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深度学习实战项目目录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xxx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 sparkSparkSession.builder().appName(FilmDataQualityAssessment).master(local[*]).config(spark.sql.shuffle.partitions,200).getOrCreate();DatasetRownetflixDfspark.read().option(header,true).option(inferSchema,true).csv(hdfs://master:9000/data/netflix_titles.csv);DatasetRowimdbDfspark.read().option(header,true).option(inferSchema,true).csv(hdfs://master:9000/data/imdb_ratings.csv);DatasetRowtmdbDfspark.read().option(header,true).option(inferSchema,true).json(hdfs://master:9000/data/tmdb_metadata.json);DatasetRowintegratedDfnetflixDf.join(imdbDf,netflixDf.col(title).equalTo(imdbDf.col(original_title)),left).join(tmdbDf,netflixDf.col(title).equalTo(tmdbDf.col(name)),left);DatasetRowcleanedDfintegratedDf.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});DatasetRowdedupDfcleanedDf.dropDuplicates(new String[]{title,release_year,director});StructType schemadedupDf.schema();String[]fieldNamesschema.fieldNames();double totalFieldsfieldNames.length;DatasetRowcompletenessScorededupDf.map((Row row)-{intnonNullCount0;for(String field:fieldNames){Object valuerow.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)));DatasetRowconsistencyScorededupDf.map((Row row)-{String netflixGenrerow.getAs(listed_in);String tmdbGenrerow.getAs(genres);double score0.0;if(netflixGenre!nulltmdbGenre!null!netflixGenre.equals(N/A)!tmdbGenre.equals(unknown)){String[]netflixArrnetflixGenre.split(,);String[]tmdbArrtmdbGenre.split(,);intmatchCount0;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)));DatasetRowaccuracyScorededupDf.map((Row row)-{Integer yearrow.getAs(release_year);Double durationrow.getAs(duration_num);Integer imdbVotesrow.getAs(imdb_votes);double score100.0;if(year!null(year1900||year2026)){score-25;}if(duration!null(duration0||duration500)){score-25;}if(imdbVotes!nullimdbVotes0){score-25;}String ratingrow.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)));DatasetRowtimelinessScorededupDf.map((Row row)-{String dateAddedrow.getAs(date_added);double score50.0;if(dateAdded!null!dateAdded.equals(N/A)!dateAdded.equals(unknown)){try{SimpleDateFormat sdfnew SimpleDateFormat(MMMM d, yyyy,Locale.US);Date datesdf.parse(dateAdded);Date nownew Date();longdiffnow.getTime()-date.getTime();longdaysdiff/(24*60*60*1000);if(days365){score100.0;}elseif(days730){score75.0;}elseif(days1095){score50.0;}else{score25.0;}}catch(ParseException e){score30.0;}}returnRowFactory.create(row.getAs(title),score);},RowEncoder.apply(StructType.fromDDL(title string, timeliness double)));DatasetRowuniquenessScorededupDf.groupBy(title,release_year).count().withColumnRenamed(count,duplicate_count);DatasetRowduplicateDfuniquenessScore.filter(uniquenessScore.col(duplicate_count).gt(1));ListStringduplicateTitlesduplicateDf.select(title).as(Encoders.STRING()).collectAsList();DatasetRowuniquenessScoreFinaldedupDf.map((Row row)-{String titlerow.getAs(title);double scoreduplicateTitles.contains(title)?60.0:100.0;returnRowFactory.create(title,score);},RowEncoder.apply(StructType.fromDDL(title string, uniqueness double)));DatasetRowqualityScorescompletenessScore.join(consistencyScore,title).join(accuracyScore,title).join(timelinessScore,title).join(uniquenessScoreFinal,title);DatasetRowfinalScorequalityScores.map((Row row)-{String titlerow.getAs(title);double completenessrow.getAs(completeness);double consistencyrow.getAs(consistency);double accuracyrow.getAs(accuracy);double timelinessrow.getAs(timeliness);double uniquenessrow.getAs(uniqueness);double totalcompleteness*0.25consistency*0.25accuracy*0.20timeliness*0.15uniqueness*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);DatasetRowgenrePiespark.sql(SELECT listed_in, COUNT(*) as cnt FROM quality_view GROUP BY listed_in ORDER BY cnt DESC LIMIT 10);DatasetRowcountryBarspark.sql(SELECT country, COUNT(*) as cnt FROM quality_view WHERE country ! unknown GROUP BY country ORDER BY cnt DESC LIMIT 15);DatasetRowratingHistspark.sql(SELECT rating, COUNT(*) as cnt FROM quality_view WHERE rating ! N/A GROUP BY rating ORDER BY rating);DatasetRowqualityScatterspark.sql(SELECT title, total_score, imdb_score FROM quality_view JOIN integratedDf ON quality_view.title integratedDf.title WHERE imdb_score IS NOT NULL);DatasetRowqualityClusterspark.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);DatasetRowfieldCoveragespark.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);DatasetRowtrendOverTimespark.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深度学习实战项目