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Project # 74
LLM-Based Approach for Holistic Analysis of Disparate Data Types
| Team Lead: | Haresh Goutham Prageeth |
| Other Team Members: | Siddharth Tyagi |
| Faculty Advisor(s): | Abhishek Santra |
| Department: | CSE |
| Project Type: | REU |
| Title: | LLM-Based Approach for Holistic Analysis of Disparate Data Types |
| Abstract: | Research has addressed the modeling and analysis of individual data types fairly successfully whether it is mining, computing objectives, or knowledge discovery. However, handling disparate data types holistically is challenging, and can benefit from new models and/or approaches. It is also important to note that holistic analysis is significantly different from traditional data mining/analysis, which deals with a specific type of knowledge discovery using specific algorithms (e.g., clustering, association rule mining, etc.) on specific data types and formats. In contrast, holistic analysis is a broader umbrella concept and is likely to need an ensemble of modeling and analysis alternatives. Hence, for the modeling and analysis of multiple data types with different characteristics, a suite of existing approaches and their extensions, as well as new ones, is required. When dealing with diverse complex data, all aspects -- data preparation, modeling, analysis, drill-down, and visualization -- become important for understanding the data as well as the knowledge discovered/objectives computed. In this project, we plan to explore our vision for the holistic modeling of data of different types from different sources to perform analysis and knowledge discovery, instead of currently used separate models for structured data, videos, text, etc. The goal is to accommodate multiple data types (structured, unstructured, image/video, and text from natural language) using models that are amenable to expressive analysis and/or infer knowledge using scalable approaches such as LLMs (Large Language Models). |
| Poster: | View Poster #74 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |