A Forensic Method for Efficient File Extraction in HDFS Based on Three-Level Mapping

The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic research on distributed file system is inadequate. To address the forensic problems, this paper uses the Hadoop distributed file system （HDFS） as a case study and proposes a forensic method for efficient file extraction based on three-level （3L） mapping. First, HDFS is analyzed from overall architecture to local file system. Second, the 3L mapping of an HDFS file from HDFS namespace to data blocks on local file system is established and a recovery method for deleted files based on 3L mapping is presented. Third, a multi-node Hadoop framework via Xen virtualization platform is set up to test the performance of the method. The results indicate that the proposed method could succeed in efficient location of large files stored across data nodes, make selective image of disk data and get high recovery rate of deleted files.

甘蓝型油菜遗传图谱的构建及单株产量构成因素的QTL分析

采用常规品系04-1139与高产多角果品系05-1054构建的F2代群体为作图群体,运用SSR(Simple sequence repeat)和SRAP(Sequence-related amplified polymorphism)构建分子标记遗传图谱并对甘蓝型油菜单株产量构成因素进行QTL分析。遗传图谱包含200个分子标记,分布于19个连锁群上,总长度1700.23cM,标记间的平均距离8.50cM。采用复合区间作图法(Composite interval mapping,CIM)对单株产量构成因素(单株有效角果数、每果粒数和千粒重)进行QTL分析,共检测到12个QTL:其中单株有效角果数4个QTL,分别解释表型变异为35.64%、12.96%、28.71%和34.02%;每果粒数获得5个QTL,分别解释表型变异为8.41%、7.87%、24.37%、8.57%和14.31%;千粒重获得3个QTL,分别解释表型变异为2.33%、1.81%和1.86%。结果表明:同一性状的等位基因增效作用可以同时来自高值亲本和低值亲本;文章中与主效QTL连锁的标记可用于油菜产量性状的分子标记辅助选择和聚合育种。