AI-Enhanced Event Tracking: A Collaborative Full-Stack Model for Tag Intelligence and Real-Time Data Validation
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P115Keywords:
AI-Enhanced Event Tracking, AI-Driven Data Analytics, Intelligent Tag Governance, Reactive Frontend Analytics, Trustworthy Digital Ecosystems, Full-Stack Analytics Architecture, Multi-Tenant Tracking InfrastructureAbstract
The current digital analytics systems strongly depend on precise tracking of events in order to measure user interactions, drive personalization pipelines, and make business decisions that are revenue critical. Nevertheless, current tagging systems still have the issue of fragmented instrumentation, the inconsistency of schema compliance, such as data drift and this interestingly missed event without any exception-in-curring serious costs in data quality and the performance of end model. To overcome them, this paper suggests an AI-Enhanced Event Tracking Framework which operates on a collaborative full-stack design to bind the front-end tag instrumentation, middleware validation logic and backend real-time anomaly detection into one intelligence-driven system. The proposed architecture takes advantage of three layers of automation: (i) an intelligent front-end instrumentation engine based on dynamically validating the DOM context, identification of broken or hand-fired tags and enrichment events with contextual metadata; (ii) policy-based middle layer based on the enforcement of event contracts, business rules, and schema constraints with the use of deterministic rules augmented by ML-based decision scoring; and (iii) a back-end AI validation engine based on prediction of event correctness by means of supervised classifier, sequence level anomaly detection by unsupervised drift model and the empirical analysis of production scale data sets substantiates that the system leads to the enhancement of data reliability end to end considerably. Findings have indicated 41-63 percent decrease in the missing or malformed events, 28 percent decrease in the duration of validation as well as 35-52 percent enhancement in the accuracy of detection of anomalies relative to the base rule-based validators. Moreover, the architecture allows high-throughput streaming operations to be maintained with real-time inference of more than 50k events per second at minimal overhead. Together, these contributions create a scalable and self-governing platform of tag intelligence, real-time data validation, and allow organizations to have constantly reliable pipelines of analytics and solid observability through the digital ecosystem
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