[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4667":3,"related-4667":54},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":37,"slug":38,"view_count":15,"doi":9,"paper":39,"created_at":53},4667,"FROM RECRUITMENT DATA SOURCES TO PLACEMENT INTELLIGENCE — AN AUTOMATED ETL PIPELINE FOR CAMPUS HIRING ANALYTICS AND DECISION SUPPORT - (HIRESTREAM)","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fview\u002F4743","Campus placement is one of the most closely watched activities in an engineering college, yet the data behind it is usually scattered across many places. Student academic records sit in the college ERP, resumes arrive as PDF files, company job descriptions come by email, aptitude test scores are exported from online assessment platforms, and interview outcomes are noted in spreadsheets by the placement cell. Preparing a simple report on how many students are eligible for a drive or why a department's placement rate fell requires days of manual copying. This paper presents HireStream, an automated extract, transform, and load pipeline that brings these recruitment data sources together and turns them into placement intelligence for the training and placement office. The pipeline extracts data on a schedule from the ERP database, a shared folder of resumes, a job description inbox, assessment platform exports, and interview feedback forms. Orchestrated by Apache Airflow, the transform stage standardises roll numbers, branch codes, and dates, validates records against rules for completeness and range, parses resumes with a spaCy-based information extraction model, and maps free-text skills onto a common skill taxonomy. The cleaned data is loaded into a PostgreSQL warehouse designed as a star schema, with fact tables for applications, assessment attempts, interview rounds, and offers, and dimension tables for students, companies, drives, skills, and time. Several analytical models run on the warehouse. A gradient boosting classifier estimates each student's probability of receiving an offer from features such as CGPA, backlogs, aptitude percentile, coding test score, internship experience, and matched skills. A matching model compares student skill profiles with job descriptions using TF-IDF and sentence embeddings and ranks eligible students for each drive. A skill gap analysis compares the skills demanded by recruiters over recent seasons with those present in each batch, and a funnel analysis shows where candidates drop out between registration, test, interview, and offer.","校园招聘是工科院校最受关注的活动之一，但其背后的数据通常分散在多个地方。学生的学业记录存放在学校的ERP系统中，简历以PDF文件形式提交，公司的职位描述通过电子邮件送达，能力倾向测试成绩从在线测评平台导出，面试结果则由就业指导中心记录在电子表格中。要生成一份关于有多少学生符合某场招聘会资格或某院系就业率为何下降的简单报告，就需要数天的人工复制工作。本文提出了HireStream，一个自动化提取、转换和加载（ETL）流水线，将上述招聘数据源整合在一起，并将其转化为面向培训与就业指导办公室的就业智能。该流水线按计划从ERP数据库、简历共享文件夹、职位描述收件箱、测评平台导出文件和面试反馈表中提取数据。在Apache Airflow的编排下，转换阶段对学号、专业代码和日期进行标准化，依据完整性和范围规则验证记录，使用基于spaCy的信息抽取模型解析简历，并将自由文本技能映射到统一的技能分类体系中。清洗后的数据被加载到设计为星型模式的PostgreSQL数据仓库中，事实表涵盖申请、测评尝试、面试轮次和录用通知，维度表涵盖学生、公司、招聘会、技能和时间。多个分析模型在该数据仓库上运行。梯度提升分类器根据CGPA、补考科目数、能力倾向百分位、编程测试成绩、实习经历和匹配技能等特征，估计每位学生获得录用通知的概率。匹配模型使用TF-IDF和句子嵌入将学生技能画像与职位描述进行比对，并为每场招聘会对符合条件的学生进行排序。技能差距分析将近期招聘季中招聘方所要求的技能与各届学生所具备的技能进行对比，漏斗分析则展示候选人在注册、测试、面试和录用各环节之间的流失情况。",null,"International Journal of Engineering Research and Science & Technology","2026-10-08T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦校园招聘数据ETL与就业决策支持，与三农、农业信息化、智慧农业、数字乡村等主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":36},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":35},"I2",8,true,[33],"decision support",[],[],"农业相关性需要复核",[],"FROMRECRUITMENTDATASOURCESTOPLACEMENTINT-4667",{"doi":9,"openalex_id":40,"authors":41,"venue":10,"cited_by_count":15,"oa_url":44,"card":45,"direction":51,"ingested_from":52},"W7221288154",[42],{"name":43,"orcid":9},"Mrs. Y. Ramya,  Balla Charrishma,  Gilleli Sreeja, Keleti Rajkumar,  Bheempav Santhosh,  Bandi Sudheer","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fdownload\u002F4743\u002F4381",{"tldr":46,"method":47,"finding":48,"direction":49,"opportunity":50},"构建HireStream自动化ETL流水线，整合校园招聘多源数据并生成就业智能分析。","Apache Airflow调度ETL，spaCy解析简历，PostgreSQL","流水线实现招聘数据自动整合与就业预测、人岗匹配、技能差距和漏斗分析。","农业人工智能与决策模型","可将该ETL与决策支持框架迁移至农业院校就业或农业人才供需匹配场景。","智慧农业 \u002F 农业物联网","openalex","2026-10-11T23:30:15.872769Z",{"total":15,"page":55,"page_size":55,"items":56},1,[]]