Patterns of Behaviour Mediated by Cognitive Scripts and Emotional Attitudes - Context-Aware Engineering of Data Mining Systems

2006 ◽  
Author(s):  
R Khosla ◽  
C Lai ◽  
B Chowdhury ◽  
Q Li
Keyword(s):  
Data Mining ◽  
Context Aware ◽  
Cognitive Scripts

A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

2021 ◽  
Author(s):  
Zainab Al-Zanbouri
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Web Services ◽  
Recommender System ◽  
Energy Efficient ◽  
Contextual Information ◽  
Three Dimensions ◽  
Context Aware ◽  
Rating Data ◽  
Recommendation Accuracy

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.

scholarly journals Context-Aware Ubiquitous Data Mining Based Agent Model for Intersection Safety

2005 ◽  
pp. 61-70 ◽  
Author(s):  
Flora Dilys Salim ◽  
Shonali Krishnaswamy ◽  
Seng Wai Loke ◽  
Andry Rakotonirainy
Keyword(s):  
Data Mining ◽  
Context Aware ◽  
Agent Model ◽  
Intersection Safety

Context Quality Impact in Context-Aware Data Mining for Predicting Soil Moisture

2020 ◽  
Vol 51 (7) ◽  
pp. 668-684 ◽  
Author(s):  
Anca Avram ◽  
Oliviu Matei ◽  
Camelia-M. Pintea ◽  
Petrica C. Pop
Keyword(s):  
Data Mining ◽  
Soil Moisture ◽  
Context Aware

scholarly journals Special issue on “context-aware data mining (CADM)”

2011 ◽  
Vol 28 (2) ◽  
pp. 249-250
Author(s):  
Chiara Renso ◽  
Vania Bogorny ◽  
Hui Xiong
Keyword(s):  
Data Mining ◽  
Context Aware ◽  
Special Issue

Building Context-Aware E-Commerce Systems

2010 ◽  
pp. 1021-1029
Author(s):  
Anahit Martirosyan ◽  
Thomas Tran ◽  
Azzedine Boukerche
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Context Aware ◽  
Work Context ◽  
Privacy Issue ◽  
Data Mining Techniques ◽  
Proposed Model ◽  
Use Context ◽  
Using Data ◽  
Different Levels

Context is any information/knowledge about an application and user that can be used by an e-commerce system to provide efficient services to the users of the system. In this article, we propose to extend usage of context as compared to previously designed context-aware e-commerce systems. While in previous work, context was mainly considered for mobile e-commerce systems, we propose to build and use context for e-commerce systems in general. The context is employed to tailor an e-commerce application to the preferences and needs of users and provide insights into purchasing activities of users and particular e-commerce stores by means of using Data Mining techniques. This article proposes a model of context that includes micro-, macro- and domain contexts that constitute knowledge about the application and its user on different levels of granularity. The article also proposes a technique for extracting groups in social networks. This knowledge is part of macro-context in the proposed model of context. Moreover, the article discusses some of the challenges of incorporating context with e-commerce systems, emphasizing on the privacy issue, with an ultimate goal of developing intelligent e-commerce systems.

Survey on Data Mining Tools Using Decision Analysis Resolution Method Applied for Context-Aware Data Mining Scenarios

2021 ◽  
pp. 544-554
Author(s):  
Anca Avram ◽  
Oliviu Matei ◽  
Ionela Chereja
Keyword(s):  
Data Mining ◽  
Decision Analysis ◽  
Context Aware ◽  
Resolution Method ◽  
Mining Tools
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