When a volcano erupts, viewers see the power of the earth unleashed. With all of this activity, it is easy to forget that the majority of a volcano’s power lies beneath the surface where nobody will ever see it — an eruption is just a pressure release valve for the limitless power beneath the surface.
This is the way we on the Vulcano Team are as well. At Vulcano, we’ve made a commitment to only discuss things that are already in progress. Some of these future development plans will be released in the very near future with the updated Vulcano Whitepaper. In the broader Vulcano economy, the movement, aggregation, compression, and verification is vital. With seismic and other sensor types coming online, the data collected by these external network components will needed to be integrated.
The purpose of this update today is to inform the community of another research opportunity which has been funded by the Vulcano Team. Soo Hyun Lee, a current student at Johns Hopkins University, is working on developing and testing a data compression model which will be used in concert with the seismic sensors, as well as other devices which are yet to have been released.* The research conducted by Ms. Lee will be vital to the long-term commercialization of the Vulcano network.
Data compression is often done by shortening clusters of repetitive characters. For example, if there is a string input of AAAABDFFF, this can be stored as A4BDF3. This saves storage space and the compression rate increases as the number of repetitive characters in a row increase. The Burrows Wheeler Transform (BWT) is a method of rearranging the characters of a string input. This method encourages the number of repetitive characters clustering next to one another. Even though it does not store the original string input, it simply re-clusters the characters with no loss of the actual input. Basically, it is a method of rearranging the data so that it becomes more easily compressible. This is particularly useful in compressing genomic sequences due to the high level of repetition.
While BWT is commonly used for compressing string input and genomic sequences, Ms. Lee will be exploring the expansion of its application to numerical data, such as that created by seismic sensors. This is not breaking new ground in terms of data compression, but it is laying the foundation for future work as VULC-enabled sensor devices and networks are constructed and deployed. Ms. Lee is a dedicated researcher and will be applying what she learns in this research context to future developments with Vulcano.
*Note: Ms. Lee, as an individual, has been given this research opportunity. She is a current student and her involvement with Vulcano should not be taken to mean that Vulcano has signed an MOU with JHU directly, as it has not. Funding for this has already been committed and a partial reimbursement for these funds is being submitted as the first governance proposal. The funding from this will not be sold for at least one year.