A Framework for Imprecise Context Reasoning

Author(s):  
Christos B. Anagnostopoulos ◽  
Panagiotis Pasias ◽  
Stathes Hadjiefthymiades
Keyword(s):  
2019 ◽  
Vol 111 ◽  
pp. 122-130
Author(s):  
Roger S. Machado ◽  
Ricardo B. Almeida ◽  
Ana Marilza Pernas ◽  
Adenauer C. Yamin

Author(s):  
Siyuan Liu ◽  
Shaojie Tang ◽  
Jiangchuan Zheng ◽  
Lionel M. Ni

Learning human mobility behaviors from location-sensing data are crucial to mobility data mining because of its potential to address a range of analytical purposes in mobile context reasoning, including exploration, inference, and prediction. However, existing approaches suffer from two practical problems: temporal and spatial sparsity. To address these shortcomings, we present two unsupervised learning methods to model the mobility behaviors of multiple users (i.e., a population), considering efficiency and accuracy. These methods intelligently overcome the sparsity in individual data by seeking temporal commonality among users’ heterogeneous location behaviors. The advantages of our models are highlighted through experiments on several real-world mobility data sets, which also show how our methods can realize the three analytical purposes in a unified manner.


2021 ◽  
Vol 17 (4) ◽  
pp. 41-59
Author(s):  
Deeba K. ◽  
Saravanaguru R. A. K.

Today, IoT-related applications play an important role in scientific world development. Context reasoning emphasizes the perception of various contexts by means of collection of IoT data which includes context-aware decision making. Context-aware computing is used to improve the abilities of smart devices and is increased by smart applications. In this paper, context-aware for the internet of things middleware (CAIM) architecture is used for developing a rule-based system using CA-RETE algorithm. The objective of context-aware systems are concentrated on 1) context reasoning methodologies and analyzing how the technologies will involve enhancing the high-level context data, 2) framework of context reasoning system, 3) implementation of CA-RETE algorithm for predicting gestational diabetes mellitus in healthcare applications.


2017 ◽  
Vol 13 (3) ◽  
pp. 39-62 ◽  
Author(s):  
Fatma Ellouze ◽  
Mohamed Amine Chaâbane ◽  
Eric Andonoff ◽  
Rafik Bouaziz

Collaborative process (CP) flexibility is an active research area in the field of business process management (BPM). It deals with both foreseen and unforeseen changes in the environment where CPs operate. In the literature, the version-based approach is largely used to cope with CP flexibility. However, BPM practitioners from various organizations can encounter some difficulties in a multi-version setting, of which when they must select the most appropriate CP version to be executed. Therefore, the aim of this article is to offer a solution to help them in this delicate task by proposing an ontology-based approach to model and query the context of versions of CP. More precisely, the authors recommend a new ontology, entitled BPM-Context-Onto, and a framework, entitled Onto-VP2M-Framework, providing support for (1) context version modeling in the BPM area, and (2) context-based querying exploiting reasoning mechanisms of the proposed ontology. The evaluation of the recommended framework shows that combining ontology with context reasoning is a promising idea in the BPM area. This novel framework has been examined within a real case study, namely the Subsea Pipeline CP.


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