In 2009, while working as a contractor for the US Army, Kinetica leveraged parallel GPU processing and visualization of real-time data in a fast-changing environment. More than a decade later, it is not only available as a Cloud service with AI/ML integration but has worked on mega-scale IoT data analytics globally.
With the advent of Generative AI and automated workflows, the company is determined to transform data analytics with its native LLMs(Large Language Models).
Operating across multiple sectors, some of its main partners and clients include T-Mobile, Ford, Mahindra Racing, the US Air Force, and many others. Kinetica has also forged strategic partnership with NVIDIA, Dell, AWS, Microsoft Azure, and Google Cloud.
“Explosion in location data availability is driving innovations in sectors like logistics, urban planning, healthcare, and marketing, opening up new possibilities for precise tracking, analysis, and improved decision-making based on spatial insights”, says Chad Meley, Chief Marketing Officer, Kinetica, in an interview with Geospatial World.
What are some of the main challenges in near real-time analysis of geospatial data?
Analyzing real-time geospatial data presents several challenges. Firstly, fusing multiple datasets involving points and polygons demands complex spatial data integration and alignment. Secondly, performing advanced geometry calculations in real-time requires significant computational resources.
Additionally, maintaining a low latency profile to capitalize on time-sensitive opportunities can strain system resources. Balancing these requirements while containing compute and engineering costs is a further challenge in real-time geospatial analysis.
What makes Kinetica’s native LLM stand apart from others?
Kinetica’s native Language-to-SQL (LLM) stands out for its specialized training, focusing on converting natural language queries into SQL queries with a strong emphasis on geospatial syntax, including complex geo-joins and ST_geometry functions.
This unique feature ensures that users can seamlessly interact with geospatial data, making complex spatial data queries more accessible and efficient. By prioritizing geospatial syntax, Kinetica’s LLM enhances the usability and effectiveness of dealing with location-based data.
First Machine Learning and now Generative AI have been transformative breakthroughs across multiple sectors. What do you think would be its impact on the geospatial domain?
Language-to-SQL models have often neglected geospatial syntax, primarily concentrating on basic SQL queries like simple counts and aggregations on straightforward table structures. However, spatial queries pose a unique challenge, as they are conceptually simple but intricate to code correctly.
As these barriers are overcome, we can anticipate a surge in the value of location-enriched data.
By enabling easier interaction with geospatial data sets, addressing this gap will empower a broader range of users to harness the insights and potential of geospatial data, unlocking new opportunities for innovation and analysis in various industries.
Dataset fusion, integration, and interoperability have emerged as some of the defining trends today in the realm of data analytics. How do you see the future of data in terms of viability as well value 5-10 years down the line?
Location-enriched data is experiencing exponential growth due to the proliferation of location-aware technologies, including GPS-enabled chips, an increase in satellite deployments, Bluetooth tags, RFID systems, CCTVs, and the widespread adoption of 5G networks.
These devices are continuously capturing and transmitting location data, providing a rich source of information for businesses and organizations.
This explosion in location data availability is driving innovations in sectors like logistics, urban planning, healthcare, and marketing, opening up new possibilities for precise tracking, analysis, and improved decision-making based on spatial insights.
