scholarly journals Context-Aware Data Prefetching in Mobile Service Environments

2014 ◽  
Author(s):  
Waldemar Hummer ◽  
Stefan Schulte ◽  
Philipp Hoenisch ◽  
Schahram Dustdar
Keyword(s):  
Mobile Service ◽  
Data Prefetching ◽  
Context Aware ◽  
Service Environments

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.

scholarly journals Context Aware Mobile Service Deployment Model of Agricultural Information System for Indian Farmers

2010 ◽  
Vol 1 (29) ◽  
pp. 8-12 ◽  
Author(s):  
S Chandrasekaran ◽  
Dipesh M Dugar ◽  
Jitendra Kumar D Jain ◽  
Kamlesh S Jain ◽  
Dinesh Kumar N Jain
Keyword(s):  
Information System ◽  
Mobile Service ◽  
Context Aware ◽  
Service Deployment

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.

Privacy Control Requirements for Context-Aware Mobile Services

2011 ◽  
pp. 151-166 ◽  
Author(s):  
Amr Ali Eldin ◽  
Zoran Stojanovic
Keyword(s):  
Web Services ◽  
Private Information ◽  
Mobile Services ◽  
End Users ◽  
Mobile Service ◽  
Context Aware ◽  
University Campus ◽  
Mobile Telecommunications ◽  
Computing Paradigm ◽  
Service Models

With the rapid developments of mobile telecommunications technology over the last two decades, a new computing paradigm known as ‘anywhere and anytime’ or ‘ubiquitous’ computing has evolved. Consequently, attention has been given not only to extending current Web services and mobile service models and architectures, but increasingly also to make these services context-aware. Privacy represents one of the hot topics that has questioned the success of these services. In this chapter, we discuss the different requirements of privacy control in context-aware services architectures. Further, we present the different functionalities needed to facilitate this control. The main objective of this control is to help end users make consent decisions regarding their private information collection under conditions of uncertainty. The proposed functionalities have been prototyped and integrated in a UMTS locationbased mobile services testbed platform on a university campus. Users have experienced the services in real time. A survey of users’ responses on the privacy functionality has been carried out and analyzed as well. Users’ collected response on the privacy functionality was positive in most cases. Additionally, results obtained reflected the feasibility and usability of this approach.

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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