Open-standard cloud-native real-time geospatial data analytics using Kinota
Abstract
The availability of real-time/low-latency data has increased immensely in the last few years. For example, networked sensors feeding streaming analytics applications provide a wealth of new data available every day. Standardized communication across disparate data sets is a consistent obstacle when trying to incorporate information from multiple sources.The ability to easily ingest and integrate data across projects, locations, or even government agencies is of obvious utility.
Here we present KinotaÔ Analytics, a real-time data integration an analytics platform that leverages OGC SensorThings API standard (STA). STA is a state-of-the-art open standard for enabling real-time communication of sensor data. Building on over a decade of OGC Sensor Web Enablement (SWE) Standards, STA offers a rich data model that can represent a range of sensor and phenomena types and is domain agnostic. Additionally, and in contrast to previous SWE standards, STA is developer-friendly, as is evident from its convenient JSON serialization, and expressive OData-based query language (with support for geospatial queries); with its Message Queue Telemetry Transport (MQTT), STA is also well-suited to efficient real-time data publishing and discovery. Kinota Analytics includes an open-source implementation of STA while including scalable cloud-native analysis tools to provide real-time insights.Kinota is the rapid integration component in a larger IoT platform designed for low latency data which includes secure, scalable, data processes, as well as predictive and descriptive analytics. Additionally, Kinota architecture is very modular allowing for customization by adopters who can choose to replace parts of the existing implementation when desirable. The architecture is also highly portable providing the flexibility to choose between cloud providers. The scalable, flexible and cloud friendly architecture of Kinota makes it ideal for use in next-generation large-scale and high-resolution real-time environmental monitoring networks used in domains such as hydrology, geomorphology, and geophysics - for example data fusion with remotely sensed data for applications such as ground deformation/geodetics or crop monitoring - as well as management applications such as flood early warning, and regulatory enforcement.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN21C0866M
- Keywords:
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- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES;
- 1964 Real-time and responsive information delivery;
- INFORMATICS;
- 4315 Monitoring;
- forecasting;
- prediction;
- NATURAL HAZARDS;
- 7924 Forecasting;
- SPACE WEATHER