gStore：

gStore是由北京大学计算机所数据管理实验室研发面向RDF知识图谱的开源图数据库系统（通常称为Triple Store）。不同于传统基于关系数据库的知识图谱数据管理方法，gStore是一种原生基于图数据模型(Native Graph Model)的RDF数据管理系统，维持了原始RDF知识图谱的图结构；其数据模型是有标签、有向的多边图，每个顶点对应着一个主体或客体。我们将面向RDF的SPARQL查询，转换为面向RDF图的子图匹配查询，利用所提出的基于图结构的索引(VS*-tree)来加速查询的性能。

gStore 拥有以下特性：

1. gStore从图数据库角度存储和检索RDF知识图谱数据；
2. gStore支持W3C定义的SPARQL 1.1标准，包括含有Union，OPTIONAL，FILTER和聚集函数的查询；gStore支持有效的增删改操作；
3. gStore单机可以支持1Billion（十亿）三元组规模的RDF知识图谱的数据管理任务；

我们组准备将用户商品评论数据使用gStore存储，在此基础上开发出适合用于检索用户特性的功能，这篇博客是对gStore中用于加速查询效率的VS*-tree树进行介绍，内容来自北大邹磊教授2014 VLDB论文《gStore:a graph-based SPARQL query engine》

（博客虽然大多英文，但稍有总结概述）

VS*-tree:

VS*-tree is 是一个作用于G*上的索引结构，用于高效地完成SPARQL查询，即找到Q*在G*上的matches;

Background：

Definition of match of Q* over G*：Q*= {v1… vn}，A set of n distinct vertices {u1… un} in G*;

Solution:

There is a straightforward method consisting of two steps，its first step is a classical inclusion query and the second step is a multiway join process;

Problem need to be solved：reduce the search space for inclusion query

S-tree is proposed:

1. A height-balanced tree similar to B+ tree;
2. Each intermediate node is formed by ORing all child signatures in S-tree;
   

Problem need to be solved: S-tree cannot support the second step, which is a NP-hard;

VS*-tree(based on S-tree) is proposed:

VS*-tree is a multi-resolution summary graph based on S-tree that can be used to reduce the search space of subgraph query processing；

Properties：

1. Each path from the root to any leaf node has the same length h, which is the height of the VS∗-tree;
2. Each leaf node corresponds to a vertex in G∗ and has a pointer to it.
3. … …
   … …
   

The example of building “super-edge”:

Definition of summary match of Q* over G^I：

Function：

Summary matches can be used to reduce the search space for subgraph search over G*;

Problem need to be solved：

The vertex encoding strategy may lead to some vertex signatures having too many 1，these
vertices can match any query vertex signature;

Solution:

We partition all of u’s neighbors into n groups {g1,…, gn} and each group gi corresponds to one instance of vertex u, denoted as u[i];

Updates to VS*-tree:

1. Inseration:

The process begins at the root of VS*-tree and iteratively chooses a child node until it reaches a suitable leaf node location where u is inserted;

Then, the summary graph at the leaf level is updated and new super edge may need to be indtoduced;

The change of leaf signature and leaf summary graph must be propagated upwards within the VS*-tree;

Rule:

The location of new node depend on both node signature & G*’s signature;

After inserting, the signature of that leaf node and super edges adjacent to it may be updated, and the change must be propagated upwards within the VS*-tree;

2. Split:

Like other height balanced trees, insertion into a node that is already full will invoke a node split;

Rule:

At each iteration, we select as the seed nodes two entities from among the B + 1. We allocate the remaining entities to these two nodes according to Equation 1.

Then, we have to update the signatures & the super edges associated with the two new nodes, and the change will be also propagated to the root of the VS∗-tree;

3. Deleation

Rule:

To delete a vertex u from VS*-tree, we find the leaf node d where u is stored, and delete u, and then adopt a bottom-up strategy to update the signature of and super edges associated with the nodes;

If some node d has less than b entries, then d is deleted and its entries are reinserted into VS*-tree;