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Cognitive Knowledge Data Lake from Multiple Complex Datasets.

Nebuli's Augmented Knowledge Discovery from complex datasets.

Applying Nebuli’s Datastack frameworks to converge multiple data silos into a smart data lake to expand a scientific publisher’s multi-million user cognitive knowledge discovery network.

Key Services

  • Hyper-personalisation

  • Behavioural Data

  • Consumer Insights

  • Knowledge Discovery

  • Gamification

  • Smart UI/UX

Key Markets

  • Personalised Nutrition

  • Personalised Health

  • Digital Health

  • Digital Biotech

Nebuli Built Smart Knowledge Data Lake from Multiple Complex Datasets.
Objectives

Despite of the availability of advanced search engines, such as Google, professionals struggle to conduct appropriate research for the problem they wish to explore. Teams also struggle to find qualified researchers in a given field to conduct accurate research in a niche, especially if those researchers are in a different discipline.

In this project, Nebuli constructed maps of the research landscape for a leading research organisation which helps their employees to navigate the research world and answer some of the questions that more traditional knowledge discovery approaches will not facilitate.

Nebuli’s Solution

Nebuli’s cognitive discovery engine helped the client’s existing data systems to read full-text datasets and conceptualise topics into Nebuli’s Data-Driven World (DDW) through the Datastack framework.

  • Connect as many research paper ecosystems and research data sources as possible for Nebuli’s smart indexing process and generate critical trends analysis based on specific parameters inputted by researchers and government agencies.

  • Creation of several of Data-Driven Worlds (DDWs) from thousands of data collections.

  • Cognitive Search of specific data elements within the DDW.

  • Data clustering and segmentation of specific data parameters defined by individual researchers within each DDW.

  • Creation of Data maps of DDW (Visualisation) using self-organising map (SOM) models and data vectors that can be displayed on Researcher.AI‘s UI and loaded within an organisation’s internal data visualisation software via API libraries.

  • Creation of an isolated system with its own database for each DDW that allows for more in-depth analysis of targeted traits of the virus, particularly when new traits are discovered and reported in various journals.

Outcomes

This model revolutionised users’ search and knowledge discovery, as Nebuli took on much of the human effort involved in compiling valuable and relevant information. The client’s team saved an average of three business days per week worth of searching per week per team member.

This model goes far beyond referencing tags and keywords and is a fully niche-specialist knowledge discovery engine that meets the client’s specific needs.

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