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The dependency propagation problem is to determine, given a view defined on data sources and a set of dependencies on the sources, whether another dependency is guaranteed to hold on the view. This paper investigates dependency propagation for recently proposed conditional functional dependencies (CFDs). The need for this study is evident in data integration, exchange and cleaning since dependencies on data sources often only hold conditionallyon the view. We investigate dependency propagation for views defined in various fragments of relational algebra, CFDs as view dependencies, and for source dependencies given as either CFDs or traditional functional dependencies (FDs). (a) We establish lower and upper bounds, all matching, ranging from PTIME to undecidable. These not only provide the firstresults for CFD propagation, but also extend the classical work of FD propagation by giving new complexity bounds in the presence of finite domains. (b) We provide the first algorithm for computing a minimal cover of allCFDs propagated via SPC views; the algorithm has the same complexity as one of the most efficient algorithms for computing a cover of FDs propagated via a projection view, despite the increased expressive power of CFDs and SPC views. (c) We experimentally verify that the algorithm is efficient.

A novel asynchronous federated architecture is proposed in this work for data prediction, aggregation, and inference with a use case for fast short-term instability mitigation in transmission systems. Existing machine learning (ML) prediction approaches rely on centralized-schemes that accumulate measurements from distributed regions at a central computing node. This centralized-schemes increase computational overhead leading to prediction delay. Federated-learning collaboratively learns a global model without centralized data processing. Nevertheless, its performance degrades when data becomes non-independent and identically-distributed due to the changes in grid dynamics, leading to expensive model re-training. This research proposes a federated-inference architecture that can exploit data-dependency in different regions to effectively exchange model parameters among high-quality local models and improve local predictive results, hence avoiding the need for global model and re-training effort. The architecture includes several major modules: 1) federated deep ML models using gated-recurrent unit for local synchrophasor data prediction in each region, 2) computationally-efficient data anomaly detection and mitigation using statistical and unsupervised autoencoders, 3) a federated inference algorithm that aggregates model weights and outputs from top-$k$ correlated neighbors to improve local performance, upon system dynamics change, 4) instability prediction and 5) a case analysis for its application in proactive short-term stability control. The efficiency and effectiveness of the proposed approach is validated on modified IEEE test systems with multiple test event scenarios with varying factors, including number of PMUs, error tolerances, and length of input and prediction windows.

Purpose: The paper formulates a bi-objective mixed-integer nonlinear programming model, aimed at minimizing the total labor hours and the workload unfairness for the multi-skilled worker assignment problem in Seru production system (SPS). Design/methodology/approach: Three approaches, namely epsilon-constraint method, non-dominated sorting genetic algorithm 2 (NSGA-II) and improved strength Pareto evolutionary algorithm (SPEA2), are designed for solving the problem. Findings: Numerous experiments are performed to assess the applicability of the proposed model and evaluate the performance of algorithms. The merged Pareto-fronts obtained from both NSGA-II and SPEA2 were proposed as final solutions to provide useful information for decision-makers. Practical implications: SPS has the flexibility to respond to the changing demand for small amount production of multiple varieties products. Assigning cross-trained workers to obtain flexibility has emerged as a major concern for the implementation of SPS. Most enterprises focus solely on measures of production efficiency, such as minimizing the total throughput time. Solutions based on optimizing efficiency measures alone can be unacceptable by workers who have high proficiency levels when they are achieved at the expense of the workers taking more workload. Therefore, study the tradeoff between production efficiency and fairness in the multi-skilled worker assignment problem is very important for SPS. Originality/value: The study investigates a new mixed-integer programming model to optimize worker-to-seru assignment, batch-to-seru assignment and task-to-worker assignment in SPS. In order to solve the proposed problem, three problem-specific solution approaches are proposed. [ABSTRACT FROM AUTHOR]