Mar 20, 2017 · The core value of this design is mining a sequential database using parallel processing so that sub-tasks can be distributed and executed by ...
Scalability is a primary issue in existing sequential pattern mining algorithms for dealing with a large amount of data. Previous work, namely sequential ...
Abstract: Scalability is a primary issue in existing sequential pattern mining algorithms for dealing with a large amount of data.
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Scalability is a primary issue in existing sequential pattern mining algorithms for dealing with a large amount of data. Previous work, namely sequential ...
The results show that SPAMC-UDLT can significantly reduce execution time, achieves extremely high scalability, and provides much better load balancing than ...
Articles. Title: Distributed and scalable sequential pattern mining through stream processing. Authors: Chen, Chun-Chieh · Shuai, Hong-Han · Chen, Ming-Syan
Dive into the research topics of 'Distributed and scalable sequential pattern mining through stream processing'. Together they form a unique fingerprint ...
Distributed and scalable sequential pattern mining through stream processing. Article 20 March 2017. Scalable and parallel sequential pattern mining using spark.
This paper seeks to give a broad overview of the distinct approaches to pattern mining in the Big Data domain.
To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications.