Next Generation Mobile Service Environment and Evolution of Context Aware Services

2006 ◽  
pp. 591-600 ◽  
Author(s):  
JungSook Bae ◽  
Jae Yong Lee ◽  
Byung Chul Kim ◽  
Seungwan Ryu
Keyword(s):  
Mobile Service ◽  
Context Aware ◽  
Next Generation ◽  
Service Environment

Context-aware semantic discovery for next generation mobile systems

2006 ◽  
Vol 44 (9) ◽  
pp. 62-71 ◽  
Author(s):  
P. Bellavista ◽  
A. Corradi ◽  
R. Montanari ◽  
A. Toninelli
Keyword(s):  
Mobile Systems ◽  
Context Aware ◽  
Next Generation ◽  
Semantic Discovery

A study on next‐generation digital library using context‐awareness technology

2013 ◽  
Vol 31 (2) ◽  
pp. 236-253 ◽  
Author(s):  
Younghee Noh
Keyword(s):  
Digital Library ◽  
Digital Libraries ◽  
Context Awareness ◽  
Theoretical Research ◽  
Context Aware ◽  
Semantic Retrieval ◽  
Next Generation ◽  
Climate Control ◽  
Content Type ◽  
Reference Services

PurposeThis study seeks to examine the concepts of context, context‐awareness, and context‐awareness technology needed for applying context‐awareness technology to the next‐generation of digital libraries, and proposed context‐aware services that can be applied to any situation by illustrating some library contexts.Design/methodology/approachThe paper investigated both theoretical research and case analysis studies before suggesting a service model for context‐awareness‐based libraries by examining the context, context‐awareness, and context‐awareness technology in depth.FindingsThis paper derived possible library services which could be provided if context‐awareness services are implemented by examining and analyzing case studies and systems constructed in other fields. A library‐applied context‐aware system could recognize users entering the library and provide optimal services tailored to each situation for both new and existing users. In addition, the context‐awareness‐based library could provide context‐awareness‐based reference services, context‐awareness‐based loan services, and cater to other user needs in the stacks, research space, and a variety of other information spaces. The context‐awareness‐based library could also recognize users in need of emergency assistance by detecting the user's behavior, movement path, and temperature, etc. Comfort or climate‐control services could provide the user with control of the temperature, humidity, illumination and other environmental elements to fit the circumstances of users, books, and instruments through context‐aware technology.Practical implicationsNext‐generation digital libraries apply new concepts such as semantic retrieval, real‐time web, cloud computing, mobile web, linked data, and context‐awareness. Context‐awareness‐based libraries can provide applied context‐awareness access service, reactive space according to the user's access, applied context‐awareness lobbies, applied context‐awareness reference services, and applied context‐awareness safety services, context‐awareness‐based comfort services and so on.Originality/valueReal instances of libraries applying context‐aware technology are few, according to the investigative results of this study. The study finds that the next‐generation digital library using context‐awareness technology can provide the best possible service for the convenience of its users.

Service-Oriented Architectures for Context-Aware Information Retrieval and Access

2010 ◽  
pp. 1549-1560
Author(s):  
Lu Yan
Keyword(s):  
Information Retrieval ◽  
Information Exchange ◽  
Exchange Process ◽  
Information Access ◽  
Mobile Service ◽  
Context Aware ◽  
Service Oriented Architectures ◽  
Information Retrieval Systems ◽  
To Come ◽  
Process Context

Humans are quite successful at conveying ideas to each other and retrieving information from interactions appropriately. This is due to many factors: the richness of the language they share, the common understanding of how the world works, and an implicit understanding of everyday situations (Dey & Abowd, 1999). When humans talk with humans, they are able to use implicit situational information (i.e., context) to enhance the information exchange process. Context (Cool & Spink, 2002) plays a vital part in adaptive and personalized information retrieval and access. Unfortunately, computer communications lacks this ability to provide auxiliary context in addition to the substantial content of information. As computers are becoming more and more ubiquitous and mobile, there is a need and possibility to provide information “personalized, any time, and anywhere” (ITU, 2006). In these scenarios, large amounts of information circulate in order to create smart and proactive environments that will significantly enhance both the work and leisure experiences of people. Context-awareness plays an important role to enable personalized information retrieval and access according to the current situation with minimal human intervention. Although context-aware information retrieval systems have been researched for a decade (Korkea-aho, 2000), the rise of mobile and ubiquitous computing put new challenges to issue, and therefore we are motivated to come up with new solutions to achieve non-intrusive, personalized information access on the mobile service platforms and heterogeneous wireless environments.

Context-Aware Recommender System for Location-Based Advertising

2011 ◽  
Vol 467-469 ◽  
pp. 2091-2096 ◽  
Author(s):  
Hyun Chul Ahn ◽  
Kyoung Jae Kim
Keyword(s):  
Mobile Devices ◽  
Recommender System ◽  
Service Industries ◽  
Classification Rule ◽  
Mobile Service ◽  
Context Aware ◽  
Good Market ◽  
Proposed Model ◽  
Market Opportunities ◽  
Marketing Tool

Demand for context-aware systems continues to grow due to the diffusion of mobile devices. This trend may represent good market opportunities for mobile service industries. Thus, context-aware or location-based advertising (LBA) has been an interesting marketing tool for many companies. However, some studies reported that the performance of context-aware marketing or advertising has been quite disappointing. In this study, we propose a novel context-aware recommender system for LBA. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user’s needs type. In particular, we employ a classification rule to understand user’s needs type using a decision tree algorithm. We empirically validated the effectiveness of the proposed model by using a real-world dataset. Experimental results show that our model makes more accurate and satisfactory advertisements than comparative systems.

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