Analytics Graph Query Solver ( AGQS ): Transforming Natural Language Queries into Actionable Insights (2024)

Abstract: In today’s era, data analytics is crucial because it allows organizations to make informed decisions based on the analysis of large amounts of data. The evolving landscape of data analytics presents a growing challenge in effectively translating natural language queries into actionable insights. To address this challenge, we introduce a novel system that seamlessly integrates natural language processing (NLP), graph-based feature representation, and code generation. Our method, called Analytics Graph Query Solver (AGQS), utilizes large language models (LLMs) to construct a dynamic graph representing keywords and engineered features. AGQS transforms textual input queries into structured descriptions and generates corresponding plans. These plans are executed stepwise to create a unified code, which is subsequently applied to our in-house virtual assistant dataset to fulfill the user’s query. Furthermore, a robust verification module ensures the reliability of the obtained results. Through experimentation, our system achieved an accuracy of 62.2%, outperforming models like GPT-4 (50.2%), Graph Reader (56.6%), Mistral3 7B (38.6%), and Llama 7B (37.6%). Overall, our approach highlights the importance of feature generation in textual query resolution and demonstrates notable improvements in accessibility and precision for data analytics. With this method, we aim to present a solution for converting natural language queries into actionable steps, ultimately generating code that provides data insights. This approach can be utilized across different datasets, empowering developers and researchers to gain valuable insights effortlessly.

## introduction in the rapidly advancing eld of data-driven analytics, the ability to extract meaningful insights from large datasets is crucial for decision-making. ## proposed methodology our methodology focuses on constructing a graph that represents keywords and engineered features and then using this graph to formulate plan to address user queries. the process consists of several steps, including graph construction, query reformulation, graph exploration and unied code genera-1. session duration 2. user engagement 3. voice assistant session the graph, denoted as @xmath0, e), where each node v @xmath1, and each edge e @xmath2. the graph's construction which is explained in detail in section 3.1, follows a systematic approach. subsequent sections 3.2 through 3.4 outline the execution ow of this approach, showing how we use the graph throughout the process. ## conclusion our work introduces a novel system that bridges the gap between natural language queries and complex data analytics through a unied framework comprising techniques of nlp, knowledge graph, and automated code generation. by constructing a dynamic graph of keywords and engineered features and leveraging large language models for query reformulation, plan generation and code creation, our system achieves a notable accuracy of 62.2%, surpassing existing models. agqs demonstrates signicant advancements in translating diverse user queries into actionable data insights, emphasizing the critical role of feature generation and keywordfeature relationships in effective step-wise planning and execution. in future work, we plan to expand the system's scalability to handle larger datasets and more complex queries, improving natural language understanding to better manage ambiguous inputs, and developing more sophisticated feature generation methods. additionally, practical improvements will be guided by user feedback, along with integrating emerging technologies that holds the potential to further enhance the system's capabilities. step 1 : query description step 2: plan generation and mapping step 3: plan verication step 4: initial node selection step 5: feature exploration step 6: unify code non technical query 1: i have a voice assistant product for smartphone. identify users who use our product a lot and really know how to make the most of its fancy features. we want to nd such users. it involves examining user interaction data to quantify usage frequency, diversity of feature usage, and prociency in utilizing complex features.", "keywords": the "fea-ture_utilization_score" serves as a baseline for engagement, indicating how features contribute to overall engagement. the "ses-sion_engagement_score" measures session engagement. it is reasonable to explore the corresponding code chunks for more detailed insights.", "score_list": [{"feature_name": "user_engagement_score", "relevance_score": "0.95"}, {"feature_name": "feature_utilization_score", "relevance_score": "0.85"}], {"feature_name": "session_engagement_score", "relevance_score": "0.9"}], "selected_features": ["user_engagement_score", "fea-ture_utilization_score", ses-sion_engagement_score] } < feature 1 @xmath3.. + <feature n @xmath4: i have a voice assistant product for smartphone. i want to nd the potential churn users over a month.