. To enable a fine-grained analysis of process model creation based on phases or the development of phase-specific modeling support, an automatic approach to detect phases is needed. While approaches exist to automatically detect modeling and reconciliation phases based on user interactions, the detection of phases without user interactions (i.e., problem understanding, method finding, and validation) is still a problem. Exploiting a combination of user interactions and eye tracking data, this paper presents a two-step approach that is able to automatically detect the sequence of phases a modeler is engaged in during model creation. The evaluation of our approach shows promising results both in terms of quality as well as computation time demonstrating its feasibility.

SGDML: Constructing accurate and data efficient molecular force fields using machine learning

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations.

Dunuen: A User-Friendly Formal Verification Tool

Formal verification allows checking the design and the behaviour of a system. One of the main limitations to the adoption of formal verification techniques is the process of model creation using specification languages. For this reason a tool supporting this activity is necessary. Actually, there are several tools allowing analysts to verify models expressed into specification languages. These tools provide support for automatically checking whether a system satisfies a property. However, to use such tools it is important to deeply know a precise notation for defining a system, i.e., the Calculus of Communicating Systems. Since systems are often expressed as time-series, to overcome this problem, we provide an user-friendly tool able to automatically generate a system model starting from the CSV - Comma-Separated Values format (the most widespread format considered to release dataset). In this way we hide the details about the model construction form the analyst, which can only focus immediately on the properties to verify.We introduce Dunuen, a tool allowing the user to firstly perform a kind of pre-processing operation starting from a CSV file, as discretization or removing attributes; subsequently it automatically creates a formal model from the pre-processed CSV file and, by invoking the model checker we embedded in Dunuen, it finally verifies whether the generated model satisfies a property expressed in temporal logic through a graphic interface, proposing formal methods as an alternative to machine learning for classification tasks.

Methodology proposal of the creation of competency models and competency model for the position of a sales manager in an industrial organisation using the AHP method and Saaty's method of determining weights

The aim of this paper is to create a competency model for the position of a sales manager in a manufacturing industry organisation according to the proposed methodology of the competency models creation. The competency model will be created using the AHP method and Saaty's method of determining weights. There is briefly explained the issue of competencies and competency models in the introductory part of the paper and then the used methods are clarified. The application part of the paper describes the methodology of the competency model creation and the process of the competency model creation for the position of a sales manager. The proposed methodology for the creation of competency models can be used in organisations of various specialisation and for any job position. Created competency models can be mainly used for employee selection, training and development, employee evaluation and remuneration.

Process Mining and Conformance Checking of Long Running Processes in the Context of Melanoma Surveillance.

Background: Process mining is a relatively new discipline that helps to discover and analyze actual process executions based on log data. In this paper we apply conformance checking techniques to the process of surveillance of melanoma patients. This process consists of recurring events with time constraints between the events. Objectives: The goal of this work is to show how existing clinical data collected during melanoma surveillance can be prepared and pre-processed to be reused for process mining. Methods: We describe an approach based on time boxing to create process models from medical guidelines and the corresponding event logs from clinical data of patient visits. Results: Event logs were extracted for 1023 patients starting melanoma surveillance at the Department of Dermatology at the Medical University of Vienna between January 2010 and June 2017. Conformance checking techniques available in the ProM framework and explorative applied process mining techniques were applied. Conclusions: The presented time boxing enables the direct use of existing process mining frameworks like ProM to perform process-oriented analysis also with respect to time constraints between events.

Process Modelling Based on Event Logs

Abstract Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, EPC). Process mining enables also conformance checking and enhancement analysis of the processes. In the paper issues related to process modelling and process mining are briefly discussed. A case study, an example of delivery process modelling with process mining technique is presented.

High-fidelity finite element models of composite wind turbine blades with shell and solid elements

A novel approach for creating highly detailed finite element models of wind turbine blades is presented. The approach is implemented as a software tool which handles all the different steps of the model creation process. The novel approach considers the blade to consist of a collection of parametric pre-defined blocks. This allows wind turbine blade models consisting of shell elements, solid elements or combinations to be created. By including the tools to accurately partition the outer mold layer, create the required offset surfaces and calculate accurate element-wise material orientations, a high level of detail and fidelity can be achieved.

Identifying the Absence of Effective Internal Controls: An Alternative Approach for Internal Control Audits

ABSTRACTAuditors face new challenges when auditing internal controls due to the increasing integration of information systems for transaction processing and the growing amount of data. Traditional manual control testing procedures become inefficient or require highly specialized and scarce technical knowledge. This study presents audit procedures that follow a new approach. Instead of manually testing internal controls, automated procedures search for the absence of those controls. Process mining techniques are combined with advanced statistical analysis where process mining serves as a data analysis technique to create process models from the recorded transaction data. These are searched for critical data constellations in combination with an exploratory factor analysis to identify systematic deficiencies in the internal control system. The manual and time-intensive inspection of individual controls is replaced by automated audit procedures that cover the totality of recorded transactions. The study follows a design science approach and uses case study data for illustration.

Exploring the Relationship between Business Processes and Contextual Information in Manufacturing and Logistics Based on Event Logs

Business processes in manufacturing and logistics are becoming more complex and change more frequently at the same time. These changes are driven by process evolution and unexpected changes. Therefore, business process documentations need continual updates. Recent works in process mining highlight the relevance of context information during automated process model creation. Our objective is to discover situational context information that affects the execution of a business process by determining the process lead time. Nave Bayesian is used for context reasoning to uncover and analyze the relationship between a particular process and its context information. We illustrate our approach with real-world event logs from manufacturing companies

NmsBuilder: Freeware to create subject-specific musculoskeletal models for OpenSim

Background and objectiveMusculoskeletal modeling and simulations of movement have been increasingly used in orthopedic and neurological scenarios, with increased attention to subject-specific applications. In general, musculoskeletal modeling applications have been facilitated by the development of dedicated software tools; however, subject-specific studies have been limited also by time-consuming modeling workflows and high skilled expertise required. In addition, no reference tools exist to standardize the process of musculoskeletal model creation and make it more efficient. Here we present a freely available software application, nmsBuilder 2.0, to create musculoskeletal models in the file format of OpenSim, a widely-used open-source platform for musculoskeletal modeling and simulation. nmsBuilder 2.0 is the result of a major refactoring of a previous implementation that moved a first step toward an efficient workflow for subject-specific model creation. MethodsnmsBuilder includes a graphical user interface that provides access to all functionalities, based on a framework for computer-aided medicine written in C++. The operations implemented can be used in a workflow to create OpenSim musculoskeletal models from 3D surfaces. A first step includes data processing to create supporting objects necessary to create models, e.g. surfaces, anatomical landmarks, reference systems; and a second step includes the creation of OpenSim objects, e.g. bodies, joints, muscles, and the corresponding model. ResultsWe present a case study using nmsBuilder 2.0: the creation of an MRI-based musculoskeletal model of the lower limb. The model included four rigid bodies, five degrees of freedom and 43 musculotendon actuators, and was created from 3D surfaces of the segmented images of a healthy subject through the modeling workflow implemented in the software application. ConclusionsWe have presented nmsBuilder 2.0 for the creation of musculoskeletal OpenSim models from image-based data, and made it freely available via nmsbuilder.org. This application provides an efficient workflow for model creation and helps standardize the process. We hope this would help promote personalized applications in musculoskeletal biomechanics, including larger sample size studies, and might also represent a basis for future developments for specific applications.

Demonstration of reduced-order urban scale building energy models

The aim of this study is to demonstrate a developed framework to rapidly create urban scale reduced-order building energy models using a systematic summary of the simplifications required for the representation of building exterior and thermal zones. These urban scale reduced-order models rely on the contribution of influential variables to the internal, external, and system thermal loads. OpenStudio Application Programming Interface (API) serves as a tool to automate the process of model creation and demonstrate the developed framework. The results of this study show that the accuracy of the developed reduced-order building energy models varies only up to 10% with the selection of different thermal zones. In addition, to assess complexity of the developed reduced-order building energy models, this study develops a novel framework to quantify complexity of the building energy models. Consequently, this study empowers the building energy modelers to quantify their building energy model systematically in order to report the model complexity alongside the building energy model accuracy. An exhaustive analysis on four university campuses suggests that the urban neighborhood buildings lend themselves to simplified typical shapes. Specifically, building energy modelers can utilize the developed typical shapes to represent more than 80% of the U.S. buildings documented in the CBECS database. One main benefits of this developed framework is the opportunity for different models including airflow and solar radiation models to share the same exterior representation, allowing a unifying exchange data. Overall, the results of this study have implications for a large-scale modeling of buildings in support of urban energy consumption analyses or assessment of a large number of alternative solutions in support of retrofit decision-making in the building industry.

Toward a reliable anomaly-based intrusion detection in real-world environments

A popular approach for detecting network intrusion attempts is to monitor the network traffic for anomalies. Extensive research effort has been invested in anomaly-based network intrusion detection using machine learning techniques; however, in general these techniques remain a research topic, rarely being used in real-world environments. In general, the approaches proposed in the literature lack representative datasets and reliable evaluation methods that consider real-world network properties during the system evaluation. In general, the approaches adopt a set of assumptions about the training data, as well as about the validation methods, rendering the created system unreliable for open-world usage. This paper presents a new method for creating intrusion databases. The objective is that the databases should be easy to update and reproduce with real and valid traffic, representative, and publicly available. Using our proposed method, we propose a new evaluation scheme specific to the machine learning intrusion detection field. Sixteen intrusion databases were created, and each of the assumptions frequently adopted in studies in the intrusion detection literature regarding network traffic behavior was validated. To make machine learning detection schemes feasible, we propose a new multi-objective feature selection method that considers real-world network properties. The results show that most of the assumptions frequently applied in studies in the literature do not hold when using a machine learning detection scheme for network-based intrusion detection. However, the proposed multi-objective feature selection method allows the system accuracy to be improved by considering real-world network properties during the model creation process.

The Methods of Knowledge Acquisition in the Product Lifecycle for a Generative Model's Creation Process

The process of automation becomes popular at different stages of the Product Lifecycle. By applying advanced CAx systems and advanced modelling techniques such as a Generative Modelling, it is possible to design, manufacture and release products onto the market in an easy and fast way. During the Product Lifecycle, a lot of knowledge about the product is generated, however, in the standard process, this knowledge is lost. By using the Knowledge-Based Engineering technique, it is possible to capture, formalise and reuse the knowledge about projects in the future. This approach has a lot of advantages, which are described in this paper.

A review of biodiesel production from microalgae

As the search for alternatives to fossil fuels continues, microalgae have emerged as a promising renewable feedstock for biodiesel. Many species contain high lipid concentrations and require simple cultivation—including reduced freshwater and land area needs—compared to traditional crops used for biofuels. Recently, technological advancements have brought microalgae biodiesel closer to becoming economically feasible through increased efficiency of the cultivation, harvesting, pretreatment, lipid extraction, and transesterification subsystems. The metabolism of microalgae can be favorably manipulated to increase lipid productivity through environmental stressors, and “green” techniques such as using flue gas as a carbon source and wastewater as a media replacement can lower the environmental impact of biodiesel production. Through life cycle assessment and the creation of process models, valuable insights have been made into the energy and material sinks of the manufacturing process, helping to identify methods to successfully scale up microalgae biodiesel production. Several companies are already exploring the microalgae industry, offsetting operating costs through isolation of co-products and careful unit operation selection. With numerous examples drawn from industry and the literature, this review provides a practical approach for creating a microalgae biodiesel facility.

Integration of scheduling and discrete event simulation systems to improve production flow planning

The increased availability of data and computer-aided technologies such as MRPI/II, ERP and MES system, allowing producers to be more adaptive to market dynamics and to improve production scheduling. Integration of production scheduling and computer modelling, simulation and visualization systems can be useful in the analysis of production system constraints related to the efficiency of manufacturing systems. A integration methodology based on semi-automatic model generation method for eliminating problems associated with complexity of the model and labour-intensive and time-consuming process of simulation model creation is proposed. Data mapping and data transformation techniques for the proposed method have been applied. This approach has been illustrated through examples of practical implementation of the proposed method using KbRS scheduling system and Enterprise Dynamics simulation system.

From phenotype to genotype in complex brain networks.

Generative models are a popular instrument for illuminating the relationships between the hidden variables driving the growth of a complex network and its final topological characteristics, a process known as the “genotype to phenotype problem”. However, the definition of a complete methodology encompassing all stages of the analysis, and in particular the validation of the final model, is still an open problem. We here discuss a framework that allows to quantitatively optimise and validate each step of the model creation process. It is based on the execution of a classification task, and on estimating the additional precision provided by the modelled genotype. This encompasses the three main steps of the model creation, namely the selection of topological features, the optimisation of the parameters of the generative model, and the validation of the obtained results. We provide a minimum requirement for a generative model to be useful, prescribing the function mapping genotype to phenotype to be non-monotonic; and we further show how a previously published model does not fulfil such condition, casting doubts on its fitness for the study of neurological disorders. The generality of such framework guarantees its applicability beyond neuroscience, like the emergence of social or technological networks.

Response: "Commentary: Utility-free heuristic models of two-option choice can mimic predictions of utility-stage models under many conditions".

In his accompanying commentary on our recent paper, Dr. Padoa-Schioppa identifies two putative errors in our manuscript (Piantadosi and Hayden, 2015). Both reflect basic misunderstandings of our arguments, as well as those of Tversky (1969), whose work ours is an extension of. First, he argues that some commodities are inherently incommensurate, such as different juice flavors (“there is no parametric dimension along which two flavors can be assigned a scalar value”). This argument seems appealing because it is difficult to think of a single algorithmic function that would describe a juice flavor as a single number. So in a colloquial sense, juices could indeed by called incommensurate. However, most models of choice assume, either tacitly or explicitly, that there is an intermediate stage during which each dimension is represented in a scalar manner and may there be deformed. A famous example is prospect theory (Kahneman and Tversky, 1979). In PT, gains are transformed using a convex utility curve and probability is deformed by a different curve; these transformed scalars are then combined into a single utility variable (Figure ​(Figure1A).1A). The same concept can be extended to thinking about very abstract goods like juice flavor, the funniness of a joke, or the intellectual appeal of a novel theory (Figure ​(Figure1B).1B). To do so, one may use a lookup table, as illustrated in our example. One example of a model that involves such a lookup table is Padoa-Schioppa and Assad (2006). In that paper, the authors propose that juices can be given a scalar value parameter, and that this parameter can be experimentally observed through preferences. Figure 1 Illustration of simplified process models for choice. (A) In Prospect Theory (Kahneman and Tversky, 1979), reward amount and probability are both transformed into a single scale, but the two scales are different dimensions. These are the combined into ... These idea of an intermediate stage is a critical part of several famous economic models (Bernoulli, 1738; Tversky, 1969; Kahneman and Tversky, 1979), including Padoa-Schioppa's own work, and has a direct neural correlate in the orbitofrontal cortex (Blanchard et al., 2015). A different possibility would be that it is impossible to construct an intermediate-stage model for a given choice process (Figure ​(Figure1C).1C). In such cases our ideas do not apply, nor would Tversky's. Some models that do not have an intermediate stage include decision by sampling, elimination by aspects, and query theory, as well as other sampling-based heuristic approaches (Tversky, 1972; Stewart et al., 2006; Johnson et al., 2007; Hayden and Platt, 2009). Second, Dr. Padoa-Schioppa is concerned that there are well-defined value comparisons to which our methods do not apply. We clearly acknowledged this point in our original manuscript. Our formalization provides a way to recognize utility functions it does apply to; this is a strength of our approach. However, while the example he gives was not discussed in our manuscript, it is straightforwardly covered using the approach we advocate. Indeed, the set of contexts to which our arguments apply is somewhat larger than we stated in the original article. Specifically, it applies to any context in which the choice can be modeled by a utility equation and then that equation can be rearranged to avoid the utility stage. Dr. Padoa-Schioppa discusses a choice between gambles with probability (P1) of reward (R1) and fixed costs (C1), where choice is determined by: P1·R1−C1>P2·R2−C2. (1) Following a method much like the one we presented (that is, first computing relative differences and then rearranging the terms), this choice is equivalent to: pP+rRcC>CP·R (2) where the uppercase letters are the average value in each dimension and the lowercase are half the difference. To translate into prose, the decision-maker does the essentially same thing as in the examples used in our paper: she computes a normalization term that depends on average values, and then computes the ratio of the dimension-free relative differences for gains, and asks whether that ratio is greater or less than the relative difference for costs. Thus, this example thus does not challenge our arguments, but instead enhances them. Moreover, it endorses our bigger (and ultimately very simple) point: given the power and flexibility of algebra, it is often possible to create process models that have no utility stage but make identical predictions to ones that do through a simple rearrangement of terms.

A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs

Process mining can be viewed as the missing link between model-based process analysis and data-oriented analysis techniques. Lion׳s share of process mining research has been focusing on process discovery (creating process models from raw data) and replay techniques to check conformance and analyze bottlenecks. These techniques have helped organizations to address compliance and performance problems. However, for a more refined analysis, it is essential to correlate different process characteristics. For example, do deviations from the normative process cause additional delays and costs? Are rejected cases handled differently in the initial phases of the process? What is the influence of a doctor׳s experience on treatment process? These and other questions may involve process characteristics related to different perspectives (control-flow, data-flow, time, organization, cost, compliance, etc.). Specific questions (e.g., predicting the remaining processing time) have been investigated before, but a generic approach was missing thus far. The proposed framework unifies a number of approaches for correlation analysis proposed in literature, proposing a general solution that can perform those analyses and many more. The approach has been implemented in ProM and combines process and data mining techniques. In this paper, we also demonstrate the applicability using a case study conducted with the UWV (Employee Insurance Agency), one of the largest “administrative factories” in The Netherlands.