Commercial Workflow Research Articles

A deep reinforcement learning-based scheduling framework for real-time workflows in the cloud environment

Benefiting from the flexibility, scalability and many other advantages of cloud computing, an increasing number of research institutions and enterprises are using cloud platforms to deploy their scientific or commercial workflow applications. From the perspective of cloud service providers, how to schedule workflows reasonably to improve user satisfaction and resource utilization is a big challenge. This challenge becomes more prominent in a dynamic cloud environment, where the workload of user-submitted workflows is constantly changing and unpredictable. Traditional scheduling methods designed for fixed workflows or real-time independent tasks are not applicable in such scenarios. In this paper, we present an online scheduling framework for multiple real-time workflows in a dynamic environment. In this framework, the scheduler employs a deep reinforcement learning based algorithm (R-DQN) to assign each task in workflows to a suitable virtual machine instance according to the current situation. Its goal is to achieve optimal task scheduling and resource allocation, so as to minimize workflow makespan and maximize resource utilization. We provide a comprehensive overview of the design and implementation of our approach, and we conduct experiments using various workflow benchmarks with different structures and scales on the WorkflowSim platform. The experimental results demonstrate that our algorithm significantly outperforms the compared algorithms in term of makespan and other metrics. Moreover, our algorithm demonstrates strong performance in generality, robustness and scalability.

Personal Authentication System Based on Iris Recognition and Digital Signature Technology

Authentication based on biometrics is being used to prevent physical access to high-security institutions. Recently, due to the rapid rise of information system technologies, biometrics is now being used in applications to access databases and commercial workflow systems. These applications need to implement measures to counter security threats. Many developers are exploring and developing novel authentication techniques to prevent these attacks. However, the most challenging problem is how to keep biometric data while maintaining the functional performance of identity verification systems. This paper presents a biometrics-based personal authentication system in which a smart card, a Public Key Infrastructure (PKI), and iris verification technologies are combined. Raspberry Pi 4 Model B+ is used as the core of hardware components with an IR Camera. Following that idea, we designed an optimal image processing algorithm in OpenCV, Python, Keras, and sci-kit learn libraries for feature extraction and recognition is chosen for application development in this project. After training, the implemented system gives an accuracy of (97% and 100%) for the left and right (NTU) iris datasets. Later, the person verification based on the iris feature is performed to verify the claimed identity and examine the system authentication. For the NTU iris dataset, the time of key generation, Signature, and Verification is 5.17sec,0.288, and 0.056, respectively. This work offers the realistic architecture to implement identity-based cryptography with biometrics using the Rivest–Shamir–Adleman (RSA) algorithm.

An alternate workflow for preparing Precision ID Ancestry and Identity Panel libraries for Illumina sequencing.

Single nucleotide polymorphisms (SNPs) are well-established for forensic applications. Although they are not compatible with existing criminal databases, they offer some advantages over short tandem repeat (STR) markers including smaller amplicons, no stutter artifacts, and biogeographic ancestry and phenotype predictions. The Precision ID NGS System, a commercial workflow by Thermo Fisher Scientific, offers a streamlined solution for genotyping forensically relevant SNPs using next-generation sequencing. The Precision ID Ancestry and Identity Panels combined target 289 SNPs, and their sensitivity, reproducibility, and accuracy have been evaluated by the forensic community. The aim of this study was to develop an alternative workflow to genotype these SNP panels using Illumina chemistry. Commercial genomic DNAs (gDNAs) (n, 3) were amplified using three uracil-tolerant polymerase master mixes. Resulting amplicons were prepared into libraries using the KAPA Hyper Prep Kit (KAPA Biosystems) and sequenced via Illumina's MiniSeq. Reads were analyzed using a published analysis pipeline to compile final genotypes with read depth information. Phusion U Multiplex PCR Master Mix (Thermo Fisher Scientific) statistically outperformed the other master mixes tested (P <0.0001), with respect to the number of SNPs genotyped. To ensure a workflow using Phusion U would be compatible across diverse samples, we optimized PCR cycle number using the same commercial gDNAs (n, 3), reference buccal swabs (n, 3), and environmental (household dust) samples (n, 6). Using the developed workflow, 93.9% of all SNPs were successfully genotyped across sample types. Implementation of the developed workflow should be straightforward for forensic laboratories and suitable for processing reference and casework samples.

A roadmap to neural automatic post-editing: an empirical approach

In a translation workflow, machine translation (MT) is almost always followed by a human post-editing step, where the raw MT output is corrected to meet required quality standards. To reduce the number of errors human translators need to correct, automatic post-editing (APE) methods have been developed and deployed in such workflows. With the advances in deep learning, neural APE (NPE) systems have outranked more traditional, statistical, ones. However, the plethora of options, variables and settings, as well as the relation between NPE performance and train/test data makes it difficult to select the most suitable approach for a given use case. In this article, we systematically analyse these different parameters with respect to NPE performance. We build an NPE “roadmap” to trace the different decision points and train a set of systems selecting different options through the roadmap. We also propose a novel approach for APE with data augmentation. We then analyse the performance of 15 of these systems and identify the best ones. In fact, the best systems are the ones that follow the newly-proposed method. The work presented in this article follows from a collaborative project between Microsoft and the ADAPT centre. The data provided by Microsoft originates from phrase-based statistical MT (PBSMT) systems employed in production. All tested NPE systems significantly increase the translation quality, proving the effectiveness of neural post-editing in the context of a commercial translation workflow that leverages PBSMT.

Control and Information Systems for the National Ignition Facility

Orchestration of every National Ignition Facility (NIF) shot cycle is managed by the Integrated Computer Control System (ICCS), which uses a scalable software architecture running code on more than 1950 front-end processors, embedded controllers, and supervisory servers. The ICCS operates laser and industrial control hardware containing 66 000 control and monitor points to ensure that all of NIF’s laser beams arrive at the target within 30 ps of each other and are aligned to a pointing accuracy of less than 50 μm root-mean-square, while ensuring that a host of diagnostic instruments record data in a few billionths of a second. NIF’s automated control subsystems are built from a common object-oriented software framework that distributes the software across the computer network and achieves interoperation between different software languages and target architectures. A large suite of business and scientific software tools supports experimental planning, experimental setup, facility configuration, and post-shot analysis. Standard business services using open-source software, commercial workflow tools, and database and messaging technologies have been developed. An information technology infrastructure consisting of servers, network devices, and storage provides the foundation for these systems. This paper is an overview of the control and information systems used to support a wide variety of experiments during the National Ignition Campaign.

Full-waveform inversion: From near surface to deep

The ancient Persian Gulf port city of Muscat provided a spectacular setting for the SEG's 2013 Workshop on Full-waveform Inversion (FWI). This active R&amp;D topic attracted about 36 oral presentations and 20 or so posters, which added up to three intense days of ideas, images, and discussion. FWI has progressed from academic research topic to commercial workflow component in roughly 10 years, with many case studies documenting improved imaging and business value and others documenting a definite need for improved understanding of algorithms and applicability. Along with fundamental research issues of worldwide importance, the meeting provided an opportunity to showcase implications of the Middle East's particular exploration challenges for the further development of FWI.

Experiment archive, analysis, and visualization at the National Ignition Facility

The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory is the world's most energetic laser, providing a scientific research center to study inertial confinement fusion and matter at extreme energy densities and pressures. A target shot involves over 30 specialized diagnostics measuring critical x-ray, optical and nuclear phenomena to quantify ignition results for comparison with computational models. The Shot Analysis and Visualization System (SAVI) acquires and analyzes target diagnostic data for display within a time-budget of 30min. Laser and target diagnostic data are automatically loaded into the NIF archive database through clustered software data collection agents. The SAVI Analysis Engine distributes signal and image processing tasks to a Linux cluster where computation is performed. Intermediate results are archived at each step of the analysis pipeline. Data is archived with metadata and pedigree. Experiment results are visualized through a web-based user interface in interactive dashboards tailored to single or multiple shot perspectives. The SAVI system integrates open-source software, commercial workflow tools, relational database and messaging technologies into a service-oriented and distributed software architecture that is highly parallel, scalable, and flexible. The architecture and functionality of the SAVI system will be presented along with examples.

A semantic approach to the discovery of workflow activity patterns in event logs

Workflow activity patterns represent a set of recurrent behaviours that can be found in a wide range of business processes. In this paper we address the problem of determining the presence of these patterns in process models. This is usually done manually by the analyst, who inspects the model and interprets its elements in terms of the semantics of those patterns. Here, we present an approach to perform this discovery based on the event log created during process execution. The approach makes use of an ontology and the semantic annotation of the event log in order to discover the patterns automatically by means of semantic reasoning. We illustrate the application of the proposed approach in a case study involving a purchase process implemented in a commercial workflow system.

Self-adapting workflow reconfiguration

Because web services are highly interoperable, they are capable of providing uniform access to underlying technologies, allowing developers to choose between competing services. Workflow languages, such as BPEL, compose and sequence Web service invocations resulting in meaningful, and sometimes, repeated tasks. Their prevalence means there may be multiple Web services that perform the same operation with some better than others depending on the situation. Their potential for being unavailable at critical workflow execution times forces a reliance on such redundant services. One remedy for unavailability and situational awareness constraints is using quality of service factors and user-directed preferences to assign priorities to workflows and services to perform run-time replacement. In this paper we describe a novel approach to self-adapting workflow reconfiguration. We discuss the implementation of our approach embodied by the Next-generation Workflow Toolkit that supports runtime workflow reconfiguration using BPEL with a commercial workflow engine. A key design feature is the decoupling of user-directed changes regarding service priority from the actual workflow execution, allowing NeWT to effectively manage and recover from workflow changes at any time. We evaluate NeWT by comparing the same example across multiple commercial systems that claim reconfiguration capabilities.

A distributed server architecture supporting dynamic resource provisioning for BPM-oriented workflow management systems

Workflow management systems have been widely used in many business process management (BPM) applications. There are also a lot of companies offering commercial software solutions for BPM. However, most of them adopt a simple client/server architecture with one single centralized workflow-management server only. As the number of incoming workflow requests increases, the single workflow-management server might become the performance bottleneck, leading to unacceptable response time. Development of parallel servers might be a possible solution. However, a parallel server architecture with a fixed-number of servers cannot efficiently utilize computing resources under time-varying system workloads. This paper presents a distributed workflow-management server architecture which adopts dynamic resource provisioning mechanisms to deal with the probable performance bottleneck. We implemented a prototype system of the proposed architecture based on a commercial workflow management system, Agentflow. A series of experiments were conducted on the prototype system for performance evaluation. The experimental results indicate that the proposed architecture can deliver scalable performance and effectively maintain stable request response time under a wide range of incoming workflow request workloads.

PS1-21: Evaluation of Commercial Workflow Engine for Modeling Clinical Processes in Quality Improvement and Decision Support

Vojtech Huser, MD, PhD, Peggy L Peissig, MBA, Carl A Christensen, MS and Justin B Starren, MD, PhD Vojtech Huser, MD, PhD, Marshfield Clinic Research Foundation (Biomedical Informatics Research Center); Peggy L Peissig, MBA, Marshfield Clinic Research Foundation (Biomedical Informatics Research Center); Carl A Christensen, MS, Marshfield Clinic Research Foundation (Biomedical Informatics Research Center); Justin B Starren, MD, PhD, Marshfield Clinic Research Foundation (Biomedical Informatics Research Center)

Adaptive workflow scheduling under resource allocation constraints and network dynamics

Workflow concepts are well suited for scenarios where many distributed entities work collaboratively together to achieve a common goal. Today, workflows are mostly used as computerized model for business processes executed in instances in commercial Workflow Management Systems. However, there are many other application domains where computer-supported cooperative work can be captured and organized by workflows. In this paper, we investigate the task of scheduling workflows in self-organizing wireless networks for disaster scenarios. Most research work in the field of workflow scheduling has been driven by temporal and causality constraints. We present an adaptive scheduling algorithm that finds a suitable execution sequence for workflow activities by additionally considering resource allocation constraints and dynamic topology changes. Our approach utilizes a multi-stage distribution algorithm which we extend with techniques to cope with network dynamics.

ITasks

In this paper we introduce the iTask system: a set of combinators to specify work flows in a pure functional language at a very high level of abstraction. Work flow systems are automated systems in which tasks are coordinated that have to be executed by humans and computers. The combinators that we propose support work flow patterns commonly found in commercial work flow systems. Compared with most of these commercial systems, the iTask system offers several advantages: tasks are statically typed, tasks can be higher order, the combinators are fully compositional, dynamic and recursive work flows can be specified, and last but not least, the specification is used to generate an executable web-based multi-user work flow application. With the iTask system, useful work flows can be defined which cannot be expressed in other systems: work can be interrupted and subsequently directed to other workers for further processing. The implementation is special as well. It is based on the Clean iData toolkit which makes it possible to create fully dynamic, interactive, thin client web applications. Thanks to the generic programming techniques used in the iData toolkit, the programming effort is reduced significantly: state handling, form rendering, user interaction, and storage management is handled automatically. The iTask system allows a task to be regarded as a special kind of persistent redex being reduced by the application user via task completion. The combinators control the order in which these redexes are made available to the application user. The system rewrites the persistent task redexes in a similar way as functions are rewritten in lazy functional languages.

Automatic capture and efficient storage of e‐Science experiment provenance

AbstractFor the first provenance challenge, we introduce a layered model to represent workflow provenance that allows navigation from an abstract model of the experiment to instance data collected during a specific experiment run. We outline modest extensions to a commercial workflow engine so it will automatically capture provenance at workflow runtime. We also present an approach to store this provenance data in a relational database. Finally, we demonstrate how core provenance queries in the challenge can be expressed in SQL and discuss the merits of our layered representation. Copyright © 2007 John Wiley &amp; Sons, Ltd.

The role of collaborative support to promote participation and commitment in software development teams

AbstractThis work discusses the use of collaborative technology as an element for extending software process culture within development teams. This discussion starts with the idea that collaboration support comprises an important element for software process improvement (SPI) and an agent for process learning. We argue that improving awareness information about work processes and about the collaboration intrinsic to it may help software development teams to better accept the idea of defining, standardizing and continuously improving their work. Our approach relies on the use of workflow systems for software process support. We have built an environment—PIEnvironment—by extending a commercial workflow system with awareness information mechanisms and participation channels. Through case studies, we have evaluated its use for the enactment of software process activities. Copyright © 2007 John Wiley &amp; Sons, Ltd.

Formulating the Data-Flow Perspective for Business Process Management

Workflow technology has become a standard solution for managing increasingly complex business processes. Successful business process management depends on effective workflow modeling and analysis. One of the important aspects of workflow analysis is the data-flow perspective because, given a syntactically correct process sequence, errors can still occur during workflow execution due to incorrect data-flow specifications. However, there have been only scant treatments of the data-flow perspective in the literature and no formal methodologies are available for systematically discovering data-flow errors in a workflow model. As an indication of this research gap, existing commercial workflow management systems do not provide tools for data-flow analysis at design time. In this paper, we provide a data-flow perspective for detecting data-flow anomalies such as missing data, redundant data, and potential data conflicts. Our data-flow framework includes two basic components: data-flow specification and data-flow analysis; these components add more analytical rigor to business process management.

Enriched workflow modelling and Stochastic Branch-and-Bound

Workflow systems provide means and techniques for modelling, designing, performing and controlling repetitive (business) processes. The quality of commercial workflow systems is usually determined to a large extent by their versatility and multi-purpose application. One of the current trends in improving workflow systems lies in enriching modelling methods and techniques in order to enlarge design alternatives. The need for such advanced methods is particularly apparent in those fields in which the process duration can be determined only vaguely, but whose completion schedules are at the same time strictly enforced by a highly competitive market by means of fines and penalties. The risk of an overrun has to be weighed against the expected costs and benefits of certain measures reducing turn-around time and their combinations. Because they can help to avoid such penalties—or, at least, keep any potential losses low by identifying critical subprocesses and evaluate appropriate measures—modelling and evaluation techniques are becoming essential features of workflow systems. Methodologically, we use Stochastic Branch-and-Bound as a technique for finding “optimal” bundles of measures. A numerical study shows the benefits of this meta-approach by means of five stepwise-developed decision scenarios requiring rich modelling. Petri nets as a modelling tool and Stochastic Branch-and-Bound as an optimization technique determine for multi-mode resource constrained workflows of varying complexity an optimal workforce strategy with respect to the number of workers and their qualification.

Construction Business Automation System

This paper introduces a system prototype, the construction business automation system, for modeling and automating construction business processes by using workflow and object technologies. Construction management tasks are developed as distributed internet applications for supporting reusability of software components, simplifying process modeling, and automating business processes. The development of the prototype has resulted in the following technologies: (1) the object-oriented reusable component technology for modeling construction management tasks; (2) the construction business process modeler based on the task based modeling methodology; (3) the request driving methodology for raising business requests and instantiating corresponding process instances; and (4) the supporting technology for interacting with a commercial workflow engine (e.g., i-Flow) for executing business process instances. A purchasing process is used as an example to illustrate these related topics and the applicability of the methodologies in construction business automation.

Applying Propositional Logic to Workflow Verification

The increasing complexity of business processes in the era of e-business has heightened the need for workflow verification tools. However, workflow verification remains an open and challenging research area. As an indication, most of commercial workflow management systems do not yet provide workflow designers with formal workflow verification tools. We propose a logic-based verification method that is based on a well-known formalism, i.e., propositional logic. Our logic-based workflow verification approach has distinct advantages such as its rigorous yet simplistic logical formalism and its ability to handle generic activity-based process models. In this paper, we present the theoretical framework for applying propositional logic to workflow verification and demonstrate that logic-based workflow verification is capable of detecting process anomalies in workflow models.

A case-based reasoning framework for workflow model management

In order to support efficient workflow design, recent commercial workflow systems are providing templates of common business processes. These templates, called cases, can be modified individually or collectively into a new workflow to meet the business specification. However, little research has been done on how to manage workflow models, including issues such as model storage, model retrieval, model reuse and assembly. In this paper, we propose a novel framework to support workflow modeling and design by adapting workflow cases from a repository of process models. Our approach to workflow model management is based on a structured workflow lifecycle and leverages recent advances in model management and case-based reasoning techniques. Our contributions include a conceptual model of workflow cases, a similarity flooding algorithm for workflow case retrieval, and a domain-independent AI planning approach to workflow case composition. We illustrate the workflow model management framework with a prototype system called Case-Oriented Design Assistant for Workflow Modeling (CODAW).