scholarly journals SLAX: An Improved Leaf-Clustering Based Approximate XML Join Algorithm for Integrating XML Data at Subtree Classes

2006 ◽  
Vol 2 ◽  
pp. 382-392 ◽  
Author(s):  
Wenxin Liang ◽  
Haruo Yokota
Keyword(s):  
Xml Data ◽  
Join Algorithm

TDTMS: An Algorithm for Subtree Results Construction on XML Data

2014 ◽  
Vol 36 (8) ◽  
pp. 1714-1728
Author(s):  
Jun-Feng ZHOU ◽  
Bo WANG ◽  
Shan-Shan TIAN ◽  
Zi-Yang CHEN ◽  
Jing-Feng GUO
Keyword(s):  
Xml Data

Efficient Entity Resolution on XML Data Based on Entity-Describe-Attribute

2011 ◽  
Vol 34 (11) ◽  
pp. 2131-2141 ◽  
Author(s):  
Ya-Kun LI ◽  
Hong-Zhi WANG ◽  
Hong GAO ◽  
Jian-Zhong LI
Keyword(s):  
Entity Resolution ◽  
Xml Data

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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