VŌR provides advanced fusion and prescriptive analytics to intelligently shift decisions windows to the left.  VŌRis a tailorable, horizontal analytics and Artificial Generic Intelligence (AGI)  solution, which encompasses the entire Big Data Life-cycle from data ingestion and munging to decision recommendations.  The driving philosophy behind VŌR is to augment analysts and help close the business translator gap through automation and discovery so organizations have the advanced analytics needed to impact positive change and growth. VŌR accomplishes this augmentation and gap narrowing with six major differentiators: Broad Spectrum Analytics, Unknown Discovery, Hypothesis Generation and Exploration, Iterative Analytics, Space/Time Representation and Storage and Curation. More details on these differentiators provided below.

VŌR generates Virtual Data Spheres™ of concentric data (like the one shown at left) to sort and search through massive data sets to identify gaps, overlaps, conflicts or missing information.  Then, proprietary Artificial Generic Intelligence (A.G.I.) components quickly and accurately identify relationships within the data. VŌR enabled mix initiative learning technologies allow subject matter experts to enhance the discovered relationships based on their intuition and gut feel. During this initial phase of data ingestion and relationships discovery, level 1 data fusion is successfully achieved.

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While VŌR™ is composed of sixteen tailorable sub components, build in a series of micro-services for rapid tailored product design, the follow six major differentiators provided information which sets  VŌR™ apart from competitors.


Broad Spectrum Analytics.  VŌR’s analytics encompass multiple techniques and methods in each form of analytics (descriptive, predictive and prescriptive) because we believe there is no single, right way to view all data.  VŌR automatically competes multiple methods and leverages them cooperatively to provide deep, rich, meaningful insights into data.  By leveraging multiple analytics views of the data, customers get high-quality, high-confidence answers that better support critical business decisions.


Unknown DiscoveryWhen confronted with large data sets, most approaches automate the simple tasks of describing the data.  However, to gain rich insight in the data and discover unknown relationships, correlations, models, behaviors, and causal triggers most leverage the cognitive power of human analysts, each with their own perspective/bias of the data.  VŌR automates the discovery of unknowns by taking an unbiased, naive approach to exploring data, exposing relationships and behaviors that it finds “interesting” and asking the user which discovery has meaning.  VŌR learns from this relationship discovery and human subject matter expert interaction and can delve further into the data and new discoveries, forming hypotheses about the unknowns and attempting to model these new discoveries so users can better exploit the knowledge VŌR presents them.  The benefit of unknown discovery is the ability to identify hidden, hard to see associations within the holistic data set, which drives a richer knowledge base, driving smarter decisions.


Hypothesis Generation and ExplorationWhat makes human analysts more insightful than traditional analytics programs?  The answer lies in humans’ ability to leverage multiple forms of reasoning, learned and experiential knowledge and to develop potential explanations or models that fit the situation. This is done through not only cognitive functions, but also through intuition, emotion and gut feel.  To date, machines have struggled with hypothesis generation as the rules for generating hypotheses are not static and not well-defined by a set of simple heuristics.  ISSAC has developed multiple methods and mechanisms to automatically generate hypotheses from data sets and explore them to understand which potential hypotheses are most likely valid and which have the biggest impact on an organization.  The Unknown Discoveries previously discussed enhance the relationships within the data that further improves the quality of these hypotheses.  The results focus potential courses of action an organization should pursue, with higher levels of confidence, driving the prescriptive outcomes of the future more accurately. The ISSAC developed Hyper Agent Simulation Programs (HASP) tool is leveraged for Hypothesis Generation and Exploration.


Iterative AnalyticsWhile there is great benefit to providing analysis based-on historical data, most organizations have critical real-time needs that require rich, deep analytics of current information as well.  VŌR provides both historical and real-time analytics with the historical analytics informing and evolving the real-time analysis quality.  Automated orchestration of historical analyses and updating real-time analytics provides an iterative framework to ensure real-time analytics and notifications provided to the user are as well informed as possible.  The result of the Iterative Analytics process is an ever-evolving enrichment of the knowledge available to the customer.  The more the system iterates on historical and real-time information, as well as interaction with subject matter experts, the more confident the hypothesis and courses of action become.  


Space/Time RepresentationThe huge influx of geo-tagged social media and Internet of Things (IoT) data has exposed many issues and limitations of space and time representation within data sets.  With trillions of potential 4-dimensional (4-d) points, the size of each point starts to matter and the math of comparison and contrasting becomes difficult and lengthy.  There are numerous solutions in the Big Data ecosystem that start to address these challenges, but none provides a holistic, single solution.  VŌR leverages ISSAC’s patent pending location representation technology to reduce 4-d point storage size by 55% or more, eliminates discontinuities and look up tables that plague other techniques, and provides an effective, accurate, simple comparative calculus.  This approach allows other VŌR analytics and ISSAC customers to achieve greater insights in shorter time, due to the reduced compute power and the simple manner used to compare and match behavior.  The impact of this technology is a drastic reduction in geo-tagging errors and very accurate 4-d positioning at the surface, sub-surface, air and space regimes with simple, precise behavioral comparisons between objects. 


Storage and CurationAs we stated earlier, Big Data necessitates multiple perspectives from which to perform analytics.  Some views and techniques require traditional database table and relational models, some views require a more NoSQL approach, and yet others require a graph-based view.  There are point solutions for each of these with some trivial cross-overs, but nothing in the ecosystem that supports all of these views without data replication.  VŌR leverages a new, patent pending technology that supports multiple views (graph, NoSQL, SQL) on the same data set to optimize the rapid retrieval of data and the exploitation of data in the system.  This approach results in a single, scalable storage and curation point for extremely large, wildly varying data sets without the need to do exotic indexing, lengthy graph traversal queries, or establishing a single data model.


CLICK HERE to download the ISSAC White Paper: "Novel Methods for system of systems modeling and simulation"