CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System
- URL: http://arxiv.org/abs/2405.14691v1
- Date: Thu, 23 May 2024 15:27:18 GMT
- Title: CityGPT: Towards Urban IoT Learning, Analysis and Interaction with Multi-Agent System
- Authors: Qinghua Guan, Jinhui Ouyang, Di Wu, Weiren Yu,
- Abstract summary: CityGPT employs three agents to accomplish thetemporal analysis of IoT data.
We have agnentized the framework, facilitated by a large language model (LLM), to increase the data comprehensibility.
Our evaluation results on real-world data with different time show that the CityGPT framework can guarantee robust performance in computing.
- Score: 4.612237040042468
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The spatiotemporal data generated by massive sensors in the Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, and stability) in real-time analysis and decision making for different IoT applications. The complexity of IoT data prevents the common people from gaining a deeper understanding of it. Agentized systems help address the lack of data insight for the common people. We propose a generic framework, namely CityGPT, to facilitate the learning and analysis of IoT time series with an end-to-end paradigm. CityGPT employs three agents to accomplish the spatiotemporal analysis of IoT data. The requirement agent facilitates user inputs based on natural language. Then, the analysis tasks are decomposed into temporal and spatial analysis processes, completed by corresponding data analysis agents (temporal and spatial agents). Finally, the spatiotemporal fusion agent visualizes the system's analysis results by receiving analysis results from data analysis agents and invoking sub-visualization agents, and can provide corresponding textual descriptions based on user demands. To increase the insight for common people using our framework, we have agnentized the framework, facilitated by a large language model (LLM), to increase the data comprehensibility. Our evaluation results on real-world data with different time dependencies show that the CityGPT framework can guarantee robust performance in IoT computing.
Related papers
- Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting.
Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server.
We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
arXiv Detail & Related papers (2024-11-24T04:56:45Z) - Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model [12.6020349733674]
We propose a novel approach to extracting and analyzing transit-related information.
Our method employs Large Language Models (LLM), specifically Llama 3, for a streamlined analysis.
Our results demonstrate the potential of LLMs to transform social media data analysis in the public transit domain.
arXiv Detail & Related papers (2024-10-19T07:08:40Z) - Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - DISCOVER: A Data-driven Interactive System for Comprehensive Observation, Visualization, and ExploRation of Human Behaviour [6.716560115378451]
We introduce a modular, flexible, yet user-friendly software framework specifically developed to streamline computational-driven data exploration for human behavior analysis.
Our primary objective is to democratize access to advanced computational methodologies, thereby enabling researchers across disciplines to engage in detailed behavioral analysis without the need for extensive technical proficiency.
arXiv Detail & Related papers (2024-07-18T11:28:52Z) - It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
Attack Graph (AG) represents the best-suited solution to support cyber risk assessment for multi-step attacks on computer networks.
Current solutions propose to address the generation problem from the algorithmic perspective and postulate the analysis only after the generation is complete.
This paper rethinks the classic AG analysis through a novel workflow in which the analyst can query the system anytime.
arXiv Detail & Related papers (2023-12-27T10:44:58Z) - Multivariate Time Series characterization and forecasting of VoIP
traffic in real mobile networks [9.637582917616703]
Predicting the behavior of real-time traffic (e.g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures.
This work proposes a forecasting analysis of crucial/QoE descriptors of VoIP traffic in a real mobile environment.
arXiv Detail & Related papers (2023-07-13T09:21:39Z) - Demonstration of InsightPilot: An LLM-Empowered Automated Data
Exploration System [48.62158108517576]
We introduce InsightPilot, an automated data exploration system designed to simplify the data exploration process.
InsightPilot automatically selects appropriate analysis intents, such as understanding, summarizing, and explaining.
In brief, an IQuery is an abstraction and automation of data analysis operations, which mimics the approach of data analysts.
arXiv Detail & Related papers (2023-04-02T07:27:49Z) - Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
literature review [62.997667081978825]
This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today.
The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
arXiv Detail & Related papers (2022-04-29T08:06:05Z) - Federated Stochastic Gradient Descent Begets Self-Induced Momentum [151.4322255230084]
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems.
We show that running to the gradient descent (SGD) in such a setting can be viewed as adding a momentum-like term to the global aggregation process.
arXiv Detail & Related papers (2022-02-17T02:01:37Z) - A Visual Analytics Framework for Reviewing Streaming Performance Data [20.61348106852359]
We introduce a visual analytic framework comprising of three modules: data management, analysis, and interactive visualization.
In particular, we introduce a set of online and progressive analysis methods for not only controlling the computational costs but also helping analysts better follow the critical aspects of the analysis results.
arXiv Detail & Related papers (2020-01-26T04:34:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.