Study on a New Structural Join Algorithm for XML Query

2009 ◽  
Author(s):  
Shaofei Wu ◽  
Yuan Huang
Keyword(s):  
Structural Join ◽  
Join Algorithm

Parallel Structural Join Algorithm on Shared-Memory Multi-Core Systems

2008 ◽  
Author(s):  
Le Liu ◽  
Jianhua Feng ◽  
Guoliang Li ◽  
Qian Qian ◽  
Jianhui Li
Keyword(s):  
Shared Memory ◽  
Structural Join ◽  
Join Algorithm

Improvement of XML Structural Join Algorithm with Weaving Multi-documents

2013 ◽  
Vol 12 (23) ◽  
pp. 7240-7244
Author(s):  
Jiang Yan ◽  
Wang Yu-Xuan ◽  
Jin Xin ◽  
Li Xin ◽  
Pan Ping
Keyword(s):  
Structural Join ◽  
Join Algorithm

A Fast Structural Join Algorithm Based on Partitioning

2009 ◽  
Vol 31 (1) ◽  
pp. 77-90
Author(s):  
Guo-Ren WANG ◽  
Bai-You QIAO ◽  
Dong-Hong HAN ◽  
Bin WANG
Keyword(s):  
Structural Join ◽  
Join Algorithm

Labelling-Scheme-Based Subgraph Query Processing on Graph Data

2011 ◽  
pp. 142-174
Author(s):  
Hongzhi Wang ◽  
Jianzhong Li ◽  
Hong Gao
Keyword(s):  
Query Processing ◽  
Structural Join ◽  
Research Issues ◽  
Graph Data ◽  
Join Algorithm ◽  
Reachability Queries ◽  
Subgraph Query ◽  
New Challenges ◽  
General Graphs ◽  
Labelling Scheme

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.

Structural Join Algorithm of XML Query Based on Exhaustive Dynamic Programming

2012 ◽  
Vol 198-199 ◽  
pp. 1527-1530
Author(s):  
Xue Min Zhang ◽  
Xiao Wen Chen ◽  
Jia Lin Jiao
Keyword(s):  
Dynamic Programming ◽  
Dynamic Programming Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Programming Algorithm ◽  
Structural Join ◽  
Join Algorithm ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Basic Ideas

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.

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

2017 ◽  
Vol 27 (02) ◽  
pp. 321-342 ◽  
Author(s):  
Samini Subramaniam ◽  
Su-Cheng Haw ◽  
Lay-Ki Soon ◽  
Kok-Leong Koong
Keyword(s):  
Query Processing ◽  
Processing Technique ◽  
Experimental Results ◽  
Structural Join ◽  
Xml Query Processing ◽  
Retrieval Time ◽  
Join Algorithm ◽  
Efficient Query Processing ◽  
Twig Queries ◽  
Pruning Technique

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.

scholarly journals In-Memory Interval Joins

2021 ◽  
Author(s):  
Panagiotis Bouros ◽  
Nikos Mamoulis ◽  
Dimitrios Tsitsigkos ◽  
Manolis Terrovitis
Keyword(s):  
Parallel Computation ◽  
State Of The Art ◽  
Complex Data ◽  
Plane Sweep ◽  
Join Algorithm ◽  
Sweep Algorithm ◽  
Join Algorithms ◽  
Domain Partitioning ◽  
Complex Data Structure ◽  
Independent Tasks

AbstractThe interval join is a popular operation in temporal, spatial, and uncertain databases. The majority of interval join algorithms assume that input data reside on disk and so, their focus is to minimize the I/O accesses. Recently, an in-memory approach based on plane sweep (PS) for modern hardware was proposed which greatly outperforms previous work. However, this approach relies on a complex data structure and its parallelization has not been adequately studied. In this article, we investigate in-memory interval joins in two directions. First, we explore the applicability of a largely ignored forward scan (FS)-based plane sweep algorithm, for single-threaded join evaluation. We propose four optimizations for FS that greatly reduce its cost, making it competitive or even faster than the state-of-the-art. Second, we study in depth the parallel computation of interval joins. We design a non-partitioning-based approach that determines independent tasks of the join algorithm to run in parallel. Then, we address the drawbacks of the previously proposed hash-based partitioning and suggest a domain-based partitioning approach that does not produce duplicate results. Within our approach, we propose a novel breakdown of the partition-joins into mini-joins to be scheduled in the available CPU threads and propose an adaptive domain partitioning, aiming at load balancing. We also investigate how the partitioning phase can benefit from modern parallel hardware. Our thorough experimental analysis demonstrates the advantage of our novel partitioning-based approach for parallel computation.

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