Streaming Analytics Architectures for Live TV Evaluation and Ad Performance Optimization
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I5P103Keywords:
Live TV Analytics, Streaming Analytics, Ad Performance, Real-Time Data Processing, Machine Learning, Apache Kafka, Edge Computing, Predictive Modelling, Broadcast Telemetry, Event-Driven ArchitectureAbstract
Broadcasting on live television (TV) remains one of the most widespread systems of distributing real-time content even in the face of over-the-top (OTT) and on-demand media. In this respect, the incorporation of streaming analytics into live TV operations has been implemented as an urgent need of advertisers who require real-time feedback. In this paper, the architecture, design thinking and algorithms of real-time analytics pipeline are explored with an eye to assess the performance of real-time televised content and optimize advertisements (ad) delivery processes. The work is given a high-velocity data ingestion, stream processing schemes, in-memory calculation, model appraisal, ad-placement cost, occasion correlation, and audience involvement evaluation. They are proposed as a set of architectural designs that involve the use of distributed message queues, stream-processing engines, micro-batch pipelines, real-time machine learning (ML) inference, multi-modal viewer attribution signals, and smart caching. The suggested architecture builds upon the legacy broadcast patterns with scalable analytics layers that are able to process heterogeneous information tracks like viewership, STB (set-top box) telemetry, second-screen interactions, content metadata, ad-impression, and social media signals. The literature review covers existing architectures as well as Lambda, Kappa and hybrid streaming-batch models and comparisons the popular streaming technology implementations Apache Kafka, Flink, Spark streaming and AWS Kinesis. New methodology, which is Adaptive Multi-layer Streaming Analytics Framework (AMLSAF), is presented to optimize ad-performance using dynamic viewer-attention modelling, real-time A/B testing, and predictive ad-fatigue scoring. The experimental findings depict that ad-conversion signals are measurably improved, latency decreases, and viewership estimation improves. The discussion identifies the difficulty of scaling, fault tolerance and guaranteeing sub-second latency in peak events of live communications. At the end of the paper, some thoughts on the development in the future are provided, including edge-based analytics, federated viewer modelling, zero-copy data movement and AI-generated dynamic ad-personalization
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