Large-scale matrix computations have become indispensable in artificial intelligence and scientific applications. It is of paramount importance to efficiently perform out-of-core computations that often entail an excessive amount of disk I/O. Unfortunately, however, most existing systems do not focus on disk I/O aspects and are vulnerable to performance degradation when the scale of input matrices and intermediate data grows large. To address this problem, we present a new out-of-core matrix computation system called PreVision. The PreVision system can achieve optimal buffer replacement by leveraging the deterministic characteristics of data access patterns, and it can also avoid redundant I/O operations by proactively evicting the pages that are no longer referenced. Through extensive evaluations, we demonstrate that PreVision outperforms the existing out-of-core matrix computation systems and significantly reduces disk I/O operations.

The density-based clustering is utilized for various applications such as hot spot detection or segmentation. To serve those applications in real time, it is desired to update clusters incrementally by capturing only the recent data. The previous incremental density-based clustering algorithms often represent clusters as a graph and suffer serious performance degradation. This is because a costly graph traversal is required to check whether a cluster is still connected whenever a point is removed. In order to address the problem of slow deletion, this paper proposes a novel incremental density-based clustering algorithm called DenForest. By maintaining clusters as a group of spanning trees instead of a graph, DenForest can determine efficiently and accurately whether a cluster is to be split by a point removed from the window in logarithmic time. With extensive evaluations, it is demonstrated that DenForest outperforms the state-of-the-art density-based clustering algorithms significantly and achieves the clustering quality comparable with that of DBSCAN.

As the world becomes increasingly data-centric, the tasks dealt with by a database management system (DBMS) become more complex and diverse. Compared with traditional workloads that typically require only a single data model, modern-day computational tasks often involve multiple data sources and rely on more than one data model. Unfortunately, however, there is currently no standard benchmark program that can evaluate a DBMS in the various aspects of multi-model databases, especially when the array data model is concerned. In this paper, we propose M2Bench , a new benchmark program capable of evaluating a multi-model DBMS that supports several important data models such as relational, document-oriented, property graph, and array models. M2Bench consists of multi-model workloads that are inspired by real-world problems. Each task of the workload mimics a real-life scenario where at least two different models of data are involved. To demonstrate the efficacy of M2Bench , we evaluated polyglot or multi-model database systems with the M2Bench workloads and unfolded the diverse characteristics of the database systems for each data model.

Given the prevalence of mobile and IoT devices, continuous clustering against streaming data has become an essential tool of increasing importance for data analytics. Among many clustering approaches, the density-based clustering has garnered much attention due to its unique advantages. The main drawback is, however, the limited scalability attributed to its relatively high computational cost, which is further aggravated when it has to update clusters continuously along with evolving data. In this paper, we present a new incremental density-based clustering algorithm called DISC optimized for the sliding window model. DISC is capable of producing exactly the same clustering results as existing methods such as Incremental DBSCAN for streaming data much more quickly and efficiently.

Researchers suffer from two problems while building a data processing pipeline for atmospheric scanning LiDAR. First, they must build an entire system that handles collecting signals, processing data, and visualizing the results. Second, they should support fast data processing to expand and deploy their system. In this paper, we introduce MISE, a fast integrated system that handles atmospheric scanning LiDAR data. MISE provides end-to-end processing, configuration options, and predefined signal-processing methods. In addition, the system uses an efficient chunking approach for fast processing with an array database. We demonstrate the construction and operation of a fine-dust particle monitoring system (based on a real-world scenario) using MISE. This demonstration demonstrates the usability and fast performance of MISE.