Abstract
Smart medical technologies, combine Internet of Things, cloud computing and artificial intelligence technologies, are redefining the family life. With the advent of the era of big data, traditional medical service systems cannot meet the needs of big data processing in the current medical system because of the limited computing resources, slow operation speed and poorly distributed processing capacity. In this chapter, cloud-based smart medical system applying MapReduce distributed processing technology is proposed to solve these problems. A new distributed k-nearest neighbour (kNN) algorithm that combines the Voronoi-inverted grid (VIG) index and the MapReduce programming framework is developed to improve the efficiency of the data processing. Here, VIG is a spatial index, which uses the grid structure and the inverted index based on Voronoi partitioning technology. The results of extensive experimental evaluations indicate the efficiency and scalability of the proposed approach with real and synthetic data sets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li J (2015) Design and implementation of a mobile- health nursing call system based on cloud computing. Xian, China
Ding H, Moodley Y (2012) A mobile-health system to manage Chronic Obstructive Pulmonary Disease patients at home. In: 34th annual international conference of the IEEE EMBS, San Diego, CA, 28 Aug–1 Sept 2012
Ji C, Wang B, Tao S et al (2016) Inverted Voronoi-based kNN query processing with MapReduce. In: Trustcom/BigDataSE/I SPA, IEEE, Tianjin, China, 23–26 Aug 2016
Gao Y, Wang Z, Ji C et al (2017) Design and implementation of a mobile-health call system based on scalable kNN query. In: e-Health networking, applications and services (Healthcom), 2017 IEEE 19th international conference on, IEEE, Dalian, China, 12–15 Oct 2017
Winter A, Stäubert S, Ammon D et al (2018) Smart medical information technology for healthcare (SMITH). Methods Inf Med 57(01):e92–e105
Mc Gregor C (2011) A cloud computing framework for real-time rural and remote service of critical care. In: IEEE symposium on Computer-Based Medical Systems, Bristol, 27–30 June 2011
Lin C, Huang L, Chou S et al (2014) Temporal event tracing on big healthcare data analytics. In: Proceedings of 2014 IEEE international congress on Big Data. IEEE, Anchorage, 27 June–2 July 2014, pp. 281–287
Nkosi M, Mekuria F (2010) Cloud computing for enhanced mobile health applications. In: 2010 IEEE second international conference on Cloud Computing Technology and Science (CloudCom), Indianapolis, 30 Nov–3 Dec 2010
Kayyali B, Knott D, Van Kuiken S (2013) The big-data revolution in US health care. Accelerating value and innovation. Mc Kinsey 2(8):1–13
Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113
The Apache Hadoop Project (2010) http://hadoop.apache.org/core/
Hesabi Z, Sellis T, Liao K (2018) DistClusTree: a framework for distributed stream clustering. In: Australasian Database Conference. https://doi.org/10.1007/978-3-319-92013-9_23
Cary A, Sun Z, Hristidis V et al (2009) Experiences on processing spatial data with MapReduce. In: International conference on Scientific and Statistical Database Management. Springer. https://doi.org/10.1007/978-3-642-02279-1_24
Ji C, Dong T, Li Y et al (2012) Inverted grid-based kNN query processing with MapReduce. In: 2012 Seventh ChinaGrid annual conference, IEEE, Beijing, 20–23 Sept 2012
Chen L, Cox S, Goble C et al (2002) Engineering knowledge for engineering grid applications. In: Proceedings of Euroweb 2002 conference
Akdogan A, Demiryurek U, Banaei-Kashani F et al (2010) Voronoi-based geospatial query processing with MapReduce. In: Cloud computing technology and science (CloudCom), IEEE, Indianapolis, 30 Nov–3 Dec 2010
Gonzalez-Lopez J, Ventura S, Cano A (2018) Distributed nearest neighbor classification for large-scale multi-label data on spark. Future Gener Comput Syst 87:66–82
Li Y, Li Z, Dong M et al (2015) Efficient subspace skyline query based on user preference using MapReduce. Ad Hoc Netw 35:105–115
Choi H, Lee M, Lee K (2012) Distributed high dimensional indexing for k-NN search. J Supercomputing 62(3):1362–1384
Zobel J, Moffat A, Ramamohanarao K (1998) Inverted files versus signature files for text indexing. ACM Trans Database Syst 23(4):453–490
Lu W, Shen Y, Chen S et al (2012) Efficient processing of k nearest neighbor joins using MapReduce. Proc VLDB Endowment 5(10):1016–1027
Pan J, Manocha D (2011) Fast GPU-based locality sensitive hashing for k-nearest neighbor computation. In: Proceedings of the 19th ACM SIGSPATIAL international conference on Advances in Geographic Information Systems, Chicago, Illinois, 1–4 Nov 2011
Stupar A, Michel S, Schenkel R (2010) RankReduce—processing k-nearest neighbor queries on top of MapReduce. In: Workshop on Large-Scale Distributed Systems for Information Retrieval, Geneva, Switzerland
Zhu P, Zhan X, Qiu W (2015) Efficient k-nearest neighbors search in high dimensions using MapReduce. In: 2015 IEEE fifth international conference on Big Data and Cloud Computing (BDCloud), IEEE, Dalian, 26–28 Aug 2015
Welcome to Apache Hadoop!. hadoop.apache.org. Accessed 16 Dec 2015
Sack J, Jorge U (eds) (1999) Handbook of computational geometry. Elsevier
Comap, the consortium for mathematics and its applications, http://www.comap.com. Accessed 26 Oct 2011
Lian X, Chen L (2008) Probabilistic group nearest neighbor queries in uncertain databases. TKDE 20(6):809–824
Acknowledgements
This work is supported by the National Nature Science Foundation of China (61702071 and 61501076), Industrial and educational cooperation and collaborative education project of the ministry of education (201702029010), Science and Technology Public Welfare Institute Fund of Liaoning Province (20170053), the Project of College Students’ Innovative and Entrepreneurial Training Program (2018004, 2018016, 2018044, 2018046, 2018066, 2018125, 2018173, 2018243), the Key Research and Development Program of Liao Ning Province of China (2017104014), the Liao Ning Provincial Ph.D. Start-up Foundation of China (20170520438), the Natural Science Foundation of Liao Ning Province of China (20180551247), the Science and Technology Innovation Fund Project of Dalian of China (2018J12GX049, 2018J13SN088), Dalian Key Laboratory of Smart Medical and Health , Liaoning optoelectronic information technology engineering laboratory.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ji, C., Gao, Y., Wang, Z., Qin, J. (2020). A Distributed Spatial Index on Smart Medical System. In: Chen, F., García-Betances, R., Chen, L., Cabrera-Umpiérrez, M., Nugent, C. (eds) Smart Assisted Living. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-25590-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-25590-9_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-25589-3
Online ISBN: 978-3-030-25590-9
eBook Packages: Computer ScienceComputer Science (R0)