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Advanced Analytics for a Complex & Dynamic World

Overview


Pattern Based Discovery


Pattern Based Prediction


Pattern Based Reasoning

Quantum Leap Pattern Based Discovery enables users to find informative patterns within large, complex data sets.  Key features include:

  1. •Ease of Use - For the first time, discovery of informative patterns in data is automatic

  2. •Transparency – Unlike any other approach, patterns are independent of a user’s domain expertise and actually project human knowledge

  3. •Interoperability - Of course, patterns may be mapped to SQL queries

  4. •Data Enrichment - Patterns reduce data volume and present information concisely

  5. •Data Integrity – The data underlying a given pattern is never lost and may be displayed in its entirety with one click

  6. •Scalability - Patterns are the only way to efficiently distill Big Data without losing the needle in the haystack

  7. •Dynamic - Patterns are automatically updated as new data is added

  8. •Flexibility - Patterns can be easily tuned or combined to create a roadmap toward desired outcomes


Key Differentiators: Nobody else can do this. Everyone is trying.

  1. •Using an extension of Shannon Information Theory, Quantum Leap is the only way to discover multi-dimensional patterns with any efficiency.

  2. •Pattern Based Analytics is the only way to deal with arbitrary data relationships, both linear and non-linear. Factor analysis assumes linear relationships for characterizing data associations. Neural networks do assume arbitrary relationships, but are black box models that are not transparent to the end user.

  3. •Only Pattern Based Analytics can deal with  arbitrary statistical data distributions. This is again a result of using Shannon Information Theory. Many statistical correlation methods implicitly assume normal or Gaussian distributions.

  4. •And only Pattern Based Analytics can deal with significant amounts of missing data using proprietary methods unique to Quantum Leap Innovations.

pattern based DISCOVERY

Patterns in data are prevalent across multiple domains. For example, technical financial market analysis often uses pattern recognition to identify profitable trading opportunities. In the life sciences, patterns of multi-gene associations can provide fundamental understanding of disease mechanisms as a basis for finding cures. In marketing analysis, patterns of customer behavior are fundamental to driving strategies that are customized for different customer segments. More generally, in the real world, patterns represent complex combinations of different variables or factors that drive outcomes. Patterns are a fundamental way in which we organize our experiential knowledge as a basis for decision making. The ability to discover new patterns in data can thus provide a key edge to decision makers in an ever more competitive and fast moving world.