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基于大数据的多源影视数据整合质量评估与可视化分析介绍

本系统《基于大数据的多源影视数据整合质量评估与可视化分析》是一个面向Netflix影视目录数据的大数据分析平台，依托Hadoop分布式存储与Spark并行计算框架，对多源异构的影视数据进行ETL清洗、整合与质量评估。系统核心能力涵盖三个方面：一是数据整合层，通过Spark SQL对多张原始影视表进行字段映射、去重、空值填充与格式统一，形成标准化的事实数据宽表；二是质量评估层，从完整性（字段缺失率）、一致性（同一影视在不同源中的信息匹配度）、准确性（数值字段的合法范围校验）、时效性（数据更新时间戳）和唯一性（重复记录占比）五个维度构建加权评分模型，对每一条影视记录输出0-100分的综合质量评分；三是可视化分析层，基于ECharts构建9张实时联动图表，包括片种结构饼图、国家分布地图、热度评分散点图、评分档位柱状图、质量聚类雷达图、字段覆盖热力图、匹配方式桑基图、类型共现弦图以及质量趋势折线图，单屏总览影视数据质量态势。系统同时提供数据管理模块，支持对原始目录数据的条件检索、分页展示和手动修正，形成“数据接入→质量评估→可视化呈现→人工干预”的闭环流程，为影视数据治理提供可落地的技术参考方案。

基于大数据的多源影视数据整合质量评估与可视化分析演示视频

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基于大数据的多源影视数据整合质量评估与可视化分析演示图片

基于大数据的多源影视数据整合质量评估与可视化分析代码展示

SparkSession spark=SparkSession.builder().appName("FilmDataQualityAssessment").master("local[*]").config("spark.sql.shuffle.partitions","200").getOrCreate();Dataset<Row>netflixDf=spark.read().option("header","true").option("inferSchema","true").csv("hdfs://master:9000/data/netflix_titles.csv");Dataset<Row>imdbDf=spark.read().option("header","true").option("inferSchema","true").csv("hdfs://master:9000/data/imdb_ratings.csv");Dataset<Row>tmdbDf=spark.read().option("header","true").option("inferSchema","true").json("hdfs://master:9000/data/tmdb_metadata.json");Dataset<Row>integratedDf=netflixDf.join(imdbDf,netflixDf.col("title").equalTo(imdbDf.col("original_title")),"left").join(tmdbDf,netflixDf.col("title").equalTo(tmdbDf.col("name")),"left");Dataset<Row>cleanedDf=integratedDf.na().fill("unknown",new String[]{"director","cast","country","rating"}).na().fill(0,new String[]{"release_year","duration_num"}).na().fill("N/A",new String[]{"listed_in","description"});Dataset<Row>dedupDf=cleanedDf.dropDuplicates(new String[]{"title","release_year","director"});StructType schema=dedupDf.schema();String[]fieldNames=schema.fieldNames();double totalFields=fieldNames.length;Dataset<Row>completenessScore=dedupDf.map((Row row)->{intnonNullCount=0;for(String field:fieldNames){Object value=row.getAs(field);if(value!=null&&!value.toString().isEmpty()&&!value.toString().equals("unknown")&&!value.toString().equals("N/A")){nonNullCount++;}}double score=(nonNullCount/totalFields)*100;returnRowFactory.create(row.getAs("title"),Math.round(score*100.0)/100.0);},RowEncoder.apply(StructType.fromDDL("title string, completeness double")));Dataset<Row>consistencyScore=dedupDf.map((Row row)->{String netflixGenre=row.getAs("listed_in");String tmdbGenre=row.getAs("genres");double score=0.0;if(netflixGenre!=null&&tmdbGenre!=null&&!netflixGenre.equals("N/A")&&!tmdbGenre.equals("unknown")){String[]netflixArr=netflixGenre.split(",");String[]tmdbArr=tmdbGenre.split(",");intmatchCount=0;for(String n:netflixArr){for(String t:tmdbArr){if(n.trim().equalsIgnoreCase(t.trim())){matchCount++;break;}}}score=(matchCount/(double)Math.max(netflixArr.length,tmdbArr.length))*100;}returnRowFactory.create(row.getAs("title"),Math.round(score*100.0)/100.0);},RowEncoder.apply(StructType.fromDDL("title string, consistency double")));Dataset<Row>accuracyScore=dedupDf.map((Row row)->{Integer year=row.getAs("release_year");Double duration=row.getAs("duration_num");Integer imdbVotes=row.getAs("imdb_votes");double score=100.0;if(year!=null&&(year<1900||year>2026)){score-=25;}if(duration!=null&&(duration<=0||duration>500)){score-=25;}if(imdbVotes!=null&&imdbVotes<0){score-=25;}String rating=row.getAs("rating");if(rating!=null&&!rating.equals("N/A")&&!rating.equals("unknown")){if(!rating.matches("^(G|PG|PG-13|R|NC-17|TV-Y|TV-Y7|TV-G|TV-PG|TV-14|TV-MA)$")){score-=25;}}returnRowFactory.create(row.getAs("title"),Math.max(0,Math.round(score*100.0)/100.0));},RowEncoder.apply(StructType.fromDDL("title string, accuracy double")));Dataset<Row>timelinessScore=dedupDf.map((Row row)->{String dateAdded=row.getAs("date_added");double score=50.0;if(dateAdded!=null&&!dateAdded.equals("N/A")&&!dateAdded.equals("unknown")){try{SimpleDateFormat sdf=new SimpleDateFormat("MMMM d, yyyy",Locale.US);Date date=sdf.parse(dateAdded);Date now=new Date();longdiff=now.getTime()-date.getTime();longdays=diff/(24*60*60*1000);if(days<=365){score=100.0;}elseif(days<=730){score=75.0;}elseif(days<=1095){score=50.0;}else{score=25.0;}}catch(ParseException e){score=30.0;}}returnRowFactory.create(row.getAs("title"),score);},RowEncoder.apply(StructType.fromDDL("title string, timeliness double")));Dataset<Row>uniquenessScore=dedupDf.groupBy("title","release_year").count().withColumnRenamed("count","duplicate_count");Dataset<Row>duplicateDf=uniquenessScore.filter(uniquenessScore.col("duplicate_count").gt(1));List<String>duplicateTitles=duplicateDf.select("title").as(Encoders.STRING()).collectAsList();Dataset<Row>uniquenessScoreFinal=dedupDf.map((Row row)->{String title=row.getAs("title");double score=duplicateTitles.contains(title)?60.0:100.0;returnRowFactory.create(title,score);},RowEncoder.apply(StructType.fromDDL("title string, uniqueness double")));Dataset<Row>qualityScores=completenessScore.join(consistencyScore,"title").join(accuracyScore,"title").join(timelinessScore,"title").join(uniquenessScoreFinal,"title");Dataset<Row>finalScore=qualityScores.map((Row row)->{String title=row.getAs("title");double completeness=row.getAs("completeness");double consistency=row.getAs("consistency");double accuracy=row.getAs("accuracy");double timeliness=row.getAs("timeliness");double uniqueness=row.getAs("uniqueness");double total=completeness*0.25+consistency*0.25+accuracy*0.20+timeliness*0.15+uniqueness*0.15;returnRowFactory.create(title,Math.round(total*100.0)/100.0,completeness,consistency,accuracy,timeliness,uniqueness);},RowEncoder.apply(StructType.fromDDL("title string, total_score double, completeness double, consistency double, accuracy double, timeliness double, uniqueness double")));finalScore.createOrReplaceTempView("quality_view");Dataset<Row>genrePie=spark.sql("SELECT listed_in, COUNT(*) as cnt FROM quality_view GROUP BY listed_in ORDER BY cnt DESC LIMIT 10");Dataset<Row>countryBar=spark.sql("SELECT country, COUNT(*) as cnt FROM quality_view WHERE country != 'unknown' GROUP BY country ORDER BY cnt DESC LIMIT 15");Dataset<Row>ratingHist=spark.sql("SELECT rating, COUNT(*) as cnt FROM quality_view WHERE rating != 'N/A' GROUP BY rating ORDER BY rating");Dataset<Row>qualityScatter=spark.sql("SELECT title, total_score, imdb_score FROM quality_view JOIN integratedDf ON quality_view.title = integratedDf.title WHERE imdb_score IS NOT NULL");Dataset<Row>qualityCluster=spark.sql("SELECT CASE WHEN total_score >= 80 THEN '优质' WHEN total_score >= 60 THEN '良好' WHEN total_score >= 40 THEN '一般' ELSE '较差' END as quality_level, COUNT(*) as cnt FROM quality_view GROUP BY quality_level");Dataset<Row>fieldCoverage=spark.sql("SELECT 'completeness' as field, AVG(completeness) as avg_score FROM quality_view UNION SELECT 'consistency', AVG(consistency) FROM quality_view UNION SELECT 'accuracy', AVG(accuracy) FROM quality_view UNION SELECT 'timeliness', AVG(timeliness) FROM quality_view UNION SELECT 'uniqueness', AVG(uniqueness) FROM quality_view");Dataset<Row>trendOverTime=spark.sql("SELECT date_added, COUNT(*) as cnt, AVG(total_score) as avg_score FROM quality_view JOIN integratedDf ON quality_view.title = integratedDf.title WHERE date_added != 'N/A' GROUP BY date_added ORDER BY date_added LIMIT 30");genrePie.write().mode("overwrite").format("jdbc").option("url","jdbc:mysql://localhost:3306/film_db").option("dbtable","genre_statistics").option("user","root").option("password","123456").save();countryBar.write().mode("overwrite").format("jdbc").option("url","jdbc:mysql://localhost:3306/film_db").option("dbtable","country_statistics").option("user","root").option("password","123456").save();ratingHist.write().mode("overwrite").format("jdbc").option("url","jdbc:mysql://localhost:3306/film_db").option("dbtable","rating_statistics").option("user","root").option("password","123456").save();qualityScatter.write().mode("overwrite").format("jdbc").option("url","jdbc:mysql://localhost:3306/film_db").option("dbtable","quality_scatter_data").option("user","root").option("password","123456").save();qualityCluster.write().mode("overwrite").format("jdbc").option("url","jdbc:mysql://localhost:3306/film_db").option("dbtable","quality_cluster_data").option("user","root").option("password","123456").save();fieldCoverage.write().mode("overwrite").format("jdbc").option("url","jdbc:mysql://localhost:3306/film_db").option("dbtable","field_coverage_data").option("user","root").option("password","123456").save();trendOverTime.write().mode("overwrite").format("jdbc").option("url","jdbc:mysql://localhost:3306/film_db").option("dbtable","trend_data").option("user","root").option("password","123456").save();spark.stop();

基于大数据的多源影视数据整合质量评估与可视化分析文档展示

💖💖作者：计算机毕业设计江挽
💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！
💛💛想说的话：感谢大家的关注与支持！
💜💜
网站实战项目
安卓/小程序实战项目
大数据实战项目
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