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.