Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents

• URL: http://arxiv.org/abs/2501.14734v1
• Date: Tue, 10 Dec 2024 05:51:11 GMT
• Title: Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents
• Authors: Jialin Wang, Zhihua Duan,
• Abstract summary: This study explores the integration of Agent AI with LangGraph to enhance real-time data analysis systems in big data environments.<n>The proposed framework overcomes limitations of static, inefficient stateful computations, and lack of human intervention.<n>System architecture incorporates Apache Spark Streaming, Kafka, and LangGraph to create a high-performance sentiment analysis system.
• Score: 1.4582633500696451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study explores the integration of Agent AI with LangGraph to enhance real-time data analysis systems in big data environments. The proposed framework overcomes limitations of static workflows, inefficient stateful computations, and lack of human intervention by leveraging LangGraph's graph-based workflow construction and dynamic decision-making capabilities. LangGraph allows large language models (LLMs) to dynamically determine control flows, invoke tools, and assess the necessity of further actions, improving flexibility and efficiency. The system architecture incorporates Apache Spark Streaming, Kafka, and LangGraph to create a high-performance sentiment analysis system. LangGraph's capabilities include precise state management, dynamic workflow construction, and robust memory checkpointing, enabling seamless multi-turn interactions and context retention. Human-in-the-loop mechanisms are integrated to refine sentiment analysis, particularly in ambiguous or high-stakes scenarios, ensuring greater reliability and contextual relevance. Key features such as real-time state streaming, debugging via LangGraph Studio, and efficient handling of large-scale data streams make this framework ideal for adaptive decision-making. Experimental results confirm the system's ability to classify inquiries, detect sentiment trends, and escalate complex issues for manual review, demonstrating a synergistic blend of LLM capabilities and human oversight. This work presents a scalable, adaptable, and reliable solution for real-time sentiment analysis and decision-making, advancing the use of Agent AI and LangGraph in big data applications.

Related papers

• Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows [1.4582633500696451]
  LangGraph framework is designed to enhance machine learning through scalability, visualization, and intelligent process optimization.<n>At its core, the framework introduces Agent AI, a pivotal innovation that leverages Spark's distributed computing capabilities.<n>The framework also incorporates large language models through the LangChain ecosystem, enhancing interaction with unstructured data.
  arXiv  Detail & Related papers  (2024-12-02T13:41:38Z)
• LAMBDA: A Large Model Based Data Agent [7.240586338370509]
  We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system. LAMBDA is designed to address data analysis challenges in complex data-driven applications. It has the potential to enhance data analysis paradigms by seamlessly integrating human and artificial intelligence.
  arXiv  Detail & Related papers  (2024-07-24T06:26:36Z)
• GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models [58.08177466768262]
  Long-context capabilities are essential for large language models (LLMs) to tackle complex and long-input tasks. We introduce GraphReader, a graph-based agent system designed to handle long texts by structuring them into a graph and employing an agent to explore this graph autonomously. Experimental results on the LV-Eval dataset reveal that GraphReader, using a 4k context window, consistently outperforms GPT-4-128k across context lengths from 16k to 256k by a large margin.
  arXiv  Detail & Related papers  (2024-06-20T17:57:51Z)
• TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
  TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
  arXiv  Detail & Related papers  (2024-01-06T06:26:49Z)
• 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)
• A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction [4.572330678291241]
  We develop a unified active learning framework specializing in software performance prediction. We investigate the impact of using different levels of information for active and passive learning. Our approach aims to improve the investment in AI models for different software performance predictions.
  arXiv  Detail & Related papers  (2023-04-06T14:00:48Z)
• EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning [92.71579608528907]
  This paper aims to design an easy-to-use pipeline (termed as EasyDGL) composed of three key modules with both strong ability fitting and interpretability. EasyDGL can effectively quantify the predictive power of frequency content that a model learn from the evolving graph data.
  arXiv  Detail & Related papers  (2023-03-22T06:35:08Z)
• Graph Neural Networks for Multi-Robot Active Information Acquisition [15.900385823366117]
  A team of mobile robots, communicating through an underlying graph, estimates a hidden state expressing a phenomenon of interest. Existing approaches are either not scalable, unable to handle dynamic phenomena or not robust to changes in the communication graph. We propose an Information-aware Graph Block Network (I-GBNet) that aggregates information over the graph representation and provides sequential-decision making in a distributed manner.
  arXiv  Detail & Related papers  (2022-09-24T21:45:06Z)
• 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)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Comparative Code Structure Analysis using Deep Learning for Performance Prediction [18.226950022938954]
  This paper aims to assess the feasibility of using purely static information (e.g., abstract syntax tree or AST) of applications to predict performance change based on the change in code structure. Our evaluations of several deep embedding learning methods demonstrate that tree-based Long Short-Term Memory (LSTM) models can leverage the hierarchical structure of source-code to discover latent representations and achieve up to 84% (individual problem) and 73% (combined dataset with multiple of problems) accuracy in predicting the change in performance.
  arXiv  Detail & Related papers  (2021-02-12T16:59:12Z)

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.