Data Center Digital Twin for Sustainable Exascale Computing

Graph-Massivizer targets “sustainable science throughput” through scalable energy-aware,  exascale  operation  and  traceable  TCO 107  understanding,  including  sustainability  indicators  and  their environmental effects (e.g., GHG emissions). The Graph-Massivizer tools will enable the creation of a novel, graph-based digital twin of a data centre; this digital twin will further support the construction of sustainable exascale computing operational models to support scientific discovery in the next decade.

The Data Center Digital Twin for Sustainable Exascale Computing Use Case in detail

High Performance Computing (HPC) plays a crucial role in scientific progress, but as systems approach exascale, maintenance becomes increasingly difficult. This Use Case introduces a graph-based digital twin of HPC centers that addresses anomaly handling, energy efficiency enhancement, and carbon emissions reduction.

The main objective of this Use Case is to develop and implement a sustainable framework to tackle various challenges encountered in HPC systems, including handling anomalies, enhancing energy efficiency, reducing carbon emissions, and ultimately optimizing system performance.

The logic of the Use Case involves the integration of various components, such as Graph Inceptor to output the graph representation of the telemetry data of the HPC for anomaly prediction models, as well as the Graph Scrutinizer to execute the BGOs, as well as the Optimizer and Choreographer to provide the computational resources for performing inference/graph queries.

Expected Outcomes of the Use Case

The targeted outcomes are the implementation of a sustainable model for data centers based on graphs, the development of an ontology for HPC systems, and the creation of a data center data model. The outcomes will be achieved by creating a graph-based digital twin of HPC centers to optimize system performance and make data centers more scalable and sustainable.

This solution will enable the implementation of complex queries that make the work easier for facility managers and engineers that are not directly possible for current monitoring systems.

The solution plans to export open-source ontologies of the Marconi100 public dataset, as well as to provide technologies to be tested in data centers in production, and it will target data centers and HPC systems for exploitation. The stakeholders involved in this use case are data center owners, operational data analytics framework developers, and data center operators.

Green AI for Sustainable Automotive Industry

The automotive  value  chain  involves  extreme  data  flows  of heterogeneous, distributed, fast-growing, disconnected, or hardly compatible information. ML methods face new challenges and opportunities to holistically analyse the massive and unprecedented data integrated across these chains, to support decisions that fundamentally change automotive manufacturing processes towards a sustainable, circular, and climate-neutral automotive industry. Graph-Massivizer enables new graph-based encoding that captures several value-chain stages to predict their outcome better and detect anomalies. Better and quicker analysis prevents defect propagation and unnecessary waste,  contributing  to  a  sustainable,  circular,  and  climate-neutral  automotive  industry. By combining graph-based ML methods with digital twins, Graph-Massivizer provides new insights and boosts the efficiency and scalability of the diagnosis beyond that of more expensive alternatives (e.g., excessive sensor deployment for continuous monitoring).

The Green AI for Sustainable Automotive Industry Use Case in Detail

The automotive industry involves a complex network of data flows that are often incompatible and diverse. To address this issue, we plan to adopt the Graph-Massivizer toolkit, which uses Machine Learning (ML) methods to analyze this data holistically. This will pave the way for a sustainable, circular, and climate-neutral automotive industry.

Graph-Massivizer uses a unique graph-based encoding that captures multiple value-chain stages, improves prediction accuracy, and anomaly detection. This enhanced analysis will help to reduce defects and waste, promoting industry sustainability. By integrating Graph-Massivizer’s graph-based ML methods with digital twins, we aim to improve diagnostic efficiency and scalability, surpassing more expensive alternatives such as continuous sensor monitoring.

The primary objective of this Use Case is to develop a solution that can predict the best production configurations for a given material type and welding machines. We will use simulated data to achieve the desired quality levels.

The Use Case aims to improve the accuracy of predicting the produced spot diameter based on an input welding program during simulation. This will help in determining the quality of the welding result and predicting the parameters required to produce a specific welding quality.

The logic or workflow is as follows. Firstly, the sensor data, welding machines, welding programs, and quality specifications are onboarded. Then, these are mapped to the welding quality ontology. Next, an algorithm is chosen (such as a neural algorithm) and a graph embedding model is trained. Once the algorithm is selected, the necessary hardware resources for the algorithm and data are acquired. Finally, the algorithm is executed, and its performance is monitored.

We plan to use the GM platform to effectively manage the streams of data produced by the numerous sensors that monitor the welding process and the machines used. This data will be integrated with other sources in the graph, and symbolic and sub-symbolic reasoning will be applied on the graph. To facilitate the integration of the sensor data in the KG, the Graph Inceptor is crucial. The Scrutinizer will be utilized to mine the produced graph. We also intend to use the Optimizer and the Greenified to pick the best resources for the selected algorithm. This will help us avoid overusing resources and most importantly, reduce failures and repeated computations caused by insufficient resources.

Expected Outcomes of the Use Case

The expected results will be obtained by generating KG embedding of the constructed welding graph and using the embedding model to predict the quality of the welding spot. This will also help in determining the most suitable program for a given material and spot.

The results will be presented in the form of an interactive view of the developed ontology and a graph generated from synthesized data. Additionally, there will be a query interface available for stakeholders to test the system and obtain the desired results.

The plan for exploitation involves the extension of the project output to create an internal platform for industrial data that is similar to the welding scenario. The aim is to adapt and expand this output to more Bosch plants, thereby addressing the needs of the industrial floors. The stakeholders involved are multiple Bosch plants that are involved in welding procedures. The sensor data is expected to provide inferred information that can assist technicians, engineers, and managers in optimizing the production process.

Global Foresight for Environment Protection

Global foresight for environment protection focuses on geopolitical and business aspects of ESG, including climate  action, responsible production and consumption patterns, clean water and sanitation, and clean and affordable energy. The foresight comprehends insights on future trends and scenarios to guide decision-making in developing better  policies. A contextual graph built through data from the Common Crawl, Linked Open Data Cloud, and global media news provides unique insights into mass media’s convergence of the three societal systems (economy, politics, science).

The Global Foresight for Environment Protection Use Case in detail

Global foresight for environmental protection focuses on the geopolitical and business aspects of ESG, which includes climate action, responsible production and consumption patterns, clean water, and affordable energy.

This approach involves analyzing news articles and extracting information about events happening in the world, particularly those concerning companies mentioned in the articles. With the help of Graph-Massivizer, past events will be used to compute potential future events to detect trends and anomalies.

Use case overview

The main objective of this use case is to identify individual relations related to global companies in online media mentions. These relations are related to various topics concerning environmental protection and business operations. Based on these extracted relations, predictions about potential future events can be made. These predictions will be based on identified generalized sequences of events.

Using Graph Massivizer, we can identify shared sequences of events across multiple companies and use those patterns to predict new events based on a sequence of recent events about a company. Besides predicting future events, the aim is also to identify specific trends and anomalies in the data by observing data in individual industries, sectors, and geographies.

The outcome of this project is the ability to identify common sub-graphs related to individual companies in large graphs of data. These patterns can be used to predict, as accurately as possible, likely future events.

This Use Case relies on cutting-edge technology to help organizations predict future events based on past data patterns. Our workflow is based on the initial provision of data as JSON objects, which are then converted to a graph structure using an established ontology. The Graph Scrutinizer is our main component for computing patterns and trends, and it operates on data imported into Graph Massivizer. By leveraging its powerful capabilities, the Graph Scrutinizer can predict potential future events for a new subgraph based on common patterns.  The Use Case relies primarily on Graph-Inceptor for ingesting new data and Graph-Scrutinizer for extracting common patterns and trends from the graph.

Expected Outcomes of the Use Case

The results of our use case are reported in the form of a list of potential events predicted for a given input sequence of past events. Each predicted event has an associated probability, giving a clear sense of the likelihood of it occurring based on the frequency of the pattern in past data.

Our prediction component is the perfect addition to your existing product, seamlessly integrating with it to offer you an additional service for an extra charge.

We are committed to offering this service to organizations in the geopolitical segment, hedge funds and investment companies, and general companies interested in monitoring their supply chain. As the primary stakeholders in this use case, analysts will benefit from our technology as it enables them to understand what is happening globally and the potential consequences of these events. By leveraging our cutting-edge tools and technology, you can be confident in your ability to predict future events and make informed decisions that will keep you ahead of the curve.

Detail of the User Interface

Green and Sustainable Finance

Graph-Massivizer aims to remove the limitations of financial market data providers (limited volume, reduced accessibility, very high costs, limited historic relevance) by enabling fast semi-automated creation of realistic and affordable synthetic extreme financial data sets, unlimited in size and accessibility. Peracton Ltd. uses the financial multiverse for improved AI-enhanced green investment and trading simulations, free of critical biases such as prior knowledge, over-fitting, and indirect contamination due to historic financial data scarcity and limitations. There are two major objectives to be achieved:

The Green and Sustainable Finance Use Case in detail

One goal of the Graph-Massivizer project is to eliminate the constraints posed by financial market data providers, such as limited volume, restricted accessibility, high costs, and limited historical relevance of historic data. The project achieves this by facilitating the rapid semi-automated generation of realistic and cost-effective synthetic financial datasets in massive volumes (petabytes), with limitless scalability and accessibility.

What is synthetic data? Synthetic data is artificially generated data that mimics real-time and historical data in terms of essential characteristics, capturing and reflecting the statistical properties of real-world data. In the context of investment and trading, synthetic data is useful for simulating various market conditions and investment scenarios, providing a rich and diverse dataset for analysis.

It can be used for different main topics at the heart of investment and trading, such as:

Synthetic data for algorithmic trading testing and modeling represents a significant paradigm shift, particularly when extreme volumes are used for such testing. This approach unlocks many new possibilities, allowing the development of adaptable and more robust trading and investment algorithms.

Use case overview

The primary outcomes of this use case include a flexible Graph-Massivizer software platform capable of generating customized synthetic data batches for various types of securities, an extensive synthetic dataset (petabytes in size) ready for testing, encompassing USA stocks (S&P500) and commodities such as Wheat, Corn, and Soya. Free samples of the synthetic dataset will be provided.

The exploitation strategy for the proposed solution includes licensing, API access fees, white-labeling, partnerships, and integrations for the Graph-Massivizer platform. Data monetization is also anticipated for bespoke synthetic datasets.