Multiple queries optimization for data streams on cloud computing

2015 ◽  
Author(s):  
Fatma M. Najib ◽  
Rasha M. Ismail ◽  
Nagwa L. Badr ◽  
M. F. Tolba
Keyword(s):  
Cloud Computing ◽  
Data Streams ◽  
Multiple Queries

scholarly journals RCD+: A Partitioning Method for Data Streams Based on Multiple Queries

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 52517-52527
Author(s):  
Ruichang Li ◽  
Chunkai Wang ◽  
Fan Liao ◽  
Honglei Zhu
Keyword(s):  
Data Streams ◽  
Multiple Queries

Cloud Computing Enhanced Service Development Architecture for the Living Usability Lab

2013 ◽  
pp. 33-53
Author(s):  
Cláudio Teixeira ◽  
Joaquim Sousa Pinto ◽  
Flávio Ferreira ◽  
André Oliveira ◽  
António Teixeira ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Streams ◽  
Ambient Assisted Living ◽  
Real Life ◽  
The Elderly ◽  
Service Development ◽  
Research Fields ◽  
Multiple Data ◽  
Processing And Storage ◽  
And Storage

As life expectancy increases, so does the number of Ambient Assisted Living (AAL) initiatives. The Living Usability Lab is a user-centered living laboratory aimed at open-innovation and evaluation of new approaches to AAL applications and services, where the different stakeholders may develop and evaluate innovative services for the elderly in near-real life conditions. These AAL initiatives often traverse several research fields, from embedded devices to multiple data streams analysis. Advanced processing, reasoning, and storage of such data streams poses a complex problem usually solved using local processing and storage resources. This chapter presents an overview of the LUL initiative, its services, and applications, and explores the problem of advanced processing, reasoning, and storage from a cloud computing perspective.

Cloud-based architectures for geo-located blogosphere dynamics detection

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Athena Vakali ◽  
Stefanos Antaris ◽  
Maria Giatsoglou
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Social Networking ◽  
Trend Analysis ◽  
Data Streams ◽  
Data Analytics ◽  
High Dimensionality ◽  
Big Data Streams ◽  
Cloud Infrastructures

Social networking data threads emerge rapidly and such crowd-driven big data streams are valuable for detecting trends and opinions. For such analytics, conventional data mining approaches are challenged by both high-dimensionality and scalability concerns. Here, we leverage on the Cloud4Trends framework for collecting and analyzing geo-located microblogging content, partitioned into clusters under cloud-based infrastructures. Different cloud architectures are proposed to offer flexible solutions for geo-located data analytics with emphasis on incremental trend analysis. The proposed architectures are largely based on a set of service modules which facilitate the deployment of the experimentation on cloud infrastructures. Several experimentation remarks are highlighted to showcase the requirements and testing capabilities of different cloud computing settings.

Dynamic pattern matching with multiple queries on large scale data streams

2020 ◽  
Vol 171 ◽  
pp. 107402
Author(s):  
S. Sukhanov ◽  
R. Wu ◽  
C. Debes ◽  
A.M. Zoubir
Keyword(s):  
Pattern Matching ◽  
Data Streams ◽  
Large Scale ◽  
Dynamic Pattern ◽  
Multiple Queries ◽  
Large Scale Data ◽  
Scale Data

Cloud-Computing: eHealth aus der Wolke

2018 ◽  
Vol 23 (11) ◽  
pp. 38-41
Author(s):  
Sebastian Krolop ◽  
Florian Benthin ◽  
Constanze Knahl
Keyword(s):  
Cloud Computing ◽  
Point Of Care

Cloud-Computing gewinnt auch in Kliniken zunehmend an Bedeutung. Über das Internet bereitgestellte Lösungen verändern nicht nur Verwaltung und Logistik – im klinischen Bereich geht es zum Beispiel um die Nutzung elektronischer Patientenakten am Point-of-Care.

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