📢 Exploring Future Opportunities: Master Research Topics with SINTEF! 🎓

An interesting opportunity for aspiring students in the field of data processing and advanced technologies has been published by Graph-Massivizer partner, SINTEF which issued three MSc research topics linked with the Graph-Massivizer project.

1. Benchmarking the MAGMA Framework:

Dive deep into the world of abstract data pipelines and graph processing. This topic invites dedicated researchers to benchmark the MAGMA framework, covering insights that can revolutionize how we perceive and process data. For a detailed overview, click here.

2. Testing and Evaluating the New Java Vector API:

Explore the intersections of graph stream processing and the Java Vector API. This research opportunity delves into the nuances of technology, offering a chance to pioneer advancements in this field. Discover more here.

3. Development of Integration of Language-Agnostic Streaming Operators:

Contribute to the evolution of language-agnostic streaming operators, enabling GNNs-ready stream processing. This research topic opens doors to innovative solutions in the realm of data processing. Explore the possibilities here.

🌟 Why Choose these Opportunities?

These research topics not only promise academic excellence but also offer a chance to pioneer future technologies. Participants will have the privilege of working closely with industry experts and contributing to real-world solutions.

🚀 How to Apply: 

If you are passionate about shaping the future of data processing and wish to embark on a transformative research journey, please direct your inquiries and applications to Daniel Thilo Schroeder (daniel.t.schroeder@sintef.no) or Brian Elvesæter (brian.elvesater@sintef.no) at SINTEF.

This is not just an opportunity; it’s a chance to be at the forefront of technological innovation. Seize the moment and be a part of the pioneering team shaping the future of data-driven industries! 🌐✨ #MScOpportunity #GraphMassivizer #ResearchInnovation #DataProcessing #SINTEFResearch 🎓🔬

EVENTREGISTRY Unveiling The Future: How Event Registry Utilizes Graph-Massivizer to Fuel UN Sustainable Development Goals 

The Pioneering Spirit of Graph-Massivizer Project

In a world drowning in data, the need for advanced processing capabilities has never been more critical. That’s where the  Graph-Massivizer Project comes into play. This game-changing initiative is designed to navigate the high seas of extreme data, turning it into an invaluable asset. The focus isn’t merely on number crunching; it’s about delivering a seamless experience, allowing vast data to be not just understandable but actionable.

Five Pillars of Excellence:

The Graph-Massivizer toolkit comprises five open-source software tools and FAIR graph datasets that consider the entire life cycle of extreme data processing. These tools focus on:

1. Usability: Intuitive design for ease of application
2. Automated Intelligence: Learning and adapting to needs
3. Performance Modelling: Anticipating requirements
4. Environmental Sustainability: Understanding the carbon footprint and optimizing accordingly
5. Computing Continuum: From high-performance computing systems to all devices, ensuring compatibility and adaptability.

The ESG Convergence

Moreover, the Graph-Massivizer project adds a layer of foresight, focusing on Environment, Social, and Governance (ESG) aspects.

Through data collected from global media, the project generates a contextual graph that offers insights into mass media’s impact on society at large.

Bridging the Gap: Data & AI for the UN Sustainable Development Goals

To combat this, the Event Registry Data Commons team has collaborated with the Graph Massivizer consortium to develop the maas scale computing for AI. This tool integrates authoritative data and offers AI-powered search functionalities, enabling policymakers, NGOs, and the general public to form data-driven strategies.

Accelerating with AI

The beauty of AI lies in its power to accelerate human efforts. Recent advances have diversified technology deployments, increased funding, and fostered talent development. Yet, barriers like fragmented efforts and lack of data accessibility persist.

Confluence of Innovations: Where Graph-Massivizer Meets UN SDGs

We see the Graph-Massivizer toolkit as a linchpin in this puzzle, an ideal companion to the UN Data Commons. Its high-performance graph processing capabilities can significantly contribute to achieving SDGs by enabling organizations to comprehend, analyze, and act on complex data patterns. Whether it’s monitoring climate change impacts, healthcare advancements, or equitable educational opportunities—Graph-Massivizer is the tool to make sense of it all.

Conclusion: Forecast as a New Frontier

We believe that innovation should serve humanity. By integrating the cutting-edge Graph-Massivizer technology with the Event Registry datasets, we can break down barriers and facilitate faster, more precise data-driven decision-making. In doing so, we hope to contribute to the UN’s mission of building a better, more sustainable, and equitable world for everyone. The question is not whether we can but whether we will. Let’s make it happen. Together.

Are you ready to be a part of this revolutionary journey? Connect with us, and let’s shape the future.

Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 2)

The preceding blog (part 1) highlighted the challenges posed by historical data, encompassing limitations related to data scarcity, data relevance, and data quality. These factors can significantly impact and curtail the effectiveness of tests conducted on financial models, particularly when integrating AI models into these financial frameworks, which demand substantial volumes of data for both training and testing purposes. In this context, the utilization of synthetic data [1], [2] emerges as a plausible remedy to address the aforementioned constraints. Although generating functional and pertinent synthetic data is a complex endeavour, the potential benefits are manifold:

Unlimited data (extreme volumes, Pb/Eb levels) for training AI and financial models:

The ability to generate vast amounts of data on an unlimited scale is of great interest for numerous domains that rely on AI and modelling. The ever-growing need for data to thoroughly test and validate AI and financial models is a continuous challenge. Synthetic data [3], [4], steps in to address this continuously expanding demand, offering a means to bridge the gap and fulfil these requirements.

Similar statistical value with the historic data

A fundamental characteristic of synthetic data is its capacity to exhibit statistical values akin to those of the original historical data. This crucial aspect guarantees that the models and algorithms are trained and evaluated using pertinent data, thereby ensuring the training remains applicable to forthcoming real historical data batches [5].

Improving AI and financial models’ accuracy while reducing noise and bias

The benefit derived from possessing highly extensive and statistically relevant synthetic data directly translates into the ability to conduct an exponentially larger number of computationally intensive simulations and tests. This, in turn, contributes to the enhancement of AI and financial models, minimizing their susceptibility to undesirable noise and inherent biases stemming from historical data [6], [7].

Enhance privacy and security

The utilization of synthetically generated data effectively eliminates any potential privacy concerns, given that synthetic data is a fabricated construct that does not correspond to actual events or entities that generated such data. Furthermore, the adoption of such data mitigates worries pertaining to security breaches or breaches of trust [6], [7], [8].

Overcoming limitations of narrow historic data scenarios / exposure to completely new types of models

In the context of conducting multiple simulations involving diverse models, historical data is confined to reflecting a finite number of past events and scenarios. Conversely, the use of synthetic data enables the creation of novel events spanning varying intensities and fluctuations. This capability facilitates the exploration of scenarios that can place stress on financial models and support the formulation of what-if scenarios that might otherwise remain unfeasible during the process of designing a financial model.

References

[1] Zewe A., ‘In machine learning, synthetic data can offer real performance improvements’, MIT News Office, November 3^rd, 2022 https://news.mit.edu/2022/synthetic-data-ai-improvements-1103

[2] Heaven, D., ‘Synthetic data for AI’, MIT Technology Review, February 23^rd, 2022, https://www.technologyreview.com/2022/02/23/1044965/ai-synthetic-data-2/

[3] Hillary, ‘Unleashing the Power of AI: Exploring the advantage of AI-Powered Assistants’, TechBullion, August 29^th, 2023  https://techbullion.com/unleashing-the-power-of-ai-exploring-the-advantages-of-ai-powered-assistants/

[4] Pradeesh, J., AI in Cybersecurity: Unlocking the Benefits and Confronting Challenges’ Forbes, August 25, 2023, https://www.forbes.com/sites/forbestechcouncil/2023/08/25/artificial-intelligence-in-cybersecurity-unlocking-benefits-and-confronting-challenges/

[5] https://www.statice.ai/

[6] Busby, L, ‘Benefits and Efficiencies Abound but AI Misses the Humanity Care’, Targeted Oncology, August 29^th, 2023 https://www.targetedonc.com/view/benefits-and-efficiencies-abound-but-ai-misses-the-humanity-of-care

[7] Heaven, W.D., ‘Synthetic data for AI, MIT Technology Review’, February 23^rd,2022 https://www.technologyreview.com/2022/02/23/1044965/ai-synthetic-data-2/

[8] Linden, A., ‘Is Synthetic Data the Future of AI’, Gartner, June 22^nd, 2022 https://www.gartner.com/en/newsroom/press-releases/2022-06-22-is-synthetic-data-the-future-of-ai

Laurentiu Vasiliu, Peracton Ltd.

Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 1)

Artificial intelligence (AI) has revolutionized the field of financial trading and investment. AI models can analyze massive amounts of data, learn from patterns and trends, and make predictions and recommendations for optimal decision-making [1] . However, to train and test these models, historical data is often used as the main source of information. Is historical data enough or even useful for testing trading and investment models in the AI era? In this blog post, we will discuss why historical data has several limitations and challenges.

The Problem of Data Scarcity

One of the main limitations of historical data is its scarcity. As we go back in time, financial data becomes less available and reliable. It is difficult to obtain comprehensive and accurate data from distant past periods, especially for emerging markets and new asset classes [2]. This limits the ability of backtesting models to capture the full complexity and diversity of real-world financial scenarios.

The Problem of Data Relevance

Another limitation of historical data is its relevance. The economy is a dynamic system that changes constantly. The factors that influenced the financial markets in the past may not be relevant or applicable in the present or future. For example, technological innovations, regulatory changes, accounting standards, corporate actions, and consumer behaviors all affect the financial landscape over time [3]. Therefore, historical data may not reflect the current or future market conditions and trends.

The Problem of Data Quality

A third limitation of historical data is its quality. Historical data may contain errors, inconsistencies, gaps, outliers, or biases that can affect the accuracy and validity of backtesting and analysis results. For example, historical data may not account for inflation effects, survivorship bias [4], look-ahead bias [5], or changes in market liquidity and volatility. Therefore, historical data may not provide a reliable basis for testing trading and investment models.

Conclusion

While historical financial data is valuable for understanding past trends and patterns, its limitations must be recognized, especially in the AI era. Scarce data, the lack of mapping between past and present, the changing corporate landscape, and evolving accounting standards all contribute to the diminishing usefulness of historical data for backtesting and investment analysis. It is crucial to strike a balance between incorporating historical insights and relying on real-time data, alternative data, synthetic data and expert oppionions to adapt to the ever-evolving financial landscape. Ultimately, the ability to adapt and leverage a wide variety of information will be key to successful trading and investment strategies in the AI era. In our next blog, we will delve into the potential of synthetic data as a unique and innovative source of financial information.”

References

[1] Cao, Longbing. “AI in Finance: Challenges, Techniques and Opportunities.” arXiv preprint arXiv:2107.09051, 2021, https://arxiv.org/abs/2107.09051

[2] Ahmed, Shamima, et al. “Artificial intelligence and machine learning in finance: A bibliometric review.” Expert Systems with Applications, Volume 61, October 2022, 101646, https://www.sciencedirect.com/science/article/pii/S0275531922000344

[3] “Artificial Intelligence, Machine Learning and Big Data in Finance Report”. OECD, 2021, https://www.oecd.org/finance/artificial-intelligence-machine-learning-big-data-in-finance.htm

[4] CFI Team, “Survivorship bias” Corporate Finance Institute, 2023, https://corporatefinanceinstitute.com/resources/capital-markets/survivorship-bias/

Laurentiu Vasiliu, Peracton Ltd.

The creation of the first holistic dataset of a tier-0 Top10 supercomputer which will serve also to Graph-Massivizer project

We are pleased to announce the publication of a new paper by our esteemed partners at University of Bologna, which showcases the valuable results obtained from their collaborative work. This groundbreaking paper presents the culmination of 10 years of research efforts, highlighting significant findings related to supercomputers and the high complex data they use. The paper serves as a comprehensive resource, providing in-depth analysis, novel methodologies, and practical applications derived from the continuous study. With its publication, the authors aim to contribute to the broader scientific community and foster further advancements in the field. This significant achievement is expected to have an impact also on future research and development endeavors of our Graph-Massivizer project.

Supercomputers, which are the most advanced computing machines available to society, play a crucial role in driving economic, industrial, and societal progress. They are utilized by scientists, engineers, decision-makers, and data analysts to tackle complex problems through computational means. However, supercomputers and their accompanying data centers are intricate and power-intensive systems themselves. Enhancing their efficiency, availability, and resilience is of utmost importance and is the focus of numerous research and engineering endeavors. Nonetheless, researchers face a significant obstacle in the form of a lack of reliable data that accurately describes the behavior of operational supercomputers.

In this paper, the authors present the outcomes of a ten-year-long project aimed at developing a monitoring framework called EXAMON, which has been implemented in the Italian supercomputers at CINECA data center. They unveil the first comprehensive dataset of a tier-0 Top10 supercomputer, encompassing management, workload, facility, and infrastructure data from the Marconi100 supercomputer over a span of two and a half years of operation. This dataset, which has been published via Zenodo, represents the largest publicly available dataset to date, with an uncompressed size of 49.9TB. Additionally, the authors offer open-source software modules that simplify data access and provide practical usage examples.

Martin Molan, PhD, Universita Di Bologna

For a more in depth analysis please check out the whole paper here: https://www.nature.com/articles/s41597-023-02174-3

The Graph-Massivizer project consortium is happy to announce the official start of this European initiative, funded by the European Commission under the Horizon Europe research and innovation programme. Graph-Massivizer aims at delivering open-source and commercial solutions that drive green digital transformation across use cases in finance, manufacturing, environment protection and exascale computing.

Leveraging graph data through an efficient and scalable digital infrastructure driving green digital transformation

Graphs are data structures that represent real-world and digital objects and their relations. In an increasingly complex world, graphs can be useful to intuitively model and represent complex scenarios and systems such as social or economic networks or digital twins.

Over the last decade, graphs have made great advances in making data findable, accessible, interoperable, and reusable. For many organisations, they have become a key instrument for extracting meaningful insights that support timely, high-impact decisions. On a larger scale, graphs are becoming crucial to innovation, competition, and prosperity. They help derive trustworthy insights to create sustainable communities and support digital transformation with better, more profitable, greener products and services. However, current graph processing platforms come with various limitations, ranging from high energy consumption and inefficiency and lack of support for diverse workloads, models, languages, and algebraic frameworks to the difficulty of use for non-experts.

Graph-Massivizer addresses these challenges by delivering an integrated toolkit to support a climate-neutral and sustainable economy based on graph data. The project partners will develop five open-source software tools for high-performance, scalable, and sustainable graph processing, as well as an enterprise-class commercial version based on the metaphactory knowledge graph platform that tightly integrates the tools in an easy-to-use-and-deploy offering to reach a broader market share.

Ambitious green use case validation in finance, environment protection, manufacturing, and high-performance computing sectors

To ensure applicability and scalability in real-world scenarios and the feasibility of commercial solutions developed on top of metaphactory as a result of the Graph-Massivizer Project, the project partners will validate the innovative toolkit on four use cases that cover the economic, societal and environmental sustainability pillars:

• sustainable green finance,
• global environment protection foresight,
• green artificial intelligence (AI) for the sustainable automotive industry, and
• data centre digital twin for exascale computing.

These use cases tackle extreme data processing and massive graph analytics challenges and are a perfect fit for the Graph-Massivizer toolkit.

Across these use cases, Graph-Massivizer aims to improve analytics efficiency by 70% and energy awareness for extract-transform-load (ETL) storage operations by 30%. Furthermore, it aims to demonstrate a possible two-fold improvement in data centre energy efficiency and over 25% lower greenhouse gas (GHG) emissions for basic graph operations.

“My vision for Graph-Massivizer is to enable a worldwide Sustainability Graph, a universal abstraction that captures, combines, models, analyses and processes knowledge about our economic, societal and environmental world. The project will contribute to this vision by providing a technological solution, coupled with field experiments and experience-sharing for a high-performance and sustainable graph processing of extreme data with a proper response for any need and organisational size by 2030,” commented the project coordinator Radu Prodan from the University of Klagenfurt.

Synthetic Financial Data Multiverse is a solution offered by Peracton Ltd. that generates fast, affordable and unlimited synthetic financial data sets, eliminating biases and increasing accessibility, overcoming traditional financial data limitations. Peracton leverages this tool for green investment and trading, aiming to reduce risks and improve the performance of financial algorithms. The Synthetic Financial Data Multiverse meets financial industry demands, de-risks algorithmic models, and addresses environmental sustainability.

“In the AI era, when there is never enough data to validate and train AI models, the Synthetic Financial Data Multiverse has the potential to radically transform the generation and use of financial markets data. ‘Real data’ problems such as biases, inaccuracies, historical irrelevance, costs, statistical relevance, overfitting will no longer apply when using bespoke synthetic financial data for testing and validating AI-enhanced financial algorithms,” says Laurentiu Vasiliu, CEO and founder of Peracton Ltd.

Global Foresight is a solution developed by Event Registry d.o.o. that empowers decision-makers with comprehensive protection insights. It allows them to stay ahead of emerging trends and scenarios and make informed, data-driven policy decisions that positively impact the environment. The solution analyses vast amounts of data from various open web sources to create an intuitive and interactive contextual graph and deliver insights and forecasts of future events.

“Global Foresight strongly emphasises the geopolitical and business aspects of environment, society and governance, providing a 360-degree view of the future landscape. From climate change to resource depletion, the solution tool will deliver comprehensive forecasts and insights to enable proactive policy-making decisions that promote sustainability practices for environmental protection,” says Gregor Leban, CEO and co-founder of Event Registry.

Green Manufacturing Line Diagnose is a solution developed by Robert Bosch GmbH that captures several value-chain stages to better predict their outcome and detect anomalies in welding control systems essential for many manufacturing processes. Better and quicker analysis prevents defect propagation and unnecessary waste, contributing to a sustainable, circular, and climate-neutral automotive industry.

“By combining graph-based AI methods with digital twins, the tool provides new insights and boosts the efficiency and scalability of the diagnosis beyond that of more expensive alternatives, such as excessive sensor deployment for continuous monitoring. The insights gained will help optimise manufacturing operations and improve the operational quality of the resulting products”, says Evgeny Kharmalov, senior expert at Bosch Center for Artificial Intelligence.

Data Center Digital Twin is a solution developed by Cineca and the University of Bologna that provides a virtual representation of the world’s fourth-fastest supercomputer Leonardo. Leonardo’s digital massive graph representation describes complex spatial, semantic, and temporal relationships between the monitoring metrics, hardware nodes, cooling equipment, and software, which are difficult to capture and express otherwise.

“Once operative, Leonardo will generate over 10 million metrics and petabytes of data that require AI analytics on massive graphs to extract operational insights for improved science throughput. The information in such a large volume of data is essential for understanding and optimising the efficiency and sustainability of future modern supercomputers operating at exascale performance,” says Andrea Bartolini, assistant professor at the University of Bologna.

Graph-Massivizer toolkit covering the sustainable lifecycle of processing extreme data as massive graphs

To support these use cases, Graph-Massivizer develops a software platform  consisting of five integrated tools for extreme data processing that will:

• translate extreme data streams or follows heuristics to generate synthetic data and persist it within a graph structure.
• use probabilistic reasoning and AI algorithms for graph pattern discovery, low-footprint graph generation, and low latency error-bounded queries.
• help co-design the most promising processing infrastructure with guaranteed performance and energy consumption estimates for specific workloads.
• use operational data centres and national energy supplier data to simulate sustainability profiles for operating graph workload analytics at scale.
• use the performance and sustainability models to deploy and orchestrate the graph analytics workloads on the computing continuum.

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About Graph-Massivizer

Graph-Massivizer r, a three-year project that started on January 1, 2023, aims to support a climate-neutral and sustainable economy by developing high-performance, scalable, and sustainable graph data processing tools. Led by the University of Klagenfurt and composed of 12 partners from 8 EU countries, the project brings together the world-leading roles of European researchers in graph processing and serverless computing and uses leadership-class European infrastructure in the computing continuum.

Project partners are: Universität Klagenfurt, IDC4EU, Peracton Ltd., SINTEF AS, University of Twente, metaphacts GmbH, Vrije Universiteit Amsterdam, Cineca Consortio Interuniversitario, Event Registry, Alma Mater Studiorum – Università di Bologna, Robert BOSCH GmbH, Jozef Stefan Institute.

The project is funded by ‘Horizon Europe’, the European Union’s key funding programme for research and innovation. Among many other R&D topics, Horizon Europe tackles climate change and helps to achieve the UN’s Sustainable Development Goals. It also aims at boosting the EU’s competitiveness and growth.

Press Contact

Prof. Radu Prodan

Institute of Information Technology, University of Klagenfurt, Austria

+43 46327003616, radu.prodan@aau.at

Project Brandbook

In our Project Brandbook you will find the project logo and announcement imagery. These images and logos are the property of the Graph-Massivizer project or the respective project partners and are provided for press use only. For different formats or special inquiries, please contact the project coordinator Radu Prodan.

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