QTwig: A Structural Join Algorithm for Efficient Query Retrieval Based on Region-Based Labeling

Dependability on XML has increased tremendously over the years. As such the need for efficient query processing technique is certainly important. Despite the fact that these techniques are able to process queries with various edge combinations, they still suffer from processing overheads by buffering large amount of intermediate results particularly for parent–child (P–C) edges. Therefore, in this paper, we propose architecture named ReLaQ, which comprises of two components, ReLab[Formula: see text] (node annotator) and QTwig (query processor) for efficient XML query processing. QTwig improves retrieval time by incorporating a pruning technique that avoids accessing irrelevant data during query processing. Experimental results indicated that ReLaQ superseded TwigStack for both path and twig queries using both regular- and skewed-structured datasets. In addition, this is also proven by means of correctness analysis of ReLaQ.

Structural Join Algorithm of XML Query Based on Exhaustive Dynamic Programming

Using the advantages of exhaustive dynamic programming algorithm, on the basic ideas of the global optimal solution is derived based on local optimal solution, this paper propose a new structural selection join algorithm. The algorithm connects to the sub-tree, and then connects to the structure of the whole. Though not guaranteed optimal solution, this algorithm can improve much in the time complexity, reduce the search space and improve efficiency.

XML Query Based on Indexed Sequential Table

The current study based on XML index and query mostly focuses on encoding and the structural relation. Region codings are widely used to improve XML query. In this paper postorder-traversal region coding is proposed. The postorder of a node’s all descendants consists of the region. Judging and ensuring structural relation of any two nodes just depend on this region, if the postorder of a node is in a region, ancestor/descendant structural relation can be ensured. Consequently, postorder-traversal region coding can effectively judge structural relation and avoid traversing the XML document tree. Based on region coding, many constructive structural query algorithms have been put forward. As we all know that Stack-Tree-Desc algorithm is one of these fine algorithms, AList and DList only need separately scan one time to judge structural relation, however some unnecessary nodes still be scanned. In order to solve this problem, Indexed Sequential Table algorithm is introduced. The optimized algorithm introduces Indexed Sequential Table to avoid scanning unwanted nodes when the two lists join to locate next node which participates in structural join. In this case, some nodes of AList and DList which don’t participate in structural joins can be jumped, the query efficiency is enhanced. As a result, ordered scanning is prevented, the consuming time of XML query shortens accordingly. Experiment results demonstrate the effectiveness of the improved coding and algorithm.

Labelling-Scheme-Based Subgraph Query Processing on Graph Data

When data are modeled as graphs, many research issues arise. In particular, there are many new challenges in query processing on graph data. This chapter studies the problem of structural queries on graph data. A hash-based structural join algorithm, HGJoin, is first proposed to handle reachability queries on graph data. Then, it is extended to the algorithms to process structural queries in form of bipartite graphs. Finally, based on these algorithms, a strategy to process subgraph queries in form of general DAGs is proposed. It is notable that all the algorithms above can be slightly modified to process structural queries in form of general graphs.