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GCN : February 2014
Although the practical use of topology in big data analysis is new, the roots of topological data analysis date back to research begun in the 1970s at Stanford University. In 2003 the university received $10 million from DARPA and the National Science Foundation to develop TDA into a practical tool, and Ayasdi was founded in 2008 to commercialize software developed from that research. "We plan to be ubiquitous," said Ben Mann, Ayasdi's vice president of federal operations. The software is being used today in the pharmaceutical, energy and nancial services sectors, as well as in government agencies including the Agriculture Department, where it has been used to study E. coli bacteria; the Director of National Intelligence's Intelligence Advanced Research Projects Activity; and the National Institute of Allergy and Infectious Diseases. Lawrence Livermore is a national leader in modeling, simulation and big data computing, working in areas ranging from climate change to national security. A key area in which the lab hopes to bring the Ayasdi system to bear is the area of public health, said Anantha Krishnan, director of the lab's Of ce of Mission Innovation. "Biodefense has been a mission of the lab for 20 years now," Krishnan said. In that time the eld has moved from simply deploying biological detection devices and into the clinical space. Now biodefense is entwined with public health. The large volume of personal medical data being gathered by public health agencies is "a very rich target for us." The lab is also at the forefront of bioinformatics, which deals with the analysis of biological data, and hopes to bring TDA into this research area. "We're talking about terabytes of data or probably more," Krishnan said. "If you want to get a handle on the global problem, you are looking at a pretty big data challenge." --William Jackson Government uses of TDA that what is being studied exists in a met- ric space in which the distance between points can be measured. In three-dimen- sional space, this can be used in tasks ranging from computer graphics to statis- tics to infer features or relationships. Us- ing TDA, data points from more complex data sets can be put into a multidimen- sional framework and relationships iden- tified based on the distances of the points from each other. "The fundamental idea is that topolog- ical methods act as a geometric approach to pattern or shape recognition within data," says a September 2013 article in the journal Science co-authored by Ayas- di CEO Gurjeet Singh. It allows "explora- tion of the data, without first having to formulate a query or hypothesis." That is, researchers can find things they did not know they were looking for. For instance, in a database of billions upon billions of phone records scientists could make sense of who was talking to whom. TDA could show these patterns across multiple databases without being queried about specific relationships. At a high level the concept is simple. But it is difficult for people living in a 3D world to make sense of the n-dimensional space in which data lives, Ayasdi s Mann said. "It is very hard for anyone to picture in his mind what that complicated shape is."Another difficulty is picturing rela- tionships not just within a data set, but between data sets that have differing formats. TDA can identify and display shapes based only on the notion of dis- tance between points regardless of the specific dimensional framework of the data set. The software developed for the Insight Discovery platform analyzes the data to produce dimensional shapes, then uses algorithms to extract relationships shown in them. The platform does not query the databases. "We let the data speak to us and illustrate features we might not have been looking for," Mann said. Mann called the Lawrence Livermore partnership "a huge opportunity for us," because of the technical expertise of the lab s staff, its access to many types of data and its advanced computing power. The lab, in Livermore, Calif., is home to two of the fastest supercomputers in the world. Its Sequoia was ranked the third fastest at 17.2 petaflops (quadrillion cal- culations per second) in the most recent TOP500 listing, and Vulcan was in ninth place at 4.3 petaflops. The collaboration arose from per- sonal connections between the lab and people at DARPA familiar with the re- search, Lawrence Livermore s Krishnan said. "Many of us became convinced this could have a major impact on what we re trying to do. One of the things we saw im- mediately was its ability to go into com- plex, heterogeneous data sets and extract patterns in a way that is query free." An agreement was reached in February 2013. The software is being made avail- able to Lawrence Livermore researchers who are looking at ways to use it. So far, "feedback has been positive," Krishnan said. • GCN FEBRUARY 2014 • GCN.COM 27