Workflow Approach Research Articles

Clustering-based multi-objective optimization considering fairness for multi-workflow scheduling on clouds

Distributed computing, such as cloud computing, provides promising platforms for orchestrating scientific workflows' tasks based on their sequences and dependencies. Workflow scheduling plays an important role in optimizing concerned objectives for distributed computing, such as minimizing the makespan and cost. Many researchers have focused on optimizing a specific single workflow with multiple objectives. Currently, there are few studies on multi-workflow scheduling, with most research focusing on objectives such as cost and makespan. However, multi-workflow scheduling requires the design of specific objectives that reflect the unique characteristics of multiple workflows. On the other hand, clustering-based approaches have garnered significant attention in the field of workflow scheduling over distributed computing resources due to their advantage in reducing data communication among tasks. Despite this, the effectiveness of clustering-based algorithms has not been extensively studied and validated in the context of multi-objective multi-workflow scheduling models. Motivated by these factors, we propose an approach for multiple workflows' multi-objective optimization (MOO), considering the new defined metric, fairness. We first mathematically formulate the fairness and define a fairness-involved MOO model. Then, we propose an advanced clustering-based resource optimization strategy in multiple workflow runs. Experimental results show that the proposed approach performs better than the compared algorithms without significant compromise of the overall makespan and cost as well as individual fairness, which can guide the simulation workflow scheduling on clouds.

A hybrid modelling approach for information processing workflow in inter-cultural heritage projects

PurposeBuilding Information Modelling in building processes brings significant efficiency gains. However, its use in restoration projects is still experimental. On the other hand, more than traditional methods are needed for communication, collaboration, and shared understanding. Hence, the main research question is how to enhance these aspects in multinational projects with information transparency challenges and the need for shared understanding among stakeholders. This research aims to develop and propose a hybrid modelling approach that integrates traditional and BIM-based information process workflows through testing and evaluating to what extent BIM can be used in restoration projects without excessive efforts.Design/methodology/approachConsidering this aim, the case study, the Mahmud Pasha Hammam as the heritage building, a 15th-century structure located in Serbia, was studied with the action research strategy to enable action-based learning by doing. Alongside the 2D documentation as the traditional method for the existing structure, restoration design proposals were also produced for the new additions to the heritage structure with BIM.FindingsA new BIM use scenario was experimented with and proposed, proving BIM implementation's potential impact on heritage restoration projects. A hybrid model was developed that meets the requirements of existing regulations and specifications, where restoration proposals were visualized quickly, quantity take-off was produced, and technical drawings were generated instantly.Originality/valueThis hybrid modelling workflow integrates HBIM with traditional methods in restoration projects to improve communication, efficiency, and collaboration in a real-time professional project.

Assessing RNA-Seq Workflow Methodologies Using Shannon Entropy.

RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies.

Sustainable energy efficient workflow classification and scheduling in geo distributed cloud datacenter

Data centers are a major source of carbon emissions and are subsequently contributing to global carbon footprints. Keeping in view of providing a sustainable solution to society, we have analyzed various factors that can help to achieve carbon neutrality and maximum sustainability. Our study pointed towards the need to follow a sustainable approach for incoming workflow throughout the life-cycle of Data centers. We analyzed that workloads need to be segregated before assigning them to the data centers so that energy-efficient resource allocation could be done. This paper demonstrates unsupervised learning techniques to cluster the incoming cloud workloads. The heterogeneous workloads were characterized using machine learning approaches and appropriate clusters were crafted. For analysis, Google Cluster Dataset is used. In order to improve the accuracy, data were normalized, and random samples of data were selected for clustering. The machine learning algorithms applied were able to successfully determine the appropriate clusters that can further be used for energy-efficient resource scheduling.

A Survey on Multiple Workflow Scheduling Algorithms in Cloud Environment

The workflow approach is a standard for displaying processes and their implementation process. With the advent of electrical sciences, more cumbersome workflows were created with more processing requirements. New distributed systems, such as grid systems and computing clouds, allow users to access heterogeneous resources that are geographically located at different points and execute their workflow tasks. Therefore, the simultaneous receipt and execution of several workflows is obvious. As a result of discussing scheduling algorithms, it is necessary to consider arrangements for implementing multiple workflows on a shared resource set. Improving the execution of multiple workflows can accelerate the process of obtaining results when sending processes to the cloud. In this paper, we first discuss the classification of multiple workflow scheduling algorithms, and then briefly describe the scheduling algorithms for the cloud environment, andfinally, the algorithms of papers were compared with each other

Efficient fault tolerant cost optimized approach for scientific workflow via optimal replication technique within cloud computing ecosystem

<span lang="EN-US">Cloud computing is one of the dispersed and effective computing models, which offers tremendous opportunity to address scientific issues with big scale characteristics. Despite having such a dynamic computing paradigm, it faces several difficulties and falls short of meeting the necessary quality of services (QoS) standards. For sustainable cloud computing workflow, QoS is very much required and need to be addressed. Recent studies looked on quantitative fault-tolerant programming to reduce the number of copies while still achieving the reliability necessity of a process on the heterogeneous infrastructure as a service (IaaS) cloud. In this study, we create an optimal replication technique (ORT) about fault tolerance as well as cost-driven mechanism and this is known as optimal replication technique with fault tolerance and cost minimization (ORT-FTC). Here ORT-FTC employs an iterative-based method that chooses the virtual machine and its copies that have the shortest makespan in the situation of specific tasks. By creating test cases, ORT-FTC is tested while taking into account scientific workflows like CyberShake, laser interferometer gravitational-wave observatory (LIGO), montage, and sipht. Additionally, ORT-FTC is shown to be only slightly improved over the current model in all cases. </span>

Development of a Paper-based Hematocrit Test and a Lateral Flow Assay to Detect Critical Fibrinogen Concentrations Using a Bottom-Up Pyramid Workflow Approach.

Fibrinogen is a coagulation factor in human blood and the first one to reach critical levels in major bleeding. Hypofibrinogenemia (a too low fibrinogen concentration in blood) poses great challenges to first responders, clinicians, and healthcare providers since it represents a risk factor for exsanguination and massive transfusion requirements. Thus, the rapid assessment of the fibrinogen concentration at the point of care has gained considerable importance in preventing and managing major blood loss. However, in whole blood measurements, hematocrit variations affect the amount (volume fraction) of plasma that passes the detection zone. In an attempt to accurately determine realistic critical levels of fibrinogen (<1.5 mg/mL) in patients needing immediate treatment and medical interventions, we have developed novel diagnostic systems capable of estimating hematocrit and critical fibrinogen concentrations. A lateral flow assay (LFA) for the detection of fibrinogen has been developed by establishing a workflow employing rapid characterization methods to streamline LFA development. The integration of two detection lines enables (i) the identification of fibrinogen (first line) present in the sample and (ii) the determination of the clinically critical fibrinogen concentrations below 1.5 mg/mL (second line). Furthermore, the paper-based separation of blood cells from plasma provides a semiquantitative estimate of the hematocrit by analyzing the fractions. Initial validation of the point-of-care (PoC) hematocrit test revealed good comparability to a standard laboratory method. The developed diagnostic systems have the ability to accelerate decision-making in cases with major bleeding.

DeepMapScaler: a workflow of deep neural networks for the generation of generalised maps

ABSTRACT The automation of map generalization has been an important research subject for decades but is not fully solved yet. Deep learning techniques are designed for various image generation tasks, so one may think that it would be possible to apply these techniques to cartography and train a holistic model for end-to-end map generalization. On the contrary, we assume that map generalization is a task too complex to be learnt with a unique model. Thus, in this article, we propose to resort to past research on map generalization and to separate map generalization into simpler sub-tasks, each of which can be more easily resolved by a deep neural network. Our main contribution is a workflow of deep models, called DeepMapScaler, which achieves a step-by-step topographic map generalization from detailed topographic data. First, we implement this workflow to generalize topographic maps containing roads, buildings, and rivers at a medium scale (1:50k) from a detailed dataset. The results of each step are quantitatively and visually evaluated. Then the generalized images are compared with the generalization performed using a holistic model for an end-to-end map generalization and a traditional semi-automatic map generalization process. The experiment shows that the workflow approach is more promising than the holistic model, as each sub-task is specialized and fine-tuned accordingly. However, the results still do not reach the quality level of the semi-automatic traditional map generalization process, as some sub-tasks are more complex to handle with neural networks.

A Version Control System for Point Clouds

This paper presents a novel version control system for point clouds, which allows the complete editing history of a dataset to be stored. For each intermediate version, this system stores only the information that changes with respect to the previous one, which is compressed using a new strategy based on several algorithms. It allows undo/redo functionality in memory, which serves to optimize the operation of the version control system. It can also manage changes produced from third-party applications, which makes it ideal to be integrated into typical Computer-Aided Design workflows. In addition to automated management of incremental versions of point cloud datasets, the proposed system has a much lower storage footprint than the manual backup approach for most common point cloud workflows, which is essential when working with LiDAR (Light Detection and Ranging) data in the context of spatial big data.

Interobserver variability in target definition for stereotactic arrhythmia radioablation.

Stereotactic arrhythmia radioablation (STAR) is a potential new therapy for patients with refractory ventricular tachycardia (VT). The arrhythmogenic substrate (target) is synthesized from clinical and electro-anatomical information. This study was designed to evaluate the baseline interobserver variability in target delineation for STAR. Delineation software designed for research purposes was used. The study was split into three phases. Firstly, electrophysiologists delineated a well-defined structure in three patients (spinal canal). Secondly, observers delineated the VT-target in three patients based on case descriptions. To evaluate baseline performance, a basic workflow approach was used, no advanced techniques were allowed. Thirdly, observers delineated three predefined segments from the 17-segment model. Interobserver variability was evaluated by assessing volumes, variation in distance to the median volume expressed by the root-mean-square of the standard deviation (RMS-SD) over the target volume, and the Dice-coefficient. Ten electrophysiologists completed the study. For the first phase interobserver variability was low as indicated by low variation in distance to the median volume (RMS-SD range: 0.02-0.02 cm) and high Dice-coefficients (mean: 0.97 ± 0.01). In the second phase distance to the median volume was large (RMS-SD range: 0.52-1.02 cm) and the Dice-coefficients low (mean: 0.40 ± 0.15). In the third phase, similar results were observed (RMS-SD range: 0.51-1.55 cm, Dice-coefficient mean: 0.31 ± 0.21). Interobserver variability is high for manual delineation of the VT-target and ventricular segments. This evaluation of the baseline observer variation shows that there is a need for methods and tools to improve variability and allows for future comparison of interventions aiming to reduce observer variation, for STAR but possibly also for catheter ablation.

Uncover: Toward Interpretable Models for Detecting New Star Cluster Members.

In this design study, we present Uncover, an interactive tool aimed at astronomers to find previously unidentified member stars in stellar clusters. We contribute data and task abstraction in the domain of astronomy and provide an approach for the non-trivial challenge of finding a suitable hyper-parameter set for highly flexible novelty detection models. We achieve this by substituting the tedious manual trial and error process, which usually results in finding a small subset of passable models with a five-step workflow approach. We utilize ranges of a priori defined, interpretable summary statistics models have to adhere to. Our goal is to enable astronomers to use their domain expertise to quantify model goodness effectively. We attempt to change the current culture of blindly accepting a machine learning model to one where astronomers build and modify a model based on their expertise. We evaluate the tools' usability and usefulness in a series of interviews with domain experts.

GIS-Based Scientific Workflows for Automated Spatially Driven Sea Level Rise Modeling

Sea level rise (SLR) poses a significant threat to shorelines and the environment in terms of flooding densely populated areas and associated coastal ecosystems. Scenario analysis is often used to investigate potential SLR consequences, which can help stakeholders make informed decisions on climate change mitigation policies or guidelines. However, SLR scenario analysis requires considerable geospatial data analytics and repetitive execution of SLR models for alternative scenarios. Having to run SLR models many times for scenario analysis studies leads to heavy computational needs as well as a large investment of time and effort. This study explores the benefits of incorporating cyberinfrastructure technologies, represented by scientific workflows and high-performance computing, into spatially explicit SLR modeling. We propose a scientific workflow-driven approach to modeling the potential loss of marshland in response to different SLR scenarios. Our study area is the central South Carolina coastal region, USA. The scientific workflow approach allows for automating the geospatial data processing for SLR modeling, while repetitive modeling and data analytics are accelerated by leveraging high-performance and parallel computing. With support from automation and acceleration, this scientific workflow-driven approach allows us to conduct computationally intensive scenario analysis experiments to evaluate the impact of SLR on alternative land cover types including marshes and tidal flats as well as their spatial characteristics.

Investigating the implementation of telehealth and digital technologies during public health crisis: A qualitative review.

Public health crises such as the Coronavirus disease 2019 (COVID-19) pandemic across the world overstrained the health care system. Accordingly, telehealth and digital technologies were implemented. Telehealth and digital technologies refer to the provision of health care at a distance using electronic means for diagnosis, evaluation, treatment, monitoring, and education of patients. This approach, while extremely useful, can be challenging for both patients and physicians, mainly as this is a new mode of health care to health practitioners and patients. Intrinsically, when managing the massive disruption to the routine of patient health care workflow, it is important to understand the important factors associated with an accelerated introduction of telehealth and digital technologies for the effective and safe continuation of healthcare during a public health crisis. Accordingly, this current study performed a qualitative review analysis of 40 scientific sources from 2019 to 2022. Findings present health practitioners that implemented telehealth and digital technologies during the public health emergency. Additionally, findings from this study presents a workflow approach for telehealth visit cycle and also discusses the current telehealth and virtual applications being implemented during public health crisis. This study provides implications to support health care managers and policymakers of health centres to be virtually informed regarding digital applications being implemented by patients and health practitioners on how to implement an effective telehealth system during public health crises.

An experimental workflow to assess the applicability of microemulsions for conformance improvement in oil-bearing reservoir

A comprehensive workflow approach is necessary to link multiple experimental tasks and identify microemulsion (ME) formulations with ‘optimal’ stability, displacement behavior and technical feasibility in the petroleum industry. In this paper, a systematic approach is described with the aid of a case study which involves the formulation of an anionic sodium dodecyl sulfate-based microemulsion. The design of such ME systems requires a proper methodology, substantial laboratory work, and functional assessment from research/industrial viewpoints. The surfactant has been screened in terms of its micellization potential, followed by phase behavior analysis and Winsor classification of prepared microemulsions. The desired composition(s) are characterized via several tools to determine droplet size, morphology, oil/water solubilization potentials and salinity scan results. The suitability of the microemulsion system for conformance improvement technology (CIT) is proposed to be assessed via physicochemical evaluation studies encompassing two attributes: rheology and stability. For a favorable ‘conforming’ drive, the microemulsion must exhibit phase stability, sufficient injectivity, and moderate-to-high viscosity under shear. Technical assessment by the industry and research team must also include factors related to cost, availability of chemicals, environmental degradation, and reservoir considerations. The article demonstrates a comprehensive all-inclusive workflow methodology to design and formulate surfactant-stabilized microemulsions via case study analysis for application in CIT. This represents a sound approach to identifying efficient, cost-effective injection fluid systems and provides a framework to identify useful parameters for ME formulation design and employ the proposed (effective) strategy for conformance control.

Systematic Literature Review of Trend and Characteristic Agile Model

Agile is a methodology and engineering approach for software development that encourages change in collaboration through tasks carried out at various stages of the software development life cycle. Scaled Agile Framework, Kanban, Scrum, Lean, Extreme Programming, Crystal, Dynamic System Development Method, and Feature Driven Development are a few of the approaches that go along with agile. Each of these approaches has distinct traits and qualities of its own. Every engineer and researcher needs to be aware of the benefits and characteristics of each method before deciding to use one. In order to assist engineers and researchers who will use one of these methods, this research will analyze it. The method used in this paper is a systematic literature review, which involved at 52 papers published in the previous eight years, from 2018 to 2022. This method is carried out by determining research questions, determining library initiation and selection, determining inclusion and exclusion criteria, and finally performing data extraction. This essay seeks to establish: (i) Study trends on each agile technique from 2018 to 2022 and (ii) Each agile method's characteristics. The results of this literature review indicate that Scrum and Extreme Programming have overtaken other agile methodologies as the most popular agile techniques over the last eight years. Through an analysis of the characteristics of each methodology, namely the development approach, suggested iteration time period, team communication, project size, project documentation, design, workflow approach, project coordinator, role assignment, coding, testing, and the nature of customer interaction, it is found that Scrum and Extreme Programming do have several advantages over other methodologies.

Open‐source workflow approaches to passive acoustic monitoring of bats

AbstractThe affordability, storage and power capacity of compact modern recording hardware have evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non‐invasive, cost‐effective tool for research and ecological management particularly useful for bats and toothed whales that orient and forage using ultrasonic echolocation. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user‐friendly and open access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats.We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open‐source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was evaluated against the performance of an expert manual analyst.Workflow performance depended strongly on the signal‐to‐noise ratio and detection algorithm used: the full deep learning workflow had the best classification accuracy (≤67%) but was computationally too slow for practical large‐scale bat PAM. Workflows using PAMGuard's detection module or triggers onboard an SM4BAT or AudioMoth accurately classified up to 47%, 59% and 34%, respectively, of calls to species. Not all workflows included noise sampling critical to estimating changes in detection probability over time, a vital parameter for abundance estimation. The workflow using PAMGuard's detection module was 40 times faster than the full deep learning workflow and missed as few calls (recall for both ~0.6), thus balancing computational speed and performance.We show that complete acoustic detection and classification workflows for bat PAM data can be efficiently automated using open‐source software such as PAMGuard and exemplify how detection choices, whether pre‐ or post‐deployment, hardware or software‐driven, affect the performance of deep learning classification and the downstream ecological information that can be extracted from acoustic recordings. In particular, understanding and quantifying detection/classification accuracy and the probability of detection are key to avoid introducing biases that may ultimately affect the quality of data for ecological management.

Supply chain logistics with quantum and classical annealing algorithms

Noisy intermediate-scale quantum (NISQ) hardware is almost universally incompatible with full-scale optimization problems of practical importance which can have many variables and unwieldy objective functions. As a consequence, there is a growing body of literature that tests quantum algorithms on miniaturized versions of problems that arise in an operations research setting. Rather than taking this approach, we investigate a problem of substantial commercial value, multi-truck vehicle routing for supply chain logistics, at the scale used by a corporation in their operations. Such a problem is too complex to be fully embedded on any near-term quantum hardware or simulator; we avoid confronting this challenge by taking a hybrid workflow approach: we iteratively assign routes for trucks by generating a new binary optimization problem instance one truck at a time. Each instance has sim 2500 quadratic binary variables, putting it in a range that is feasible for NISQ quantum computing, especially quantum annealing hardware. We test our methods using simulated annealing and the D-Wave Hybrid solver as a place-holder in wait of quantum hardware developments. After feeding the vehicle routes suggested by these runs into a highly realistic classical supply chain simulation, we find excellent performance for the full supply chain. Our work gives a set of techniques that can be adopted in contexts beyond vehicle routing to apply NISQ devices in a hybrid fashion to large-scale problems of commercial interest.

An optimization approach for hybrid workflows in platform-enabled private service marketplaces

The future of work is likely to include private platform-enabled marketplaces which remain a new, fertile ground for research. This paper focuses on hybrid workflows which utilize the expertise of in-house (IH) and on-demand (OD) workers to perform complex knowledge intensive tasks under service constraints. Our primary contributions are in identifying and modeling an innovative application of OR in the new economy, proving its complexity and proposing a high-quality heuristic. We formulate a scenario-based mathematical programming model of a private talent marketplace for Knowledge Intensive Business Services (KIBS). This model considers uncertainty in task, worker, and OD marketplace characteristics. Problem complexity is illustrated. We develop a simplified queuing theory-based model of PKIBS to characterize the structure of the OD marketplace. Propositions from the queueing model inform IH workforce size, IH and OD workforce composition, and OD pricing decisions. A Tabu-based One Period Look Ahead heuristic based on propositions from the simplified queuing theory-based model is proposed. Heuristic performance is evaluated using a problem upper bound and MIP solution for small problems, and experiments based on a cyber security use case. Results illustrate the mix of OD and IH workforce and sophisticated hybrid workflows for different market conditions.

Abstract TP29: Nurse-led Acute Stroke Process Promotes Advocacy And Treatment Times: A Case Series

Background: Reperfusion therapies for acute ischemic strokes are highly time sensitive. Centers across the world have implemented various approaches in treating these patients when presenting to the ED using multidisciplinary team approaches, straight-to-CT, and straight-to-angio suite. Several studies recently published the efficiency of an ED nurse-led stroke process on improving timeliness and decreasing door-to-treatment times. Purpose: Little to no literature has focused on how a stroke RN leading a stroke alert process serves as a patient advocate to assist the decision-making process. Methods: Our CSC built a stroke program using a multidisciplinary team, parallel workflow approach led by a Stroke Responder, filled by a neuroscience RN specially trained to lead the acute stroke process. This case series exemplifies how the Stroke Responder influenced the decision-making process in 2 stroke alerts. Case Study 1: A 60-year old high-profile public figure presented to the ED with mild aphasia. With a low NIHSS, some providers may be reluctant to offer thrombolysis based off risk vs benefit. In this patient’s case, mild aphasia was debilitating to life and career. The stroke RN emphasized this resulting in the patient receiving thrombolysis and making a full recovery to baseline. Case Study 2: For an incoming stroke alert on a 90+ year old, the stroke RN received pertinent patient information and a family contact number from EMS via telemedicine. The stroke RN was able to confirm information with family and transferred call to neurologist for consent of thrombolysis prior to patient arrival. Patient was transferred straight-to-CT upon arrival, administered the thrombolytic within 12 min, and subsequently taken for mechanical thrombectomy in the right MCA bifurcation when procedure was aborted due to NIHSS from 13 to 3. Conclusion: Highly specialized neuroscience nurses assigned to lead a stroke alert process benefit the patient beyond providing effective care coordination and assisting the neurologist with assessments. They can help drive the decision-making process resulting in higher treatment rates and faster door-to-treatment times.

Data scarce modelling the impact of present and future groundwater development on Jordan multiaquifer groundwater resources

Rapidly growing demands and climate change stresses water resources worldwide and leads to highly competitive situations between the environment and socio-economic development of a region, calling for a smart and modelling driven water resources management. However, data scarcity often prevents the realisation of a comprehensive, nation-wide resources model, which provides reliable and spatially discretized results of water resources development.We present a workflow approach to set up a large-scale multi-aquifer model, overcoming data shortage by stepwise calibration and integrating hydrological and numerical groundwater flow modelling into a coupled system. The study aims to develop such a system to assess how groundwater resources react on anthropogenic impacts on the example of the Kingdom of Jordan, one of the water poorest countries on globe.Simulated heads reliably resembled the monitored ones in >70 % of the observation wells. That makes us confident, the model represents all the states well from 1970, prior to the intense development of the country until 2015. The water balance shows an annual deficit of 1.16 million cubic meter (MCM) due to an overdraft. The discharge to the Dead Sea increased from 564 MCM/yr to 696 MCM/yr over the time period. Regional drawdowns of >250 m and groundwater depression with an extension of approx.100 km are observable in both large aquifer complexes. Most severe areas in the upper calcareous aquifer are located in the north of Amman and practically in all urban and agricultural agglomerations across the country. Groundwater tables in the deeper sandstone aquifer are particularly affected in the south as well as in the wider vicinity of the Dead Sea as consequence of its continuous dropping. Simulations of the future development of the groundwater tables indicate a severe deterioration of the situation with further declines in groundwater levels of up to 70 m.