Finding the unknown unknowns.
ENSIGN solves a critical problem in the application of machine learning in data science – how to gain deep insight from the entirety of massive-scale multidimensional unlabeled data while supporting, but not requiring, heroic up-front feature engineering. The approach builds on advanced, well-founded techniques from the field of spectral hypergraph analytics.
These techniques have been extended and made computational tractable using Reservoir’s patented data structures and supporting proprietary algorithmic advances. Combined with supporting tools, these advances enable a broad range of potential use cases, scalable and streaming operation, and the ability to leverage a variety of computing configurations.
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ENSIGN is written in ANSI C and can run on any size platform from desktop PCs to massive shared and distributed-memory supercomputers with or without GPUs. Using Dask for parallelism and an in-memory Python workflow, ENSIGN supports massive scale data from end to end.
No need for labeling or heroic feature engineering. Just ingest and go. ENSIGN supports a variety of multi-domain use cases in security, forensic analysis, and scientific discovery. Exploratory tools provide novel ways to examine and visualize interesting correlations.
Hypergraphs are more than just expanded graphs. The time of being limited to statistical measures and basic graph metrics like centrality is over. ENSIGN provides a way to make smarter use of large-volume linked data that exploits rather than flattens intrinsic multidimensional correlations.
Joint analysis allows two or more datasets with overlapping dimensions to be analyzed together without the combinatorial explosion of a relational join. Streaming analysis allows new data to be added to an existing analysis. Both operations provide massive, practical computational efficiency for modern use cases.
ENSIGN includes support for generating reports and visualizing results. With ENSIGN, it is possible to see the patterns in data and understand the influence of specific actors.
ENSIGN is callable from a Python interface and includes supporting analytic tools written in Python. This means ENSIGN can seamlessly interoperate with leading edge data science tools and packages such as Anaconda.
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632 Broadway, Suite 803
New York, New York 10012
632 Broadway,
Suite 803 New York,
New York 10012
