Cluster Computing

, Volume 18, Issue 3, pp 1285–1294 | Cite as

A network approach for managing and processing big cancer data in clouds

  • Wei Xing
  • Wei Jie
  • Dimitrios Tsoumakos
  • Moustafa Ghanem


Translational cancer research requires integrative analysis of multiple levels of big cancer data to identify and treat cancer. In order to address the issues that data is decentralised, growing and continually being updated, and the content living or archiving on different information sources partially overlaps creating redundancies as well as contradictions and inconsistencies, we develop a data network model and technology for constructing and managing big cancer data. To support our data network approach for data process and analysis, we employ a semantic content network approach and adopt the CELAR cloud platform. The prototype implementation shows that the CELAR cloud can satisfy the on-demanding needs of various data resources for management and process of big cancer data.


Big data Data network Cloud computing 



We thank the Scientific Computing team and RNA Biology Group at CRUK MI for their helpful comments. We would like to thank EU CELAR project partners, in particular, the Laboratory for Internet Computing (LINC), University of Cyprus.


  1. 1.
    Lawrence, M., Stojanov, P., Mermel, C., Robinson, J., Garraway, L., Golub, T., Meyerson, M., Gabriel, S., Lander, E., Getz, G.: Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505(5), 495–501 (2014)CrossRefGoogle Scholar
  2. 2.
    Chen, R., Mias, G., Li-Pook-Than, J., Jiang, L., Lam, H., Chen, R., Miriami, E., Karczewski, K., Hariharan, M., Dewey, F., Cheng, Y., Clark, M., Im, H., Habegger, L., Balasubramanian, S., O’Huallachain, M., Dudley, J., Hillenmeyer, S., Haraksingh, R., Sharon, D., Euskirchen, G., Lacroute, P., Bettinger, K., Boyle, A., Kasowski, M., Grubert, F., Seki, S., Garcia, M., Whirl-Carrillo, M., Gallardo, M., Blasco, M., Greenberg, P., Snyder, P., Klein, T., Altman, R., Butte, A.J., Ashley, E., Gerstein, M., Nadeau, K., Tang, H., Snyder, M.: Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148(6), 1293–1307 (2012)CrossRefGoogle Scholar
  3. 3.
    Hanahan, D., Weinberg, R.: Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2011)CrossRefGoogle Scholar
  4. 4.
    Weinberg, R.A.: Coming full circlefrom endless complexity to simplicity and back again. Cell 157(1), 267–271 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Giannakopoulos, I., Papailiou, N., Mantas, C., Konstantinou, I., Tsoumakos, D., Koziris, N.: CELAR: Automated Application Elasticity Platform. IEEE International Conference on Big Data (2014)Google Scholar
  6. 6.
    Copil, G., Moldovan, D., Le, D.-H., Truong, H.-L., Dustdar, S., Sofokleous, C., Loulloudes, N., Trihinas, D., Pallis, G., Dikaiakos, M.D., Sheridan, C., Floros, E., Loverdos, C.K., Star, K., Xing, W.: On controlling elasticity of cloud applications in celar. In: Emerging Research in Cloud Distributed Computing Systems. Software Engineering, and High Performance Computing Book Series, Advances in Systems Analysis (2015)Google Scholar
  7. 7.
    Xing, W., Corcho, O., Goble, C., Dikaiakos, M.D.: An ActOn-based semantic information service for Grids. J. Future Gener. Comput. Syst. 26(3), March (2010)Google Scholar
  8. 8.
    Xing, W., Corcho, O., Goble, C., Dikaiakos, M.: Active ontology: an information integration approach for highly dynamic information sources. In: European Semantic Web Conference. Innsbruck, Austria (2007)Google Scholar
  9. 9.
    Wang, L., Khan, S.U., Chen, D., Kolodziej, J., Ranjan, R., Xu, C., Zomaya, A.Y.: Energy-aware parallel task scheduling in a cluster. Future Gener. Comput. Syst. 29(7), 1661–1670 (2013)CrossRefGoogle Scholar
  10. 10.
    Wang, L., Kunze, M., Tao, J., von Laszewski, G.: Towards building a cloud for scientific applications. Adv. Eng. Softw. 42(9), September (2011)Google Scholar
  11. 11.
    Wang, L., Chen, D., Hu, Y., Ma, Y., Wang, J.: Towards enabling cyberinfrastructure as a service in clouds. Comput. Electr. Eng. 39(1), 3–14 (2013)CrossRefGoogle Scholar
  12. 12.
    Xing, W., Liabotis, I., Tsoumakos, D., Sofokleous, S., Floros, V., Loverdos, C.: Translational cancer detection pipeline design (v1.0). Tech. Rep, EU CELAR Project (March 2013)Google Scholar
  13. 13.
    EMBL-EBI Services.
  14. 14.
    Rebholz-Schuhmann, D., Kirsch, H., Gaudan, S., Arregui, M., Nenadic, G.: Annotation and disambiguation of semantic types in biomedical text: a cascaded approach to named entity recognition. In: Proceedings of the EACL Workshop on Multi-Dimensional Markup in NLP, Trente, Italy (2006)Google Scholar
  15. 15.
    del Castillo, J.C.: Bioalma’s text mining solutions for biomedical research. ALMA Bioinformatics, S.L. (2002)Google Scholar
  16. 16.
    Fernandez, J., Hoffmann, R., Valencia, A.: iHOP web services family. In: Freitas, A., Navarro, A. (eds.) Bioinformatics for Personalized Medicine, ser. Lecture Notes in Computer Science, vol. 6620, pp. 102–107 (2012)Google Scholar
  17. 17.
  18. 18.
    Zdobnov, E.M., Lopez, R., Apweiler, R., Etzold, T.: The EBI SRS server recent developments. Bioinformatics 18(2), 368–373 (2002)CrossRefGoogle Scholar
  19. 19.
    Hekkelman, H.L., Vriend, G.: MRS: a fast and compact retrieval system for biological data. Nucl. Acids Res. 33(Web-Server-Issue), 766–769, 2005Google Scholar
  20. 20.
    Xiong, P., Chi, Y., Zhu, S., Moon, H.J., Pu, C., Hacigumus, H.: Intelligent management of virtualized resources for database systems in cloud environment. In: IEEE 27th International Conference on Data Engineering, pp. 87–98, April (2011)Google Scholar
  21. 21.
    Wang, L., von Laszewski, G., Dayal, J., He, X., Younge, A.J., Furlani, T.R.: Towards thermal aware workload scheduling in a data center. In: Proceedings of the 10th International Symposium on Pervasive Systems, Algorithms, and Networks, pp. 116–122, December (2009)Google Scholar
  22. 22.
    Wang, L., von Laszewski, G., Younge, A.J., He, X., Kunze, M., Tao, J., Fu, C.: Cloud computing: a perspective study. New Gener. Comput. 28(2), 137–146 (2010)CrossRefzbMATHGoogle Scholar
  23. 23.
    Rao, J., Bu, X., Xu, C.-Z., Wang, K.: A distributed self-learning approach for elastic provisioning of virtualized cloud resources. In: 2011 IEEE 19th International Symposium on Modeling, Analysis Simulation of Computer and Telecommunication Systems, pp. 45–54, July (2011)Google Scholar
  24. 24.
    Sharma, U., Shenoy, P., Sahu, S., Shaikh, A.: A cost-aware elasticity provisioning system for the cloud. In: 31st International Conference on Distributed Computing Systems, pp. 559–570, June (2011)Google Scholar
  25. 25.
    Giannakopoulos, I., Papailiou, N., Mantas, C., Konstantinou, I., Tsoumakos, D., Koziris, N.: CELAR: automated application elasticity platform. In: 2014 IEEE International Conference on Big Data, Big Data 2014, pp. 23–25 (2014)Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Wei Xing
    • 1
  • Wei Jie
    • 2
  • Dimitrios Tsoumakos
    • 3
  • Moustafa Ghanem
    • 4
  1. 1.Cancer Research UK Manchester InstituteUniversity of ManchesterManchesterUK
  2. 2.School of Computing and TechnologyUniversity of West LondonLondonUK
  3. 3.Computing Systems LaboratoryNational Technical University of AthensAthensGreece
  4. 4.Department of Computer ScienceUniversity of MiddlsexLondonUK

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