Very cool! I'm particularly interested in this section:
> SpaceTime database is built on a unique, multidimensional data index that automatically adapts to mutable and possibly highly skewed data distribution (which is usually the case with data coming from moving objects). There is one single multi-columnar index which indexes all associated columns of records at once. There are no secondary indices!
> The physical model intertwines index and record data – as a consequence, records that are logically close (based on id, position and timestamp) are at the same time also physically close. In turn, this maximizes the throughput of disk reads.
> The main ideas for inventing SpaceTime index came from several inspiring scientific papers. It is similar to the kd-tree family of indices, but with two major improvements: first, the index tree in SpaceTime is built using the bottom-up approach (as opposed to the top-down kd-tree construction) and second, the process of index creation adapts to particular space-time distribution of data. Kd-trees work well on a large scale only with static datasets; our bottom-up approach overcomes this.
Can you provide links to the scientific papers that were mentioned?
Are there any plans to publish a more detailed description of SpaceTime's index data structure and algorithm?
Hi, thanks for the interest! Yes, we are planning to publish more details about the novel index structure, so feel free to visit https://blog.mireo.hr/ every 2 weeks.
Very soon we will also launch a live demo with 200,000 vehicles, so you'll be able to try SpaceTime capabilities on your own :)
> SpaceTime database is built on a unique, multidimensional data index that automatically adapts to mutable and possibly highly skewed data distribution (which is usually the case with data coming from moving objects). There is one single multi-columnar index which indexes all associated columns of records at once. There are no secondary indices!
> The physical model intertwines index and record data – as a consequence, records that are logically close (based on id, position and timestamp) are at the same time also physically close. In turn, this maximizes the throughput of disk reads.
> The main ideas for inventing SpaceTime index came from several inspiring scientific papers. It is similar to the kd-tree family of indices, but with two major improvements: first, the index tree in SpaceTime is built using the bottom-up approach (as opposed to the top-down kd-tree construction) and second, the process of index creation adapts to particular space-time distribution of data. Kd-trees work well on a large scale only with static datasets; our bottom-up approach overcomes this.
Can you provide links to the scientific papers that were mentioned?
Are there any plans to publish a more detailed description of SpaceTime's index data structure and algorithm?