[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122559-en":3,"doc-seo-122559-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122559,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",6,"Technology","Intelligent Spark Agents - A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows","This paper presents a Spark-based modular LangGraph framework to strengthen machine learning workflows through scalability, visualization, and intelligent process optimization. The core innovation introduces Agent AI, combining Spark distributed computing with LangGraph workflow orchestration to automate data preprocessing, feature engineering, and model evaluation. Agents interact dynamically with data via Spark SQL and DataFrame agents, executing graph-structured tasks with real-time feedback for reliable decisions in distributed environments. The framework also leverages large language models through LangChain to enhance unstructured-data interaction and data-driven analysis, supported by experimental results showing improved efficiency, scalability, and accuracy.","arXiv :2412 .0 1490v4 [ cs .AI] 6 Dec 2024  \nIntelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows  \nJialin Wang 1 and Zhihua Duan2  \n1 Executive Vice President,Ferret Relationship Intelligence  \nBurlingame, CA 94010, USA  \n[jialinwangspace@gmail.com](jialinwangspace@gmail.com)[ ](jialinwangspace@gmail.com)[https://www.linkedin.com/in/starspacenlp/](https://www.linkedin.com/in/starspacenlp/)  \n2 Intelligent Cloud Network Monitoring Department  \nChina Telecom Shanghai Company,Shanghai, China [duanzh.sh@chinatelecom.cn](duanzh.sh@chinatelecom.cn)  \nAbstract. This paper presents a Spark-based modular LangGraph framework, designed to enhance machine learning workflows through scalability, visualization, and intelligent process optimization. At its core, the framework introduces Agent AI, a pivotal innovation that leverages Spark’s distributed computing capabilities and integrates with LangGraph for workflow orchestration.  \nAgent AI facilitates the automation of data preprocessing, feature engineering, and model evaluation while dynamically interacting with data through Spark SQL and DataFrame agents. Through LangGraph’s graphstructured workflows, the agents execute complex tasks, adapt to new inputs, and provide real-time feedback, ensuring seamless decision-making and execution in distributed environments. This system simplifies machine learning processes by allowing users to visually design workflows, which are then converted into Spark-compatible code for high-performance execution.  \nThe framework also incorporates large language models through the LangChain ecosystem, enhancing interaction with unstructured data and enabling advanced data analysis. Experimental evaluations demonstrate significant improvements in process efficiency and scalability, as well as accurate data-driven decision-making in diverse application scenarios.  \nThis paper emphasizes the integration of Spark with intelligent agents and graph-based workflows to redefine the development and execution of machine learning tasks in big data environments, paving the way for scalable and user-friendly AI solutions.  \nKeywords: Large Language Model · Agent · LangChain · LangGraph ChatGPT · ERNIE-4 · GLM-4 · Big Data · Machine learning · Apache Spark · Data analysis.  \n1 Introduction  \nThe development of information technology brings convenience to life and fastgrowing data. With the maturity of big data analysis technology represented by machine learning, big data has tremendous effect on social and economic life and provided a lot of help for business decision-making. For example, in the e-commerce industry, Taobao recommends suitable goods professionally to users after analyzing from large amounts of transaction data; in the advertising industry, online advertising predicts users’ preferences by tracking users’ clicks to improve users’ experience.  \nHowever, the traditional business relational data management system has been unable to deal with the characteristics of big data including large capacity, diversity, and high-dimension . [1] In order to solve the problem of big data analysis, distributed computing has been widely used, among which the Apache Hadoop [2] is one of the widely used distributed systems in recent years. Hadoop adopts MapReduce as a rigorous computing framework. The emergence of Hadoop has promoted the popularity of large-scale data processing platforms. Spark [3], a big data architecture developed by AMPLab of the University of Berkeley, is also widely used. Spark integrates batch analysis, flow analysis, SQL processing, graph analysis, and machine learning applications. Compared with Hadoop, Spark is fast, flexible, and fault-tolerant, which is the ideal choice to run machine learning analysis programs. However, Spark is a tool for developers, which requires analysts to have certain computer skills and spend a lot of time creating, deploying and maintaining system","cbCaiqrNm8Z3EIGR","https://ap.wps.com/l/cbCaiqrNm8Z3EIGR","pdf",2559162,1,20,"English","en",105,"# Introduction\n## Motivation from big data and distributed computing\n## Modular workflow tool for easier ML development\n## Integration of Spark, agents, LangChain, and LangGraph","[{\"question\":\"What is the main contribution of the proposed framework?\",\"answer\":\"It provides a Spark-based modular LangGraph framework centered on Agent AI to orchestrate scalable, visualizable machine learning workflows with intelligent optimization.\"},{\"question\":\"How do the agents support the machine learning workflow execution?\",\"answer\":\"Agents automate preprocessing, feature engineering, and model evaluation, interact with data through Spark SQL and DataFrame agents, and run graph-structured tasks with real-time feedback.\"},{\"question\":\"How are large language models used in the framework?\",\"answer\":\"Large language models are integrated via the LangChain ecosystem to improve interaction with unstructured data and enable advanced data analysis within the workflow.\"}]","Intelligent Spark Agents - A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows | PDF",1785811299,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"intelligent-spark-agents-a-modular-langgraph-framework-for-scalable-visualized-and-enhanced-big-data-machine-learning-workflows","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/intelligent-spark-agents-a-modular-langgraph-framework-for-scalable-visualized-and-enhanced-big-data-machine-learning-workflows/122559/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main contribution of the proposed framework?","Question",{"text":75,"@type":76},"It provides a Spark-based modular LangGraph framework centered on Agent AI to orchestrate scalable, visualizable machine learning workflows with intelligent optimization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the agents support the machine learning workflow execution?",{"text":80,"@type":76},"Agents automate preprocessing, feature engineering, and model evaluation, interact with data through Spark SQL and DataFrame agents, and run graph-structured tasks with real-time feedback.",{"name":82,"@type":73,"acceptedAnswer":83},"How are large language models used in the framework?",{"text":84,"@type":76},"Large language models are integrated via the LangChain ecosystem to improve interaction with unstructured data and enable advanced data analysis within the workflow.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,112,117,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":111},"technology",{"id":113,"doc_module":4,"doc_module_name":46,"category_name":114,"show_sort_weight":115,"slug":116},7,"Healthcare",40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]