System for providing event-driven distributed data mesh analytics

Abstract

A system for event-driven distributed data network analysis, the system includes: a multitude of domain data nodes, each comprising a stream storage, a multitude of transformation pods, and a product API, with each domain data node ingesting heterogeneous event streams, validating schema compliance, performing enrichments, and making data products available under versioned schema contracts; each domain data node further comprises a hardware-based edge intelligence appliance comprising an enclosure, a compute module with heterogeneous processing units selected from CPUs, GPUs, and TPUs, a secure enclave module for confidential execution of transformation code on encrypted data, a protocol-structured stream storage medium supporting multi-level retention, and industrial I / O interfaces configured for direct interaction with sensors, programmable logic controllers, and cloud-native services; an event mesh backbone processor configured to connect the multitude of domain data nodes via a publish-subscribe architecture, with the backbone guaranteeing exactly one-time delivery, partition-level sorting, and geo-replicated durability; a Contextual Intelligence Engine coupled with the Event Mesh Backbone processor, the engine comprising a semantic graph memory and a Streaming Graph Reasoner configured to map raw events into an ontology, perform graph joins in real time, and recognize composite causal patterns across multiple domain data nodes; a governance and policy control unit integrated into both the data and control layers. This layer enforces zero-trust security, role-based access control, real-time provenance tracking, and regulatory compliance; and An actuation interface coupled with the contextual intelligence engine and the governance layer. The actuation interface is configured to generate control commands for operational technology devices and digital workflows, thus closing the loop from data acquisition to autonomous decision-making.

Description

Technical field

[0001] The present invention relates to distributed data management and analysis. More specifically, it is an event-driven, domain-oriented data mesh architecture that aggregates heterogeneous real-time streams from both physical assets, such as IoT sensors and industrial machines, and digital assets, such as microservices and enterprise SaaS systems. The system eliminates centralized data lake bottlenecks, provides low-latency contextual analytics, and integrates a physical machine device in the form of an edge intelligence appliance that hosts domain data nodes and enables secure real-time computations. Background of the invention

[0002] Traditional data lake and warehouse solutions are optimized for historical reporting rather than real-time decision-making. Centralizing all data leads to latency, cost, and governance bottlenecks that fail to meet modern industrial requirements such as predictive maintenance, technical trading, or autonomous robot coordination. While approaches to distributed data meshes exist, most current implementations are limited to batch ingestion or lack the intrinsic event-driven architecture required for continuous, low-latency intelligence. Furthermore, no state-of-the-art hardware or appliance implementation can perform domain node computations at the edge while adhering to zero-trust security requirements.

[0003] Accordingly, there is a need for a fully event-driven distributed data network architecture that integrates semantic thinking, policy-as-code governance, and real-time activation, while extending to a physical device that embodies the concept of distributed nodes in tangible machine form.

[0004] Over the past two decades, companies and institutions across industries have attempted to build data infrastructures capable of capturing, storing, and analyzing the rapidly growing volume of digital and physical information. The dominant paradigm has been the centralized data warehouse, later augmented by more flexible forms of data lakes. These systems were based on the principle that an organization should maintain a single, authoritative repository—a so-called "single source of truth"—into which data from various business units, applications, and sensors are fed, harmonized, and queried. In the age of static reporting and historical analysis, this architecture was relatively effective, as business intelligence was primarily retrospective, and latencies of hours or days were acceptable.However, as companies increasingly rely on real-time insights to control industrial processes, respond to cybersecurity threats, or personalize digital experiences, the limits of centralization are increasingly being reached.

[0005] One of the biggest challenges of the centralized model is the unavoidable latency. Because all data must pass through an ingestion layer and into a warehouse or data storage facility before it is available for analysis, network hops, queues, and storage introduce an inherent delay. This delay may not be significant for traditional reporting, but in areas such as high-frequency trading, predictive maintenance on a factory floor, or fraud detection in financial transactions, decisions must be made within milliseconds. The detour via a central storage facility makes such responsiveness impossible. Equally problematic is the single point of failure that arises when all analyses depend on a single data warehouse.If centralized storage experiences failures, damage, or even scheduled maintenance, all downstream analytics capabilities are crippled. Organizations managing critical infrastructure cannot tolerate such fragility when business continuity is essential.

[0006] Another drawback stems from the rigidity inherent in schema governance within centralized data lakes. Business units such as human resources, finance, or supply chain operations often develop their data needs at different intervals, and some require rapid iterations to remain competitive. Centralized governance enforces a uniform schema development cycle, stifling agile teams and preventing them from quickly deploying domain-specific models. This rigidity not only inhibits innovation but also prevents organizations from leveraging local optimizations that could provide a competitive edge. The cost dimension also undermines the potential of centralized data lakes. As sensor payloads, log files, and clickstream data grow into the petabyte range, the economic footprint of continuously copying and storing nearly duplicate data in cold storage becomes superlinear.What was once touted as a cost-efficient “cheap Hadoop” infrastructure has become prohibitively expensive to maintain, especially when a large portion of the copied data is never queried.

[0007] In response to these limitations, the concept of the data mesh emerged as an alternative paradigm. Data mesh proponents argue that data should be treated as a product and owned by domain-specific teams, with each team responsible for publishing its data in a shareable, well-documented format. Instead of pumping everything into a central data pool, the data remains distributed but is accessible through defined contracts. While this represents an important step toward decentralization, most implementations are still immature. Many so-called data mesh systems continue to rely on periodic batch jobs or micro-batch streams to move data between nodes. This again introduces latency and prevents truly event-driven operation of the system.The lack of a real-time backbone means that order guarantees, on-time delivery, and temporal context are often compromised. Without these capabilities, cross-domain reasoning engines cannot reliably identify causal patterns such as "a temperature increase in a turbine leading to a backlog in the supply chain and thus an increase in customer service."

[0008] Several prior art publications highlight the shortcomings of existing attempts to implement distributed data platforms. For example, Korean patent KR20180049274A describes a mesh platform for aggregating user attributes from multiple sources, but its focus is limited to identity enrichment workflows.

[0009] Continuous event routing, complex pattern recognition, and cross-domain knowledge representation—all essential for real-time enterprise intelligence—are not addressed. Similarly, US 2017 / 0352123 A1 proposes a platform that provides real-time views of consolidated data, but its architecture still aggregates all data streams into a single consolidation layer. This revives the central bottleneck that meshes are meant to avoid, undermining resilience and scalability. US 12254022 B1 goes a step closer by defining declarative pipeline units with automated provisioning, but treats the mesh as an acyclic build artifact: once a pipeline creates a table, there is no mechanism to preserve streaming origin, enforce zero-trust access, or argue across heterogeneous physical and digital domains.In practice, this severely limits its usefulness in regulated or safety-critical environments.

[0010] The academic and industry literature reveals similar gaps. Articles describing the benefits of data meshes typically present them as an organizational solution rather than a technical implementation, emphasizing the principles of federated ownership and data as a product, but acknowledging that real-time reference implementations are still in their infancy. Case studies, such as the use of event streaming at Saxo Bank, illustrate how topics can be partitioned along business boundaries, but provide little detail on semantic context modeling, knowledge graph integration, or automated activation across operational and information technology silos.Consequently, while these approaches show gradual improvements over traditional data lakes, they do not achieve the holistic, event-driven distributed architecture required for deterministic intelligence in the millisecond range.

[0011] Existing systems fundamentally suffer from three shortcomings. First, centralization—explicit or hidden. Even when marketed as distributed, many meshes rely on hub-and-spoke designs for stateful joins or consolidated views. This increases latency, especially under high traffic, and prevents true horizontal fault tolerance. Second, the lack of semantic context. Without a unified ontology or graph reasoning layer, raw events remain isolated within domains. This renders the system blind to causal relationships that transcend organizational boundaries. Human analysts must then retrospectively piece together insights, thus eliminating the possibility of automated decision-making. Third, weak governance. Security, compliance, and provenance are often treated as additional batch processes rather than being natively embedded in the runtime structure.In a world characterized by zero-trust mandates and strict regulations such as the GDPR or HIPAA, this disjointed approach cannot guarantee that every automated activation is both authorized and verifiable.

[0012] Market demand for real-time, cross-domain intelligence continues to grow. However, existing solutions lack a unified architecture that addresses the combined requirements for latency, semantic reasoning, and governance. Centralized data lakes are too slow and fragile. Batch-oriented networks cannot guarantee reliable streaming. Current patents are limited to identity workflows or dashboards, or they neglect the crucial integration of policy enforcement and semantic enrichment. Industry implementations demonstrate how event streaming can partition topics, but they offer neither true knowledge integration nor intent-driven activation. The result: organizations pursuing operational AI, autonomous systems, or digital twins remain trapped between outdated, high-speed-unsuitable data repositories and experimental networks that don't understand the real-time language of events.

[0013] Therefore, a system is needed that distributes streaming computations across domain nodes while maintaining global query capability, overlays a live knowledge graph to support cross-domain thinking, and embeds governance directly into the data plane. Such a system must offer exactly-once delivery, order guarantees, and semantic context with millisecond latencies, while also meeting compliance and audit requirements. Only by closing these gaps can organizations securely and effectively leverage heterogeneous data streams from IoT devices, ERP systems, cloud services, and robot controllers within a unified, event-driven architecture. This recognition of unmet needs underscores why existing solutions are insufficient and motivates the development of the present invention. Summary of the invention

[0014] The invention presents a system comprising the following: (1) a multitude of domain data nodes (DDNs), each implemented as software modules or physical edge devices, which receive, validate, transform and disclose events as data products; (2) a low-latency event backbone that supports exactly-once delivery semantics and topic-level security; (3) a Contextual Intelligence Engine (CIE) that overlays semantic models and real-time graph reasoning across nodes; (4) a governance and policy enforcement layer that integrates zero-trust access control, provenance tracking, and compliance verification; and (5) an actuation interface for translating insights into autonomous control or workflow triggers.

[0015] In one embodiment, the invention provides a machine device called an Edge Intelligence Appliance (EIA). This device is a robust, modular, rack-mountable or field-deployable structure with compute processors, stream storage modules, secure enclaves, and industrial communication interfaces. Each EIA functions as a concrete DDN, hosts transformation pods, executes machine learning models, and provides product APIs directly at the point of data generation.

[0016] The main objective of the present invention is to provide an event-driven distributed data network system that overcomes the shortcomings of centralized data lakes and immature batch-oriented networks by enabling low-latency, real-time analytics across heterogeneous physical and digital domains. The invention aims to localize computations within distributed domain data nodes while maintaining a unified and queryable network. This eliminates the bottlenecks of centralized consolidation and ensures fault tolerance even during peak loads or partial network outages. A further objective is to enrich raw event streams with semantic context by integrating a contextual intelligence engine that can overlay ontologies, knowledge graphs, and machine learning, enabling the automatic recognition of cross-domain causal relationships without human intervention.Another key objective of the invention is to embed governance as a native component of the system to ensure zero-trust security, policy-as-code enforcement, immutable provenance tracking, and real-time regulatory compliance, rather than relying on post-process batch reconciliation. Furthermore, the invention aims to provide a control interface that enables insights from the mesh to be directly translated into operational commands, industrial control signals, or IT workflow triggers. This closes the loop from ingestion to decision to action with a latency of less than one second. A further objective of the invention is to enable scalability and independent lifecycle management, allowing each domain node to evolve its schema, pipelines, and models without impacting the rest of the mesh.The invention also aims to embody its principles in a tangible device structure, an edge intelligence appliance. This serves as a physical instantiation of a domain data node, bringing computing power, storage capacity, and secure policy enforcement directly to the point of data generation. Taken together, these objectives ensure that the invention provides a robust, future-proof architecture for predictive maintenance, supply chain stability, digital twin synchronization, fraud detection, and other mission-critical real-time applications, while simultaneously ensuring trust, compliance, and operational reliability. BRIEF DESCRIPTION OF THE FIGURE

[0017] These and other features, aspects, and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of an event-driven distributed data network system with an embedded edge intelligence appliance.

[0018] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0019] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0020] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0021] References in this specification to “an aspect”, “another aspect”, or similar expressions mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0022] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The system, methods, and examples provided here serve only for illustration and are not to be construed as a limitation.

[0024] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0025] In Fig.Figure 1 shows a block diagram of a quantum-based system for detecting fake news. The system comprises: multiple domain data nodes (102), each containing a stream storage, multiple transformation pods (102a), and a product API, with each domain data node receiving heterogeneous event streams, validating schema compliance, performing enrichments, and delivering data products under versioned schema contracts; an event mesh backbone processor (104) that connects the multiple domain data nodes via a publish-subscribe architecture and guarantees exactly-once delivery, partition-level sorting, and geo-replicated durability; and a contextual intelligence engine (106) coupled to the event mesh backbone processor.The engine includes a semantic graph memory and a streaming graph reasoner that maps raw events into an ontology, performs graph joins in real time, and recognizes composite causal patterns across multiple domain data nodes; a governance and policy control unit (108) integrated into the data and control layer, with the layer enforcing zero-trust security, role-based access, real-time provenance tracking, and regulatory compliance; and an actuation interface (110) coupled with the contextual intelligence engine and the governance layer, with the actuation interface configured to generate control commands for operational technology devices and digital workflows, thus completing the loop from capture to autonomous decision-making.

[0026] In one embodiment, each domain data node (102) also includes a hardware-based edge intelligence application comprising an enclosure, a compute module with heterogeneous processing units selected from CPUs, GPUs and TPUs, a secure enclave module for confidential execution of transformation code on encrypted data, a protocol-structured stream storage medium supporting multi-level retention, and industrial I / O interfaces configured for direct interaction with sensors, programmable logic controllers and cloud-native services.

[0027] In one embodiment, the transformation pods (102a) running within each domain data node are containerized microprocesses orchestrated by a distributed scheduler. The pods are capable of performing inline machine learning inferences using ONNXRuntime or TensorRT, with the inference results published as derived event streams that are version-controlled and origin-stamped before leaving the domain boundary.

[0028] In one embodiment, the Event Mesh backbone processor (104) partitions topics by domain, product and event type and also includes transactional write mechanisms and idempotent producer protocols, so that the system maintains the Exactly Once semantics under conditions of network partitioning, producer retries or consumer restarts, thus ensuring deterministic provision of event sequences in safety-critical control environments.

[0029] In one embodiment, the Contextual Intelligence Engine (106) comprises a semantic graph store implemented as a label property graph or RDF triple store, wherein the graph store accepts event attributes and maps them to typed nodes and edges, and a streaming graph configured to apply complex event processing rules, graph neural network embeddings, and top-k pattern recognition, thus enabling the detection of cross-domain incident patterns such as "sensor anomaly persisting beyond threshold, open work order present, and inventory deficit detected".

[0030] In one embodiment, the governance and policy control unit (108) executes policy-as-code rules written in a declarative language, evaluates permissions based on user roles, event confidentiality labels, and purpose-specific consent tokens, and enforces field-level redaction, differential privacy insertion, and column-level encryption during query execution. Furthermore, it records immutable lineages in append-only logs secured by Merkle chains of evidence to ensure regulatory-level auditability.

[0031] In one embodiment, the actuation interface (110) translates decisions of the contextual intelligence engine into operational commands that are transmitted to industrial devices via OPC UA, Modbus TCP or MQTT Sparkplug protocols and simultaneously to IT workflows via REST or gRPC APIs, with each outgoing command being subjected to effect-based authorization verification, retry logic and circuit breaker safety measures to prevent unsafe or unauthorized execution.

[0032] In one embodiment, the edge intelligence application also includes a policy processor implemented as a dedicated microcontroller coupled with the secure enclave module. The policy processor is configured to perform line-rate validation of the governance rules for each event, append cryptographically signed origin stamps, and forward validation results to both the local audit memory and the federated governance layer. This ensures that governance enforcement is deterministic even under disconnected or compromised network conditions.

[0033] The present invention, as described in the preceding claims, introduces a distributed data network system that operates natively in an event-driven manner and extends its functionality to specific device-level embodiments via the edge intelligence appliance. The underlying architecture and techniques of the system are designed to achieve deterministic, low-latency, context-aware intelligence while simultaneously meeting stringent requirements for regulatory compliance, fault tolerance, and cross-domain interoperability.

[0034] The system is based on domain data nodes. These are autonomous computing units that can ingest raw event streams, validate them against agreed-upon schemas, perform domain-specific enrichments, and publish them as data products under versioned contracts. Each domain data node is implemented as a software-defined platform that can be hosted in the cloud, on local servers, or within the Edge Intelligence Appliance, a physical device optimized for field deployment. Each node's ingestion pipeline employs a three-stage validation technique: schema compliance checks using a distributed ledger, privacy-preserving transformations such as tokenization of personal data, and authentication through digital signatures or message authentication codes.After validation, events are transferred to a protocol-structured stream memory, which ensures immutability and supports an ordered playback semantics.

[0035] Transformation pods, orchestrated as containerized microprocesses, handle events flowing through stream memory. Each transformation is represented as a directed acyclic graph of operators, which may include filtering, normalization, reference table enrichment, and inference using embedded machine learning models. For example, a technique employed in a transformation pod might process vibration sensor events, calculate a moving Z-score anomaly value over a moving time window, and output an enriched event annotated with anomaly safety. These transformations are executed with support for parallelization and vectorized computation, ensuring that throughput scales linearly with the workload. Each output event is annotated with metadata specifying the transformation code reference, container hash, and timestamp.This allows downstream systems to reconstruct the origin of each derived data product.

[0036] The event mesh backbone processor connects all domain nodes via a publish-subscribe architecture, in which events are partitioned by domain, product, and event type. The backbone technically guarantees exactly-once semantics by combining transactional write protocols with idempotent producer tokens. If a producer attempts to retransmit during operation due to network instability, the backbone technology compares its transaction identifier and offset sequence with existing data records, discards duplicates, and ensures orderly transmission. Partition leaders are chosen through consensus-based coordination techniques such as Raft or ZooKeeper, thus ensuring availability and continuity even in the event of failures. Georeplication further ensures that partitions are mirrored across regions. Snapshot shipping and write-ahead logging enable disaster recovery without exceeding latency limits.

[0037] Once events in the backbone have stabilized, the Contextual Intelligence Engine overlays the semantic reasoning. The engine integrates a semantic graph memory that maps incoming event attributes to typed nodes and edges, thus representing real-world entities such as machines, work orders, or transactions in an evolving knowledge graph. The Graph Reasoner then applies continuous queries and complex event processing windows to detect higher-order patterns. A technique implemented in the Reasoner integrates graph neural network embeddings, trained on historical incidents, with top-K pattern matching across real-time event flows.For example, the Reasoner can detect a composite condition consisting of "vibration anomaly lasting over three minutes, coolant pressure drop exceeding ten percent, and an open work order in the maintenance system" and output a highly reliable risk score indicating an impending equipment failure. The streaming nature of the Graph Reasoner ensures that these conditions are detected in less than a second, even if the events originate from different domains.

[0038] Governance and policy enforcement are embedded as an integral part of the runtime structure. A distributed policy engine evaluates every data access and transformation against rules defined in a declarative language such as Rego. The evaluation technique checks user roles, event sensitivity classifications, and consent tokens before queries or transformations are permitted. For sensitive operations, transformations are executed in secure enclaves to ensure confidentiality, even with respect to system operators. Each transformation automatically generates W3C PROV-N source records, captures the input entity → activity → output entity relationships, and cryptographically signs them with Merkle proof chains to guarantee immutability.For aggregated queries, differential privacy techniques are applied to ensure compliance with statistical disclosure control requirements. By embedding these controls at runtime, the system ensures that every inference and action is both authorized and auditable in real time.

[0039] The actuation interface translates the inferences generated by the context intelligence engine into real-world effects. A technique that governs this translation first verifies authorization by the governance layer and applies effect-based permissions to ensure that only authorized actors can trigger control actions. After validation, the actuation pipeline uses protocol adapters for command transmission. For operational domains, the system transmits control messages via industrial protocols such as OPC UA or Modbus TCP. For IT workflows, it sends REST or gRPC API calls to ERP systems or cloud orchestrators. Reliability techniques such as retrigger loops, circuit breakers, and compensation handlers ensure that commands are transmitted exactly once and that errors are automatically reset to a safe state.For example, if a command to throttle a machine spindle is not acknowledged within a timeout window, the system retries the operation with exponential backoff and simultaneously notifies a human supervisor. This integration of technical safeguards prevents unsafe or inconsistent update results.

[0040] The edge intelligence appliance provides a physical instantiation of the domain data node concept, integrating the aforementioned technologies into a concrete device. Its compute module integrates multi-core CPUs, GPUs, and optional FPGAs to accelerate inference workloads. A secure enclave module performs privacy-compliant transformations and policy checks, while a dedicated policy processor microcontroller conducts line-rate validation of governance rules and applies cryptographically signed origin stamps. The appliance architecture includes industrial-grade interfaces for direct connection to sensors and programmable logic controllers (PLCs), enabling event capture and processing without traversing the remote cloud infrastructure. The appliance's internal burst control technology manages the producer flow by monitoring buffer utilization and forwarding backpressure signals to prevent sensor overflows.Tiered storage techniques manage hot data partitions on SSDs for fast access while archiving colder segments in distributed object storage to achieve both performance and cost efficiency.

[0041] The technical end-to-end lifecycle within the system can be illustrated using a concrete operational example. A vibration sensor connected to an edge intelligence appliance sends a measurement stream encapsulated in the CloudEvents format. The appliance's ingest technique validates the payload using schema fingerprints, removes sensitive identifiers, and verifies the digital signature. The validated event is stored in the protocol-structured stream storage and processed by a transformation pod that calculates anomaly metrics. The enriched event is published in the backbone and forwarded to the contextual intelligence engine, where graph reasoning links the anomaly to open work orders and inventory data. A machine learning inference running in the reasoner determines a failure risk score.The governance layer verifies that an automated throttling command is authorized, confirms role-based permissions, and logs the immutable origin. The actuation technology then sends an OPC UA command to slow the machine spindle, makes a REST API call to open a maintenance job, and sends a notification to a supervisor. All these actions are completed within a latency of less than 500 milliseconds, thus closing the loop from capture to understanding to actuation.

[0042] Through these integrated techniques, encompassing validation, transformation, backbone provisioning, semantic reasoning, governance, and activation, the present invention creates a system that enables deterministic, policy-compliant, real-time intelligence across distributed domains. By coupling this architecture with the physical embodiment of the edge intelligence appliance, the invention ensures that computation, security, and activation are brought directly to the point of data generation, thereby overcoming the limitations of previous centralized or pseudo-distributed approaches.

[0043] At the perimeter, various producers such as IoT sensors, mobile clients, and enterprise applications send out events in standardized formats. Each incoming stream is processed through an ingest and validation layer to ensure schema compliance, data privacy, and cryptographic authenticity. Events are then stored in the stream storage of a DDN, which performs transformation pipelines and enrichment through containerized tasks.

[0044] The event mesh backbone processor connects all DDNs, enabling unique publish / subwrite deployments across domains. A contextual intelligence engine enriches raw events with semantic metadata, stores it in a graph model, and applies real-time reasoning rules or machine learning models to recognize composite patterns.

[0045] The governance and policy control unit enforces zero-trust principles. It evaluates every access and transformation against declarative policies, records immutable provenance, and supports confidential computing through enclave execution. Finally, the activation interface transforms derived insights into operational commands, including industrial control signals (e.g., OPC UA, Modbus) or IT workflows (e.g., REST, gRPC). Device: Edge Intelligence Appliance (EIA)

[0046] In a preferred embodiment, an appliance architecture is introduced. The Edge Intelligence Appliance is a robust computer that serves as a standalone or clustered DDN. Its architecture comprises: • Housing and frame: A shock-resistant, thermally controlled housing with modular slots for accommodating computing and storage blades. • Compute module: A heterogeneous processing unit that integrates multi-core CPUs, GPUs and optionally FPGAs or TPUs, and is optimized for stream analysis and ML inference. • Secure Enclave module: Hardware-based trusted execution environments (Intel SGX, AMD SEV) that perform sensitive transformations on encrypted data. • Stream storage module: A protocol-structured, high-throughput storage medium that supports multi-level event stream retention. • Policy processor: A dedicated microcontroller for performing policy checks, consent validations, and origin stamps in real time. • Industrial I / O interfaces: Robust connectors supporting Ethernet, MQTT, OPC-UA, Modbus-TCP and wireless connectivity for integration with industrial equipment. • Cooling and power supply subsystem: Adaptive cooling channels and redundant power supplies ensure reliability in harsh industrial environments.

[0047] This device embodies the abstract concept of a DDN as a physical, deployable structure that enables companies to place compute near machines, factory floors, or other data sources. Each EIA can operate autonomously or connect to others via the event backbone, thus extending the distributed mesh fabric into a concrete edge infrastructure.

[0048] The present invention relates to distributed data management and real-time analytics architectures. In particular, it is an event-driven, domain-oriented data network system that unifies heterogeneous event streams across physical and digital environments and enables real-time semantic enrichment, government-compliant processing, and automated activation. The invention lies at the intersection of edge computing, event streaming, knowledge graph reasoning, and secure distributed systems and is applicable in industries requiring continuous intelligence and closed-loop control, such as manufacturing, finance, logistics, healthcare, and critical infrastructure management.

[0049] The drawing and the preceding description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use. The range of embodiments is at least as broad as specified in the following claims.

[0050] Advantages, further benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and all components that can lead to an advantage, benefit, or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of individual or all claims. REFERENCES 100 A quantum-based system for detecting fake news. 102 domain data nodes 102a Transformation capsules 104 Event Mesh Backbone Processor 106 Contextual Intelligence Engine 108 Unit for Governance and Political Control 110 Actuating interface QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] KR 20180049274A

[0008] US 2017 / 0352123 A1

[0009] US 12254022 B1

[0009]

Claims

[1] A system for event-driven distributed data network analysis, the system includes: a multitude of domain data nodes, each comprising a stream storage, a multitude of transformation pods, and a product API, with each domain data node ingesting heterogeneous event streams, validating schema compliance, performing enrichments, and making data products available under versioned schema contracts; each domain data node further comprises a hardware-based edge intelligence appliance comprising an enclosure, a compute module with heterogeneous processing units selected from CPUs, GPUs, and TPUs, a secure enclave module for confidential execution of transformation code on encrypted data, a protocol-structured stream storage medium supporting multi-level retention, and industrial I / O interfaces configured for direct interaction with sensors, programmable logic controllers, and cloud-native services; an event mesh backbone processor configured to connect the multitude of domain data nodes via a publish-subscribe architecture, with the backbone guaranteeing exactly one-time delivery, partition-level sorting, and geo-replicated durability; a Contextual Intelligence Engine coupled with the Event Mesh Backbone processor, the engine comprising a semantic graph memory and a Streaming Graph Reasoner configured to map raw events into an ontology, perform graph joins in real time, and recognize composite causal patterns across multiple domain data nodes; a governance and policy control unit integrated into both the data and control layers. This layer enforces zero-trust security, role-based access control, real-time provenance tracking, and regulatory compliance; and An actuation interface coupled with the contextual intelligence engine and the governance layer. The actuation interface is configured to generate control commands for operational technology devices and digital workflows, thus closing the loop from data acquisition to autonomous decision-making. [2] System according to claim 1, wherein the transformation pods executed in each domain data node are containerized microprocesses orchestrated by a distributed scheduler, wherein the pods can perform inline machine learning inferences, wherein the results of the inference are published as derived event streams that are version-controlled and origin-stamped before leaving the domain boundary. [3] System according to claim 1, wherein the event mesh backbone processor partitions topics by domain, product and event type and also includes transactional write mechanisms and idempotent producer protocols, such that the system maintains the Exactly Once semantics under conditions of network partitioning, producer retries or consumer restarts and thereby ensures deterministic transmission of event sequences in safety-critical control environments. [4] System according to claim 1, wherein the Contextual Intelligence Engine comprises a semantic graph store implemented as a label property graph or RDF triple store, wherein the graph store accepts event attributes and maps them to typed nodes and edges, and a streaming graph reasoner configured to apply complex event processing rules, graph neural network embeddings, and top-k pattern recognition, thereby enabling the detection of cross-domain incident patterns such as "sensor anomaly persisting beyond threshold, open work order present, and inventory deficit detected". [5] System according to claim 1, wherein the governance and policy control unit executes policy-as-code rules written in a declarative language, evaluates permissions based on user roles, event confidentiality labels and purpose-specific consent tokens, and enforces field-level redaction, differential privacy insertion and column-level encryption during query execution, and also records immutable lineages in append-only logs secured by Merkle chains of evidence to ensure regulatory auditability. [6] System according to claim 1, wherein the actuation interface translates decisions of the contextual intelligence engine into operational commands which are transmitted to industrial devices via OPC UA, Modbus TCP or MQTT Sparkplug protocols and simultaneously to IT workflows via REST or gRPC APIs, wherein each outgoing command is subjected to effect-based authorization verification, retry logic and circuit breaker safety measures to prevent unsafe or unauthorized execution. [7] System according to claim 2, wherein the edge intelligence application further comprises a policy processor implemented as a dedicated microcontroller coupled to the secure enclave module, the policy processor being configured to perform line-rate validation of the governance rules for each event, append cryptographically signed origin stamps, and forward validation results to both the local audit memory and the federated governance layer, thereby ensuring that governance enforcement is deterministic even under disconnected or compromised network conditions.

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