The Bright Future of Semantic Graphs and Big Connected Data by Alex Woodie.
From the post:
Semantic graph technology is shaping up to play a key role in how organizations access the growing stores of public data. This is particularly true in the healthcare space, where organizations are beginning to store their data using so-called triple stores, often defined by the Resource Description Framework (RDF), which is a model for storing metadata created by the World Wide Web Consortium (W3C).
One person who’s bullish on the prospects for semantic data lakes is Shawn Dolley, Cloudera’s big data expert for the health and life sciences market. Dolley says semantic technology is on the cusp of breaking out and being heavily adopted, particularly among healthcare providers and pharmaceutical companies.
“I have yet to speak with a large pharmaceutical company where there’s not a small group of IT folks who are working on the open Web and are evaluating different technologies to do that,” Dolley says. “These are visionaries who are looking five years out, and saying we’re entering a world where the only way for us to scale….is to not store it internally. Even with Hadoop, the data sizes are going to be too massive, so we need to learn and think about how to federate queries.”
By storing healthcare and pharmaceutical data as semantic triples using graph databases such as Franz’s AllegroGraph, it can dramatically lower the hurdles to accessing huge stores of data stored externally. “Usually the primary use case that I see for AllegroGraph is creating a data fabric or a data ecosystem where they don’t have to pull the data internally,” Dolley tells Datanami. “They can do seamless queries out to data and curate it as it sits, and that’s quite appealing.”
This is leading-edge stuff, and there are few mission-critical deployments of semantic graph technologies being used in the real world. However, there are a few of them, and the one that keeps popping up is the one at Montefiore Health System in New York City.
Montefiore is turning heads in the healthcare IT space because it was the first hospital to construct a “longitudinally integrated, semantically enriched” big data analytic infrastructure in support of “next-generation learning healthcare systems and precision medicine,” according to Franz, which supplied the graph database at the heart of the health data lake. Cloudera’s free version of Hadoop provided the distributed architecture for Montefiore’s semantic data lake (SDL), while other components and services were provided by tech big wigs Intel (NASDAQ: INTC) and Cisco Systems (NASDAQ: CSCO).
This approach to building an SDL will bring about big improvements in healthcare, says Dr. Parsa Mirhaji MD. PhD., the director of clinical research informatics at Einstein College of Medicine and Montefiore Health System.
“Our ability to conduct real-time analysis over new combinations of data, to compare results across multiple analyses, and to engage patients, practitioners and researchers as equal partners in big-data analytics and decision support will fuel discoveries, significantly improve efficiencies, personalize care, and ultimately save lives,” Dr. Mirhaji says in a press release. (emphasis added)
If I hadn’t known better, reading passages like:
the only way for us to scale….is to not store it internally
learn and think about how to federate queries
seamless queries out to data and curate it as it sits
I would have sworn I was reading a promotion piece for topic maps!
Of course, it doesn’t mention how to discover valuable data not written in your terminology, but you have to hold something back for the first presentation to the CIO.
The growth of data sets too large for ETL are icing on the cake for topic maps.
Why ETL when the data “appears” as I choose to view it? My topic map may be quite small, at least in relationship to the data set proper.
OK, truth-in-advertising moment, it won’t be quite that easy!
And I don’t take small bills. 😉 Diamonds, other valuable commodities, foreign deposit arrangements can be had.
People are starting to think in a “topic mappish” sort of way. Or at least a way where topic maps deliver what they are looking for.
That’s the key: What do they want?
Then use a topic map to deliver it.