Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Cluster Computing
Erschienen in: Cluster Computing 4/2019

01.11.2017

Coordinate descent based ontology sparse vector computing strategy and its applications

verfasst von: Wei Gao, Muhammad Shoaib Sardar, Sohail Zafar, Zohaib Zahid

Erschienen in: Cluster Computing | Sonderheft 4/2019

Einloggen

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

search-config
loading …

Abstract

In recent years, as a semantic analysis and computational tool, ontology has been widely applied in many engineering applications. Many cases suggests that it’s confronted with countless big data source with the complex data structures. In order to relieve the dilemma, the sparse learning algorithms are introduced into the ontology similarity measuring and ontology mapping. In this setting, it should be a high dimensional expression of each ontology vertex, and the ontology algorithm should extract key component information effectively. Under such background, we consider the ontology sparse vector learning algorithm and application in different engineering applications. In this article, by means of coordinate descent minimization tricks, we present the ontology sparse vector optimization strategy and discuss the different transformation in different settings. At last, the new ontology sparse vector learning proceeding is applied to four engineering applications respectively to get its efficiency verified.
Vorheriger Artikel The clustering analysis on data and lending strategy of supply chain with information asymmetry method
Nächster Artikel An evaluation of the FRWMA chart for dependent interval-valued data

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
Literatur
1.
Gregor, D., Toral, S., Ariza, T., Barrero, F., Gregor, R., Rodas, J., Arzamendia, M.: A methodology for structured ontology construction applied to intelligent transportation systems. Comput. Stand. Interfaces 47, 108–119 (2016)
2.
Kaminski, M., Nenov, Y., Grau, B.C.: Datalog rewritability of Disjunctive Datalog programs and non-Horn ontologies. Artif. Intell. 236, 90–118 (2016)MathSciNetMATH
3.
Forsati, R., Shamsfard, M.: Symbiosis of evolutionary and combinatorial ontology mapping approaches. Inf. Sci. 342, 53–80 (2016)MathSciNet
4.
Pesaranghader, A., Matwin, S., Sokolova, M., Beiko, R.G.: simDEF: definition-based semantic similarity measure of gene ontology terms for functional similarity analysis of genes. Bioinformatics 32(9), 1380–1387 (2016)
5.
Huntley, R.P., Sitnikov, D., Orlic-Milacic, M., et al.: Guidelines for the functional annotation of microRNAs using the Gene Ontology. RNA 22(5), 667–676 (2016)
6.
Brown, R.B.K., Beydoun, G., Low, G., Tibben, W., Zamani, R., Garcia-Sanchez, F., Martinez-Bejar, R.: Computationally efficient ontology selection in software requirement planning. Inf. Syst. Front. 18(2), 349–358 (2016)
7.
Palmer, C., Urwin, E.N., Pinazo-Sanchez, J.M., Cid, F.S., Rodriguez, E.P., Pajkovska-Goceva, S., Young, R.I.M.: Reference ontologies to support the development of global production network systems. Comput. Ind. 77, 48–60 (2016)
8.
Terblanche, C., Wongthongtham, P.: Ontology-based employer demand management. Softw.-Pract. Exp. 46(4), 469–492 (2016)
9.
Farid, H., Khan, S., Javed, M.Y.: DSont: DSpace to ontology transformation. J. Inf. Sci. 42(2), 179–199 (2016)
10.
Suarez-Figuero, C.M., Gomez-Perez, A., Fernandez-Lopez, M.: Scheduling ontology development projects. Data Knowl. Eng. 102, 1–21 (2016)
11.
Agapito, G., Milano, M., Guzzi, P.H., Cannataro, M.: Extracting cross-ontology weighted association rules from gene ontology annotations. IEEE-ACM Trans. Comput. Biol. Bioinform. 13(2), 197–208 (2016)
12.
Chicco, D., Masseroli, M.: Ontology-based prediction and prioritization of gene functional annotations. IEEE-ACM Trans. Comput. Biol. Bioinform. 13(2), 248–260 (2016)
13.
Umadevi, S., Premkumar, K., Valarmathi, S., Ayyasamy, P.M., Rajakumar, S.: Identification of novel genes related to diabetic retinopathy using protein-protein interaction network and gene ontologies. J. Biol. Syst. 24(1), 117–127 (2016)MathSciNet
14.
Bajenaru, L., Smeureanu, I., Balog, A.: An ontology-based e-learning framework for healthcare human resource management. Stud. Inf. Control 25(1), 99–108 (2016)
15.
Sherimon, P.C., Krishnan, R.: OntoDiabetic: an ontology-based clinical decision support system for diabetic patients. Arab. J. Sci. Eng. 41(3), 1145–1160 (2016)
16.
Bobillo, F., Straccia, U.: The fuzzy ontology reasoner fuzzyDL. Knowl.-Based Syst. 95, 12–34 (2016)
17.
Trokanas, N., Cecelja, F.: Ontology evaluation for reuse in the domain of process systems engineering. Comput. Chem. Eng. 85, 177–187 (2016)
18.
Mazandu, G.K., Chimusa, E.R., Mbiyavanga, M., Mulder, N.J.: A-DaGO-fun: an adaptable gene ontology semantic similarity-based functional analysis tool. Bioinformatics 32(3), 477–479 (2016)
19.
Auffeves, A., Grangier, P.: Contexts, systems and modalities: a new ontology for quantum mechanics. Found. Phys. 46(2), 121–137 (2016)MathSciNetMATH
20.
Hoyle, D.C., Brass, A.: Statistical mechanics of ontology based annotations. Phys. A-Stat. Mech. Its Appl. 442, 284–299 (2016)MathSciNetMATH
21.
Gao, W., Zhu, L.L., Wang, K.Y.: Ranking based ontology scheming using eigenpair computation. J. Intell. Fuzzy Syst. 31(4), 2411–2419 (2016)
22.
Gao, W., Guo, Y., Wang, K.Y.: Ontology algorithm using singular value decomposition and applied in multidisciplinary. Clust. Comput.-J. Netw. Softw. Tools Appl. 19(4), 2201–2210 (2016)
23.
24.
Gao, Y., Farahani, M.R., Gao, W.: Ontology optimization tactics via distance calculating. Appl. Math. Nonlinear Sci. 1(1), 159–174 (2016)MathSciNetMATH
25.
Gao, W., Gao, Y., Zhang, Y.G.: Strong and weak stability of \(k\)-partite ranking algorithm. Information 15(11A), 4585–4590 (2012)MathSciNetMATH
26.
27.
28.
Gao, W., Baig, A.Q., Ali, H., Sajjad, W., Farahani, M.R.: Margin based ontology sparse vector learning algorithm and applied in biology science. Saudi J. Biol. Sci. 24(1), 132–138 (2017)
29.
Gao, W., Wang, W.F.: The fifth geometric arithmetic index of bridge graph and carbon nanocones. J. Differ. Equ. Appl. 23(1–2), 100–109 (2017)MathSciNetMATH
30.
Gao, W., Yan, L., Shi, L.: Generalized Zagreb index of polyomino chains and nanotubes. Optoelectron. Adv. Mater. Rapid Commun. 11(1–2), 119–124 (2017)
31.
Beck, A.: The 2-coordinate descent method for solving double-sided simplex constrained minimization problems. J. Optim. Theory Appl. 162(3), 892–919 (2014)MathSciNetMATH
32.
Arablouei, R., Dogancay, K., Adali, T.: Unbiased recursive least-squares estimation utilizing dichotomous coordinate-descent Iterations. IEEE Trans. Signal Process. 62(11), 2973–2983 (2014)MathSciNetMATH
33.
Richtarik, P., Takac, M.: Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Math. Program. 144(1–2), 1–38 (2014)MathSciNetMATH
34.
Necoara, I., Patrascu, A.: A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints. Comput. Optim. Appl. 57(2), 307–337 (2014)MathSciNetMATH
35.
Patrascu, A., Necoara, I.: Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization. J. Glob. Optim. 61(1), 19–46 (2015)MathSciNetMATH
36.
Fercoq, O., Richtarik, P.: Accelerated, parallel, and proximal coordinate descent. SIAM J. Optim. 25(4), 1997–2023 (2015)MathSciNetMATH
37.
Patrascu, A., Necoara, I.: Random coordinate descent methods for l(0) regularized convex optimization. IEEE Trans. Autom. Control 60(7), 1811–1824 (2015)MathSciNetMATH
38.
McGaffin, M.G., Fessler, J.A.: Edge-preserving image denoising via group coordinate descent on the GPU. IEEE Trans. Image Process. 24(4), 1273–1281 (2015)MathSciNetMATH
39.
Dumitrescu, B.: On the relation between the randomized extended Kaczmarz algorithm and coordinate descent. BIT Numer. Math. 55(4), 1005–1015 (2015)MathSciNetMATH
40.
Rinaldi, M., Tampere, C.M.J.: An extended coordinate descent method for distributed anticipatory network traffic control. Transp. Res. Part B-Methodol. 80, 107–131 (2015)
41.
Park, H., Konishi, S.: Robust coordinate descent algorithm robust solution path for high-dimensional sparse regression modeling. Commun. Stat.-Simul. Comput. 45(1), 115–129 (2016)MathSciNetMATH
42.
Bayon, L., Fortuny, A.P., Otero, J.A., Suarez, P.M., Tasis, C.: Cyclic coordinate descent in a class of bang-singular-bang problems. J. Comput. Appl. Math. 291, 304–316 (2016)MathSciNetMATH
43.
Nguyen, V.D.L., Minh, L.Q., Tram, N.P., Bahadori, A., Lee, M.: Novel retrofit designs using a modified coordinate descent methodology for improving energy efficiency of natural gas liquid fractionation process. J. Nat. Gas Sci. Eng. 33, 458–468 (2016)
44.
Michoel, T.: Natural coordinate descent algorithm for L1-penalised regression in generalised linear models. Comput. Stat. Data Anal. 97, 60–70 (2016)MathSciNetMATH
45.
Jabr, R.A., Dzafic, I.: Sensitivity-based discrete coordinate-descent for Volt/VAr control in distribution networks. IEEE Trans. Power Syst. 31(6), 4670–4678 (2016)
46.
Richtarik, P., Takac, M.: On optimal probabilities in stochastic coordinate descent methods. Optim. Lett. 10(6), 1233–1243 (2016)MathSciNetMATH
47.
Craswell, N., Hawking, D.: Overview of the TREC: web track. In: Proceeding of the Twelfth Text Retrieval Conference, Gaithersburg, Maryland, NIST Special Publication, vol. 2003, pp. 78–92 (2003)
Metadaten
Titel
Coordinate descent based ontology sparse vector computing strategy and its applications
verfasst von
Wei Gao
Muhammad Shoaib Sardar
Sohail Zafar
Zohaib Zahid
Publikationsdatum
01.11.2017
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 4/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1283-8

Weitere Artikel der Sonderheft 4/2019

Cluster Computing 4/2019 Zur Ausgabe

OriginalPaper

An on-line detection method for outliers of dynamic unstable measurement data

OriginalPaper

The methods of big data fusion and semantic collision detection in Internet of Thing

OriginalPaper

Virtual reality technology applied in digitalization of cultural heritage

OriginalPaper

Design and realization of bank history data management system based on Hadoop 2.0

OriginalPaper

Iterative tighter nonparallel hyperplane support vector clustering with simultaneous feature selection

OriginalPaper

Achieving energy efficient wireless sensor network by choosing effective cluster head