Mining Popular Patterns: A Novel Mining Problem and Its Application to Static Transactional Databases and Dynamic Data Streams

2015 ◽  
pp. 115-139 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Fan Jiang ◽  
Carson K. Leung ◽  
Dacheng Liu ◽  
Aaron Peddle ◽  
...  
Keyword(s):  
Data Streams ◽  
Dynamic Data ◽  
Transactional Databases

scholarly journals Mining maximal frequent patterns in transactional databases and dynamic data streams: A spark-based approach

2018 ◽  
Vol 432 ◽  
pp. 278-300 ◽  
Author(s):  
Md. Rezaul Karim ◽  
Michael Cochez ◽  
Oya Deniz Beyan ◽  
Chowdhury Farhan Ahmed ◽  
Stefan Decker
Keyword(s):  
Data Streams ◽  
Frequent Patterns ◽  
Dynamic Data ◽  
Transactional Databases

Classification in Dynamic Data Streams with a Scarcity of Labels

2021 ◽  
pp. 1-1
Author(s):  
Conor Fahy ◽  
Shengxiang Yang ◽  
Mario Gongora
Keyword(s):  
Data Streams ◽  
Dynamic Data

scholarly journals Similarity Search for Dynamic Data Streams

2020 ◽  
Vol 32 (11) ◽  
pp. 2241-2253 ◽  
Author(s):  
Marc Bury ◽  
Chris Schwiegelshohn ◽  
Mara Sorella
Keyword(s):  
Data Streams ◽  
Similarity Search ◽  
Dynamic Data

Visual Analysis of Dynamic Data Streams

2009 ◽  
Vol 8 (3) ◽  
pp. 212-229 ◽  
Author(s):  
George Chin ◽  
Mudita Singhal ◽  
Grant Nakamura ◽  
Vidhya Gurumoorthi ◽  
Natalie Freeman-Cadoret
Keyword(s):  
Real Time ◽  
Data Streams ◽  
High Volume ◽  
Scientific Data ◽  
General Strategy ◽  
Dynamic Visualization ◽  
Time Data ◽  
Dynamic Data ◽  
Intensive Training ◽  
Real Time Data

For scientific data visualizations, real-time data streams present many interesting challenges when compared to static data. Real-time data are dynamic, transient, high-volume and temporal. Effective visualizations need to be able to accommodate dynamic data behavior as well as Abstract and present the data in ways that make sense to and are usable by humans. The Visual Content Analysis of Real-Time Data Streams project at the Pacific Northwest National Laboratory is researching and prototyping dynamic visualization techniques and tools to help facilitate human understanding and comprehension of high-volume, real-time data. The general strategy of the project is to develop and evolve visual contexts that will organize and orient high-volume dynamic data in conceptual and perceptive views. The goal is to allow users to quickly grasp dynamic data in forms that are intuitive and natural without requiring intensive training in the use of specific visualization or analysis tools and methods. Thus far, the project has prototyped five different visualization prototypes that represent and convey dynamic data through human-recognizable contexts and paradigms such as hierarchies, relationships, time and geography. We describe the design considerations and unique features of these dynamic visualization prototypes as well as our findings in the exploration and evaluation of their use.

Info-fuzzy algorithms for mining dynamic data streams

2008 ◽  
Vol 8 (4) ◽  
pp. 1283-1294 ◽  
Author(s):  
Lior Cohen ◽  
Gil Avrahami ◽  
Mark Last ◽  
Abraham Kandel
Keyword(s):  
Data Streams ◽  
Dynamic Data ◽  
Fuzzy Algorithms
Sign in / Sign up

Export Citation Format

Share Document