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Solution

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Gathering & Analytics

Capture from Multiple Sources

Internal and External Data

Define Relevant Data Capture Processes

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Structuring and Prediction

Processing / Analysis

NLP & Taxonomy Structuring & Predictive Reasoning

Measurable Values via Non-Linear Inference

Train System to Individual Topics

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Interactive Presentation

Present Results

User-friendly Dashboard

Searched Documents & Calculated Predictions

Implementation Timeline

01
Set up / Parametrize

1 - 2 days

02
First Operational Solution

3 - 4 weeks

03
Continuous Refinement of Solution

Ongoing

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The Data Loading and Data Preparation Layer handles all data loading and document pre-processing activities.

Data capture is supported from three different sources: internal data, social media screening and web-data crawling/RSS feeds.
During the data loading process, each loaded document is processed via a complete NLP (Natural Language Processing) stack consisting of:
For supporting document analytics the NLP stack further includes:

The Learning and Knowledge Capture Layer consists of all services required to train the text analytics models (NLP) to individual knowledge domains, to build individual taxonomy and ontology structures for semantic content categorisation and to build predictive reasoning models using Dydon’s NeuroFuzzy concept.

The Presentation Layer has all generated results presented using parameterizable, easy to use and intuitive dashboards.

The Process

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Dydon Technology & Architecture

Java core base with open-source components

Database: MongoDB, NoSQL & REST APIs​

AI modules: Apache Lucene/Solr, OpenNLP, Mallet, Fuzzy4j​

Fully web-based, cloud and on-premise operation​

NLP Stack Components

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