2022· International Journal of Data Engineering and Intelligent Computing· Vol 5, pp. 01-08· 0 citations
TL;DR
This paper discusses the design, implementation, and evaluation of a conversational interface that provides users with real-time, context-aware responses to queries, and demonstrates how such AI-powered systems enhance user experience, improve query accuracy, and streamline data discovery processes.
Abstract
Data catalogs are essential tools for managing and discovering organizational data, but traditional navigation methods can be cumbersome, especially as data volumes grow. This paper explores the integration of generative AI-based conversational interfaces to enable intuitive and efficient navigation of unified data catalogs. By leveraging advanced natural language processing (NLP) models, users can interact with data catalogs through human-like dialogues, eliminating the need for complex query languages and manual search processes. We discuss the design, implementation, and evaluation of a conversational interface that provides users with real-time, context-aware responses to queries. Through a case study and usability testing, we demonstrate how such AI-powered systems enhance user experience, improve query accuracy, and streamline data discovery processes. Finally, we address the challenges and future directions of deploying generative AI in data catalog systems, emphasizing the need for scalable and secure solutions.
The resulting prototype confirms that a cloud-hosted multimodal LLM, when combined with a minimal and well-structured web stack, can serve as a practical foundation for next-generation digital assistants suitable for customer support, education, and personal productivity applications.
G. Vamsi, Vinay Kumar Male· International Scientific Jou...· 0 citations
A novel, layered conceptual framework is introduced that organizes research in CDR across four key dimensions: User Layer, System Layer, Data Layer, and Evaluation Layer and identifies core challenges in CDR, including the lack of standardized evaluation benchmarks and limited support for ambiguous or evolving user intent.
Lisa-Yao Gan, Johanna Walker, E. Simperl et al.· Information Systems Frontier...· 0 citations
Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language. Although these systems can significantly reduce manual effort, it remains difficult to diagnose their behavior and steer the reasoning process when failures or unexpected outputs occur. We present MUSE, an interactive meta-agent that enhances user understanding and control of agentic data science systems by (1) dynamically restructuring low-level execution traces into multiple semantic levels that support navigation from high-level overviews to low-level implementation details; (2) enabling users to reference specific workflow steps in context to ask grounded questions, provide feedback, and revise problematic steps without manually locating relevant execution history; and (3) supporting mixed-initiative steering by surfacing suspicious steps for inspection, scaffolding the repair process, and translating user repair intent into contextualized instructions for the underlying agent. In a between-subjects study (n = 15), MUSE improved task efficiency and increased users'confidence in understanding and steering agentic data science workflows.
Wei-Hao Chen, Weixi Tong, Yuan Tian et al.· 0 citations
In today’s data-driven environment, the ability to explore and manipulate datasets is essential, however, many existing data analysis tools require technical expertise in platforms such as Excel, Python, or Power BI, creating accessibility barriers for non-technical users. This project presents Quiksight, a web-based conversational data analysis system powered by Large Language Models (LLMs) that enables users to interact with datasets using natural language. The objective of this work was to develop an intuitive system that simplifies data analysis by allowing users to upload Excel or CSV files and perform operations such as filtering, sorting, summarization, column modification, missing-value handling, and data export without writing code. The system was implemented using Google Gemini as the LLM, FastAPI for backend processing, and HTML, JavaScript, and Tailwind CSS for the frontend interface.Evaluation showed that Quiksight successfully interpreted and executed a wide range of natural language data manipulation queries with high accuracy. Testing recorded an average query response time of approximately 2 seconds, while dataset uploads up to 30MB remained below 20 seconds under suitable network conditions. Compared with existing platforms such as Julius.ai and PowerDrill.ai, Quiksight demonstrated advantages through its lightweight architecture, simplified interaction model, and focus on everyday users. The project demonstrates the feasibility of LLM-powered conversational interfaces for democratizing data analysis. Future improvements include database integration, support for larger datasets, and advanced analytical capabilities such as statistical modelling.
Wumi Ajayi, Ihuanyachi O. OGBONNA, Daniel I. OGHINAN· Global Journal of Engineerin...· 0 citations
Generative user interfaces (GenUIs) promise on-demand components tailored to users'needs. As users iterate on information tasks, they construct personal structures over information they encounter---how items are grouped, what gets prioritized, and what terms mean in their context. Yet, current systems leave these structural decisions to the model at each generation, ignoring the structural logic users have established. Without a persistent representational structure shared between user and system, GenUIs have no basis to remain aligned with what users have established. We draw on Information Architecture (IA), a design practice for organizing and structuring information, as a shared language to bridge user-constructed structure and system generation. We present a framework identifying four IA elements---partition, hierarchy, order, and vocabulary---and characterize how each maps to concrete UI generation decisions. We instantiate this framework in Maru, a conversational system that captures user prompts and interactions as IA preferences, persisting as rules both user and system draw on across generations. A user study revealed that IA persistence kept generated UIs aligned as sessions progressed, while alignment without it degraded, with diverse patterns emerging across users and contexts, pointing to the value of IA persistence in aligning GenUI to individual needs.
Eunhye Kim, DaEun Choi, Bryan Min et al.· 0 citations
For decades, search and recommendation systems have been optimized as distinct components within large-scale discovery platforms. The rise of generative AI is beginning to blur this boundary. At Spotify, we are exploring how large language models can evolve from tools that retrieve content into systems that reason over users, catalogs, and intent, while remaining steerable through natural language and user interaction. This talk presents lessons from deploying and studying generative retrieval and recommendation systems across Spotify's content ecosystem. I will describe how semantic identifiers enable language models to operate directly over large, heterogeneous catalogs, allowing search, recommendation, retrieval, explanation, and user understanding to be expressed within a common generative framework. I will discuss recent work on production-scale podcast discovery, language-steerable recommendation, and the NEO framework for unifying search, recommendation, and reasoning across multiple content types. These systems demonstrate how grounding language models in catalog entities and user behavior can improve discovery while preserving the flexibility of natural-language interaction. More broadly, they suggest a path toward discovery systems in which retrieval, recommendation, and reasoning are no longer separate stages, but capabilities of a shared generative model. Beyond model frameworks, I will discuss the emerging challenges of alignment and evaluation in discovery systems. Unlike traditional retrieval problems, generative recommendation often has many valid answers. I will present approaches for learning from large-scale behavioral signals, preference-aware optimization, and profile-aware LLM-as-a-judge evaluation, along with lessons from online experimentation at Spotify. These experiences suggest that future discovery systems will require new forms of personalization, controllability, and evaluation that extend beyond conventional ranking metrics. I will conclude with a research agenda for generative discovery systems, including language-steerable interfaces, unified retrieval-and-reasoning models, preference-aligned generation, and evaluation frameworks designed to measure user-specific relevance at scale. As search, recommendation, and conversational AI continue to converge, these directions point toward a new generation of discovery systems that can understand intent, reason over large catalogs, and help users navigate increasingly complex information spaces.
Paul N. Bennett· Proceedings of the 32nd ACM...· 0 citations
