Structured PREreview of Automating Business Intelligence Requirements with Generative AI and Semantic Search

Does the introduction explain the objective of the research presented in the preprint?

Yes

The introduction successfully explains the objective of the research by presenting AUTOBIR, a novel AI-driven system leveraging Generative AI, specifically Large Language Models and Semantic Search, to automate and accelerate the specification of Business Intelligence (BI) requirements. This system addresses the significant challenge of eliciting and specifying BI requirements in rapidly evolving business environments, a process that is traditionally labor-intensive and error-prone. AUTOBIR aims to reduce the time and effort needed for BI system development while ensuring accuracy by providing a no-code conversational interface that translates natural language inquiries into executable queries, prototype analytic code, descriptions, data dependencies, and detailed test-case reports. Furthermore, the paper explores the technical architecture of this system, its integration of AI and data discovery technologies, and the broader potential of Generative AI in transforming data engineering practices for managing and evolving large-scale BI systems

Are the methods well-suited for this research?

Highly appropriate

The methods employed in the AUTOBIR system are well-suited for the research objective of automating and accelerating Business Intelligence requirements specification because they directly address the inherent challenges of traditional, labor-intensive, and error-prone requirements elicitation processes. The core approach leverages Generative AI, specifically Large Language Models (LLMs), and Semantic Search to provide a no-code conversational interface that translates natural language inquiries into actionable BI outputs. This approach is effective because LLMs are used for Text-to-Query transformation, generating executable queries, prototype analytic code, and natural language explanations, which expedites the development timeline and enhances user comprehension. Furthermore, the system’s reliance on semantic search and OWL ontologies is crucial for aligning user inputs with underlying data structures, allowing the system to capture the semantic nuances of data schemas and ensure accurate, contextually relevant queries across diverse and distributed data sources. The system also integrates self-debugging mechanisms featuring syntax, semantic, and execution checkers, which are vital for maintaining the accuracy and integrity of the generated analytics specifications, thereby mitigating the risk of errors associated with automated query generation.

Are the conclusions supported by the data?

Highly supported

The conclusions presented in the preprint are directly supported by the findings, system architecture, and evaluation insights detailed within the sources. The core claim that AUTOBIR is a no-code framework capable of streamlining the creation of analytics requirements is backed by the explanation of its technical components, which automate the generation of query code, data models, natural language explanations, execution outcomes, and visual representations of data. The paper confirms the system’s practical relevance and effectiveness by describing its implementation and refinement during client engagements across four distinct domains: Security, Air Defense, Retail, and Banking. This iterative refinement process involved collecting qualitative feedback from 23 Subject Matter Experts, demonstrating how the system was adjusted to enhance user-friendliness and efficiency in crafting analytical specifications. Additionally, the conclusion regarding the system’s robustness is supported by a comprehensive discussion of threats to validity and the mitigation measures implemented, such as integrating self-debugging mechanisms that use syntax, semantic, and execution checkers to correct LLM-generated errors, and utilizing datasets like Spider and BIRD to ensure broader evaluation beyond small-scale benchmarks

Are the data presentations, including visualizations, well-suited to represent the data?

Highly appropriate and clear

The data presentations, including visualizations, are well-suited to represent the data as they are comprehensive and designed to be accessible to both technical and non-technical users. The system utilizes Generative AI to produce reports that extend beyond traditional tabular formats, incorporating visual aids that deepen user comprehension and provide a robust foundation for validating and refining Business Intelligence requirements. Outputs generated from a user's natural language inquiry include the automatically generated analytics query, a natural language interpretation of that query, the requisite ontological data model, mappings to physical data sources, and detailed test-case reports. Specifically, the system offers an optional feature that generates a natural language explanation of the query to improve explainability and alignment with user intent. Furthermore, the system abstracts the underlying physical database structures into conceptual relationships within the displayed data model, for example showing foreign key relationships as object properties, which specifically enhances usability for non-technical stakeholders. If visualization is requested, the Data Visualizer uses an LLM to generate graphical representations concurrently with query execution results, and these generated reports and visualizations are archived as test cases for future validation.

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Very clearly

The authors clearly discuss the findings and interpret the practical implications of the AUTOBIR system based on qualitative evaluations and implementation insights across four domains: Security, Air Defense, Retail, and Banking. The evaluation, which incorporated feedback from 23 Subject Matter Experts, confirms the system's effectiveness in enhancing user-friendliness, streamlining complex data analysis, and accelerating the prototyping of analytical dashboards. Key lessons learned emphasize the critical need for semantically rich logical data models to support Large Language Models, especially since many physical models suffer from cryptic or ambiguous names, hindering BI adoption. The conclusions affirm that AUTOBIR is a robust, no-code framework that successfully generates query code, data models, explanations, and visualizations, thereby bridging the gap between technical implementation and business goals. Regarding future steps, the authors outline continuous development goals, including the plan for a large-scale user study to assess the system's effectiveness and further expansion of testing with heterogeneous and large datasets like Spider 2.0. They also highlight ongoing research needs, specifically in advanced techniques such as automatic ontology discovery, enhanced semantic search, and developing robust methodologies for guided, secure, and explainable query generation.The authors clearly discuss the findings and interpret the practical implications of the AUTOBIR system based on qualitative evaluations and implementation insights across four domains: Security, Air Defense, Retail, and Banking. The evaluation, which incorporated feedback from 23 Subject Matter Experts, confirms the system's effectiveness in enhancing user-friendliness, streamlining complex data analysis, and accelerating the prototyping of analytical dashboards. Key lessons learned emphasize the critical need for semantically rich logical data models to support Large Language Models, especially since many physical models suffer from cryptic or ambiguous names, hindering BI adoption. The conclusions affirm that AUTOBIR is a robust, no-code framework that successfully generates query code, data models, explanations, and visualizations, thereby bridging the gap between technical implementation and business goals. Regarding future steps, the authors outline continuous development goals, including the plan for a large-scale user study to assess the system's effectiveness and further expansion of testing with heterogeneous and large datasets like Spider 2.0. They also highlight ongoing research needs, specifically in advanced techniques such as automatic ontology discovery, enhanced semantic search, and developing robust methodologies for guided, secure, and explainable query generation.

Is the preprint likely to advance academic knowledge?

Somewhat likely

The preprint is likely to advance academic knowledge by introducing AUTOBIR, which the authors identify as a novel AI-driven system leveraging Generative AI, specifically Large Language Models and Semantic Search, to automate and accelerate the comprehensive specification of Business Intelligence requirements. The authors assert that, to their knowledge, no existing approach utilizes recent advancements in Artificial Intelligence to automate this comprehensive specification process, positioning their work as a substantial advancement in AI-driven data engineering and the automation of requirements elicitation. The research also contributes significantly by exploring the broader potential of Generative AI in transforming the landscape of data engineering and opens new research avenues focused on deducing, evaluating, and refining formal ontological representations. Furthermore, the paper outlines critical directions for future academic work, including continuous research into advanced techniques such as automatic ontology discovery, enhanced semantic search, and establishing robust methodologies for guided, secure, and explainable query generation.

Would it benefit from language editing?

No

The preprint would not benefit from language editing because the language is generally clear and professional, adhering to academic standards, ensuring that the objectives, methodology, and conclusions regarding the AUTOBIR system are fully comprehensible.

Would you recommend this preprint to others?

Yes, it’s of high quality

This preprint is highly recommended, particularly for those interested in Generative AI applications in data engineering and Business Intelligence, because it presents AUTOBIR, a novel AI-driven system designed to automate the comprehensive specification of BI requirements using Large Language Models and semantic search technologies.

Is it ready for attention from an editor, publisher or broader audience?

Yes, as it is

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.