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Encyclopedia of Social Network Analysis and Mining pp 2621–2634Cite as

Social Internet of Mobile Things and Decision Support Tools

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Synonyms

SIoT; Smart Social Objects; Social network of IoT devices; Social Virtual Objects

Glossary

Decision support system:

According to Geoffrion’s definition, a DSS has six characteristics (Geoffrion 1983): (1) Is designed to solve ill- or semi-structured problems, i.e., where objectives cannot be fully or precisely defined; (2) Has an interface that is both powerful and easy to use; (3) Enables the user to combine models and data in a flexible manner; (4) Helps the user explore the solution space (the options available to them) by using the models in the system to generate a series of feasible alternatives; (5) Supports a variety of decision-making styles, and easily adapted to provide new capabilities as the needs of the user evolve; (6) Allows an interactive and recursive process in which decision-making proceeds by multiple passes, perhaps involving different routes, rather than a single linear path

Internet of Things (IoT):

Smart-connected things with the ability (1) to be...

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References

  • Abrol S, Rajasekar G, Khan L (2015) Real-time stream data analytics for multi-purpose social media applications. Inf Reuse Integ (IRI):25–30

    Google Scholar 

  • Atzori L, Iera A, Morabito G, Nitti M (2012) The social internet of things (siot)–when social networks meet the internet of things: Concept, architecture and network characterization. Comput Netw 56(16):3594–3608

    Article  Google Scholar 

  • Babu P. apache licenced open source. Available via https://github.com/P7h/StormTweetsSentimentD3Viz. Cited 18 Aug 2015

  • Barker A, Hemert JV (2007) Scientific workflow: a survey and research directions. In: Parallel processing and applied mathematics. Springer, Heidelberg, pp 746–753

    Google Scholar 

  • Biswas AR (2014) IoT and cloud convergence: Opportunities and challenges. In: 2014 I.E. World Forum on Internet of Things (WF-IoT). IEEE, Piscataway, pp 375–376

    Chapter  Google Scholar 

  • Cha S, Wachowicz M (2015) Towards real-time streaming analytics based on cloud computing. Inter J Big Data (IJBD) 2:28–40

    Google Scholar 

  • Chua SL, Marsland S, Guesgen HW (2009) Behaviour recognition from sensory streams in smart environments. In: Australasian conference on artificial intelligence. Springer, New York/London 5866, p 666–675

    Google Scholar 

  • Compute Canada. Available via https://www.computecanada.ca/. Cited 20 Aug 2015

  • Dubba KSR, Cohn AG, Hogg DC (2010) Event model learning from complex videos using ILP. In: Proceedings ECAI. IOS Press, 215,p 93–98

    Google Scholar 

  • Ericsson Research blog. Available via http://www.ericsson.com/research-blog/data-knowledge/apache-storm-vs-spark-streaming/. Cited 8 Sept 2015

  • Fiske AP (1992) The four elementary forms of sociality: framework for a unified theory of social relations. Psychol Rev 99:689–723

    Article  Google Scholar 

  • Gartner G, Huang H (2016) Progress in Location-Based Services 2016. Springer Heidelberg

    Google Scholar 

  • Geoffrion AM (1983) Can OR/MS evolve fast enough? “Source for six essential characteristics of DSS”. Interfaces 13:10

    Article  Google Scholar 

  • Holmquist LE, Mattern F, Schiele B, Alahuhta P, Beigl M, Gellersen W (2001) Smart-its friends: a technique for users to easily establish connections between smart artefacts. In: International conference on ubiquitous computing. Springer, Berlin/Heidelberg

    Google Scholar 

  • Hua Y, He W, Liu X, Feng D (2015) SmartEye: Realreal-time and efficient cloud image sharing for disaster environments. In: The proceedings of IEEE conference on computer communications. IEEE, Piscataway, pp 1616–1624

    Google Scholar 

  • Kardeby V (2011) Automatic sensor clustering: connectivity for the internet of things. Licentiate thesis, Mid Sweden University, Department of Information Technology and Media, Sundsvall

    Google Scholar 

  • Lee I. Software system. Available via http://www.cis.upenn.edu/~lee/07cis505/Lec/lec-ch1-DistSys-v4.pdf. Cited 4 Sept 2015

  • Li X, Lu R, Liang X, Shen X, Chen J, Lin X (2011) Smart community: an internet of things applications. IEEE Commun Mag 49(11):68–75

    Article  Google Scholar 

  • Lin F, Cohen W (2010) Power iteration clustering. In: Proceedings of the 27th international conference on machine learning (ICML-10). p 655–662

    Google Scholar 

  • Lovász L. (1993) Random walks on graphs. Combinatorics, Paul erdos is eighty 2: 1–46

    Google Scholar 

  • Magdy A, Alsrabi L, Harthi SA, Muslesh M, Ghanem TM, Ghani S, Mokbel MF (2015) Demonstration of Taghreed: a system for querying, analyzing, and visualizing geotagged microblogs. Data Eng (ICDE) 1:1416–1419

    Google Scholar 

  • Magdy A, Aly A, Mokbel M, Elnikety S (2014) Mars: realreal-time spatio-temporal queries on microblogs. Data Engineering (ICDE). 1238–1241

    Google Scholar 

  • Nadler B, Galun M (2006) Fundamental limitations of spectral clustering. In: Advances in neural information processing systems. 1017–1024

    Google Scholar 

  • Noll M (2013) Running Hadoop on Ubuntu Linux (Multi-Node Cluster). Retrieved from http://cs.smith.edu/dftwiki/images/MichaelNollHadoopTutorial2.pdf on February 2017

  • Osman A. EI-Refaey M, Elnaggar A (2013) Towards real-time analytics in the cloud. In: The proceedings of the 9th IEEE World congress on services. p. 428–435

    Google Scholar 

  • Raina I, Gujar S, Shah P, Desai A, Bodkhe B (2014) Twitter sentiment analysis using apache storm. In: the international journal of recent technology and engineering (IJRTE) 3(5): 23–26

    Google Scholar 

  • Riedy J, Bader D (2013) Multithreaded community monitoring for massive streaming graph data. In: 2013 I.E. 27th international parallel and distributed processing symposium workshops & PhD forum. 1646–1655

    Google Scholar 

  • Solaimani M, Iftekhar M, Khan L, Thuraisingham B (2014) Spark-based anomaly detection over multi-source VMware performance data in real-time. In: IEEE symposium on computational intelligence in cyber security (CICS) 1–8

    Google Scholar 

  • Song M, Kim MC (2013) RT2M: Real-time Twitter trend mining system. In: the proceedings of international conference on Social Intelligence and Technology 64–71

    Google Scholar 

  • Tsai CW, Lai CF, Chiang MC, Yang LT (2014) Data mining for internet of things: a survey. IEEE Commun Surv Tutorials 15(1):77. First Quarter

    Article  Google Scholar 

  • Thang ND et al (2013) Deflation-based power iteration clustering. Appl Intell 39(2):367–385

    Article  Google Scholar 

  • Thuraisingham B (2014) Secure sensor semantic web and information fusion. Texas University, Dallas Richardson

    Book  Google Scholar 

  • Tran L, Wachowicz M (2017) Spectral Clustering for discovering location patterns of mobile IoT devices. Transactions in GIS, submitted

    Google Scholar 

  • Veen JS, Waaij B, Lazovik E, Wijbrandi W, Meijer RJ (2015) Dynamically scaling apache storm for the analysis of streaming data. In: the proceedings of 1st international conference on big data computing service and applications 154–161

    Google Scholar 

  • Welbourne E, Cole G, Gould K, Rector K, Raymer S, Balazinska M, Borriello G (2009) Building the internet of things using RFID: The RFID ecosystem experience. In: the journal of IEEE internet 13(3)

    Google Scholar 

  • Xu K, Qu Y, Yang K (2016) A tutorial on the internet of things: from a heterogeneous network integration perspective. IEEE Network, 30(2):102–8

    Google Scholar 

  • Yang G, Xie L, Mantysalo M, Zhou X, Pang Z, Da Xu L, Kao-Walter S, Chen Q, Zheng L (2014) A health –IoT platform based on the integration of intelligent packaging, unobtrusive Bio-sensor, and intelligent medicine box. IEEE Trans Ind Inf 10(4):2180–2191

    Article  Google Scholar 

  • Yang X, Ghoting A, Ruan Y, Parthasarathy S (2012) A framework for summarizing and analyzing twitter feeds. In: the proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining 370–378

    Google Scholar 

  • Ye W, et al. (2016) FUSE: full spectral clustering. In: 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

    Google Scholar 

  • Zheng VW, Zheng Y, Xie X, Yang Q (2010) Collaborative location and active recommendations with GPS history data. In: Proceedings of the 19th international conference on World Wide Web 1029–1038

    Google Scholar 

Recommended Reading

  • Granville V (2014) Developing analytical talent: becoming a data scientist. Wiley Press, Hoboken

    Google Scholar 

  • Li S, Da Xu L, Zhao S (2015) The internet of things: a survey. Inf Syst Front 17(2):243–259

    Article  Google Scholar 

  • Vermesan O, Friess P, Guillemin P, Gusmeroli S, Sundmaeker H, Bassi A, Jubert IS, Mazura M, Harrison M, Eisenhauer M, Doody P. Internet of things strategic research roadmap. Vermesan O, Friess P, Guillemin P, Gusmeroli S, Sundmaeker H, Bassi A, et al. (2011) Internet of things: global technological and societal trends 1: 9–52

    Google Scholar 

  • Memon N, Xu JJ, Hicks DL, Chen H (2010) Data mining for social network datadata. Annals of information systems, vol 12. Springer, New York/London, pp 47–74

    Google Scholar 

  • Yu S, Lin X, Misic J, Shen X (2015) Networking for big data. CRC Press, Boca Raton

    Google Scholar 

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Acknowledgments

This research has been funded by the NSERC/Cisco Industrial Research Chair in real-time mobility analytics and partially supported by H2020 project BASMATI (GA 723131).

Author information

Authors and Affiliations

  1. University of New Brunswick, People in Motion Lab, Fredericton, Canada

    Monica Wachowicz & Sangwhan Cha

  2. ISTI Institute of National Research Council, Pisa, Italy

    Chiara Renso

Authors
  1. Monica Wachowicz
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  2. Sangwhan Cha
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  3. Chiara Renso
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Corresponding author

Correspondence to Chiara Renso .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

Section Editor information

  1. Nanyang Technological University (NTU), Singapore, Singapore

    Gao Cong

  2. LinkedIn, Mountain View, CA, United States

    Bee-Chung Chen

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Wachowicz, M., Cha, S., Renso, C. (2018). Social Internet of Mobile Things and Decision Support Tools. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_327

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