DAGR – DAG Based Context Reasoning: An Architecture for Context Aware Applications

2008 ◽  
Author(s):  
Bernd Niklas Klein ◽  
Sian Lun Lau ◽  
Andreas Pirali ◽  
Tino Löffler ◽  
Klaus David
Keyword(s):  
Context Aware ◽  
Context Reasoning

Context Reasoning for Predicting Gestational Diabetes Mellitus Using CA-RETE Algorithm

2021 ◽  
Vol 17 (4) ◽  
pp. 41-59
Author(s):  
Deeba K. ◽  
Saravanaguru R. A. K.
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Smart Devices ◽  
Context Aware ◽  
Healthcare Applications ◽  
Context Aware Computing ◽  
Context Reasoning ◽  
Reasoning System ◽  
High Level

Today, IoT-related applications play an important role in scientific world development. Context reasoning emphasizes the perception of various contexts by means of collection of IoT data which includes context-aware decision making. Context-aware computing is used to improve the abilities of smart devices and is increased by smart applications. In this paper, context-aware for the internet of things middleware (CAIM) architecture is used for developing a rule-based system using CA-RETE algorithm. The objective of context-aware systems are concentrated on 1) context reasoning methodologies and analyzing how the technologies will involve enhancing the high-level context data, 2) framework of context reasoning system, 3) implementation of CA-RETE algorithm for predicting gestational diabetes mellitus in healthcare applications.

scholarly journals Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application

2017 ◽  
Author(s):  
Sara Saeedi ◽  
Xueyang Zou ◽  
Mariel Gonzales ◽  
Steve Liang
Keyword(s):  
Web Application ◽  
Management Practices ◽  
Recommendation System ◽  
Contextual Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Context Information ◽  
Context Aware ◽  
Context Reasoning ◽  
Past Behavior

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

scholarly journals Artificial Neural Network Context-Aware Reasoning Engine Model for Predicting User-Ranked Activities

2021 ◽  
Vol 5 (6) ◽  
pp. 36-42
Author(s):  
Samuel King Opoku
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Context Aware ◽  
Back Propagation Algorithm ◽  
Engine Model ◽  
Context Reasoning ◽  
Stochastic Learning ◽  
Artificial Neural ◽  
Reasoning Engine

The choice of users’ activity in a context-aware environment depends on users’ preferences and background. Users tend to rank concurrent activities and select their preferred activity. Researchers and developers of context-aware applications have sought various mechanisms to implement context reasoning engines. Recent implementations use Artificial Neural Networks (ANN) and other machine learning techniques to develop a context-aware reasoning engine to predict users’ activities. However, the complexities of these mechanisms overwhelm the processing capabilities and storage capacity of mobile devices. The study models a context-aware reasoning engine using a multi-layered perceptron with a gradient descent back-propagation algorithm to predict activity from user-ranked activities using a stochastic learning mode with a constant learning rate. The work deduced that working with specific rules in training a neural network is not always applicable. Training a network without approximation of neuron’s output to the nearest whole number increases the accuracy level of the network at the end of the training.

scholarly journals Rule-based solution for context-aware reasoning on mobile devices

2014 ◽  
Vol 11 (1) ◽  
pp. 171-193 ◽  
Author(s):  
Grzegorz Nalepa ◽  
Szymon Bobek
Keyword(s):  
Mobile Devices ◽  
Rapid Evolution ◽  
Preliminary Evaluation ◽  
Context Aware ◽  
Context Modeling ◽  
Network Access ◽  
Mobile Platforms ◽  
Rule Based ◽  
Real Time Processing ◽  
Context Reasoning

With the rapid evolution of mobile devices, the concept of context aware applications has gained a remarkable popularity in recent years. Smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, and GPS, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows real-time processing of data gathered by their sensors. Universal network access viaWiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Most of existing frameworks for context-aware systems, are usually dedicated to static, centralized, clientserver architectures. However, mobile platforms require from the context modeling language and inference engine to be simple and lightweight. The model should also be powerful enough to allow not only solving simple context identification tasks but more complex reasoning. The original contribution of the paper is a proposal of a new rule-based context reasoning platform tailored to the needs of such intelligent distributed mobile computing devices. It contains a proposal of a learning middleware supporting context acquisition. The platform design is based on a critical review and evaluation of existing solutions given in this paper. A preliminary evaluation of the platform is given along with use cases including a social system supporting crime detection and investigation.

CREATING CONTEXT-AWARE COLLABORATIVE WORKING ENVIRONMENTS

2011 ◽  
Vol 20 (01) ◽  
pp. 195-207 ◽  
Author(s):  
M. ANTONIA MARTÍNEZ-CARRERAS ◽  
ANDRÉS MUÑOZ ◽  
JUAN BOTÍA ◽  
ANTONIO F. GÓMEZ-SKARMETA
Keyword(s):  
Team Work ◽  
Context Information ◽  
Context Aware ◽  
Team Members ◽  
Context Reasoning ◽  
Design And Implementation ◽  
Reasoning System ◽  
Working Environments ◽  
Current State ◽  
Collaborative Working

Context-aware systems are intended for providing services adapted to the needs of people, by taking into account their state and the information related to their environment. One alternative to represent this context information resides in the use of Semantic Web ontologies. They provide a formal vocabulary which allows to easily express and share knowledge. Additionally, several types of automatic knowledge manipulation and reasoning processes become available thanks to the formal features of such ontologies. The inclusion of context information through ontologies in Collaborative Working Environments (CWEs) may bring important benefits to team work inside an organization, such as an automatic selection between different collaborative services according to the team members' preferences and their current state. This paper describes the design and implementation of a context-reasoning system which has been integrated into a CWE architecture to take advantage of context-awareness.

An Ontology-Based Context-Aware Infrastructure for Smart Homes

2011 ◽  
pp. 272-299 ◽  
Author(s):  
Bin Guo ◽  
Daqing Zhang ◽  
Michita Imai
Keyword(s):  
Semantic Web ◽  
Smart Homes ◽  
Context Aware ◽  
End User ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Context Reasoning ◽  
Series Of Experiments

A general infrastructure that can facilitate the development of context-aware applications in smart homes is proposed. Unlike previous systems, our system builds on semantic web technologies, and it particularly concerns the contexts from human-artifact interaction. A multi-levels’ design of our ontology (called SS-ONT) makes it possible to realize context sharing and end-user-oriented customization. Using this infrastructure as a basis, we address some of the principles involved in performing context querying and context reasoning. The performance of our system is evaluated through a series of experiments.

scholarly journals An ontology for context-aware pervasive computing environments

2003 ◽  
Vol 18 (3) ◽  
pp. 197-207 ◽  
Author(s):  
HARRY CHEN ◽  
TIM FININ ◽  
ANUPAM JOSHI
Keyword(s):  
Pervasive Computing ◽  
Privacy Protection ◽  
Context Aware ◽  
Web Ontology Language ◽  
Agent Architecture ◽  
Ongoing Research ◽  
Context Reasoning ◽  
Meeting Room ◽  
Ontology Language ◽  
Computing Environments

This document describes COBRA-ONT, an ontology for supporting pervasive context-aware systems. COBRA-ONT, expressed in the Web Ontology Language OWL, is a collection of ontologies for describing places, agents and events and their associated properties in an intelligent meeting-room domain. This ontology is developed as a part of the Context Broker Architecture (CoBrA), a broker-centric agent architecture that provides knowledge sharing, context reasoning and privacy protection supports for pervasive context-aware systems. We also describe an inference engine for reasoning with information expressed using the COBRA-ONT ontology and the ongoing research in using the DAML-Time ontology for context reasoning.

Fuzzy Bayesian Context-Aware System to Reduce Electricity Consumption

2021 ◽  
Vol 2 (1) ◽  
pp. 86-98
Author(s):  
Kavita Pankaj Shirsat ◽  
Girish P. Bhole
Keyword(s):  
Electricity Consumption ◽  
Context Aware ◽  
Usage Pattern ◽  
Incorrect Decision ◽  
Context Reasoning ◽  
Main Challenge ◽  
Occupancy Detection ◽  
Reduce Electricity Consumption ◽  
Iot Devices ◽  
Context Aware System

Context-awareness develops smart, intelligent IoT devices that can adapt to changing needs and act autonomously on behalf of the user. The main challenge of context-aware internet of things is to interpret the context effectively. There is an abundance of CAIOT in literature. Understanding of the meaning of the context is, however, almost ignored. Misinterpretation of context can lead to an incorrect decision that motivates to develop a system that emphasis context reasoning and decision making using the fuzzy Bayesian approach. The current investigation aims to build a context-aware IoT system using occupancy detection for energy management. The performance evaluation for the proposed system uses data collected in the tutorial room to detect occupancy. Extensive experiments highlight the utility of the proposed approach, which significantly reduces energy than the traditional ON/OFF usage pattern through customer access via mobile phone or personal computer.

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