The technology at Ayasdi, based on topological data analysis, is one of the top 10 innovations developed at DARPA in the last decade. It is key to unlocking some of the biggest national security
challenges that we face today.

Tony Tether
former DARPA Director

Ayasdi For Public Sector

Predict and Prevent Threats

You have unprecedented and exponentially growing amounts of data at your disposal. This presents both an opportunity as well as a challenge. With the right analytical tools, data analysis offers the potential to discover critical intelligence that can help accelerate mission outcomes, and furthermore, improve the efficiency, quality and cost-effectiveness of the services that your departments and agencies deliver.

Ayasdi’s advanced analytics solution helps government and public sector organizations rapidly uncover critical insights from their data that was previously hidden or overlooked. Based on Topological Data Analysis (TDA) technology created at the Defense Advanced Research Projects Agency (DARPA), Ayasdi’s solution represents a credible, new approach to analyzing highly complex and disparate data. Government organizations use Ayasdi’s analytics solution to improve target discovery and patterns of life detection, refine models and reduce false positives, complement existing signal processing methods, and refine public health initiatives.

Target Discovery and Anomaly Detection

Federal agencies have access to more data than they can possibly analyze. Ayasdi’s solution automatically categorizes newly arriving data into previously segmented groupings. It increases the thoroughness with which higher value data is analyzed, thereby improving the chances of discovering targets and identifying anomalies.

Agencies are using Ayasdi’s solution to analyze complex and disparate data sets to create more precise profiles of individuals, entities and populations that helps with target discovery. The solution combines advanced machine learning techniques with topological data analysis to automatically analyze thousands of attributes simultaneously to uncover subtle correlations and links between events and individuals and segments of populations that other analytical methods would have missed or taken months to identify. By analyzing and segmenting the existing data, it makes it easier for agencies to then compare newly arriving data with identified targets or segments of interest and immediately flag anomalies for further investigation.

Patterns of Life Detection

Agencies are looking for ways to mine through complex data from disparate sources to uncover sequences of activities or “patterns of life” leading up to a significant event. By understanding the subtleties within a sequence of activities that preceded an event, organizations can better predict future events.

Ayasdi’s advanced analytics solution is helping agencies fuse and analyze highly complex sets of data to identify patterns of activities that resulted in a threat. It uses advanced machine learning algorithms and topological data analysis to detect subtle yet meaningful patterns in sequences of activities - previously hidden or overlooked. By identifying and establishing these patterns as baselines, agencies are better equipped to analyze future sequences of events to proactively thwart potential threats.

Model Improvement

Agencies are in dire need of effective ways of refining their models to reduce the number of false negatives and false positives that can result in savings of millions of dollars, or meeting other mission needs.

Organizations have used Ayasdi’s advanced analytics solution to improve models, classifiers and entity extractors across a broad range of domains. This includes malware classifiers, dangerous material sensors, as well as financial disclosure, credit card and online transaction fraud. The solution helps provide a better understanding of weaknesses in existing models by automatically segmenting failures into groups that can then be improved upon individually.

Signal Processing for Threat Detection

Traditional signal processing tools struggle to differentiate “signal” from “noise” and often overlook or process out valuable information. Historically, specialized sensors have been deployed at great expense to capture and understand certain expected signals. While these processing techniques are good at examining an anticipated signal, they often dismiss what they perceive to be noise as irrelevant. Ayasdi’s analytics solution can be used to extract hidden structure from that noise. Finding new insights in this data can prove to be extremely valuable to government organizations.

Ayasdi’s advanced analytics solution can complement traditional signal processing tools by extracting new insights and relationships from previously analyzed data. It allows for the additional exploitation of signal data without further acquisition costs.

For example, DARPA used Ayasdi’s analytics solution to analyze acoustic data tracks. The analysis identified signals in what had been previously classified as unstructured noise, using traditional signal processing methods. By correlating and analyzing specific phenomena in the background, Ayasdi’s solution was able to distinguish and identify additional signal from the noise.

Public Health Surveillance

With the right analytics offering, public health organizations have the opportunity to capitalize on the influx of data to drive better health outcomes and reduce treatment costs.

Ayasdi’s advanced analytics solution helps public health organizations analyze disparate datasets from public and private sources – all in one place. It allows them to mine through complex sets of data to uncover previously hard-to-find correlations that help precisely segment populations to better predict the risk of disease and prevent outbreaks. For instance, organizations can use Ayasdi’s analytics solution to correlate health-related data (e.g., microbiome data) with non-health related data (e.g., social media) to gain a better understanding of infectious diseases, predict when and where outbreaks may occur, and create early warning systems.

Food safety organizations can use Ayasdi’s analytics solution to analyze both social media data as well as filed complaints to monitor and predict public health outbreaks.

Organizations can also benefit from using Ayasdi’s analytics solution to mine medical data by geography, eating habits as well as other behaviors that in turn, inform the design of improved public health policies and initiatives.

Environmental health organizations can gain deeper insight into the associations between air quality and health, for instance, by using Ayasdi’s solution to automatically identify correlations between environmental and medical data.

Ayasdi’s analytics solution can discover insights that help shape Health Affordability initiatives. It can be used to analyze data and identify families that are at risk of high healthcare expenses as a result of serious illnesses. Public outreach initiatives can then be fine-tuned using this information to promote preventive care to these families.

Public health organizations also have the opportunity to apply Ayasdi’s advanced analytics solution to correlating and analyzing data from government and clinical databases to identify the most cost-effective treatments for a variety of conditions.

Text and Unstructured Data Analysis

There is no shortage of solutions for natural language processing (NLP), sentiment analysis and entity extraction. However, most of these solutions fail to uncover the subtle nuances and patterns within data that can potentially provide a deeper understanding of sub-groups of populations.

Ayasdi’s advanced analytics solution complements existing approaches to analyzing text and other unstructured data. It uses advanced machine learning algorithms and topological data analysis to augment the output from existing solutions, further improving the quality and sensitivity of these systems. For example, Ayasdi’s solution can help organizations automatically uncover insights into topics that are significant to specific subpopulations. It identifies emerging conversations, provides a means to monitor the evolution of groups and uncovers time-sensitive patterns.

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