TritanDB: Time-series Rapid Internet of Things Analytics

TL;DR

TritanDB is a high-performance time-series database optimized for IoT data, enabling efficient data management, integration, and analysis across resource-constrained devices and cloud environments.

Contribution

The paper introduces TritanDB, a novel IoT-optimized time-series database with advanced compression, low-overhead query translation, and support for rich data models like RDF.

Findings

Order of magnitude performance improvement over existing databases

Effective compression techniques tailored for IoT data

Supports complex analyses like forecasting

Abstract

The efficient management of data is an important prerequisite for realising the potential of the Internet of Things (IoT). Two issues given the large volume of structured time-series IoT data are, addressing the difficulties of data integration between heterogeneous Things and improving ingestion and query performance across databases on both resource-constrained Things and in the cloud. In this paper, we examine the structure of public IoT data and discover that the majority exhibit unique flat, wide and numerical characteristics with a mix of evenly and unevenly-spaced time-series. We investigate the advances in time-series databases for telemetry data and combine these findings with microbenchmarks to determine the best compression techniques and storage data structures to inform the design of a novel solution optimised for IoT data. A query translation method with low overhead even on resource-constrained Things allows us to utilise rich data models like the Resource Description Framework (RDF) for interoperability and data integration on top of the optimised storage. Our solution, TritanDB, shows an order of magnitude performance improvement across both Things and cloud hardware on many state-of-the-art databases within IoT scenarios. Finally, we describe how TritanDB supports various analyses of IoT time-series data like forecasting.