System for real-time processing and analysis of distributed data in scalable computer networks

Abstract

Data processing system for real-time processing and analysis of distributed data in scalable computer networks, comprehensive a plurality of processing units distributed from one another, a data stream acquisition unit for the continuous recording of incoming data streams, a coordinating control unit for assigning data processing tasks to the processing units, as well as an intermediate storage unit for the temporary storage of data and processing results, characterized by the fact that The data processing system has a hardware-implemented synchronization and consistency control unit, which is set up to synchronize time-distributed data streams in real time using deterministic timestamp mechanisms and to physically suppress inconsistent data states before further processing, with the processing units performing parallel analysis processes and the results being merged into a consistent global state space.

Description

Technical field

[0001] The present invention relates to the field of data processing systems, in particular systems for the real-time processing and analysis of distributed data in scalable computer networks. The invention lies in the technical area of ​​distributed information processing, real-time data stream processing, and system-level control of data analysis systems. Specifically, the invention relates to a device for processing continuously incoming data streams using multiple distributed processing units, wherein temporally deterministic synchronization and consistency checking of the data is performed. Furthermore, the invention includes hardware-based control and synchronization units for coordinating parallel analysis processes and ensuring a consistent overall result.The technical field also extends to systems for load balancing, data stream segmentation, real-time synchronization, and the consistent merging of distributed processing results in scalable network environments. State of the art

[0002] In the field of modern data processing, distributed computer networks are increasingly used to process and analyze large amounts of data in real time. Such systems are used particularly in applications such as financial transactions, industrial monitoring systems, telecommunications networks, sensor networks, and digital platforms.

[0003] Incoming data streams are distributed in parallel across multiple processing units to enable fast and efficient analysis.

[0004] Data processing systems based on distributed architectures are known from the state of the art. These systems process data streams using software mechanisms such as stream processing frameworks. They utilize time-based windowing mechanisms, buffer memory, and distributed computing nodes to analyze data. Processing coordination is typically handled by software-based control components, which manage task allocation and result aggregation.

[0005] A key problem with these well-known systems is that the synchronization of data streams is often non-deterministic. Time delays, network latencies, and differing processing times lead to inconsistent data states that are only detected or corrected subsequently. This can result in erroneous analysis results, especially in applications with high real-time requirements.

[0006] Furthermore, systems are known that employ load balancing mechanisms to optimize processing capacity. These systems consider parameters such as CPU utilization or network status; however, the adjustment typically occurs at the software level without direct hardware-based control. This can lead to delays and inaccuracies in the distribution of data processing tasks.

[0007] Methods for checking data consistency are also known, but these are usually performed after the actual processing. In these systems, inconsistent data is often only detected retrospectively, leading to additional computational effort and delays. Suppressing inconsistent data states early on, before processing, is not adequately implemented in the current state of the art.

[0008] Furthermore, aggregation mechanisms are known that combine partial results from different processing units. This aggregation often occurs based on time windows or heuristic rules, which means that the deterministic order of the results is not always guaranteed. This can lead to inconsistencies in the overall result, especially with highly dynamic data streams.

[0009] Overall, the known solutions show approaches to distributed data processing, load balancing and result aggregation, but lack a technically integrated system that ensures deterministic real-time synchronization, hardware-supported consistency control before processing and a consistent and orderly merging of results in a scalable computer network. Object of the invention

[0010] The present invention is based on the objective of providing a data processing system that enables reliable, deterministic and scalable real-time processing and analysis of distributed data in computer networks, thereby overcoming the disadvantages of known systems.

[0011] In particular, the task is to create a technical solution in which incoming data streams are synchronized in time and checked for consistency before processing, so that inconsistent or delayed data states are detected and suppressed before analysis.

[0012] Another object of the invention is to provide a hardware-based control of data processing that enables an efficient and adaptive distribution of processing tasks across multiple processing units, taking into account current system states and network parameters.

[0013] Furthermore, the invention aims to ensure a deterministic merging of partial results from parallel analysis processes, so that a consistent and reproducible overall result is generated.

[0014] Furthermore, a system should be created that enables continuous real-time processing of large data streams with simultaneously high processing speed and low latency.

[0015] Ultimately, the object of the invention is to provide a modular and scalable data processing system that can be flexibly integrated into different network environments and ensures stable and efficient data analysis even with increasing data volumes. Summary of the invention

[0016] The present invention relates to a data processing system for the real-time processing and analysis of distributed data in scalable computer networks. The system comprises a plurality of distributed processing units, a data stream acquisition unit for the continuous recording of incoming data, and a coordinating control unit for the allocation of processing tasks. Furthermore, a hardware-implemented synchronization and consistency control unit is provided, which synchronizes incoming data streams using deterministic timestamping mechanisms and suppresses inconsistent data states before further processing.

[0017] The processing units execute parallel analysis processes, the results of which are merged in a globally consistent state space. Adaptive control logic enables the dynamic distribution of processing tasks depending on current system states and network parameters. Furthermore, a result aggregation and merging unit is provided, which deterministically combines the generated partial results and excludes inconsistent results.

[0018] The invention enables reliable, consistent and low-latency processing of large data streams in real time while maintaining high scalability. Detailed description of the invention

[0019] The present invention relates to a data processing system for real-time processing and analysis of distributed data in scalable computer networks, in which deterministic, consistent and low-latency processing of continuously incoming data streams is enabled.

[0020] The data processing system includes an input interface in the form of a data stream acquisition unit, which is configured to continuously capture incoming data streams from various sources and feed them into the system. The captured data streams are first fed to a hardware-implemented synchronization and consistency control unit. This unit is designed to align incoming data temporally using deterministic timestamping mechanisms and simultaneously performs a check for structural and temporal consistency.

[0021] The synchronization and consistency control unit includes means for detecting discrepancies between data packets originating from different sources.

[0022] In particular, timestamps, data sequences, and defined consistency conditions are evaluated. Data packets that do not meet these conditions are suppressed or removed from the data stream before further processing. This ensures that only consistent and synchronized data is passed on to downstream processing units.

[0023] After preprocessing, the data is forwarded to a multiple distributed processing units. These processing units are configured to perform parallel analysis processes. Each processing unit comprises a segmented analysis pipeline in which the incoming data is divided into several sequential processing segments. Within each segment, defined analysis operations are performed, and the results of the individual segments are combined into sub-results.

[0024] A central control unit is provided to coordinate the processing units and distribute data processing tasks. This control unit includes hardware-based load balancing logic that continuously monitors the status of each processing unit. Parameters such as utilization, processing time, and communication delay are recorded and evaluated. Based on these parameters, incoming data streams are dynamically assigned to suitable processing units, ensuring even load distribution and efficient use of available resources.

[0025] The partial results generated by the processing units are passed to a result aggregation and merging unit. This unit is configured to combine the partial results according to deterministic ordering rules. This ensures that the data processing sequence is maintained and that delayed or inconsistent partial results are not included in the final result. The aggregation takes into account the temporal assignment and the logical dependencies of the individual data streams.

[0026] Furthermore, the system is designed to manage a global state representation in which all relevant processing results are consolidated. This global state representation serves as a consistent basis for downstream applications and ensures that all analysis results are based on a uniform and reliable data set.

[0027] In a preferred embodiment, the data processing system has a modular design, allowing additional processing units to be integrated without disrupting ongoing operations. Communication between the individual system components takes place via standardized interfaces, with data transmission optimized to meet real-time requirements.

[0028] In another embodiment, the synchronization and consistency control unit operates adaptively and dynamically adjusts its test parameters. Historical data and system states are taken into account to continuously optimize the consistency check and adapt it to different data streams.

[0029] The present invention thus enables a technical solution in which distributed data streams are synchronized and checked for consistency before the actual processing. The combination of hardware-based synchronization, parallel data processing, adaptive load balancing, and deterministic result aggregation ensures reliable real-time processing of large data volumes.

[0030] The system according to the invention is particularly suitable for applications with high demands on data consistency, processing speed, and scalability, such as in industrial control systems, financial applications, telecommunications networks, or data-intensive analysis platforms. The system can be used in both centralized and distributed architectures.

[0031] It is understood that the described embodiments are merely exemplary and that modifications and adaptations are possible without leaving the scope of protection of the invention.

Claims

[1] Data processing system for real-time processing and analysis of distributed data in scalable computer networks, comprising a plurality of processing units distributed from one another, a data stream acquisition unit for the continuous recording of incoming data streams, a coordinating control unit for assigning data processing tasks to the processing units, as well as an intermediate storage unit for the temporary storage of data and processing results, characterized by , that The data processing system has a hardware-implemented synchronization and consistency control unit, which is set up to synchronize time-distributed data streams in real time using deterministic timestamp mechanisms and to physically suppress inconsistent data states before further processing, with the processing units performing parallel analysis processes and the results being merged into a consistent global state space. [2] Data processing system according to claim 1, characterized by that the coordinating control unit includes hardware-based load balancing logic, which dynamically adjusts the allocation of data processing tasks depending on current processing states, memory usage and network delay. [3] Data processing system according to claim 1, characterized by, that the synchronization and consistency control unit is set up to continuously check incoming data streams for temporal and structural consistency and only release data for analysis that meets defined consistency conditions. [4] Data processing system according to claim 1, characterized by , that each processing unit includes a segmented analysis pipeline in which data is divided into successive processing segments, with each segment being processed independently and subsequently reconstructed into an overall result. [5] Data processing system according to claim 1, characterized by , that a result aggregation and merging unit is provided which combines the partial results generated by the processing units into a consistent overall result using deterministic ordering rules and excludes inconsistent or delayed partial results.