CPOI：一种压缩的版本化RDF三元集归档方法
CPOI: A Compact Method to Archive Versioned RDF Triple-Sets
摘要：
最近产生和发布了大量RDF/S数据，一些现代应用程序需要在这些数据集上提供版本控制和归档服务。在本文中，我们提出了一种新的存储索引，用于归档这些数据集的版本，称为CPOI（紧凑偏序索引），它利用了RDF知识库（KB）是一个图（或等效的一组三元组）的事实，因此它没有唯一的序列化（因为它发生在文本中）。如果我们想保存多个版本，我们实际上想存储多组三元组。CPOI是用于存储这样的集合的数据结构，其目的在于减少存储空间，因为这不仅对于减少存储成本是重要的，而且对于减少各种通信成本以及使得能够在主存储器中托管（并且因此有效地处理）大量数据也是重要的。CPOI基于三元组标识符集合上的偏序结构，其中三元组标识符使用可变长度编码方案以间隙形式表示。对于这个指数，我们评估分析和实验各种标识符分配技术及其节省空间。实验结果表明，该方法可以节省大量的存储空间，尤其是在大规模的真实合成数据集上，压缩后的数据集的存储空间仅为未压缩数据集的8%左右。
英文摘要：
Large amounts of RDF/S data are produced and published lately, and several modern applications require the provision of versioning and archiving services over such datasets. In this paper we propose a novel storage index for archiving versions of such datasets, called CPOI (compact partial order index), that exploits the fact that an RDF Knowledge Base (KB), is a graph (or equivalently a set of triples), and thus it has not a unique serialization (as it happens with text). If we want to keep stored several versions we actually want to store multiple sets of triples. CPOI is a data structure for storing such sets aiming at reducing the storage space since this is important not only for reducing storage costs, but also for reducing the various communication costs and enabling hosting in main memory (and thus processing efficiently) large quantities of data. CPOI is based on a partial order structure over sets of triple identifiers, where the triple identifiers are represented in a gapped form using variable length encoding schemes. For this index we evaluate analytically and experimentally various identifier assignment techniques and their space savings. The results show significant storage savings, specifically, the storage space of the compressed sets in large and realistic synthetic datasets is about the 8% of the size of the uncompressed sets.
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