Qbeast & Telefonica Research Collaboration

31st of March, 2022

Using Qbeast Spark to make the Machine Learning models more efficient.

In January, 2018 the I-BiDaaS project* was successfully launched with 13 participating organizations from 8 different countries. The Barcelona Supercomputing Center (BSC) was a member of the I-BiDaaS consortium, and Qbeast’s solution took part in the I-BiDaaS platform as BSC’s contribution. Telefonica participated as the organization leading the telecommunications use case, with the purpose being the use of big data to predict congestion points in the cellular network and employ bots in call centers that are more anthropomorphic and realistic.  

After a successful cooperation between Telefonica and Qbeast Ioannis Arapakis, Research Scientist at Telefonica with Adrià Correas, Junior Engineer at Qbeast are working together on a follow-up project which addresses the high-value problem of cellular network  workload forecasting using Recurrent Graph Neural Networks. 

Rapid urbanization brings with it an influx of people into cities, posing enormous mobility and sustainability issues. With the introduction of IoT-based technologies to the market and the broad adoption of mobile devices by end-users, the growing need for telecommunication services is being exaggerated even more. 

Currently, billions of mobile users access the Internet while on the move. Because it is the only global-scale infrastructure with ubiquitous mobility support, the cellular network is critical for their Internet access. A high-value service is at the heart of its mobility management: performance predictions of workloads predicted to be generated in the near future.

Graph Neural networks are a hot topic right now, as they allow us to train Machine Learning models with information about the whole structure of a graph, not just node features. What Ioannis and Adrià are trying to do is improve the previous approaches in order to solve this problem using this technique. The Qbeast Spark extension will push this approach forward as it can help make this computationally expensive task more efficient.

What is proposed is the creation of a centralized network solution to handle the aforementioned challenge in this project. Typically, there are hundreds of thousands of sectors providing tens of measurements at a constant (almost instantaneous) rate, making accurate and timely forecasting of congestion points particularly challenging, which makes it a valuable service to mobile network operators. Ioannis and Adrià will identify and quantify the most prominent patterns in a cellular network and research a solution, which can be executed in a cloud environment that can handle resource-hungry AI algorithms. 

*The I-BiDaaS project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 780787. 

Telefónica, S.A. is a Spanish multinational telecommunications company headquartered in Madrid, Spain. It is one of the largest telephone operators and mobile network providers in the world. It provides fixed and mobile telephony, broadband, and subscription television, operating in Europe and the Americas. For more information, visit telefonica.com
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I-BiDaaS Project with Qbeast Completed With Great Success

I-BiDaaS Project with Qbeast Completed With Great Success

March 26, 2021

The Barcelona Supercomputing Center(BSC) was a member of the I-BiDaaS consortium, and Qbeast’s solution took part in the I-BiDaaS platform as BSC’s contribution. Qbeast’s visualization tool Qviz, which will be part of the Qbeast Platform product, was used in the banking use cases(CaixaBank) that were conducted during the project.

Industrial-Driven Big Data as a Self-Service Solution (I-BiDaaS) an EU-funded project that aimed to encourage IT and non-IT big data experts to easily apply and collaborate with big data technologies by developing, creating, and demonstrating a unified solution that significantly increases data analysis speed while coping with the pace of data asset development, and promotes cross-domain data-flow towards a thriving data-driven EU economy.

The vision of I-BiDaaS was to shift the power balance within an organization, improving efficiency, lowering costs, generating greater employee empowerment, and increasing profitability. To create a stable environment for methodological big data exploration in order to develop new products, services, and technologies. To build innovations that will boost the productivity and competitiveness of all EU companies and organizations that deal with large, complex data sets.

The I-BiDaaS project was successfully launched in January, 2018 with 13 participating organizations from 8 different countries and the duration of the project lasted for 36 months.

Qbeast, as part of the I-BiDaaS tools, was tested and analyzed in the context of fraud detection in the use cases of advanced analysis of bank transfer payment in financial terminal and enhance control of customers to online banking. Qbeast was credited with a 30% reduction in data processing time and a potential cost reduction in commercial data analytics solutions licenses.

CaixaBank concluded: the most important conclusion of the use case was the ability to perform big data clustering analytics in a very agile way, based on existing or custom-tailored clustering algorithms.”

I-BiDaaS tools were validated for the full cycle of big data processing, as a self-service for non-IT and intermediate users, with advanced users able to customize their big-data analysis.

“One of the key gains Qbeast has obtained from the I-BiDaaS project is clearly the close contact we have had with the industry. Having CaixaBank, Telefónica I+D and Centro Ricerche Fiat in the project, and being able to work with them so closely, has had a paramount impact on how Qbeast is now, and how Qbeast will be shaped in the future.” said Raül Sirvent, Principal Investigator for BSC during the I-BiDaaS project and Senior Researcher at the Department of Computer Science, BSC.

For further information on I-BiDaaS, please visit the I-BiDaaS website.

About Qbeast
Qbeast is here to simplify the lives of the Data Engineers and make Data Scientists more agile with fast queries and interactive visualizations. For more information, visit qbeast.io
© 2020 Qbeast. All rights reserved.
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How Qbeast solves the pain chain of Big Data analytics

Are you ready to find out how speeding up data analysis by up to 100x solves data teams’ pain points?

Well, first let me give you some background information. According to a survey conducted by Ascend.io and published in July 2020, 97% of data teams are above or at work capacity.¹ Given that every day more and more data is generated and stored, this is not good news for data teams and organizations. Yet, the capability to leverage data in business has never been more critical.

The pain chain

The survey states that the ability to meet data needs is significantly impacted by slow iteration cycles in data teams. This aligns with the feedback that we received from our customers’ data teams as well.

To explain why iteration cycles are slow, let’s use the concept of the pain chain. The pain chain was first introduced by Keith M. Eades and is a map to describe a sequence of problems in an organization’s process.² The pain of one role in the company causes the pain of another function. In our case, the data pain chain starts with the Data Engineer, follows to the Data Scientist, and finally involves the decision-makers. To keep in mind, the data engineer is the one who prepares the data. The data scientist uses this data to create valuable and actionable insights. And well, the decision-maker is a project manager, for example, who wants to get a data-driven project done.

The survey found that data scientists are the most impacted by the dependency on others, such as data engineers, to access the data and the systems (48%). On the other hand, data engineers spend most of their time maintaining existing and legacy systems (54%).

How does this impact the decision-maker? Well, it leads to a significant loss of value due to delayed implementation of data products or because they cannot be implemented at all.

How do we solve it

Qbeast’s solution tackles the pain chain on several fronts to eliminate it altogether.

Front 1: Data Engineering

There is nothing more time consuming and nerve-racking than maintaining and building complex ETL pipelines.

Less complexity and more flexibility with an innovative storage architecture

Can’t we just work without ETL pipelines? You may say yes, we can use a data lake instead of a data warehouse. We can keep all the data in the data lake and query it directly from there. The downside? Querying is slow and processing all the data is expensive. But what if you could query all the data directly without sacrificing speed and cost?

With Qbeast, you can store all the data in your data lake. We organize the data so that you can find what exactly you are looking for. Even better, we can answer queries by reading only a small sample of the dataset. And you can use your favorite programming languages, be it Scala, Java, Python, or R.

How do we do this? With our storage technology, we combine multidimensional indexing and statistical sampling. Check out this scientific paper³ to find out more.

Our technology’s advantage is that we can offer superior query speed than data warehouses while keeping the data lakes’ flexibility. No ETL pipelines but fast and cost-effective. The best of both worlds, so to speak.

Front 2: Data Science

We know that if you are a data scientist, you do not care so much about pipelines. You want to get all the data you need to tune your model. And it is a pain to rely on a data engineer every time you need to query a large dataset. You are losing time, and you can’t focus on the things that matter. But what if you could decide the time required to run your query yourself?

Data Leverage

By analyzing the data with a tolerance limit, you can decide how long to wait for a query and adjust the precision to your use case. Yes, this means that you can run a query on whatever you want. Do you want to know the number of sales in the last months? Full precision! But do you really need to scan your whole data lake to see the percentage of male users? Probably not.

With Qbeast, you can get the results you need while accessing only a minimum amount of available data. We call this concept Data Leverage. With this option, you can speed up queries by up to 100x compared to running state-of-the-art query engines such as Apache Spark.

Conclusion

A storage system, which unites multidimensional indexing techniques and statistical sampling, solves the data analytics pain chain by speeding up queries, reducing complexity, and adding flexibility. This results in a significant speed-up of iteration cycles in data teams. Increased productivity and speed of data analysis itself have a colossal impact on the ability to meet data needs and to create superior data products. And above all, alleviating the pain chain results in a happy data team, decision-makers, and customers.

But the pain chain doesn’t end here! Now it is time for the application developers to pick up all the insights uncovered by the data scientists and use them to build amazing products! That’s a topic for another post, but I bet you have guessed; we have a solution for that too.

References

1. Team Ascend. “New Research Reveals 97% of Data Teams Are at or Over Capacity”, Ascend.io, 23 July 2020, New Research Reveals 97% of Data Teams Are at or Over Capacity. Accessed 28 December 2020.

2. Eades, Keith M., The New Solution Selling: The Revolutionary Sales Process That is Changing the Way People Sell, McGraw-Hill, 2004.

3. C. Cugnasco et al., “The OTree: Multidimensional Indexing with efficient data Sampling for HPC,” 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 433–440, doi: 10.1109/BigData47090.2019.9006121.

For further information on BTTG, please visit the BTTG website.

About Qbeast
Qbeast is here to simplify the lives of the Data Engineers and make Data Scientists more agile with fast queries and interactive visualizations. For more information, visit qbeast.io
© 2020 Qbeast. All rights reserved.
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