Deep Data Analytics
1st Open Call
EDI-2018-3-JOT_1. Temporal pattern recognition in searches on the
Web search and the underlying user intent is highly volatile and influenced by a plethora of factors. Marketers are challenged in optimizing PPC campaigns based on sparse click data.
To support marketing and budget decisions Deep Data Analytics aims to develop a deep neural net to detect non-trivial patterns in searches on the web to gain impactful insights into user behavior.
Centered around Natural Language Processing, Deep Data Analytics will enrich the data provided with geospatial information and additional data sources to enable the Neural Net to recognize signifikant patterns in the web searches that would escape human attention.
80% of global data is unstructured and cannot be analyzed with classic methods. Deep Data Analytics closes this gap with Artificial Intelligence.
Deep Data Analytics’ mission is to categorize, analyze and understand unstructured data to gain actionable insights and a competitive edge over competitors following three concepts:
- Actionable: DDA extracts actionable information from unstructured data.
- Adaptable: The world is changing, so is data. Whether email, documents, social media or messages. DDA uses all kinds of data sources to generate information.
- Scalable: DDA models scale to datasets of any size and deliver insights quickly and accurately.