Process Instances Research Articles

Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures

Distributed stream processing engines are designed with a focus on scalability to process big data volumes in a continuous manner. We present the Theodolite method for benchmarking the scalability of distributed stream processing engines. Core of this method is the definition of use cases that microservices implementing stream processing have to fulfill. For each use case, our method identifies relevant workload dimensions that might affect the scalability of a use case. We propose to design one benchmark per use case and relevant workload dimension. We present a general benchmarking framework, which can be applied to execute the individual benchmarks for a given use case and workload dimension. Our framework executes an implementation of the use case's dataflow architecture for different workloads of the given dimension and various numbers of processing instances. This way, it identifies how resources demand evolves with increasing workloads. Within the scope of this paper, we present 4 identified use cases, derived from processing Industrial Internet of Things data, and 7 corresponding workload dimensions. We provide implementations of 4 benchmarks with Kafka Streams and Apache Flink as well as an implementation of our benchmarking framework to execute scalability benchmarks in cloud environments. We use both for evaluating the Theodolite method and for benchmarking Kafka Streams' and Flink's scalability for different deployment options.

Time-oriented interactive process miner: a new approach for time prediction

Everyday information systems collect a different kind of process instances of a business flow. As time goes on, the size of the collected data builds up speedily and constitutes a huge amount of data. It is a very challenging task to obtain valuable information and features of processes from such big data. Considering in advance, the trend and different features of the ongoing process are essential. Especially, time management is crucial in designing and conducting business processes. In this article, a novel process miner algorithm is proposed for time prediction, named time-oriented İnteractive process miner (T-IPM), which predicts the remaining and completion time of each process in a business workflow. The goal is to develop a process miner algorithm for time prediction that is able to work on a huge amount of event logs, and to incorporate the execution records of ongoing processes into the discovered process model instantly. In this study, three datasets from different domains that include event logs of repair, hospital, and traffic process flows were used to carry out the experimental studies in order to show the effectiveness of the proposed time prediction algorithm. The experimental studies show that the proposed algorithm is capable of performing process analysis on a huge amount of event logs with low memory consumption and high prediction accuracy.

Conceptualising transdisciplinary integration as a multidimensional interactive process

Integration is a key process in transdisciplinary research and knowledge co-production. Nonetheless, it is often used as a buzzword without specifying what exactly it means or what actually happens during integration. We propose conceptualizing integration as a multidimensional interactive process. We characterize it as an open-ended learning process without pre-determined outcomes. Integration designates relations established throughout a transdisciplinary research process between elements that were not previously related. Those elements are participants in the process and their thought-styles and thought-collectives and more specifically pieces of knowledge, ideas, or practices from different thought-collectives as well as views of individual researchers and practitioners. Integration can happen at manifold instances of a transdisciplinary research process. It can take place among two, several, or all participants and can be one-sided or mutual. It might include insights, practices, frameworks, or concepts shared by two, several, or all participants. Consensus is only one along with other ways of retaining plurality of thought-styles and seeing integration as a balance between them that remains subject to continuous revision. To analyse or achieve effective integration, further dimensions beyond the cognitive have to be taken into account including at least an emotional and a social-interactional dimension.

POP-ON: Prediction of Process Using One-Way Language Model Based on NLP Approach

In business process management, the monitoring service is an important element that can prevent various problems in advance from before they occur in companies and industries. Execution log is created in an information system that is aware of the enterprise process, which helps predict the process. The ultimate goal of the proposed method is to predict the process following the running process instance and predict events based on previously completed event log data. Companies can flexibly respond to unwanted deviations in their workflow. When solving the next event prediction problem, we use a fully attention-based transformer, which has performed well in recent natural language processing approaches. After recognizing the name attribute of the event in the natural language and predicting the next event, several necessary elements were applied. It is trained using the proposed deep learning model according to specific pre-processing steps. Experiments using various business process log datasets demonstrate the superior performance of the proposed method. The name of the process prediction model we propose is “POP-ON”.

Violante, Violente, Violenta: la novella I. 42 di Matteo Bandello e le sue riscritture tra Francia, Inghilterra e Spagna

ABSTRACT This article gives an instance of the translation process involving the Italian novella in Renaissance Europe. It focuses on a meaningful case study, comparing one of Matteo Bandello’s novellas (I. 42) with its French, English, and Spanish rewritings by Pierre Boaistuau, William Painter, and Vicente de Millis Godínez. It is argued that these three translators changed the Italian plot in order to make the story acceptable from a moralistic point of view, specifically by re-characterising the female protagonist. The article examines Painter’s rewriting in particular: it suggests that the Elizabethan writer drew directly on both the Italian text and its French translation, but chose to follow Boaistuau’s changes on specific literary and cultural grounds. Indeed, the French model showed Painter a way to endow his work with moral rectitude, while preserving the violent and tragic narrative events that would satisfy his readers’ tastes.

Coarsening in algebraic multigrid using Gaussian processes

Multigrid methods have proven to be an invaluable tool to efficiently solve large sparse linear systems arising in the discretization of Partial Differential Equations (PDEs). Algebraic multigrid methods and in particular adaptive algebraic multigrid approaches have shown that multigrid efficiency can be obtained without having to resort to properties of the PDE. Yet the required setup of these methods poses a not negligible overhead cost. Methods from machine learning have attracted attention to streamline processes based on statistical models being trained on the available data. Interpreting algebraically smooth error as an instance of a Gaussian process, we develop a new, data driven approach to construct adaptive algebraic multigrid methods. Based on Gaussian a priori distributions, kriging interpolation minimizes the mean squared error of the a posteriori distribution, given the data on the coarse grid. Going one step further, we exploit the quantification of uncertainty in the Gaussian process model in order to construct efficient variable splittings. Using a semivariogram fit of a suitable covariance model we demonstrate that our approach yields efficient methods using a single algebraically smooth vector.

Comparative analysis of clustering-based remaining-time predictive process monitoring approaches

Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). Various studies have been explored to develop models with higher predictive power. However, comparing the various studies is difficult as different datasets, parameters and evaluation measures have been used. This paper seeks to address this problem with a focus on studies that adopt a clustering-based approach to predict the remaining time to the end of the process instance. A systematic literature review is undertaken to identify existing studies which adopt a clustering-based remaining-time predictive process monitoring approach and performs a comparative analysis to compare and benchmark the output of the identified studies using five real-life event logs.

КРИПТОГРАФИЧЕСКАЯ КОНТЕЙНЕРИЗАЦИЯ ДАННЫХ В ОБРАБОТКЕ НЕЙРОННЫХ СЕТЕЙ ГЛУБОКОГО ОБУЧЕНИЯ

Introduction: the existing development of methods for ensuring information security in the field of information technology allows you to control the confidentiality of personal data at variable levels. Hardware solutions can be portable cryptographic keys, as well as narrowly targeted biometric identification devices. Software solutions are methods of static verification using sequences and side-tools for deploying data virtualization systems. The main problem that stands out among the methods described above is the need for additional hardware that acts as tokens, infrared cameras, and biometric identification sensors. An alternative problem is the lack of security of software solutions, since the presence of zero-day vulnerabilities can reduce security, which leads to the possibility of interception of confidential data by third parties. Purpose of the research is expressed in the analysis and development of software methods and algorithms for ensuring data security based on containerization methods. The developed software solution allows you to perform application tasks in a secure container, while the implemented authentication methods support the ability to identify an individual both using sequences and using the Windows Hello application programming interface. Methods used: security methods and algorithms aimed at implementing a unique cryptographic algorithm. The results of the studied methods and algorithms for scanning a bounded shell allow us to variatively select the optimal outcomes of internal process instances along with the implementation of cryptographic methods. Application software is based on the joint use of virtualization tools and methods of interaction through neural network technologies. The isolation of this solution from alternative methods lies in the possibility of using the container without significant hardware resources and lack of primary knowledge in configuring the software module. Practical significance lies in the possibility of using methods and algorithms in the construction of secure jobs in small and medium-sized businesses.

Business Process Event Prediction Through Scalable Online Learning

Predictive process monitoring techniques aim to forecast outcomes of running business process instances. These techniques are based on using predictive models built from past observed behavior, i.e., in an offline setting. However, business process behavior usually changes over time and predictive models are therefore at risk of becoming obsolete. Because of this, the definition of systems that build predictive models through an online setting has recently gained attention. Nevertheless, the scalability of this kind of setting within a context where the amount of data available is experiencing rapid growth is an outstanding issue. To solve this problem, this paper aims to define a framework for event sequence prediction capable of taking advantage of modern distributed processing platforms. An implementation over this framework based on Apache Flink is presented and it is tested upon two different case studies to prove its validity and its capacity to scale.

Decision-making support for input data in business processes according to former instances

Business Processes facilitate the execution of a set of activities to achieve the strategic plans of a company. During the execution of a business process model, several decisions can be made that frequently involve the values of the input data of certain activities. The decision regarding the value of these input data concerns not only the correct execution of the business process in terms of consistency, but also the compliance with the strategic plans of the company. Smart decision-support systems provide information by analyzing the process model and the business rules to be satisfied, but other elements, such as the previous temporal variation of the data during the former executed instances of similar processes, can also be employed to guide the input data decisions at instantiation time. Our proposal consists of learning the evolution patterns of the temporal variation of the data values in a process model extracted from previous process instances by applying Constraint Programming techniques. The knowledge obtained is applied in a Decision Support System (DSS) which helps in the maintenance of the alignment of the process execution with the organizational strategic plans, through a framework and a methodology. Finally, to present a proof of concept, the proposal has been applied to a complete case study.

Changes in artefact‐centric business process instances and their correctness prediction

Change of artefact-centric business process instances is important for an enterprise to keep competitive. However, to preserve the correctness of changes of an artefact-centric business process instance is still a big challenge. The hardness mainly stems from the fact that whether a business process instance can reach a final state has been proved to be undecidable. As such, finding a reliable verification algorithm for preserving correctness becomes impossible. In this study, the authors propose a random-forest-based approach to predict the correctness of changes in a business process instance. The availability of the proposed method is validated by comparing it to the traditional formal verification method. They also propose two optimisations of the proposed method and validate their effectiveness through extensive experimental analysis.

POTUS: Predictive Online Tuple Scheduling for Data Stream Processing Systems

Most online service providers deploy their own data stream processing systems in the cloud to conduct large-scale and real-time data analytics. However, such systems, e.g., Apache Heron, often adopt naive scheduling schemes to distribute data streams (in the units of tuples) among processing instances, which may result in workload imbalance and system disruption. Hence, there still exists a mismatch between the temporal variations of data streams and such inflexible scheduling scheme designs. Besides, the fundamental limits of benefits of predictive scheduling to data stream processing systems remain unexplored. In this article, we focus on the problem of tuple scheduling with predictive service in Apache Heron. With a careful choice in the granularity of system modeling and decision making, we formulate the problem as a stochastic network optimization problem and propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">POTUS</i> , an online predictive scheduling scheme that aims to minimize the response time of data stream processing by steering data streams in a distributed fashion. Theoretical analysis and simulation results show that POTUS achieves an ultra-low response time with a stability guarantee. Moreover, POTUS only requires mild-value of future information to effectively reduce the response time, even with mis-prediction.

Data-driven innovation processes within federated networks

PurposeWithin digital innovation, there are two significant consequences of the pervasiveness of digital technology: (1) the increasing connectivity is enabling a wider reach and scope of innovation structures, such as innovation networks and (2) the unprecedented availability of digital data is creating new opportunities for innovation. Accordingly, there is a growing domain for studying data-driven innovation (DDI), especially in contemporary contexts of innovation networks. The purpose of this study is to explore how DDI processes take form in a specific type of innovation networks, namely federated networks.Design/methodology/approachA multiple case study design is applied in this paper. We draw our analysis from data collected over six months from four cases of DDI. The within-analysis is aimed at constructing the DDI process instance in each case, while the crosscase analysis focuses on pattern matching and cross-case synthesis of common and unique characteristics in the constructed processes.FindingsEvidence from the crosscase analysis suggests that the widely accepted four-phase digital innovation process (including discovery, development, diffusion and post-diffusion) does not account for the explorative nature of data analytics and DDI. We propose an extended process comprising an explicit exploration phase before development, where refinement of the innovation concept and exploring social relationships are essential. Our analysis also suggests two modes of DDI: (1) asynchronous, i.e. data acquired before development and (2) synchronous, i.e. data acquired after (or during) development. We discuss the implications of these modes on the DDI process and the participants in the innovation network.Originality/valueThe paper proposes an extended version of the digital innovation process that is more specifically suited for DDI. We also provide an early explanation to the variation in DDI process complexities by highlighting the different modes of DDI processes. To the best of our knowledge, this is the first empirical investigation of DDI following the process from early stages of discovery till postdiffusion.

Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach

Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, though social contextual factors are widely acknowledged to impact the way cases are handled, as yet there have been no studies which have investigated the impact of social contextual features in the predictive process monitoring framework. These factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. This paper seeks to address this problem by investigating the impact of social contextual features in the predictive process monitoring framework utilising a survival analysis approach. We propose an approach to censor an event log and build a survival function utilising the Weibull model, which enables us to explore the impact of social contextual factors as covariates. Moreover, we propose an approach to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs and it outperforms these approaches.

A motivated information processing perspective on the antecedents of empowering leadership

AbstractEmpowering leadership is an important influence on team effectiveness. This makes the question of what the antecedents of team empowering leadership are an important issue to consider. To address this question, we propose a motivated information processing perspective that holds that engaging in empowering leadership is based on social information processing, and that there are individual differences in how elaborate that information processing is. We argue that a key consideration in shifting control from the leader to the team is the extent to which empowering leadership is driven by leaders’ consideration of their trust in the team (i.e., an instance of social information processing), and that leader need for closure (a trait capturing the disposition to carefully consider decisions and actions) moderates the relationship between leader trust in team and empowering leadership. A survey of N = 156 work teams supported these hypotheses.

Consumer engagement in occupational therapy health‐related research: A scoping review of the Australian Occupational Therapy Journal and a call to action

Consumer engagement in research is becoming an ethical, political, and moral imperative. The aim of this scoping review was to provide a snapshot of the current status of the emerging area of consumer engagement in occupational therapy health-related research, as published in the Australian Occupational Therapy Journal. A scoping review was conducted of all health-related original research published in the Australian Occupational Therapy Journal for 5½ years, plus Early View, as at June 2019. Eligible papers were examined for consumer engagement content which included any active choice or control by consumers, beyond being a research participant. A recognised six stage methodology was used with quantitative and qualitative data analysed. Two consumers collaborated in interpreting the data including finalising themes, leading theme naming, developing key discussion points, and producing recommendations. Of the 123 eligible papers, 48 (39.02%) included consumer engagement. However, only two incorporated consumer engagement across all research phases-preparation, execution and translation. A total of 103 consumer engagement activities were charted and categorised across all papers. There were limited instances of consumer collaboration (14/103, 13.59%) or consumer-led research processes (15/103, 14.56%) reported. Four themes emerged: Parity in research partnerships; It's important to know the Who, What, When, How and So What of consumer involvement; Consumer engagement must be a two-way process-not a dead end street; and Meeting the challenge-being diverse and inclusive. Comprehensive consumer-researcher partnerships may not be common-place in health-related occupational therapy research in the Australian Occupational Therapy Journal. This paper is a call to action. Occupational therapists must embrace consumer research partnerships as a demonstration of the key philosophies of enabling and empowering consumers and communities. Collaboration with two consumers in finalising data analysis, results interpretation and reporting added a diverse and valuable perspective.

A novel framework for seamless mosaic of Cartosat-1 DEM scenes

Digital elevation model (DEM) is used as a source of elevation data in wide range of applications. The applications involving larger area than an individual DEM scene often need a mosaic of DEM scenes. A simple stitching exhibits seams along the borders of DEM scenes in the mosaic output. The presence of seam lines varies with the number of scenes in an overlapping region and their overlap extents. The conventional feathering-based blend method, by virtue of the method itself, considers only two input scenes in an instance of the mosaic process. This method requires a suitable ordering of the input DEM scenes, especially in the overlapping regions containing more than two scenes. In this paper, we propose a framework that automatically handles the multiple overlapping regions and creates a seamless mosaic of Cartosat-1 DEM scenes for a given region of interest. We use a tree data structure to arrange input DEM scenes hierarchically based on their metadata. A traversal algorithm is devised to establish an order of precedence among the DEM scenes. This determines a unique input sequence of DEM scenes during mosaic process. In addition, two variants of feathering-based blend method are formulated to handle all possible cases of the overlapping regions. The experiments have been conducted on Cartosat-1 DEM scenes over 1°×1° region of interest. We analyze and compare our proposed methodology with average method and feathering-based blend method. We demonstrate that our framework exhibits a smooth transition of elevation values in the overlapping regions of the DEM mosaic output.

Process Intensification through Directly Coupled Autothermal Operation of Chemical Reactors

Summary Autothermal operation of a chemical reactor involves coupling exothermic and endothermic chemical reactions for the purpose of thermal management without resorting to external energy sinks or sources. Often this is accomplished through regenerative or recuperative heat exchange between spatially or temporally separated exothermic and endothermic reactions. However, it is also possible to directly couple these reactions simultaneously within the same reactor volume, eliminating the heat-transfer bottleneck that characterizes much of chemical manufacture. It is not widely recognized that directly coupled autothermal operation allows dramatic process intensification. This perspective defines autothermal operation and contrasts it with conventional heat transfer for thermal management of chemical processes. The intensification and cost savings that can be achieved are quantified, and the implications to modular chemical process intensification are presented. Guidelines are proposed for designing directly coupled autothermal processes. Diverse examples are presented. Several challenges to expanding the field are critically discussed.

Improving the bed movement physics of inclined grate biomass CFD simulations

Extensive research has been performed in the field of biomass combustion, particularly in terms of numerical simulations. However, only a few studies have deepened the understanding of the dynamics of packed beds and boiler grates.This paper presents and assesses a new physical solution under a comprehensive Eulerian model that tackles the complex problem of biomass movements inside a packed boiler bed. In addition, this paper improves the precision of the obtained results, expanding the application cases of the simulation model produced by the GTE research group at the University of Vigo (Spain). The modelling algorithms are based on mechanisms that represent the crumbling of biomass particles due to gravity and the movements caused by the push generated by the feeding system. The movements’ logic affects the variables involved in the biomass thermal conversion formulation, which is integrated with other combustion models previously presented by the authors.To test the performance of the presented models, a 3D experimental grate was printed and fed with biomass pellets. The performance was compared to that of the evolution of the simulated bed. Several experiments were performed to analyse parameters, such as the bed size, slope and residence time of different fuel batches. The results of the comparison show agreement in the behaviour of the model in terms of the bed size, shape and slope for different instances of the feeding process. Otherwise, the experiments show that the internal distribution of the bed layers of different batches presents some differences with the predictions. Therefore, possible improvements, such as considering a friction factor, may be applied to the model to upgrade its performance.

Cause vs. effect in context-sensitive prediction of business process instances

Predicting undesirable events during the execution of a business process instance provides the process participants with an opportunity to intervene and keep the process aligned with its goals. Few approaches for tackling this challenge consider a multi-perspective view, where the flow perspective of the process is combined with its surrounding context. Given the many sources of data in today’s world, context can vary widely and have various meanings. This paper addresses the issue of context being cause or effect of the next event and its impact on next event prediction. We leverage previous work on probabilistic models to develop a Dynamic Bayesian Network technique. Probabilistic models are considered comprehensible and they allow the end-user and his or her understanding of the domain to be involved in the prediction. Our technique models context attributes that have either a cause or effect relationship towards the event. We evaluate our technique with two real-life data sets and benchmark it with other techniques from the field of predictive process monitoring. The results show that our solution achieves superior prediction results if context information is correctly introduced into the model.