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Recurring batch data pipelines are a staple of the modern enterprise-scale data warehouse. As a data warehouse scales to support more products and services, a growing number of interdependent pipelines running at various cadences can give rise to periodic resource bottlenecks for the cluster. This resource contention results in pipelines starting at unpredictable times each day and consequently variable landing times for the data artifacts they produce. The variability gets compounded by the dependency structure of the workload, and the resulting unpredictability can disrupt the project workstreams which consume this data. We present Clockwork, a delay-based global scheduling framework for data pipelines which improves landing time stability by spreading out tasks throughout the day. Whereas most scheduling algorithms optimize for makespan or average job completion times, Clockwork's execution plan optimizes for stability in task completion times while also targeting predefined pipeline SLOs. We present this new problem formulation and design a list scheduling algorithm based on its analytic properties. We also discuss how we estimate the resource requirements for our recurring pipelines, and the architecture for integrating Clockwork with Dataswarm, Facebook's existing data workflow management service. Online experiments comparing this novel scheduling algorithm and a previously proposed greedy procrastinating heuristic show tasks complete almost an hour earlier on average, while exhibiting lower landing time variance and producing significantly less competition for resources in the cluster.

Background: Salvianolate, a common drug for stabilizing heart disease and Angina Pectoris, is considered to be off-label for preventing venous thromboembolism (VTE) or anticoagulation at present. However, many clinical studies have showed that salvianolate can effectively inhibit the deep-vein thrombosis (DVT) incidence, and prevent VTE of perioperative patients in the real world in China. Objective: This analysis aimed to evaluate the effectiveness and safety of salvianolate in preventing VTE in perioperative patients. Methods: Databases of PubMed, Cochrane Library, Embase, CNKI, Wanfang and VIP were searched until July 2019. Literature retrieval, data extraction and quality assessment were independently completed by two researchers and checked with each other. Review Manager 5.2 software was applied for meta-analysis. Results: A total of 429 studies were retrieved, including 11 randomized controlled trials (RCTs) with a total of 1149 subjects. Compared with low molecular weight heparin (LMWH) group alone, salvianolate combined LMWH group had lower DVT incidence in preventing perioperative thrombosis (2.75% and 14.23%, OR: 0.21, 95% CI:[0.08,0.53]; P = .0009). The incidence of adverse reactions of experimental group was similar to that of control group (1.79% and 2.31%, OR: 0.65, 95% CI:[0.18,2.35]. P = .51). Compared with the control group, D-dimer level (D-D), platelet count (PLT), fibrinogen (FIB), whole blood high shear viscosity (WBHSV), and whole blood low shear viscosity (WBLSV) were all significantly decreased (P

Real-time monitoring of bioprocesses by the integration of analytics at critical unit operations is one of the paramount necessities for quality by design manufacturing and real-time release (RTR) of biopharmaceuticals. A well-defined process analytical technology (PAT) roadmap enables the monitoring of critical process parameters and quality attributes at appropriate unit operations to develop an analytical paradigm that is capable of providing real-time data. We believe a comprehensive PAT roadmap should entail not only integration of analytical tools into the bioprocess but also should address automated-data piping, analysis, aggregation, visualization, and smart utility of data for advanced-data analytics such as machine and deep learning for holistic process understanding. In this review, we discuss a broad spectrum of PAT technologies spanning from vibrational spectroscopy, multivariate data analysis, multiattribute chromatography, mass spectrometry, sensors, and automated-sampling technologies. We also provide insights, based on our experience in clinical and commercial manufacturing, into data automation, data visualization, and smart utility of data for advanced-analytics in PAT. This review is catered for a broad audience, including those new to the field to those well versed in applying these technologies. The article is also intended to give some insight into the strategies we have undertaken to implement PAT tools in biologics process development with the vision of realizing RTR testing in biomanufacturing and to meet regulatory expectations.

Additional file 1: Supplementary figure 1. Conceptual framework. Supplementary table 1 Internal consistency and confirmatory factor analysis (CFA) results.