Data Analytics

Finding the unknown unknowns.


Spectral hypergraph analytics at massive scale

Finding deep patterns at enterprise scale

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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Core Capabilities

Analyze Data at Massive Scale

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.

Discover Deep Insights

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.

True Multidimensional Analysis

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 and Streaming Data Analysis

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.

Visualize Results

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.

Full Python 3 Integration

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.

The Latest

Reservoir Labs Presents New Research at IEEE HPEC 2020

The 2020 IEEE High Performance Extreme Computing Conference, happening virtually from September 22 – 24, will feature novel, breakthrough research on an array of Reservoir Labs’ R&D including R-Stream, ENSIGN, and Algorithms. Reservoir engineers will be presenting their findings during

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Recent Publications

Efficient and scalable computations with sparse tensors

In a system for storing in memory a tensor that includes at least three modes, elements of the tensor are stored in a mode-based order for improving locality of references when the elements are accessed during an operation on the tensor. To facilitate efficient data reuse in a tensor transform

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Systems and methods for selective expansive recursive tensor analysis

A system for performing tensor decomposition in a selective expansive and/or recursive manner, a tensor is decomposed into a specified number of components, and one or more tensor components are selected for further decomposition. For each selected component, the significant elements thereof are identified, and using the indices of the

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