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Author de Morais, Wagner O. ♦ Lundström, Jens ♦ Wickström, Nicholas
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Multidisciplinary Digital Publishing Institute
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Computer science, information & general works ♦ Library & information sciences ♦ Philosophy & psychology ♦ Psychology ♦ Natural sciences & mathematics ♦ Mathematics ♦ Life sciences; biology ♦ Physiology & related subjects ♦ Natural history of organisms ♦ Technology ♦ Medicine & health ♦ Human physiology ♦ Incidence & prevention of disease ♦ Diseases
Subject Domain (in MeSH) Eukaryota ♦ Organisms ♦ Behavioral Disciplines and Activities ♦ Psychiatry and Psychology ♦ Mathematical Concepts ♦ Biological Sciences ♦ Non-Medical Public and Private Facilities ♦ Technology and Food and Beverages ♦ Information Science ♦ Information Science
Subject Keyword Discipline Biotechnology ♦ Assisted Living Facilities ♦ Methods ♦ Database Management Systems ♦ Instrumentation ♦ Information Storage And Retrieval ♦ Artificial Intelligence ♦ Confidentiality ♦ Databases, Factual ♦ Humans ♦ Software ♦ Journal Article
Abstract As an alternative to the existing software architectures that underpin the development of smart homes and ambient assisted living (AAL) systems, this work presents a database-centric architecture that takes advantage of active databases and in-database processing. Current platforms supporting AAL systems use database management systems (DBMSs) exclusively for data storage. Active databases employ database triggers to detect and react to events taking place inside or outside of the database. DBMSs can be extended with stored procedures and functions that enable in-database processing. This means that the data processing is integrated and performed within the DBMS. The feasibility and flexibility of the proposed approach were demonstrated with the implementation of three distinct AAL services. The active database was used to detect bed-exits and to discover common room transitions and deviations during the night. In-database machine learning methods were used to model early night behaviors. Consequently, active in-database processing avoids transferring sensitive data outside the database, and this improves performance, security and privacy. Furthermore, centralizing the computation into the DBMS facilitates code reuse, adaptation and maintenance. These are important system properties that take into account the evolving heterogeneity of users, their needs and the devices that are characteristic of smart homes and AAL systems. Therefore, DBMSs can provide capabilities to address requirements for scalability, security, privacy, dependability and personalization in applications of smart environments in healthcare.
Description Country affiliation: Sweden
Author Affiliation: de Morais WO ( School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, Halmstad 30118, Sweden. wagner.demorais@hh.se.); Lundström J ( School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, Halmstad 30118, Sweden. jens.lundstrom@hh.se.); Wickström N ( School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, Halmstad 30118, Sweden. nicholas.wickstrom@hh.se.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-08-12
Publisher Place Switzerland
e-ISSN 14248220
Journal Sensors
Volume Number 14
Issue Number 8


Source: WHO-Global Index Medicus