Data Workflow Research Articles

Emergency department length of stay following discontinuation of routine oral contrast material.

To determine the effect of discontinuing routine oral contrast material on emergency department (ED) length of stay (LOS), time from order to CT completion, and preliminary report turnaround time (TAT). A HIPAA-compliant, IRB-waived, single-institution, retrospective cohort study was conducted on adult patients presenting with abdominal pain to the ED from October 2015 to April 2019. Routine oral contrast material was administered prior to July 2018 and discontinued thereafter. CT workflow (ED LOS, exam completion time, report TAT) data were analyzed in a univariate analysis before and after discontinuation of oral contrast. Pre- versus post-policy data were compared with 2-sided t tests. The primary outcome was ED LOS. Data were analyzed on a process control chart and confidence limits were adjusted using established criteria. There were 5020 included abdominopelvic CTs. After routine oral contrast material was discontinued, ED LOS (13.4h vs 10.7h, p < 0.001) and time from CT order to CT completion (2.7h vs 2.1h, p < 0.001) declined. However, control chart analysis revealed improvement in overall LOS preceded the policy change by 9months, while improvement in time to CT completion coincided with the policy change. Preliminary report TAT increased by 4min after the policy change (29min vs. 33min, p < 0.001). Discontinuation of routine oral contrast material in the ED accelerated time to CT completion but had a minor non-significant effect on overall ED LOS. Much of the reduction in overall LOS likely was due to unrelated process improvements.

Assessing Lysosomal Disorders in the NGS Era: Identification of Novel Rare Variants

Lysosomal storage diseases (LSDs) are a heterogeneous group of genetic disorders with variable degrees of severity and a broad phenotypic spectrum, which may overlap with a number of other conditions. While individually rare, as a group LSDs affect a significant number of patients, placing an important burden on affected individuals and their families but also on national health care systems worldwide. Here, we present our results on the use of an in-house customized next-generation sequencing (NGS) panel of genes related to lysosome function as a first-line molecular test for the diagnosis of LSDs. Ultimately, our goal is to provide a fast and effective tool to screen for virtually all LSDs in a single run, thus contributing to decrease the diagnostic odyssey, accelerating the time to diagnosis. Our study enrolled a group of 23 patients with variable degrees of clinical and/or biochemical suspicion of LSD. Briefly, NGS analysis data workflow, followed by segregation analysis allowed the characterization of approximately 41% of the analyzed patients and the identification of 10 different pathogenic variants, underlying nine LSDs. Importantly, four of those variants were novel, and, when applicable, their effect over protein structure was evaluated through in silico analysis. One of the novel pathogenic variants was identified in the GM2A gene, which is associated with an ultra-rare (or misdiagnosed) LSD, the AB variant of GM2 Gangliosidosis. Overall, this case series highlights not only the major advantages of NGS-based diagnostic approaches but also, to some extent, its limitations ultimately promoting a reflection on the role of targeted panels as a primary tool for the prompt characterization of LSD patients.

МЕТОД АНАЛИТИЧЕСКОЙ СИНХРОНИЗАЦИИ ДАННЫХ МОНИТОРИНГА РАБОЧЕГО ПРОЦЕССА ТРАНСПОРТНЫХ ДИЗЕЛЕЙ В ЭКСПЛУАТАЦИИ

An analytical synchronization for data of the transport diesel engines working process is a key issue of this publication. Of particular importance is that workflow data refers to engines in use. The lack of accuracy in determining the current characteristics of the working process is the cause of a significant error in determining power, fuel consumption, errors in monitoring the operation of engine systems, and its diagnosis. Calculation of the effective power of marine diesel engines is necessary not only to control the workflow and diagnose problems. It is also important for energy efficiency management as part of the Ship Energy Efficiency Management Plan (SEEMP). The task of analytical synchronization is formulated as the transfer of data of a function by time to a function by the angle of rotation of the crankshaft. In this case, it becomes possible to determine the top dead center (TDC) most correctly. As the basis of the method of analytical data synchronization for determining TDC coordinates, it is proposed to use the sequential execution of three stages: linear, sinusoidal, and differential (compression pressure’s first derivative is equal to zero). In addition, refinements have been made to linear and sinusoidal synchronization algorithms. They differ from their existing counterparts in using detailed restrictions. This allows one to have a further reduction of the error in TDC determining to a range of 0.1 ... 0.3 degrees of the crankshaft rotation. Also, this ensures the accuracy of the indicated power calculation and other basic workflow parameters with a maximum relative error of up to 2.5%. Obviously, such high accuracy allows avoiding diagnostic errors as much as possible, predicting the engine load, performing more exact calculations of parameters and characteristics, and taking measures to achieve higher energy efficiency and economy. Undoubtedly, this helps to increase the efficiency of both transport engines in general and marine engines in particular.

Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments

In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient data placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic cloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we propose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators (DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation results show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid cloud environments.

Implementation Knowledge Management in Management Thesis for High Education in Indonesia

Management thesis in high education have some lackness in knowledge management. Every busines process that happen in management thesis don’t have efective approach to integrate data workflow and to acuisition the knowledge. Therefore, knowledge management system for management thesis that suit with high education are needed. This paper gives suggestions about new architecture software that offer service for management thesis in high education to make business process more efective and efficien. Methodology that is used for development new software uses model process software development life cycle(SDLC) with web platform , that will increase collaboration in business process management thesis. The result of this implementation can increased efective and eficiency business process management thesis significantly.

Findable and reusable workflow data products: A genomic workflow case study

While workflow systems have improved the repeatability of scientific experiments, the value of the processed (intermediate) data have been overlooked so far. In this paper, we argue that the intermediate data products of workflow executions should be

Visual Workflow Process Modeling and Simulation Approach Based on Non-Functional Properties of Resources

With the emergence of big data and cloud technologies, companies now evolve in complex IT environments. This situation requires good practices for data process automation to be adopted to ensure system interoperability. Visual computing helps companies to describe and organize the ways in which information systems, devices, and people must interact. It incorporates a number of fields including modeling and simulation (M&amp;S). This paper focused on M&amp;S of data workflow processes that are key steps to bridging the gap between business views and goals on the one side, and operational implementations on the other side. Simulation adds a dynamic view to static modeling; it increases understanding of the behavior of process mechanisms and the identification of weak areas in data flow. Several research projects have been focused on control flow and data flow, but less attention has been paid to resource characteristics. This work is based on the MDSEA approach and the eBPMN language, and proposes an approach that aims to distinguish the types of resources carrying out process tasks. Furthermore, it introduces a new composite resource made from the relationship between a user (human resource) and a task form (IT resource). Moreover, it proposes a resource aggregation based on process performance combination in order to run and display a global performance measurement of a process path.

JA:2021-25. Increasing Efficiency and Reliability of Agricultural Injury and Fatality Coding with Standard Operating Procedures: Lessons Learned from AgInjuryNews.org

ABSTRACT Purpose: News media reports of agricultural injuries can be useful to better inform safety and health stakeholders and prevention efforts. These data are particularly useful for youth incidents, since the National Institute of Occupational Safety and Health discontinued their survey-based agriculture surveillance program in 2015, leaving a gap in reliable, consistent data on both youth and adult agricultural injuries and fatalities. In this study we describe how coding data derived from news reports, with a detailed Standard Operating Procedure (SOP) and refined inclusion/exclusion criteria, can lead to more efficient workflows to capture and code agricultural injury data. Methods: To capture quality data and improve inter-rater reliability we employ a multi-coder process. The first coder enters primary data into the Case Report Form (CRF), and a second coder reviews these data points for agreement. While reviewing for agreement, the second coder diverges into two other occupational/agricultural injury coding schemas (Occupational Illness and Injury Classification System [OIICS] and Farm and Agricultural Injury Classification [FAIC]) and employs an additional multi-coder and interrater reliability process, adding to the multifaceted workflow and detailed data output. This secondary coding schema and reliability ratings are discussed in a separate manuscript. Any discrepancies from the initial review are then discussed and settled between first and second coders. All CRFs are sent to another team member who reviews >10% and publishes the reports for public use on AgInjuryNews.org. International cases endure a similar process, but without OIICS or FAIC coding. In partnership with the Canadian Agricultural Safety Association, our team began Canadian injury report capture and coding in the fall of 2019. Findings: We developed a structured and detailed SOP for coding agricultural data obtained from news reports. Use of the SOP and refined inclusion/exclusion criteria were crucial when: 1) onboarding additional coders; 2) employing multiple coders; 3) refining search terms in data collection; 4) coding of emerging technologies and trends; and 5) improving the efficiency and quality of the data. Translation: Coders’ ability to reference a thoroughly vetted SOP and inclusion/exclusion criteria may lead to improved data coding for agricultural injuries and fatalities.

Sharing SRP data to reduce environmentally associated disease and promote transdisciplinary research.

The National Institute of Environmental Health Sciences (NIEHS) Superfund Basic Research and Training Program (SRP) funds a wide range of projects that span biomedical, environmental sciences, and engineering research and generate a wealth of data resulting from hypothesis-driven research projects. Combining or integrating these diverse data offers an opportunity to uncover new scientific connections that can be used to gain a more comprehensive understanding of the interplay between exposures and health. Integrating and reusing data generated from individual research projects within the program requires harmonization of data workflows, ensuring consistent and robust practices in data stewardship, and embracing data sharing from the onset of data collection and analysis. We describe opportunities to leverage data within the SRP and current SRP efforts to advance data sharing and reuse, including by developing an SRP dataset library and fostering data integration through Data Management and Analysis Cores. We also discuss opportunities to improve public health by identifying parallels in the data captured from health and engineering research, layering data streams for a more comprehensive picture of exposures and disease, and using existing SRP research infrastructure to facilitate and foster data sharing. Importantly, we point out that while the SRP is in a unique position to exploit these opportunities, they can be employed across environmental health research. SRP research teams, which comprise cross-disciplinary scientists focused on similar research questions, are well positioned to use data to leverage previous findings and accelerate the pace of research. Incorporating data streams from different disciplines addressing similar questions can provide a broader understanding and uncover the answers to complex and discrete research questions.

USAGE OF INFORMATION-SEARCH ENGINE AT HIGHER EDUCATION INSTITUTION ARCHIVAL WORK MANAGEMENT

The purpose of the article is to justify the need to use the information-search engine in universities’ archival affairs management, as well as to develop data workflows diagrams to understand the process of information searching automation in universities’ archival funds. The methodology uses business process modeling techniques, namely DWFD data workflow modeling. A functional approach is used to determine the minimum number of objects in the subject area and the relations between the objects. Structural-logical query generation method was also used to work with structured information databases. The scientific novelty of the work is to create a data workflow diagram (DWFD), which is included in the functional model. DWFD will allow, at the functional modeling stage, to determine the basic data requirements and to trace how information is exchanged, both within the archived information-search engine being designed and the system as a whole with the external information environment. Conclusions. With the help of structural modeling tools, DWFD the process of information-search engine development for archival units of higher education institutions, taking into account the international archival standard ISAD (G) was designed. Thus, the proposed project to develop an information-search engine that is able to accumulate information and output it on multidimensional requests will allow the university archival department staff to increase the efficiency of fulfilling requests for university graduated and employees’ personal data verification. As a prospect for further scientific research is the digitization of archival documents with the assignment of a certain indexation, that is, metadata and uploading them to the database, as well as the ability to create thematic virtual exhibitions of the archive fund on the based on the digitization of archival materials.Key words: information-search engine, automation, archival subdivision, higher education institution informatization, university informatization, ISAD (G), verification, projecting, archival affairs management.

Using data to optimise the EHRs and performance

The use of data to improve the efficiency and the utilisation of the electronic health record has become an increasingly important topic in informatics. This paper looks briefly at why it is important to focus on the development of metrics and key indicators for provider and clinician utilisation and efficiency within the electronic health record. With the use of this data workflows can be developed within the electronic health record to decrease variation and provide a more efficient and value-driven approach to provide healthcare delivery. Workflow improvement and standardisation can also lead to improved clinical and financial performance.

A multicentric IT platform for storage and sharing of imaging-based radiation dosimetric data.

The MEDIRAD project is about the effects of low radiation dose in the context of medical procedures. The goal of the work is to develop an informatics service that will provide the researchers of the MEDIRAD project with a platform to share acquired images, along with the associated dosimetric data pertaining to the radiation resulting from the procedure. The authors designed a system architecture to manage image data and dosimetric data in an integrated way. DICOM and non-DICOM data are stored in separated repositories, and the link between the two is provided through a semantic database, i.e., a database whose information schema in aligned with an ontology. The system currently supports CT, PET, SPECT, and NM images as well as dose reports. Currently, two workflows for non-DICOM data generated from dosimetric calculations have been taken into account, one concerning Monte Carlo-based calculation of organ doses in Chest CT, and the other estimation of doses in nontarget organs in 131I targeted radionuclide therapy of the thyroid. The system is currently deployed, thus providing access to image and related dosimetric data to all MEDIRAD users. The software was designed in such a way that it can be reused to support similar needs in other projects.

A dynamic VM provisioning and de-provisioning based cost-efficient deadline-aware scheduling algorithm for Big Data workflow applications in a cloud environment

Cloud computing is the fastest growing distributed computing paradigm that provides online IT resources on demand by following a pay-as-you-go billing model. The success of this computing paradigm enables cloud providers to offer an extensive collection of parallel computing resources to deal with Big Data workflow scheduling problems. Although, workflow scheduling has been extensively studied, however, most of them are unable to achieve user-specified deadline constraints at the cheap cost. In this paper, a Dynamic Cost-Efficient Deadline-Aware (DCEDA) heuristic algorithm is proposed for scheduling Big Data workflow that produces the cheapest schedule while achieving the deadline constraints. DCEDA dynamically takes appropriate scheduling decisions for workflow tasks based on the fact that deadline constraint is not violated in the future. Also, it continuously monitors the VM pool for identifying the active idle VMs that incur extra costs and overheads, and subsequently de-provision them. The experimental analysis based on Montage workflow and randomly generated synthetic workflow with various characteristics prove that DCEDA delivers better performance in comparison to the existing algorithms.

QUEUING THEORY BASED ON DECENTRALIZED ONLOAD DATA ARRIVAL USING EXPECTED MAX-MIN PROBABILISTIC DECISION FOR REDUCING WORKLOAD

Calculating probabilistic decision using the queuing theory, is a new technology with the aim of making it easier for the user to obtain the pre-requisite services in hardware and software access to a wide range of Internet-based services. That is, an important aspect of modeling the delivery of workflow data services in the process of efficient load balancing in a shared environment of arrival. In order to provide a service, matrix theory has become a very essential and important aspect between data requests and wait and see responses. Load balancing, as there is less response time and higher output separation including equal load. Various mechanisms have been proposed to provide an efficient load balance. To realize the desired level of computation time, we propose a maximum precedence algorithm for scheduling Meta tasks by static illustration to use Enhanced Max-min Algorithm with (EMM) optimized Enhanced decentralized onload data arrival Mode (DO-DAM) with hyper switching methods.by arriving the service distributions are notated as Kendall Notations as (Max-A/S/Min-R/J/D). A queuing model improves the utilization and consumption to manage the resources that uses switch over Probability service time distribution to allot the job to data allotment support on scheduling application on static queue demands. This proposed queuing theory implements to improve the time consumption, utilization, scalability, make span and throughput performance.

Big Data Processing Workflows Oriented Real-Time Scheduling Algorithm using Task-Duplication in Geo-Distributed Clouds

Scheduling big data processing workflows involves both large-scale tasks and transmission of massive intermediate data among tasks, thus optimizing their completion time and monetary cost becomes a challenging issue. Besides, data streams are continuously generated, and dynamically submitted to clouds for real-time or near real-time processing. Naturally, responsive schedules are required to keep pace with such dynamic environments and this further aggravates the difficulty of the workflow scheduling problem. To address these issues, we first derive two theorems to minimize the completion time of a set of parallel workflow tasks and the start time of each workflow task, and then define the latest finish time for workflow tasks, which is also proved its advantage in reducing costs without delaying the completion of workflows. On the basis of these theorems, we propose a novel r eal- t ime s cheduling a lgorithm using t ask- d uplication, RTSATD, such that minimizing both the completion time and monetary cost of processing big data workflows in clouds. The performance of RTSATD is analyzed by using both synthesized and real-world workflows. The experimental results demonstrate the superiority of the proposed algorithm with respect to completion time (up to 28.73 percent) and resource utilization (up to 46.31 percent) over two existing approaches.

Ensuring Computers Understand Manual Operations in Production: Deep-Learning-Based Action Recognition in Industrial Workflows

In this study, we consider fully automated action recognition based on deep learning in the industrial environment. In contrast to most existing methods, which rely on professional knowledge to construct complex hand-crafted features, or only use basic deep-learning methods, such as convolutional neural networks (CNNs), to extract information from images in the production process, we exploit a novel and effective method, which integrates multiple deep-learning networks including CNNs, spatial transformer networks (STNs), and graph convolutional networks (GCNs) to process video data in industrial workflows. The proposed method extracts both spatial and temporal information from video data. The spatial information is extracted by estimating the human pose of each frame, and the skeleton image of the human body in each frame is obtained. Furthermore, multi-frame skeleton images are processed by GCN to obtain temporal information, meaning the action recognition results are predicted automatically. By training on a large human action dataset, Kinetics, we apply the proposed method to the real-world industrial environment and achieve superior performance compared with the existing methods.

Kmasker plants - a tool for assessing complex sequence space in plant species.

Many plant genomes display high levels of repetitive sequences. The assembly of these complex genomes using short high-throughput sequence reads is still a challenging task. Underestimation or disregard of repeat complexity in these datasets can easily misguide downstream analysis. Detection of repetitive regions by k-mer counting methods has proved to be reliable. Easy-to-use applications utilizing k-mer counting are in high demand, especially in the domain of plants. We present Kmasker plants, a tool that uses k-mer count information as an assistant throughout the analytical workflow of genome data that is provided as a command-line and web-based solution. Beside its core competence to screen and mask repetitive sequences, we have integrated features that enable comparative studies between different cultivars or closely related species and methods that estimate target specificity of guide RNAs for application of site-directed mutagenesis using Cas9 endonuclease. In addition, we have set up a web service for Kmasker plants that maintains pre-computed indices for 10 of the economically most important cultivated plants. Source code for Kmasker plants has been made publically available at https://github.com/tschmutzer/kmasker. The web service is accessible at https://kmasker.ipk-gatersleben.de.

On privacy-aware eScience workflows

Computing-intensive experiments in modern sciences have become increasingly data-driven illustrating perfectly the Big-Data era. These experiments are usually specified and enacted in the form of workflows that would need to manage (i.e., read, write, store, and retrieve) highly-sensitive data like persons’ medical records. We assume for this work that the operations that constitute a workflow are 1-to-1 operations, in the sense that for each input data record they produce a single data record. While there is an active research body on how to protect sensitive data by, for instance, anonymizing datasets, there is a limited number of approaches that would assist scientists with identifying the datasets, generated by the workflows, that need to be anonymized along with setting the anonymization degree that must be met. We present in this paper a solution privacy requirements of datasets used and generated by a workflow execution. We also present a technique for anonymizing workflow data given an anonymity degree.

Multi-objective scheduling of extreme data scientific workflows in Fog

The concept of “extreme data” is a recent re-incarnation of the “big data” problem, which is distinguished by the massive amounts of information that must be analyzed with strict time requirements. In the past decade, the Cloud data centers have been envisioned as the essential computing architectures for enabling extreme data workflows. However, the Cloud data centers are often geographically distributed. Such geographical distribution increases offloading latency, making it unsuitable for processing of workflows with strict latency requirements, as the data transfer times could be very high. Fog computing emerged as a promising solution to this issue, as it allows partial workflow processing in lower-network layers. Performing data processing on the Fog significantly reduces data transfer latency, allowing to meet the workflows’ strict latency requirements. However, the Fog layer is highly heterogeneous and loosely connected, which affects reliability and response time of task offloading. In this work, we investigate the potential of Fog for scheduling of extreme data workflows with strict response time requirements. Moreover, we propose a novel Pareto-based approach for task offloading in Fog, called Multi-objective Workflow Offloading (MOWO). MOWO considers three optimization objectives, namely response time, reliability, and financial cost. We evaluate MOWO workflow scheduler on a set of real-world biomedical, meteorological and astronomy workflows representing examples of extreme data application with strict latency requirements.

Climate: An R Package to Access Free In-Situ Meteorological and Hydrological Datasets For Environmental Assessment

Freely available and reliable meteorological datasets are highly demanded in many scientific and business applications. However, the structure of publicly available databases is often difficult to follow, especially for users who only deal with this kind of dataset on occasion. The “climate” R package aims to fill this gap with an easy-to-use interface for downloading global meteorological data in a fast and consistent way. The package provides access to different sources of in-situ meteorological data, including the Ogimet website, atmospheric vertical sounding gathered at the University of Wyoming’s webpage, and hydrological and meteorological measurements collected by the Institute of Meteorology and Water Management—National Research Institute (i.e., Polish Met Office). This article also provides a quick overview of the key functionalities available within the climate R package, and gives examples of an efficient and tidy workflow of meteorological data within the R based environment. The automation procedures included in the packages allow one to download data in a user-defined time resolution (from hourly to annual), for a user-defined time span, and for a specified group of stations or countries. The package also contains metadata, including a list of available stations, their geospatial information, and measurement descriptions with their units. Finally, the obtained datasets can be processed in R or exported to external tools (e.g., spreadsheets or GIS software).