Data Analytics
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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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
Supporting the 2020 NY Metro Joint Cyber Security Conference
This year the NY Metro Joint Cyber Security Coalition is hosting its annual conference virtually on Thursday, October 22, an event where cyber security professionals in the Greater New York City come to gain insight into various aspects of information
CLSAC 2020: Capturing Spatial and Temporal Variation in Behaviors Related to COVID-19 using ENSIGN
At the Chesapeake Large-Scale Analytics Conference (CLSAC) this week, Reservoir Labs demonstrates how tensor decomposition methods can be applied to gain insight into the efficacy of Non-Pharmaceutical Interventions (NPIs) used to contain the spread of COVID-19 in the Random Access Session:
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
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
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
Multiscale Data Analysis Using Binning, Tensor Decompositions, and Backtracking
Large data sets can contain patterns at multiple scales (spatial, temporal, etc.). In practice, it is useful for data exploration techniques to detect patterns at each relevant scale. In this paper, we develop an approach to detect activities at multiple scales using tensor decomposition, an unsupervised high-dimensional data analysis technique