nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Evaluating Decision Analytics from Mobile Big Data using Rough Set Based Ant Colony

verfasst von : Soumya Banerjee, Youakim Badr

Erschienen in: Mobile Big Data

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The significance of mobile centric data from various sensors, mobile phones and from other corresponding sources has already been identified across different sections of applications from commercial services to decision making applications. However, uncertainty and volume of mobile big data solicits appropriate analytics and decision making ability to be inferred from such data sources. Primarily, the data source and analytics to be chosen from the perspective of adaptive yet intelligent technique. The proposed chapter elaborates such solution while deploying rough set, which is capable of handling imprecise and uncertain contexts of mobile big data. In addition to, ant colony pheromone deposition and evaporation process assists in optimal feature selection mechanism for resolved decisions. The proposed model is supported by case study of hazards event and the information of the event is propagated through mobile data derived from social network. The data is represented as social tweets and posts. It has been analyzed with rough set based ant colony.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Electromagnetic Interference and Discontinuity Effects of Interconnections on Big Data Performance of Integrated Circuits

Nächstes Kapitel Energy-Aware Issues for Handling Big Data in Mobile Cloud Computing

Cheng, S., Z, Q.Q.Q. : Big data analytic with swarm intelligence. Ind. Manag. Data Syst. (2016)

Chatzimilioudis, G., Konstantinidis, A., Laoudias, C., Zeinalipour-Yazti, D.: Crowdsourcing with smartphones. IEEE Int. Comput. 16(5), 36–44 (2012)CrossRef

Chen, Y., Miao, D., Wang, R.: A rough set approach to feature selection based on ant colony optimization. Pattern Recogn. Lett. 31, 226–233 (2010)CrossRef

Cheng, S., Liu, B., Ting, T.O., Qin, Q., Shi, Y., Huang, K.: Survey on data science with population-based algorithms. Big Data Anal. 1(1), 3 (2016)CrossRef

Choudhury De, M., Kiciman, E., Dredze, M., Coppersmith, G., Kumar, M.: Discovering shifts to suicidal ideation from mental health content in social media. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI ’16, pp. 2098–2110 (2016)

Cisco: Cisco visual networking index: global mobile data traffic forecast update 2015–2020, White Paper (2016)

Cooper, G., Yeager,V., Burkle, F., Subbarao, I.: Twitter as a potential disaster risk reduction tool. part 1: introduction, terminology, research and operational applications. PLoS Curr. Disast. (2015)

Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. Commun. ACM 51, 107–113 (2008)CrossRef

Decuyper, A., Rutherford, A., Wadhwa, A., Bauer, J., Krings, G., Gutierrez, T., Blondel, V.D., Luengo-Oroz, M.A.: Estimating food consumption and poverty indices with mobile phone data. CoRR. https://doi.org/abs/1412.2595 (2014)

10.

Donoho, D.: 50 Years of Data Science. Technical report, Stanford University, (2015)

11.

Eagle, N., Pentland, A., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Nat. Acad. Sci. 106(36), 15274–15278 (2009)CrossRef

12.

Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRef

13.

Houston, J.B., Hawthorne, J., Perreault, M.F., Park, E.H., Goldstein Hode, M., Halliwell, M.R., Turner McGowen, S.E., Davis, R., Vaid, S., McElderry, J.A., Griffith, S.A.: Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters 39(1), 1–22 (2015)CrossRef

14.

Iglesia de la, B.: Evolutionary computation for feature selection in classification problems. Wiley Interdis. Rev. Data Mining Knowl. Disc. 3, 381–407 (2013)

15.

Jia, X., Tang, Z., Liao, W., Shang, L.: On an optimization representation of decision-theoretic rough set model. Int. J. Approx. Reason. 55(1), 156–166 (2014)CrossRefMathSciNetMATH

16.

Jin, X., Wah, B.W., Cheng, X., Wang, Y.: Significance and challenges of big data research. Big Data Res. 2(2), 59–64 (2015)CrossRef

17.

LeCun, Y., Bengio, Y.: H.G: Deep learning. Nature 521(4), 36–44 (2016)

18.

Li, T., Lu, J., Luis, M.: Preface: intelligent techniques for data science. Int. J. Intel. Syst. 30(8), 851–853 (2015)CrossRef

19.

Li, S., Li, T., Zhang, Z., Chen, H., Zhang, J.: Parallel computing of approximations in dominance-based rough sets approach. Know. Based Syst. 87, 102–111 (2015)CrossRef

20.

Luo, C., Li, T.: Incremental Three-Way Decisions with Incomplete Information, pp. 128–135. Springer International Publishing, (2014)

21.

Nokia: https://research.nokia.com/mdc, Nokia Research

22.

Otero, F.E., Freitas, A.A.: Improving the interpretability of classification rules discovered by an ant colony algorithm. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO ’13, pp. 73–80 (2013)

23.

Otero, F.E., Freitas, A.A., Johnson, C.G.: Inducing decision trees with an ant colony optimization algorithm. Applied Soft Computing 12(11), 3615–3626 (2012)CrossRef

24.

Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2, 1–135 (2008)CrossRef

25.

Pawlak, Z.: Rough Sets-Theoretical Aspects of Reasoning About Data. Kluwer, Boston, London, Dordrecht (1991)MATH

26.

Peralta, D., Rio, S., Gallego, S.R., Triguero, J.B.I., Herrera, F.: Evolutionary feature selection for big data classification: a mapreduce approach. Math. Prob, Eng (2015)

27.

Rio, S., Lopez, V., Benitez, J., Herrera, F.: A mapreduce approach to address big data classification problems based on the fusion of linguistic fuzzy rules. Int. J. Comput. Intell. Syst. 8, 422–437 (2015)CrossRef

28.

Sevenich, M., Hong, S., van Rest, O., Wu, Z., Banerjee, J., Chafi, H.: Using domain-specific languages for analytic graph databases. PVLDB 9(13), 1257–1268 (2016)

29.

Shannon, C.: Understanding community-level disaster and emergency response preparedness. Disaster Med. Public Health Prepared. 9(3), 239–244 (2015)CrossRef

30.

Sun, B., Ma, W., Zhao, H.: A fuzzy rough set approach to emergency material demand prediction over two universes. Appl. Math. Model. 37(10–11), 7062–7070 (2013)CrossRefMathSciNet

31.

Tan, I.W.T.M., Wang, L.: Towards ultrahigh dimensional feature selection for big data. J. Mach. Learn. Res. 15, 1371–1429 (2014)MathSciNetMATH

32.

Tan, S., Zhang, J.: An empirical study of sentiment analysis for chinese documents. Expert System with Applications 34(4), 2622–2629 (2008)CrossRef

33.

Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., Ohsaki, H.: Recognizing depression from twitter activity. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI ’15, pp. 3187–3196, (2015)

34.

White, T.: Hadoop: The Definitive Guide, 4th edn. O’Reilly Media Inc, Sebastopol (2015)

35.

Zhang, E., Zhang, Y.: F-Measure, pp. 1147–1147. Boston, MA: Springer US, (2009)

36.

Zhou, Z.H., Chawla, N.V., Jin, Y., Williams, G.J.: Big data opportunities and challenges: Discussions from data analytics perspectives [discussion forum]. IEEE Comput. Intell. Mag. 9(4), 62–74 (2014)CrossRef

Titel: Evaluating Decision Analytics from Mobile Big Data using Rough Set Based Ant Colony
verfasst von: Soumya Banerjee
Youakim Badr
Verlag: Springer International Publishing
Buch: Mobile Big Data
Print ISBN: 978-3-319-67924-2

Electronic ISBN: 978-3-319-67925-9

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-67925-9_9

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"