Multi-Tenant API Data Synchronization and AI-Powered Event-Based Data Processing Architecture

Abstract

The invention relates to a data processing system for securely and consistently processing data from heterogeneous external API sources (1) in a multi-tenant computer network environment. The system includes an API adapter selector layer (2) that selects adapters based on source, transaction type, and tenant context; a normalization layer (3) that converts source-specific data into a common technical data model; a tenant context and data isolation layer (4) that associates data with tenant ID and transaction key; an event-based synchronization queue (5) that identifies repetitive events and reprocesses incomplete events; a central event / status store (6) that stores transaction results; a log and error queue (7) that records errors in a traceable manner; web and mobile clients (8, 9) that feed from the same status store; a secure AI query agent (10) that limits the scope of the query and data according to tenant authorization and masks sensitive fields; and AI modules (11) that generate output from authorized data.

Description

1 TARIFF Multi-K API Data Synchronization and AI-Powered Event-Driven Approach Data Processing M mar s TECHNICAL AREA 5 The invention relates to heterogeneous external application programming interface (API) sources. The acquired data is synchronized with a multi-threaded API and supported by artificial intelligence. data processing architecture related to computer-based data processing systems area le lg ld r. STATE OF THE ART In the case of a technologically advanced system, it communicates with external data sources via API. integration systems, multi-factor databases, asynchronous message queues, Web and mobile systems, AI-powered data analytics components are separate. It is available for use. 15 The domain names, data types, status codes, error codes, and other API resources are included. Because timestamps are different from each other, data relating to the same technical event... Data loss or erroneous mapping during conversion to a common data structure This can occur. The same event being received repeatedly, API response delay, or the call itself... Even if an interruption or partial update occurs, the transaction status remains 20. It can become inconsistent. In multi-tenant structures, tenant identity and authorization scope are limited to database access. (implemented adapter selection, message queue, status log, error log, stemc notification) or assign to different tenants if not protected during AI query The mixing of flows creates a risk. Also, web and mobile clients have different 25 the state of being fed from the sources of the process states at different times What is displayed; is the direct access of artificial intelligence modules to the data source. This can lead to the processing of areas outside the scope of authority. The technical problem identified in the previous article is not solely related to the marketplace sales process. It's not about managing; it's about the multifaceted nature of data from heterogeneous API sources and their multiplicity. 30 The main technical problem is that the stemc lbr environment cannot be reliably processed. It consists of the following sub-problems: 2 1. Different API schemes may handle the same product, stock, order, or shipping event in different areas. Names, data types, status codes, and timestamps are common to all communication purposes. An error occurred during the conversion of the technical data model. 2. Receiving the same event multiple times, API call interrupted, delay. The reason for the response not being returned or a partial update occurring on the marketplace side is 5. The stock / share status becomes inconsistent. 3. Multi-story architecture with different vendors having the same physical infrastructure Despite its processing, data isolation, access control, and technical logging are essential. The mechanisms are not strong enough. 4. The same order, stock, or error status should be maintained between the web panel and the mobile application. Appearing at different times; critical technical events can reliably inform the user. as let lememes. 5. Directly working with vendor data for LLM or other artificial intelligence modules If the database query is generated without authorization, it may result in an incorrect query or data leak. or exceeding the tenant limit r sk . 15 6. Product images may come in different sizes, backgrounds, qualities, and formats. Standardization, debugging, and output traceability in a batch visualization pipeline. inability to provide. For these reasons, heterogeneous API data is transmitted via an adapter suitable for the source. normalize, maintain tenant context, and process throughout the pipeline, preventing repetitive events. Securely re-processing by specifying the transaction key, shared with clients. integrated system that generates situations and limits the scope of artificial intelligence. br tekn km mar ye ht yaç vardır. DESCRIPTION OF THE INVENTION 25 The purpose of the invention is to analyze heterogeneous data from various API sources, including source and processing. Based on the type and tenant context, select the adapter and share the common technical data model. Transformation and tenant context preservation, and event-based processing. to provide. The other purpose of the invention is to obtain the same event repeatedly or to interrupt the process. If it fails, the transaction key, retry counter, and transaction status will be displayed. using data integrity protection; the outcome of the process is a common event / situation 3 by storing it in the repository, the same technical situation applies to web and mobile systems. It is produced. Another purpose of the invention is to define user queries within the tenant's identity and authorization scope. It artificially limits, masks sensitive areas, and only summarizes specific data. The goal is to create a secure query path that transmits intelligence modules. 5 The technical advantages provided by the invention are as follows:  Transforming heterogeneous API data into a common data model. Reducing errors and the need for channel-based development.  Differences are ensured by protecting the tenant's identity and authorization information throughout the process. Separating the data assigned to users. 10  Asynchronous queuing, impotent operation switch, and retry. Preserving the integrity of the space and data.  Ensuring transaction status consistency between web and mobile systems.  Artificial intelligence uses masking and querying rules. restriction. 15  Visual processing steps can be monitored along with error logs. hiding. Explanation of the Figures Figure 1. Schematic representation of a multi-tenant, event-based API data processing system. 20 Reference List 1. Harc API data sources 2. Select API adapter, layer C. 3. Normalization layer 25 4. Tenant context and data isolation layer 5. Event-based synchronization queue 6. Central event / situation repository 7. Log and error queue 8. Web stemc s 30 9. Mobile system 10. Secure AI query agent 11. Artificial intelligence modules 4 The subject of the invention is the system, which uses API adapters to receive data from API data sources (1). select c layer (2) and normalization layer (3) common technical data What does the model transform; through the tenant context and data isolation layer (4) with tenant identity, scope of authorization, source system and transaction key l şk lend rmekted r. 5 Tenant context preserved events in event-based synchronization queue (5) Processed asynchronously; repetitive events via the processing key. Events that were not completed are being reviewed and reprocessed. Process results. Errors that occur during processing are stored in the central event / situation repository (6). It is recorded in the log and error queue (7). 10 Web stemc s (8) and mobile stemc (9) from the same central event / status repository (6) It is fed. Queries directed to artificial intelligence modules (11) are secure artificial It is transmitted via the AI ​​query agent (10); secure AI query agent (10), It restricts the scope of queries and data based on the tenant context and authorization information. By masking sensitive areas, only z nl ver y artificial intelligence modules ne (11) 15 It transmits. These components work together; common source schemas, tenant Maintaining boundaries along the asynchronous processing line, repetitive events The situation is being processed without generating updates, common among STEMs. situation creation and artificial intelligence in terms of data scope 20 The limitations imposed are causing technical effects. DETAILED DESCRIPTION OF THE INVENTION External API data sources (1), one or more data generating or data providing It includes a remote system. From these sources, product, stock, order, and shipping status are available. At least one of the following: category, payment, or visual data; resource-specific domain names, data The data is retrieved using status codes, error codes, and timestamps. API adapter select layer (2), incoming data source information, transaction type and tenant By evaluating its context, the adapter corresponding to the API data source (1) The selected adapter component is chosen. The selected adapter component shares common 30 data fields with the source-specific data fields. The technical data model applies mapping between fields and data... It transfers to the normalization layer (3). Normalization layer (3) common technical data model of the resource-specific data fields It is converting. This layer requires mandatory field checking and data type validation. error and status codes specific to the API data source (1) are being created; It is mapped to the error and status codes used by the verification system. As a result, it was determined that the tenant context and data isolation 5 can be processed. to the layer (4), data containing validation error and error queue (7) is being transferred. Tenant context and data isolation layer (4), normalized data tenant identity, with authorization scope, transaction time, source system and transaction key. It connects to the tenant context created; event-based synchronization 10 in the queue (5), in the central event / status repository (6), in the log and error queue (7), web stemc s (8) and mobile stemc (9) access and secure artificial intelligence query It is protected in the agent (10). Thus, each data record, event, status, error and The query is processed within the scope of the tenant. Event-based synchronization queue (5) categorizes events associated with the tenant context. It receives it asynchronously. The operation key for each event in the queue is repeated. The attempt counter and transaction status are recorded. Each item carries the same transaction key. When the event is retrieved again, the previous transaction record is checked; completed event The event is terminated without generating a new status update; it's an incomplete event. Considering the retry counter and the current transaction status, retry 20 It is being processed. Central event / status repository (6), in event-based synchronization queue (5) The results of the processed events are shown along with the tenant context and transaction key. It stores the product, stock, order, shipping, and error statuses in the same state. The system is being updated with the help of the system; thus, different systems can use the same common 25 for the same operation. Access to the status record is provided. Log and error queue (7), occurring during normalization or event processing Errors are categorized by tenant context, transaction key, error code, and transaction status. It records the error by connecting the data. The error log allows for tracking the related event and event-based analysis. Determining the event to be reprocessed by the synchronization queue (5) 30 It provides. Web stemm s (8) contains technical events, errors and in the central event / status repository (6). Synchronization statuses are displayed within the tenant's permissions. Mobile 6 stemc (9) is fed from the same central event / state repository (6); kr tk as For specified situations, notifications are received while preserving the tenant context. Secure AI query agent (10), AI of user query modules what (11) or the agent that prevents direct transmission to the data source It is a layer. The secure AI query agent (10) processes the query with user authorization and 5 It evaluates the scope of the permissions related to the query based on the tenant context. It is obscuring, masking personally or commercially sensitive areas, and only z nl data summaries transfer to artificial intelligence modules (11). AI modules (11) by secure AI query agent (10) It produces analysis, summary, or warning output on the provided data. Artificial intelligence 10 In the application of modules nn (11), text data is pre-processed and tokenized. Similarity and recommendation scores are generated; product images are used in another application. Size, format, background and quality are checked, unsuitable options are selected. The images are either logged for errors or pre-processed, and the resulting output is... The tenant context is stored with the transaction key. 15 OPERATING PRINCIPLE OF THE SYSTEM When the system is started, data received from the API data sources (1) is transferred to the API adapter Select C layer (2) is transferred. API adapter Select C layer (2), source, process Adapter selection is based on the type and tenant context. The selected adapter is then used... The received data, in the normalization layer (3) common technical data model 20 It is being converted. The data verified in the normalization layer (3), tenant context and data isolation layer (4) by tenant k ml ğ, scope of authority, transaction Time is associated with the source system and the transaction key. This relationship is the event. The event transferred to the synchronization queue (5) is preserved. Event-based synchronization queue (5), previous record with the same operation key 25 It is checking whether it exists or not. The previous record must be complete. In this case, a status update is not being generated for the same event. The record... If it is incomplete, the event will be restarted, the timer will start, and the process will begin. Depending on the situation, it is reprocessed. If an error occurs, the error information is logged. error queue (7), successful or failed operation status center 30 The event / status repository (6) is written together with the tenant context. Web stemc s (8) and mobile stemc (9), central within the tenant authority s. The center uses the same status record in the event / status repository (6). 7 The situations identified as critical in the event / situation repository (6) are sent to the mobile client (9) It is transmitted as a notification. The query directed to the AI ​​modules (11) by the user is a secure artificial intelligence. It is taken into the AI ​​query agent (10). Secure AI query agent (10), The tenant determines the sensitive areas based on the context and scope of authority. It masks and transmits the data summary to artificial intelligence modules (11). Artificial The output of the intelligence modules nn (11) is given to the user while preserving the lg l tenant context. It is presented. The subject of the invention is computer-applied data processing carried out by the system. Method; obtaining API data, source, transaction type and tenant context 10 Adapter selection, conversion of source-specific data to a common technical data model, Converted data, tenant identity, scope of authority, source system, and transaction. coding with the key, asynchronous queuing of the related event, process Identifying a repeated or incomplete event through the key, the event processing or reprocessing, the result of the process in the tenant context and the center 15 The event / status is recorded in the event / status repository, and the AI ​​query is configured within the tenant context. Artificial intelligence that masks and restricts access to information according to its scope of authority. It includes the steps for transferring it to the module. 25

Claims

8 REQUESTS 1. Multi-tasking computing of data obtained from heterogeneous API data sources. It is a computer-based data processing system that is geared towards processing data in a network environment, feature; external API data sources (1) data source, operation type and select adapter according to tenant context API adapter select c layer (2), select len ​​5 Source-specific data received via adapter, common technical data model. Converting normalization layer (3), converted data tenant k ml ğ, authorization By associating the scope with the source system and transaction key, the relationship in question is defined as follows: tenant context and data isolation layer (4) created on the event, tenant The context-preserved event corresponds to the asynchronous field and transaction key of 10. A completed event that carries the same transaction key as the previous transaction record. This will prevent the status update from being updated, select the incomplete event. event-based system structured to guide the process back to the next step synchronization queue (5) and event processing result in the same tenant context and process central event / situation repository (6) which keeps the key, is characterized by its character. 15 2. According to claim 1, it is a computer-based data processing system, and its characteristic is; API adapter. Select the c layer (2), each br har c API data source (1) for source-specific data fields adapter that includes mapping between fields in the common technical data model The character is defined by choosing one of its components.

3. According to claim 1, it is a computer-based data processing system, and its characteristic is; normalization 20 layer (3) performs mandatory field checking and data validation and the hard API Error and status codes specific to the data source (1) with system error and status codes The character is damaged by the matching.

4. According to claim 1, it is a computer-based data processing system, and its characteristic is event-based. The synchronization queue (5) has a retry counter and operation 25 for each event. The status is maintained and the incomplete event is retried using a counter for the current process. Depending on the situation, it is characterized by its re-processing.

5. A computer-based data processing system like any of the previous systems. and its feature is; an error that occurs during normalization or event processing, tenant 30 that records the context, transaction key, error code, and transaction status. log and error queue (7) is included. 9 6. According to claim 1, it is a computer-based data processing system, and its characteristic is; central The event / state repository (6) produces the same state for different data types. The character is damaged by the updating of the setting.

7. According to Claim 1, it is a computer-based data processing system, and its characteristic is; tenant authority. Within the scope of the central event / status repository (6), the same status record is accessed via web 5 It is characterized by containing client (8) and mobile client (9).

8. According to claim 7, it is a computer-based data processing system, and its characteristic is; mobile stemc nn (9), from the situation determined as kr tk in the central event / situation repository (6) The tenant context is preserved while the notification received is characterized by its nature.

9. According to claim 1, it is a computer-based data processing system, and its feature is; user 10 By evaluating the query based on the tenant context and authorization information, permission is granted. By defining its scope, and masking sensitive areas within the defined scope, it provides privacy. Secure artificial intelligence that generates a data summary and transmits that summary to the output. query agent (10) character zed r.

10. According to claim 9, it is a computer-based data processing system, and its characteristic is; secure 15 Direct data communication with the data source, depending on the output of the artificial intelligence query tool (10). masked images that are not available and are only provided by the vehicle in question. at least one artificial intelligence that produces analysis, summary, or warning output based on the data summary. module (11) is characterized by its inclusion.

11. According to claim 10, it is a computer-based data processing system, and its feature is artificial intelligence 20 from the module (11), analysis, summary or warning output on masked data It is characterized by producing at least brin.

12. A computer-based data processing system according to Claim 10 or Claim 11, Feature ğ; artificial intelligence module (11), text version preprocessing and tokenization It is characterized by producing at least one of the similarity and recommendation scores by holding down a tab. 25 13. A computer-based data processing system like any of the previous systems. and features of the artificial intelligence module (11), visual data size, format, background and quality control checks, and records any visual defects that are not satisfactory. or pre-processing the data and processing the resulting output within the tenant context. It is to keep it hidden with the key. 30 14. A computer-based data processing system like any of the previous systems. and features include the tenant context and the data isolation layer (4), the converted data Additionally, it correlates transaction time information and tenant context with event-based processes. synchronization queue (5), central event / status repository (6), log and error queue (7), web stemc s (8), mobile stemc (9) and secure artificial intelligence query agent (10) Its character is damaged by its protection throughout.

15. Multi-purpose computing of data obtained from heterogeneous API data sources. Synchronization in a network environment using a high-speed computer application for data processing 5 It is a method, and its characteristic is;  Obtaining data from the source API (1),  Select the API adapter based on the source, transaction type, and tenant context. adapter selected by layer (2),  common technical data in the normalization layer of the source-specific data (3) 10 What model needs to be converted?  transformed data by tenant context and data isolation layer (4) tenant identity, scope of authorization, source system and transaction key l şk lend r lmes ,  l şk l event-based synchronization queue (5) asynchronously 15 taking,  Identifying repeated or incomplete events via the transaction key and the re-processing or re-processing of the event,  The result of the operation is sent to the central event / status repository with the tenant context (6) save lmes, 20  tenant context in secure AI query tool (10) of user query and its limitation according to the scope of authority,  Masking of sensitive areas and data transfer to artificial intelligence module (11) transfer, The process steps are characterized by their inclusion. 25 16. According to claim 15, it is a computer-based data processing method, and its characteristic is; The normalization or event processing error occurs within the tenant context, process The key is associated with the error code and operation status and added to the log and error queue (7) The save step includes the character zed r.

17. According to claim 15, the computer is an applied data processing method, and its characteristic is; web 30 center within the scope of tenant authorization by stemc s (8) and mob l stemc (9) Accessing the same status record in the event / status repository (6) and critically 11 Steps to transmit the determined situation to the mobile client (9) as a notification The character of Çermes is damaged.

18. According to claim 15, computer-based data processing is a method whose characteristic is; artificial intelligence. Analysis, summary or warning on masked data by intelligence module (11) It is characterized by the fact that it includes at least one step of producing one unit from its output. 5 15 25

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