Dynamic Data Types Optimization in Multimedia and Communication Applications

2014 ◽  
pp. 93-134
Author(s):  
David Atienza Alonso ◽  
Stylianos Mamagkakis ◽  
Christophe Poucet ◽  
Miguel Peón-Quirós ◽  
Alexandros Bartzas ◽  
...  
Keyword(s):  
Data Types ◽  
Dynamic Data

Cloud4NFICA-Nearness Factor-Based Incremental Clustering Algorithm Using Microsoft Azure for the Analysis of Intelligent Meter Data

2020 ◽  
Vol 10 (2) ◽  
pp. 21-39
Author(s):  
Archana Yashodip Chaudhari ◽  
Preeti Mulay
Keyword(s):  
Smart Grids ◽  
Clustering Algorithm ◽  
Incremental Clustering ◽  
Clustering Method ◽  
Data Types ◽  
Dynamic Data ◽  
Real Dataset ◽  
Processing Power ◽  
Microsoft Azure ◽  
Socioeconomic Data

Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.

scholarly journals Platforms as service ecosystems: Lessons from social media

2019 ◽  
Vol 35 (1) ◽  
pp. 25-48 ◽  
Author(s):  
Cristina Alaimo ◽  
Jannis Kallinikos ◽  
Erika Valderrama
Keyword(s):  
Social Media ◽  
Data Types ◽  
Dynamic Data ◽  
Content Sharing ◽  
Social Media Platforms ◽  
Service Ecosystems ◽  
Media Platform ◽  
Three Stages ◽  
Platforms And Ecosystems ◽  
Service Ecosystem

The growing business expansion of social media platforms is changing their identity and transforming the practices of networking, data and content sharing with which social media have been commonly associated. We empirically investigate these shifts in the context of TripAdvisor and its evolution since its very establishment. We trace the mutations of the platform along three stages we identify as search engine, social media platform and end-to-end service ecosystem. Our findings reveal the underlying patterns of data types, technological functionalities and actor configurations that punctuate the business expansion of TripAdvisor and lead to the formation of its service ecosystem. We contribute to the understanding of the current trajectory in which social media find themselves as well as to the literature on platforms and ecosystems. We point out the importance of services that develop as commercially viable and constantly updatable data bundles out of diverse and dynamic data types. Such services are essential to the making of the complementarities that are claimed to underlie ecosystem formation.

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