Yon Dohn Chung

The execution time of an Apache Spark application is heavily influenced by its configuration settings. Accordingly, Bayesian Optimization (BO) is commonly used for automated tuning, employing the acquisition function, Expected Improvement (EI). However, existing works did not compare the performance to the other acquisition functions empirically. In this paper, we show that EI may not work well for Spark applications due to a huge search space compared to the other optimization problems. In addition, we demonstrate the performance of BO based on Probability of Improvement (PI), which achieves exploration via rich random initialization and exploitation via the PI acquisition function. Through the experimental evaluations, we show that the PI-based BO outperforms the EI-based BO in both optimal time and optimization cost.

Apache Spark stands out as a well-known solution for big data processing because of its efficiency and rapid processing capabilities. One of its modules, Spark SQL, serves as a prominent big data query engine. However, executing Spark SQL applications with massive data can be time-intensive, and the execution time can vary significantly depending on its configurations. Recent studies try to reduce application execution times by searching optimal configurations for applications. While Bayesian optimization is recognized as a powerful method in recent studies for configuration optimization, it faces challenges such as computational costs and time-consuming computations, especially when dealing with large search spaces Due to these challenges, we propose QHB+, designed to rapidly search optimal configurations. QHB+ utilizes the Successive Halving Algorithm-based optimization methods, performing well in hyperparameter optimization of machine learning models, for configuration optimization of Spark SQL applications. Through empirical evaluations against established benchmarks, we show the efficiency of QHB+, highlighting them as swift alternatives to conventional optimization method for optimizing Spark SQL configurations.

Federated learning is a promising approach for training machine learning models using distributed data from multiple mobile devices. However, privacy concerns arise when sensitive data are used for training. In this paper, we discuss the challenges of applying local differential privacy to federated learning, which is compounded by limited resources of mobile clients and the asynchronicity of federated learning. To address these challenges, we propose a framework called LAFD, that stands for Local-differentially Private and Asynchronous Federated Learning with Direct Feedback Alignment. LAFD consists of two parts: (a) LFL-DFALS: Local differentially private Federated Learning with Direct Feedback Alignment and Layer Sampling, and (b) AFL-LMTGR: Asynchronous Federated Learning with Local Model Training and Gradient Rebalancing. LFL-DFALS effectively reduces the computation and communication costs via direct feedback alignment and layer sampling. AFL-LMTGR handles the problem of stragglers via local model training, and gradient rebalancing. We demonstrate the performance of LFL-DFALS and AFL-LMTGR through experiments.

This paper demonstrates Waffle, a self-driving grid indexing system for moving objects. We introduce system architecture, system workflow, and user scenarios. Waffle enables the management of moving objects with less human effort while automatically improving performance.

Background: Automatic cell tracking methods enable practitioners to analyze cell behaviors efficiently. Notwithstanding the continuous development of relevant software, user-friendly visualization tools have room for further improvements. Typical visualization mostly comes with main cell tracking tools as a simple plug-in, or relies on specific software/platforms. Although some tools are standalone, limited visual interactivity is provided, or otherwise cell tracking outputs are partially visualized. Results: This paper proposes a self-reliant visualization system, CellTrackVis, to support quick and easy analysis of cell behaviors. Interconnected views help users discover meaningful patterns of cell motions and divisions in common web browsers. Specifically, cell trajectory, lineage, and quantified information are respectively visualized in a coordinated interface. In particular, immediate interactions among modules enable the study of cell tracking outputs to be more effective, and also each component is highly customizable for various biological tasks. Conclusions: CellTrackVis is a standalone browser-based visualization tool. Source codes and data sets are freely available at http://github.com/scbeom/celltrackvis with the tutorial at http://scbeom.github.io/ctv_tutorial.