Knowledge Reactor: A Contextual Computing Work in Progress for Eldercare
The world-wide population of people over 60 years
of age is growing rapidly. The explosion is placing increasingly
onerous demands on individual families, multiple industries and
entire countries. Current, human-intensive approaches to eldercare
are not sustainable, but IoT and AI technologies can help. The
Knowledge Reactor (KR) is a contextual, data fusion engine built to
address this and other similar problems. It fuses and centralizes IoT
and System of Record/Engagement data into a reactive knowledge
graph. Cognitive applications and services are constructed with its
multiagent architecture. The KR can scale-up and scaledown, because
it exploits container-based, horizontally scalable services for graph
store (JanusGraph) and pub-sub (Kafka) technologies. While the KR
can be applied to many domains that require IoT and AI technologies,
this paper describes how the KR specifically supports the challenging
domain of cognitive eldercare. Rule- and machine learning-based
analytics infer activities of daily living from IoT sensor readings. KR
scalability, adaptability, flexibility and usability are demonstrated.
[1] A. Newell, “Some problems of basic organization in problem-solving
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[2] “Janusgraph.” (Online). Available: http://janusgraph.org.
[3] “Kafka.” (Online). Available: http://kafka.apache.org.
[4] “Watson IoT.” (Online). Available: http://www.ibm.com/Watson/IoT.
[5] “Docker.” (Online). Available: https://www.docker.com.
[6] “Tinkerpop3.” (Online). Available: http://tinkerpop.apache.org.
[7] “Node-red.” (Online). Available: http://nodered.org.
[8] “Spark.” (Online). Available: https://spark.apache.org.
[9] “Jupyter.” [Online]. Available: http://jupyter.org
[10] “Rxjava.” (Online). Available: https://github.com/ReactiveX/RxJava.
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w3.org/RDF.
[12] “Web ontology language.” (Online). Available: hhttps://www.w3.org/
OWL.
[13] B. Balaji, A. Bhattacharya, G. Fierro, J. Gao, J. Gluck, D. Hong,
A. Johansen, J. Koh, J. Ploennigs, Y. Agarwal, M. Berges, D. Culler,
R. Gupta, M. B. Kjærgaard, M. Srivastava, and K. Whitehouse,
“Brick: Towards a unified metadata schema for buildings,” in
Proceedings of the 3rd ACM International Conference on Systems
for Energy-Efficient Built Environments, ser. BuildSys ’16. New
York, NY, USA: ACM, 2016, pp. 41–50. (Online). Available:
http://doi.acm.org/10.1145/2993422.2993577.
[14] “Sensor model language.” (Online). Available: http://www.
opengeospatial.org/standards/sensorml#schemas.
[15] “W3C semantic sensor net ontology.” (Online). Available: http://www.
w3.org/2005/Incubator/ssn/wiki/Semantic Sensor Net Ontology.
[16] “Review of sensor and observations ontologies.”
(Online). Available: http://www.w3.org/2005/Incubator/ssn/wiki/
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ontologydesignpatterns.org/wiki/Submissions:ContentOPs.
[18] “Quantities unitsdimensions and types.” (Online). Available: http:
//www.qudt.org.
[19] H. Lee and J. Kwon, “Ontology model-based situation and
socially-aware health care service in a smart home environment,” vol. 7,
pp. 239–250, 09 2013.
[20] “Indoorgml.” (Online). Available: http://docs.opengeospatial.org/is/
14-005r4/14-005r4.html#12.
[21] “Citygml.” (Online). Available: http://www.opengeospatial.org/
standards/citygml.
[22] T. Gu, X. H. Wang, H. K. Pung, and D. Q. Zhang, “An ontology-based
context model in intelligent environments,” in In Proceedings of
Communication Networks and Distributed Systems Modeling and
Simulation Conference, 2004, pp. 270–275.
[23] M. Miraoui, S. El-etriby, C. Tadj, and A. Abid, “Ontology-based context
modeling for a smart living room,” 10 2015.
[24] “Daml+oil.” (Online). Available: http://www.daml.org/2001/03/daml+
oil-index.html.
[25] S. Tazari, “universaal/ontology.” (Online). Available: https://github.com/
universAAL/ontology/wiki.
[26] “Sample decision ontology.”
[27] “Relationship: A vocabulary for describing relationships between
people.” (Online). Available: http://vocab.org/relationship/.
[28] “Avamere.” (Online). Available: https://www.avamere.com/.
[29] “Smartthings.” (Online). Available: https://www.smartthings.com.
[30] “Netatmo.” (Online). Available: https://www.netatmo.com.
[31] I. Ihianle, U. Naeem, and S. Islam, “Ontology-driven activity recognition
from patterns of object use,” pp. 654–657, 09 2017.
[32] F. J. Ordonez, P. de Toledo, and A. Sanchis, “Activity recognition
using hybrid generative/discriminative models on home environments
using binary sensors,” Sensors, vol. 13, no. 5, pp. 5460–5477, 2013.
(Online). Available: http://www.mdpi.com/1424-8220/13/5/5460.
[1] A. Newell, “Some problems of basic organization in problem-solving
programs,” RAND, Tech. Rep. RM-3283-PR, 1962.
[2] “Janusgraph.” (Online). Available: http://janusgraph.org.
[3] “Kafka.” (Online). Available: http://kafka.apache.org.
[4] “Watson IoT.” (Online). Available: http://www.ibm.com/Watson/IoT.
[5] “Docker.” (Online). Available: https://www.docker.com.
[6] “Tinkerpop3.” (Online). Available: http://tinkerpop.apache.org.
[7] “Node-red.” (Online). Available: http://nodered.org.
[8] “Spark.” (Online). Available: https://spark.apache.org.
[9] “Jupyter.” [Online]. Available: http://jupyter.org
[10] “Rxjava.” (Online). Available: https://github.com/ReactiveX/RxJava.
[11] “Resource description framework.” (Online). Available: https://www.
w3.org/RDF.
[12] “Web ontology language.” (Online). Available: hhttps://www.w3.org/
OWL.
[13] B. Balaji, A. Bhattacharya, G. Fierro, J. Gao, J. Gluck, D. Hong,
A. Johansen, J. Koh, J. Ploennigs, Y. Agarwal, M. Berges, D. Culler,
R. Gupta, M. B. Kjærgaard, M. Srivastava, and K. Whitehouse,
“Brick: Towards a unified metadata schema for buildings,” in
Proceedings of the 3rd ACM International Conference on Systems
for Energy-Efficient Built Environments, ser. BuildSys ’16. New
York, NY, USA: ACM, 2016, pp. 41–50. (Online). Available:
http://doi.acm.org/10.1145/2993422.2993577.
[14] “Sensor model language.” (Online). Available: http://www.
opengeospatial.org/standards/sensorml#schemas.
[15] “W3C semantic sensor net ontology.” (Online). Available: http://www.
w3.org/2005/Incubator/ssn/wiki/Semantic Sensor Net Ontology.
[16] “Review of sensor and observations ontologies.”
(Online). Available: http://www.w3.org/2005/Incubator/ssn/wiki/
Review of Sensor and Observations Ontologies.
[17] “Ontology design patterns.” (Online). Available: http://
ontologydesignpatterns.org/wiki/Submissions:ContentOPs.
[18] “Quantities unitsdimensions and types.” (Online). Available: http:
//www.qudt.org.
[19] H. Lee and J. Kwon, “Ontology model-based situation and
socially-aware health care service in a smart home environment,” vol. 7,
pp. 239–250, 09 2013.
[20] “Indoorgml.” (Online). Available: http://docs.opengeospatial.org/is/
14-005r4/14-005r4.html#12.
[21] “Citygml.” (Online). Available: http://www.opengeospatial.org/
standards/citygml.
[22] T. Gu, X. H. Wang, H. K. Pung, and D. Q. Zhang, “An ontology-based
context model in intelligent environments,” in In Proceedings of
Communication Networks and Distributed Systems Modeling and
Simulation Conference, 2004, pp. 270–275.
[23] M. Miraoui, S. El-etriby, C. Tadj, and A. Abid, “Ontology-based context
modeling for a smart living room,” 10 2015.
[24] “Daml+oil.” (Online). Available: http://www.daml.org/2001/03/daml+
oil-index.html.
[25] S. Tazari, “universaal/ontology.” (Online). Available: https://github.com/
universAAL/ontology/wiki.
[26] “Sample decision ontology.”
[27] “Relationship: A vocabulary for describing relationships between
people.” (Online). Available: http://vocab.org/relationship/.
[28] “Avamere.” (Online). Available: https://www.avamere.com/.
[29] “Smartthings.” (Online). Available: https://www.smartthings.com.
[30] “Netatmo.” (Online). Available: https://www.netatmo.com.
[31] I. Ihianle, U. Naeem, and S. Islam, “Ontology-driven activity recognition
from patterns of object use,” pp. 654–657, 09 2017.
[32] F. J. Ordonez, P. de Toledo, and A. Sanchis, “Activity recognition
using hybrid generative/discriminative models on home environments
using binary sensors,” Sensors, vol. 13, no. 5, pp. 5460–5477, 2013.
(Online). Available: http://www.mdpi.com/1424-8220/13/5/5460.
@article{"International Journal of Electrical, Electronic and Communication Sciences:77268", author = "Scott N. Gerard and Aliza Heching and Susann M. Keohane and Samuel S. Adams", title = "Knowledge Reactor: A Contextual Computing Work in Progress for Eldercare", abstract = "The world-wide population of people over 60 years
of age is growing rapidly. The explosion is placing increasingly
onerous demands on individual families, multiple industries and
entire countries. Current, human-intensive approaches to eldercare
are not sustainable, but IoT and AI technologies can help. The
Knowledge Reactor (KR) is a contextual, data fusion engine built to
address this and other similar problems. It fuses and centralizes IoT
and System of Record/Engagement data into a reactive knowledge
graph. Cognitive applications and services are constructed with its
multiagent architecture. The KR can scale-up and scaledown, because
it exploits container-based, horizontally scalable services for graph
store (JanusGraph) and pub-sub (Kafka) technologies. While the KR
can be applied to many domains that require IoT and AI technologies,
this paper describes how the KR specifically supports the challenging
domain of cognitive eldercare. Rule- and machine learning-based
analytics infer activities of daily living from IoT sensor readings. KR
scalability, adaptability, flexibility and usability are demonstrated.", keywords = "Ambient sensing, AI, artificial intelligence, eldercare,
IoT, internet of things, knowledge graph.", volume = "12", number = "4", pages = "291-8", }
