Infrastructures for Development of Context-Aware Mobile Applications

2011 ◽  
pp. 1104-1118
Author(s):  
Hugo Feitosa de Figueirêdo ◽  
Tiago Eduardo da Silva ◽  
Anselmo Cardoso de Paiva ◽  
José Eustáquio Rangel de Queiroz ◽  
Cláudio De Souza Baptista
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Contextual Information ◽  
Wireless Networking ◽  
Context Aware ◽  
Context Model ◽  
Technological Advances ◽  
Communication Method ◽  
Context Aware System

Context-aware mobile applications are becoming popular, as a consequence of the technological advances in mobile devices, sensors and wireless networking. Nevertheless, developing a context-aware system involves several challenges. For example, what will be the contextual information, how to represent, acquire and process this information and how it will be used by the system. Some frameworks and middleware have been proposed in the literature to help programmers to overcome these challenges. Most of the proposed solutions, however, neither have an extensible ontology-based context model nor uses a communication method that allows a better use of the potentialities of the models of this kind.

Developing Context-Aware System using the Conceptual Context Model

2006 ◽  
Author(s):  
Chung-seong Hong ◽  
Kang-woo Lee ◽  
Young-ho Suh ◽  
Hyoung-sun Kim ◽  
Hyun Kim ◽  
...  
Keyword(s):  
Context Aware ◽  
Context Model ◽  
Context Aware System

scholarly journals IMPLEMENTASI CONTEXT AWARE PADA SISTEM TRAVEL PLANNER BERBASIS MOBILE

2021 ◽  
Vol 5 (1) ◽  
pp. 23-27
Author(s):  
Viery Darmawan ◽  
◽  
Rengga Asmara ◽  
Ira Prasetyaningrum
Keyword(s):  
Contextual Information ◽  
The Internet ◽  
Context Aware ◽  
New Approach ◽  
Tourist Attractions ◽  
Technological Advances ◽  
Long Time ◽  
Travel Efficiency ◽  
Opening And Closing

In the era of technological advances, tourists will first seek information about the tourist object to be addressed, even tourists often don't have a destination, so they have to search one by one via the internet. In determining travel plans, it is often to see one by one the review of tourist attractions and conclude the results will take a long time, while tourists need actual and fast information to determine the travel plans. In this study, the authors take a new approach, namely by creating a mobile-based travel planner system that compiles travel plans automatically by considering contextual information related to tourist location points, whether of tourist locations during travel days, travel opening and closing hours, so that it will increase travel efficiency without having to do the research manually which takes a long time. The system can also provide travel recommendations based on visitor comments sentiment on Google Places and is equipped with a trip route that will be generated automatically. This research is useful for helping tourists plan their trip actually because of the consideration of contextual information so that it will make it easier and save tourists time.

Context-Aware and Adaptive Units of Learning in mLearning

2012 ◽  
Vol 3 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Sergio Gómez ◽  
Ramón Fabregat
Keyword(s):  
Language Learning ◽  
Mobile Devices ◽  
Second Language Learning ◽  
Contextual Information ◽  
Technology Enhanced Learning ◽  
Context Aware ◽  
Desktop Computers ◽  
Conditional Statements ◽  
Enhanced Learning ◽  
Interest And Motivation

In technology-enhanced learning, the use of mobile applications is increasing, which improves students’ learning experiences, allowing them to carry out daily activities anytime, anywhere. However, the majority of the available learning contents have been designed for desktop computers; thus, accessing that information is limited by the technical capabilities of mobile devices. As a result, students might lose interest and motivation to learn using their mobile devices if content adaptation and learning personalization processes are not appropriately designed. In this paper, the authors present a context-aware adaptation architecture for mobile learning. In the architecture, two mechanisms based on conditional statements from the IMS Learning Design specification and a transcoding mechanism are presented. Moreover, which learner’s contextual information can be represented to design the learning process and retrieved to adapt activities and resources is explained by the description of a context-aware mobile-assisted second language learning scenario.

Toward a Knowledge-Driven Context-Aware System for Surgical Assistance

2017 ◽  
Vol 02 (03) ◽  
pp. 1740007 ◽  
Author(s):  
Hirenkumar Nakawala ◽  
Giancarlo Ferrigno ◽  
Elena De Momi
Keyword(s):  
Image Processing ◽  
Surgical Training ◽  
Contextual Information ◽  
Surgical Instruments ◽  
Context Aware ◽  
Semantic Technologies ◽  
Surgical Instrument ◽  
Test Scenario ◽  
Surgical Assistance ◽  
Context Aware System

Complex surgeries complications are increasing, thus making an efficient surgical assistance is a real need. In this work, an ontology-based context-aware system was developed for surgical training/assistance during Thoracentesis by using image processing and semantic technologies. We evaluated the Thoracentesis ontology and implemented a paradigmatic test scenario to check the efficacy of the system by recognizing contextual information, e.g. the presence of surgical instruments on the table. The framework was able to retrieve contextual information about current surgical activity along with information on the need or presence of a surgical instrument.

scholarly journals A Contextual Data Offloading Service With Privacy Support

2019 ◽  
Author(s):  
Francisco Gomes ◽  
Lincoln Rocha ◽  
Fernando Trinta
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Context Aware ◽  
Computational Power ◽  
Context Model ◽  
Data Offloading ◽  
Contextual Data ◽  
Data History ◽  
Remote Environment ◽  
The Impact

Mobile and context-aware applications are now a reality thanks to the increased capabilities of mobile devices. In the last twenty years, researchers had proposed several software infrastructures to help the development of context-aware applications. We verified that most of them do not store contextual data history and that few of these infrastructures take into account the privacy of contextual data. This article presents a service named COP (Contextual data Offloading service with Privacy support) to mitigate these problems. Its foundations are: (i) a context model; (ii) a privacy policies; and (iii) a list of synchronization policies. The COP aims at storing and processing the contextual data generated from several mobile devices, using the computational power of the cloud. We have implemented one experiment evaluated the impact of contextual filter processing in the mobile device and the remote environment. In this experiment, we measured the processing time and the energy consumption of COP approach. The analysis detected that the migration of data from mobile device to a remote environment is advantageous.

scholarly journals CFDIL: A Context-aware Feature Deep Interaction Learning for  App Recommendation

2021 ◽  
Author(s):  
Qingbo Hao ◽  
Ke Zhu ◽  
Chundong Wang ◽  
Peng Wang ◽  
Xiuliang Mo ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Historical Data ◽  
Contextual Information ◽  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Context Aware ◽  
Interaction Data ◽  
Learning Potential ◽  
The One

Abstract The rapid development of Mobile Internet has spa-wned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method.

scholarly journals Paas EUD Tool for Developing Expert Context-Aware Mobile Applications

2021 ◽  
Vol 10 (2) ◽  
pp. 215-224
Author(s):  
Sahar Elshafei ◽  
◽  
Ehab Hassanein ◽  
Hanan Elazhary ◽  
◽  
...  
Keyword(s):  
Mobile Applications ◽  
Expert Knowledge ◽  
Contextual Information ◽  
Quality Data ◽  
Software Systems ◽  
Context Aware ◽  
Software Developers ◽  
Platform As A Service ◽  
Context Data ◽  
Current Context

Context-awareness enables systems to be tailored to the needs of users and their real circumstances at certain times. A noteworthy trend in software development is that an increasing number of software systems are being developed by individuals with expert knowledge in other sectors. Because most of the current context-aware development toolkits are intended for software developers, these types of systems cannot be easily developed by non-technical consumers. The development of tools for designing context-aware frameworks by consumers who are not programming experts but are specialists in the area of implementation would result in faster adoption of such services by businesses. This paper provides a cloud-based framework for people without programming experience to create context-aware mobile applications. The platform can provide a lightweight distribution of packaged applications that allows experts to send specified information to mobile users based on their context data without overlapping between the rules of the application. An energy-efficient mobile application was developed to acquire contextual information from the user device and to create quality data accordingly. The framework adopts Platform as a Service (PaaS) and containerization to facilitate development of context-aware mobile applications by experts in various domains rather than developing a tool for each domain in isolation, while considering multitenancy.

ConChi

2016 ◽  
pp. 798-820
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users' requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

ConChi

2014 ◽  
pp. 263-285
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users’ requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

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