Analyzing data streams using a dynamic compact stream pattern algorithm

Oyewale, A; Hughes, CJ; Saraee, MH

Abstract

In order to succeed in the global competition, organizations need to understand and monitor the rate of data influx. The acquisition of continuous data has been extremely outstretched as a concern in many fields. Recently, frequent patterns in data streams have been a challenging task in the field of data mining and knowledge discovery. Most of these datasets generated are in the form of a stream (stream data), thereby posing a challenge of being continuous. Therefore, the process of extracting knowledge structures from continuous rapid data records is termed as stream mining. This study conceptualizes the process of detecting outliers and responding to stream data. This is done by proposing a Compressed Stream Pattern algorithm, which dynamically generates a frequency descending prefix tree structure with only a singlepass over the data. We show that applying tree restructuring techniques can considerably minimize the mining time on various datasets.

Citation

Oyewale, A., Hughes, C., & Saraee, M. (2018, July). Analyzing data streams using a dynamic compact stream pattern algorithm. Presented at The Eighth International Conference on Advances in Information Mining and Management, Barcelona, Spain

• Presentation Conference Type: Other
• Conference Name: The Eighth International Conference on Advances in Information Mining and Management
• Conference Location: Barcelona, Spain
• Start Date: Jul 22, 2018
• End Date: Jul 26, 2018
• Publication Date: Jul 22, 2018
• Deposit Date: Aug 6, 2018
• Publicly Available Date: Aug 9, 2018
• Book Title: IMMM 2018, The Eighth International Conference on Advances in Information Mining and Management
• ISBN: 9781612086545
• Publisher URL: https://www.thinkmind.org/index.php?view=article&articleid=immm_2018_2_20_50039
• Related Public URLs: https://www.thinkmind.org/index.php?view=instance&instance=IMMM+2018
• Additional Information: Event Type : Conference