The Digital Knowledge Layer for Microsoft® Azure

The Digital Knowledge Layer for Microsoft Azure
Maana Founder, President and CTO, Donald Thompson

Hear Maana Founder, President and CTO, Donald Thompson talk about Maana’s “Knowledge First” approach and how Maana’s Computational Knowledge Graph helps form and answer the problem questions.

Knowledge Portal

Build your Knowledge Layer with a highly-intuitive visual experience.

  • Create and query the knowledge graph
  • Hydrate domain concepts in the knowledge graph with data
  • Trigger bots to enrich the knowledge graph with dynamic links
  • Create and compose services with function composition features
  • Add and orchestrate services on the knowledge graph

Command Line

Provides interactive and scripted access to convenient system actions.

  • Schema management, data loading, querying and administration. The command line interface is easily extensible with custom plug-ins
  • Allows extendibility with custom plugins making it easy for developers to add functionality

Customer-Owned Applications

Knowledge applications are use cases developed by customers on Maana platform.

  • They provide AI-driven recommendations into operational decisions
  • A knowledge application is made up by decision models that perform real-time calculations

A customer does not have access to knowledge applications developed by other customers.

Computational Knowledge Graph™

The core of the Maana Knowledge Platform™ is the patented Maana Computational Knowledge Graph™.

  • It is not a graph database, nor relies on ontologies and description logics
  • The Computational Knowledge Graph separates the structure and computations to be performed from the data itself
  • It enables a fluidity of modeling, allowing data from any source or format to be seamlessly integrated, searched, analyzed, operationalized and re-purposed
  • Each resulting model is a combination of subject-matter expertise, relevant data, and the algorithms to be performed
  • The Maana Computational Knowledge Graph is dynamic because it can represent conceptual and computational models
  • It is designed to perform complex transformations and calculations at interactive speeds, making it a game-changing technology for agile development knowledge applications

Data and Domain Models

Create models that represent both your business domains and data, whatever its source.

  • Data from any source and in any format can be seamlessly integrated, modeled and leveraged as a part of the Knowledge Layer
  • Subject-matter experts can leverage the Knowledge Portal to encode their expertise in the form of domain models
  • This forms the digitized, reusable tacit knowledge that is often the competitive edge for businesses
  • Domain modeling is not a one time task and models are able to be easily edited to account for the fast paced, ever changing nature of the modern enterprise

Search and Query

The Search capability is foundational to the application development on the Platform for the developers of knowledge applications.

  • They can search for everything on the computational knowledge graph
  • They extend from basic building blocks such as Functions and Kinds (and their instances) to higher order components such as Knowledge/Function/Query Graphs and Services
  • The results are disambiguated into six major buckets that make it easy for the developer to find exactly what they are looking for
  • Once the right components are found they are dragged into the Knowledge Portal to select, connect, curate, and populate the graph of kinds and services that form their Workspace

Calculate and Transform

Much more than just data look-ups.

  • Using Maana’s extensive library of machine learning services, data scientists can ensure that the right algorithm, in the development language of choice, is always a few clicks away
  • This allows for complex transformations and calculations to be performed at interactive speeds, making it a game-changing technology for agile development of AI-driven knowledge applications
  • In addition to the existing library of transformational services, users can easily add desired or created services to the Maana services catalog via a graphQL endpoint

Learn and Reason

The ability to Observe, Learn, Reason and Decide are foundational to developing knowledge applications to produce AI-driven recommendations.

  • The Maana Platform introduces a problem-decomposition methodology
  • It is implemented via the Function Decomposition feature
  • It guides developers to think about the “business” problem they are solving via services and functions in stages reasoning and learning

The latest machine learning technologies are available to developers in the form of Azure and others existing services wrapped in GraphQL, like machine learning libraries.

Bots and Assistants

Bots help developers automate many of the tasks of building machine learning models and perform automatic event-driven actions or notify other bots or users to take certain actions. Examples of bots that come with the Platform are:

  • Form knowledge and create the graph
  • Capture expertise
  • Integrate data into graph
  • Extract and add knowledge 
  • Derive value from knowledge
  • Automate building machine learning models
  • Extract complex knowledge from data 
  • Reason over knowledge

In addition to bots and assistants that come with the Platform, developers can develop additional bots and assistants and make them part of their Maana platform deployment.

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  • Accenture
  • Microsoft Azure

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