A generalized scalable software architecture for analyzing temporally structured big data in the cloud

Advances in Intelligent Systems and Computing

Volumn 275 AISC, Issue VOLUME 1, 2014, Pages 559-569

  Westerlund, Magnus ^a   Hedlund, Ulf ^a   Pulkkis, Göran ^a   Björk, Kaj Mikael ^a  

^a ARCADA UNIVERSITY OF APPLIED SCIENCES   (Finland)

Author keywords

Predictive analytics; Runtime models; Selfadaptive systems; System level design; Temporal data

Indexed keywords

INFORMATION SYSTEMS; RESEARCH; SOFTWARE ARCHITECTURE;

PREDICTIVE ANALYTICS; RUNTIME MODELS; SELF-ADAPTIVE SYSTEM; SYSTEM LEVEL DESIGN; TEMPORAL DATA;

BIG DATA;

EID: 84904685863     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-05951-8_53     Document Type: Conference Paper

References (29)

1. Demirkan, H., Delen, D.: Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems 55(1), 412-421 (2013)
2. Chen, Q., Hsu, M., Zeller, H.: Experience in continuous analytics as a service (CaaaS). In: Proc. EDBT 2011 (March 2011)
3. Talia, D.: Clouds for Scalable Big Data Analytics. Computer 46(5), 98-101 (2013)
4. Mell, P., Grance, T.: The NIST Definition of Cloud Computing. NIST Special Publication 800-145 (September 2011), http://csrc.nist.gov/publications/nistpubs/800-145/ SP800-145.pdf(last accessed on August 8, 2013)
5. Timmermann, A.: Forecast Combinations. CEPR Discussion Papers 5361 (2005)
6. SVM-Support Vector Machines, http://www.support-vector-machines.org/ (last accessed on August 24, 2013)
7. Haykin, S.O.: Neural Networks and Learning Machines, 3rd edn. Pearson Prentice Hall, USA (2009)
8. Konstantinou, I., Angelou, E., Boumpouka, C., Tsoumakos, D., Koziris, N.: On the Elasticity of NoSQL Databases over Cloud Management Platforms. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2385-2388. ACM (October 2011)
9. Begoli, E.: A Short Survey on the State of the Art in Architectures and Platforms for Large Scale Data Analysis and Knowledge Discovery from Data. In: Proceedings of the WICSA/ECSA 2012 Companion Volume, pp. 177-183. ACM (August 2012)
10. Fox, G.C.: Large Scale Data Analytics on Clouds. In: Proceedings of the Fourth International Workshop on Cloud Data Management, pp. 21-23. ACM (October 2012)
11. Valvåg, S.V., Johansen, D., Kvalnes, Å.: Position Paper: Elastic Processing and Storage at the Edge of the Cloud. In: Proceedings of the 2013 International Workshop on Hot Topics in Cloud Services, pp. 43-49. ACM (April 2013)
12. Rupprecht, L.: Exploiting In-network Processing for Big Data Management. In: Proceedings of the 2013 Sigmod/PODS Ph.D. Symposium on PhD Symposium, pp. 1-5. ACM (June 2013)
13. Ghazal, A., Rabl, T., Hu, M., Raab, F., Poess, M., Crolotte, A., Jacobsen, H.-A.: BigBench: Towards an Industry Standard Benchmark for Big Data Analytics. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 1197-1208. ACM (June 2013)
14. SOA Manifesto, http://www.soa-manifesto.org/(last accessed on August 24, 2013)
15. Laney, D.: 3D Data Management: Controlling Data Volume, Velocity and Variety. Meta Group (Gartner) (February 2001)
16. Welcome to ApacheTM Hadoop®!, http://hadoop.apache.org/(last accessed on August 21, 2013)
17. Welcome to Apache Pig!, http://pig.apache.org/(last accessed on August 21, 2013)
18. Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: Sixth Symp. Operating System Design and Implementation (OSDI 2004), San Francisco, CA (December 2004)
19. Lustig, I., Dietrich, B., Johnson, C., Dziekan, C.: The Analytics Journey. An IBM view of the structured data analysis landscape: descriptive, predictive and prescriptive analytics. Analytics, 11-18 (November/December 2010), http://www.analyticsmagazine.org/november-december-2010/54-the-analytics journey.html(last accessed on January 17, 2014)
20. Lee, J., et al.: SAP HANA distributed in-memory database system: Transaction, session, and metadata management. In: IEEE 29th Int. Conf. Data Engineering (ICDE), pp. 1165-1173 (April 2013)
21. Plale, B., et al.: CASA and LEAD: Adaptive Cyberinfrastructure for Real-Time Multiscale Weather Forecasting. Computer 39(11), 56-64 (2006)
22. Sadashiv, N., Kumar, S.M.D.: Cluster, Grid and Cloud Computing: A Detailed Comparison. In: Proc. 6th Int. Conf. Computer Science & Education (ICCSE 2011), pp. 477-482 (2011)
23. Yuxi, L., Jianhua, W.: Research on Comparison of Cloud Computing and Grid Computing. Research J. Applied Sciences, Engineering and Technology 4(2), 120-122 (2012)
24. Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud Computing and Grid Computing 360-Degree Compared. In: Proc. Grid Computing Environments Workshop (GCE 2008) (2008)
25. Amazon Web Services, http://aws.amazon.com/(last accesed on August 24, 2013)
26. Arel, I., Rose, D.C., Karnowski, T.P.: Deep Machine Learning-A New Frontier in Artificial Intelligence Research [Research Frontier]. IEEE Computational Intelligence Magazine 5(4), 13-18 (2010)
27. Widodo, A., Budi, I.: Combination of time series forecasts using neural network. In: Int. Conf. Electrical Engineering and Informatics (ICEEI), pp. 1-6 (July 2011)
28. Savola, R., Frühwirth, C., Pietikäinen, A.: Risk-Driven Security Metrics in Agile Software Development-An Industrial Pilot Study. J. Universal Computer Science 18(12), 1679-1702 (2012)
29. Encog Machine Learning Framework, http://www.heatonresearch.com/encog (last accessed on August 21, 2013)