Context-Aware Duplicate Detection in Semi-structured Data Streams

2014 ◽  
Author(s):  
Parijat Shukla ◽  
Arun K. Somani
Keyword(s):  
Data Streams ◽  
Structured Data ◽  
Context Aware ◽  
Duplicate Detection

scholarly journals CAMeL: A Self-Adaptive Framework for Enriching Context-Aware Middlewares with Machine Learning Capabilities

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Nicola Bicocchi ◽  
Damiano Fontana ◽  
Franco Zambonelli
Keyword(s):  
Machine Learning ◽  
Contextual Information ◽  
Structured Data ◽  
Machine Learning Algorithms ◽  
Context Aware ◽  
Mobile Platforms ◽  
Context Management ◽  
Video Signals ◽  
Learning Capabilities ◽  
General Goal

Context-aware middlewares support applications with context management. Current middlewares support both hardware and software sensors providing data in structured forms (e.g., temperature, wind, and smoke sensors). Nevertheless, recent advances in machine learning paved the way for acquiring context from information-rich, loosely structured data such as audio or video signals. This paper describes a framework (CAMeL) enriching context-aware middlewares with machine learning capabilities. The framework is focused on acquiring contextual information from sensors providing loosely structured data without the need for developers of implementing dedicated application code or making use of external libraries. Nevertheless the general goal of context-aware middlewares is to make applications more dynamic and adaptive, and the proposed framework itself can be programmed for dynamically selecting sensors and machine learning algorithms on a contextual basis. We show with experiments and case studies how the CAMeL framework can (i) promote code reuse and reduce the complexity of context-aware applications by natively supporting machine learning capabilities and (ii) self-adapt using the acquired context allowing improvements in classification accuracy while reducing energy consumption on mobile platforms.

scholarly journals Schema-based Scheduling of Event Processors and Buffer Minimization for Queries on Structured Data Streams

2004 ◽  
pp. 228-239 ◽  
Author(s):  
Christoph Koch ◽  
Stefanie Scherzinger ◽  
Nicole Schweikardt ◽  
Bernhard Stegmaier
Keyword(s):  
Data Streams ◽  
Structured Data ◽  
Buffer Minimization

scholarly journals Enabling Context-Aware Search using Extracted Insights from IoT Data Streams

2020 ◽  
Author(s):  
V. Janeiko ◽  
R. Rezvani ◽  
N. Pourshahrokhi ◽  
S. Enshaeifar ◽  
M. Krogbak ◽  
...  
Keyword(s):  
Data Streams ◽  
Context Aware
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