scholarly journals Hash-Based Structural Join Algorithms

2006 ◽  
pp. 136-149 ◽  
Author(s):  
Christian Mathis ◽  
Theo Härder
Keyword(s):  
Structural Join ◽  
Join Algorithms

Efficient Processing of XML Path Queries Using BI Index

2011 ◽  
Vol 63-64 ◽  
pp. 119-123
Author(s):  
Xiang Yu Hu ◽  
Yun Yin Mo ◽  
Hai Wei Zhang ◽  
Xiao Jie Yuan
Keyword(s):  
Information Exchange ◽  
Data Representation ◽  
Query Languages ◽  
Structural Join ◽  
Join Algorithms ◽  
Efficient Processing ◽  
Path Queries ◽  
Labeling Scheme ◽  
And Storage ◽  
Parent Child

XML has been widely used for information exchange and storage as the de facto data representation format nowadays. Several XML query languages such XPath, XQuery and XML-QL have been proposed. Many structural join algorithms have been proposed for processing XPath queries, Although holistic twig join algorithms has been proved to be I/O optimal in terms of input and output sizes for queries with only ancestor-descendant edges, it cannot control the size of intermediate results for queries with parent-child edges. We address the problem of efficient path queries with mixed of ancestor-descendant and parent-child edges on a simple but novel index, called BI (i.e. Binary Index) based on Dewey labeling scheme. And we propose a new holistic path join algorithm, namely PSBI, which has the same performance as PathStack for query path with only ancestor-descendant edges, but it is significantly more efficient than PathStack for queries with the presence of parent-child edges. Experimental results demonstrate that the PSBI and BI index has a substantial performance improvement compared to original PathStack algorithm.

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.

scholarly journals Cache-efficient sweeping-based interval joins for extended Allen relation predicates

2021 ◽  
Author(s):  
Danila Piatov ◽  
Sven Helmer ◽  
Anton Dignös ◽  
Fabio Persia
Keyword(s):  
Data Structure ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Temporal Databases ◽  
Access Method ◽  
Wide Range ◽  
Interval Relation ◽  
Cache Efficient ◽  
Join Algorithms ◽  
Better Than

AbstractWe develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.

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

Incorporating Clustering into Set Similarity Join Algorithms: The SjClust Framework

2016 ◽  
pp. 185-204 ◽  
Author(s):  
Leonardo Andrade Ribeiro ◽  
Alfredo Cuzzocrea ◽  
Karen Aline Alves Bezerra ◽  
Ben Hur Bahia do Nascimento
Keyword(s):  
Similarity Join ◽  
Join Algorithms
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