Active in-database processing to support ambient assisted living systems. de Morais O., W.; Lundström, J.; and Wickström, N. Sensors (Basel, Switzerland), 14(8):14765-14785, MDPI, 12, 2014.
Active in-database processing to support ambient assisted living systems [link]Website  abstract   bibtex   
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.;
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 title = {Active in-database processing to support ambient assisted living systems},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {Artificial Intelligence,Assisted Living Facilities/*methods,Confidentiality,Database Management Systems/*instrumentation,Databases, Factual,Humans,Information Storage and Retrieval/*methods,Software},
 pages = {14765-14785},
 volume = {14},
 websites = {http://login.ezproxy.library.ualberta.ca/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=cmedm&AN=25120164&site=ehost-live&scope=site},
 month = {12},
 publisher = {MDPI},
 city = {School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, Halmstad 30118, Sweden. wagner.demorais@hh.se.; School of Information Science, Computer and Electrical Engineering, Halmstad University, Box 823, Halmstad 30},
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 notes = {ID: 25120164; Accession Number: 25120164. Language: English. Date Created: 20140814. Date Completed: 20150916. Update Code: 20150916. Publication Type: Journal Article. Journal ID: 101204366. Publication Model: Electronic. Cited Medium: Internet. NLM ISO Abbr: Sensors (Basel). PubMed Central ID: PMC4179057. Comment: Cites: Int J Med Inform. 2013 Nov;82(11):e232-41. (PMID: 21482182). Cites: IEEE J Biomed Health Inform. 2013 May;17(3):579-90. (PMID: 24592460). Cites: Health Aff (Millwood). 2002 Sep-Oct;21(5):78-89. (PMID: 12224911). Cites: Comput Methods Programs Biomed. 2008 Jul;91(1):55-81. (PMID: 18367286). Cites: J Am Geriatr Soc. 2010 Aug;58(8):1579-86. (PMID: 20646105). Cites: Gerontologist. 2003 Oct;43(5):761-5. (PMID: 14570973). Cites: J Am Med Inform Assoc. 2007 May-Jun;14(3):269-77. (PMID: 17329725). Cites: Yearb Med Inform. 2008;:33-40. (PMID: 18660873). Linking ISSN: 14248220. Subset: IM; Date of Electronic Publication: 2014 Aug 12. ; Original Imprints: Publication: Basel, Switzerland : MDPI, c2000-},
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 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.;},
 bibtype = {article},
 author = {de Morais O., Wagner and Lundström, Jens and Wickström, Nicholas},
 journal = {Sensors (Basel, Switzerland)},
 number = {8}
}
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