The Graph-Massivizer project researches and develops a holistic neuro-symbolic AI reasoning and inference platform for sustainable graph processing of extreme data based on five integrated tools.
Graph-Inceptor adopts knowledge graphs based on Resource Description Framework (RDF) to symbolically represent extreme data from the formal semantic ontological specification (based on Web Ontology Language (OWL)) of categories, properties, and relations between the concepts or entities. It combines the RDF Mapping Language (RML) with the formal ontology to convert heterogeneous streams of time series data into massive graphs with billions of nodes and trillions of edges, materialised within a graph database underneath, such as GraphDB for RDF graphs. For performance and compatibility, it offers the possibility of creating smaller in-memory virtual graphs, for example, integrating existing relational tabular databases and translating declarative SQL into semantic SPARQL queries for symbolic question answering.
Graph-Scrutinizer researches on top of the symbolic KG representation of data approximate and neural reasoning methods defined and encapsulated as basic graoh operations (BGO) to derive insights. BGO categories include classic graph creation methods (e.g. load, store, conversion), time-series to graph methods (e.g. natural visibility, quantile, optimal partition), traversal algorithms (e.g. depth and breadth-first search, shortest path search), computation of node properties (e.g. clustering coefficient, rank, degree, betweenness centrality), discovery of topological properties (e.g. diameter, hop-plot), sampling (e.g. node, edge, traversal-based) and summarisation algorithms, or neural network inferences. For the latter, it employs GNN for generating personalised graph embeddings for each use case problem that consider not only the structure but also the nodes’ and edges’ semantic properties (as labels) for training advanced ML inference methods with improved accuracy compared to traditional methods operating on tabular data.
Graph-Optimizer designs a BGO repository containing various categorised BGO implementations optimised for multiple shared and distributed memory platforms (e.g. CPU, GPU, FPGA). It further defines analytical symbolic models to predict the performance and energy consumption of BGOs, calibrated using microbenchmarks to the heterogeneous input graph and hardware characteristics. The calibrated symbolic models evaluate the best-performing BGO implementation for the given context.
Graph-Greenifier scales the graph analytics at the data centre level and beyond by combining open operational traces (e.g. SURF) with their measurements and the European Network of Transmission System Operators for Electricity (ENTSO-E) data. It evaluates and labels existing BGO workloads using sustainability metrics related to the effective use of green energy, production of greenhouse gas emissions, or total operational costs.
Graph-Choreographer employs a graph orchestration engine that specifies advanced data analytics based on BGO workflow compositions and selects the “best” implementations from the repository for serverless deployment, scheduling and execution on the computing continuum with optimised time and energy tradeoffs. Graph-Choreographer federates the infrastructure resources across the seven distributed Graph-Massivizer sites described in Section 1.4 applying state-of-the-art cybersecurity measures.
