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
Author(s):  
Farhad Daneshgar

Like many existing ERP models (e.g., Podolsky, 1998; Van Stijn & Wensley, 2001), the OOAB framework is also based on a widely accepted assumption that a corporate-wide information system consists of a set of potentially related subsystems; and as a result, information flows among these subsystems must be identified, and required resources planned, using an appropriate ERP methodology. However, up until now there existed no formalised framework that facilitates sharing of contextual knowledge in ERP processes. A unique attribute of the OOAB framework is that it treats ERP processes as a collaborative processes where various roles/actors collaboratively perform tasks in order to achieve a common overall goal. An object-oriented framework is presented in this article that facilitates sharing the contextual knowledge/resources that exist within ERP processes. Context is represented by a set of relevant collaborative semantic concepts or “objects”. These are the objects that are localised/contextualised to specific sub-process within the ERP process.


2011 ◽  
pp. 1515-1535
Author(s):  
Katarzyna Wac ◽  
Richard Bults ◽  
Bert-Jan van Beijnum ◽  
Hong Chen

Mobile service providers (MoSPs) emerge, driven by the ubiquitous availability of mobile devices and wireless communication infrastructures. MoSPs’ customers satisfaction and consequently their revenues, largely depend on the quality of service (QoS) provided by wireless network providers (WNPs) available at a particular location-time to support a mobile service delivery. This chapter presents a novel method for the MoSP’s QoS-assurance business process. The method incorporates a location- and time-based QoS-predictions’ service, facilitating the WNP’s selection. The authors explore different business cases for the service deployment. Particularly, they introduce and analyze business viability of QoSIS.net, an enterprise that can provide the QoS-predictions service to MoSPs, Mobile Network Operators (as MoSPs), or directly to their customers (i.e. in B2B/B2C settings). QoSIS.net provides its service based on collaborative-sharing of QoS-information by its users. The authors argue that this service can improve the MoSP’s QoS-assurance process and consequently may increase its revenues, while creating revenues for QoSIS.net.


Author(s):  
Lu Yan

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.


Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2832 ◽  
Author(s):  
Pantaleone Nespoli ◽  
Mattia Zago ◽  
Alberto Huertas Celdrán ◽  
Manuel Gil Pérez ◽  
Félix Gómez Mármol ◽  
...  

Continuous authentication was introduced to propose novel mechanisms to validate users’ identity and address the problems and limitations exposed by traditional techniques. However, this methodology poses several challenges that remain unsolved. In this paper, we present a novel framework, PALOT, that leverages IoT to provide context-aware, continuous and non-intrusive authentication and authorization services. To this end, we propose a formal information system model based on ontologies, representing the main source of knowledge of our framework. Furthermore, to recognize users’ behavioral patterns within the IoT ecosystem, we introduced a new module called “confidence manager”. The module is then integrated into an extended version of our early framework architecture, IoTCAF, which is consequently adapted to include the above-mentioned component. Exhaustive experiments demonstrated the efficacy, feasibility and scalability of the proposed solution.


2011 ◽  
Vol 467-469 ◽  
pp. 2091-2096 ◽  
Author(s):  
Hyun Chul Ahn ◽  
Kyoung Jae Kim

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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