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QBeast Utility Analysis to marKet and Enterprise

Periodic Reporting for period 1 - Quake (QBeast Utility Analysis to marKet and Enterprise)

Reporting period: 2018-04-01 to 2019-09-30

The objective of the Quake project was to help in getting a research result (Qbeast) ready for the market, and help with the needed steps to create a BSC spin-off company (such as patenting the technology) to exploit it in relevant markets (data harvesting companies and data driven companies). The Qbeast indexing technology enables faster interactions (queries) with databases, thus making more efficient working with them, achieving results faster, and using less computing resources.

Since Qbeast improves efficiency in computing systems, this helps society by using less energy to achieve the same result, lowering the power needs for computation and making them more energy efficient. Besides, the creation of a spin-off company will create jobs related to the IT sector.
The objectives of the project Quake have been met, these aims were described in section 1.1 of the DoA and their results can be found in the different submitted deliverables:
1. To develop a clear understanding of the potential markets for our technology: Described in D2.1 and D2.2
2. To develop a prototype as a demonstrator for BI market outreach while exploiting our available prototype for the Biotech sector: See deliverable D5.1 for further information
3. To ultimately create a comprehensive business plan that will outline the relevant steps required to launch our technology to the market through the creation of a spin-off company: The business plan can be consulted in deliverable D3.2 although it is a confidential deliverable.

The most notable result of the project is the set of tools produced able to leverage the Qbeast indexing technology:
1. QDB: a Qbeast powered DataBase, able to answer queries faster than competitors.
2. QViz: a set of visualisation tools that leverage Qbeast to speed up visualisation of data representations needed by data analysts.
3. QML: a set of Machine Learning algorithms, that have been modified to take advantage of the Qbeast index to speed up data reading while they run.

Our dissemination strategy changed during the project. At the first part of the project, we targeted general audiences and scientific communities, to explain the benefits of the project and the technology respectively. Then, during the second half of the project, our efforts have been devoted to participate in events where possible investors for the new spin-off to be created may be found. And finally, since the work will continue after the project, we also listed some future events targeted to continue with the search for potential investment, to ensure the company creation. All details about our outreach activities can be found at Deliverable D5.2: Report on Outreach Results.
We have studied both scientific and commercial database solutions, and the Qbeast indexing technology is unique compared to the state of the art. The results of the project (QDB, QViz and QML) can be tested in the Qbeast.io website (http://qbeast.io/) being our main demonstrator of the technology.

The socio-economic impact that can be reached by the Qbeast spin-off company is to be able to reduce operational costs on data analysis for companies (both large and small companies). Many companies nowadays devote big amounts of their budgets in business analytics and a variety of data analyses performed in the data they own from their users, not only to try to improve their sells, but also to increase security, gain efficiency, and so on. If Qbeast is able to penetrate the Big Data analytics market, this can also serve as an example to many other markets that can benefit from Qbeast, since database technologies are used in many different markets, as a basic IT service.
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