Intel on Wednesday outlined pilots for its Trusted Analytics Platform, which is a set of open source tools designed to make big data projects easier to implement.
The Trusted Analytics Platform (TAP) was mentioned at the Intel Developer Forum in August. At Strata + Hadoop World in New York, Ron Kasabian, general management of Intel's big data unit, outlined the effort.
TAP includes a series of algorithms, tools and engines to analyze and collaborate with data scientists. The software is open source and includes integration with Intel architecture and hardware. TAP includes:
- A data layer that includes Apache Hadoop and Spark optimized for Intel hardware.
- An open analytics layer that can support predictive application programming interfaces.
- Support for cloud native apps.
In an interview, Kasabian said that Intel has deployed a series of big data proofs of concept and TAP aggregates that knowledge. "With TAP we're reusing different components to build modular proof of concepts," said Kasabian. He added that enterprises need to scale proofs of concepts to show potential returns on big data projects.
The chip giant has been a key player in the big data ecosystem and has partnered with Cloudera. Intel has also open sourced its tools to optimize big data applications to speed up compute time with enhanced security. Intel's business interest in big data primarily revolves around selling more server processors.
As for the pilots, Intel outlined a series of projects that have involved TAP. The TAP architecture is being used by the following:
- Penn Medicine is using analytics to combine patient data from multiple repositories to forecast risk of disease and readmission.
- Icahn School of Medicine of Mount Sinai is hoping to use data science to find new drug therapies.
- Oregon Health & Science University is developing a cancer cloud that aims to allow hospitals to share genomic data to discover potential cures.
Of those projects, the Oregon Health & Science University pilot is most notable. Research hospitals see their genomic data as their intellectual property. Kasabian said TAP was used to create a "distributed big data" architecture. The idea is that researchers could share data in aggregate via on instance in a "secure enclave" used by a virtual machine.
When the necessary data was shared, the virtual machine and the information was decommissioned. The setup allowed "data to remain at the institution but still be shared," said Kasabian. "No one was giving up their data but allowed it to be used for research."