Uncertain context data management in dynamic mobile environments
PBN-AR
Instytucja
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej (Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie)
Informacje podstawowe
Główny język publikacji
EN
Czasopismo
Future Generation Computer Systems-The International Journal of Grid Computing and eScience
ISSN
0167-739X
EISSN
1872-7115
Wydawca
Elsevier Science BV
DOI
Rok publikacji
2016
Numer zeszytu
Strony od-do
110--124
Numer tomu
66
Link do pełnego tekstu
Identyfikator DOI
Liczba arkuszy
1
Autorzy
(liczba autorów: 2)
Słowa kluczowe
EN
context-aware systems
big data management
rule-based models
uncertainty handling
mobile systems
Streszczenia
Język
EN
Treść
Building systems that acquire, process and reason with context data is a major challenge. Model updates and modifications are required for the mobile context-aware systems. Additionally, the nature of the sensor-based systems implies that the data required for the reasoning is not always available nor it is certain. Finally, the amount of context data can be significant and can grow fast, constantly being processed and interpreted under soft real-time constraints. Such characteristics make it a case for a challenging big data application. In this paper we argue, that mobile context-aware systems require specific methods to process big data related to context, at the same time being able to handle uncertainty and dynamics of this data. We identify and define main requirements and challenges for developing such systems. Then we discuss how these challenges were effectively addressed in the KnowMe project. In our solution, the acquisition of context data is made with the use of the AWARE platform. We extended it with techniques that can minimise the power consumption as well as conserve storage on a mobile device. The data can then be used to build rule models that can express user preferences and habits. We handle the missing or ambiguous data with number of uncertainty management techniques. Reasoning with rule models is provided by a rule engine developed for mobile platforms. Finally, we demonstrate how our tools can be used to visualise the stored data and simulate the operation of the system in a testing environment. © 2016 Elsevier B.V. All rights reserved.
Cechy publikacji
original article
peer-reviewed
Inne
System-identifier
idp:100526
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