Leveraging AI/GenAI and data processing techniques to extract data from technical documents has proliferated due to recent advances in LLM capabilities and open source tools. However, being able to contextualize suitable GenAI/LLMs capabilities coupled with enterprise constraints on cost, data regulation, and quality is still challenging. Especially, AI/GenAI resource-constrained enterprises must deal with complex domainspecific technical documents of assets and designs supplied by multiple vendors. This paper presents novel practical methods that incorporate contexts into the design and execution of activities for industrial technical document extraction applications. We consider resource-constrained environments in which enterprises are with edge GenAI/LLMs infrastructures and non-AI engineers. We devise context-aware composition and adaptation for extraction pipelines to deal with diverse attributes of GenAI/LLMs and extraction quality control. We experiment our methods with technical documents for telco operators.

In a large-scale mobile network, due to the diversity of data characteristics, detection purposes of operation teams, and analytics and machine learning algorithm abilities, building big data anomaly detection pipelines without considering different analytics and team situations may not yield expected quality of analytics, including detection relevancy, performance and quality. This is especially for analytics subjects, such as mobile network zones, of which characteristics are dynamic and contextual. Moreover, due to the lack of labeled data and the high cost of creating labeled data, building anomaly detection analytics models based on (supervised) deep learning or advanced models is even more challenging from various aspects of effort, cost and deployment. In this paper, we present a novel framework that enables anomaly detection through context-aware, composable components to provide efficient detection pipelines suitable for lightweight, resource constrained and geographical operation teams. First, we identify and categorize different types of analytics feature contexts and evaluate existing algorithms suitable for these contexts, mapping anomaly detection algorithms, patterns and configurations for data pre-processing and unsupervised detection tasks in individual analytics functionality. These context-specific pipelines detect anomalies and their relevancy for dynamic analytics subjects such as mobile network zones. Then we develop dynamic configuration and combination techniques for such pipelines to produce highly relevant, multi-context detection of anomalies. Our framework provides flexibility and configurations for team contexts to carry out the anomaly detection in the team’s operations. We will demonstrate our work through real data gathered for a large-scale mobile network covering multiple types of sites with different geographical zones and equipment. We especially focus on district zones and user-defined zones as analytics subjects that must be managed by teams in our experiments.

This paper presents our approach for supporting machine learning (ML)-based analytics of quality of experience (QoE) related issues in a variety of Radio Access Networks (V-RAN). We focus on key problems in a holistic analytics infrastructure for engineers without strong ML skills and powerful computing infrastructures. We characterize types of relevant data and existing data systems to follow a specific data mesh approach suitable for engineers. The paper presents key steps in establishing the participation of engineers and the acquisition of domain knowledge. We introduce models for representing analytics subjects and their dependencies, and for managing relevant ML techniques and methods for analytics subjects. We explain our work through examples from a large-scale mobile network of approximately 4 million subscribers.