Feature Driven Survey of Big Data Systems

Cigdem Avci Salma, Bedir Tekinerdogan, Ioannis N. Athanasiadis

2016

Abstract

Big Data has become a very important driver for innovation and growth for various industries such as health, administration, agriculture, defence, and education. Storing and analysing large amounts of data are becoming increasingly common in many of these application areas. In general, different application domains might require different type of big data systems. Although, lot has been written on big data it is not easy to identify the required features for developing big data systems that meets the application requirements and the stakeholder concerns. In this paper we provide a survey of big data systems based on feature modelling which is a technique that is utilized for defining the common and variable features of a domain. The feature model has been derived following an extensive literature study on big data systems. We present the feature model and discuss the features to support the understanding of big data systems.

References

  1. Araùjo, J., Baniassad, E., Clements, P., Moreira, A., Rashid, A., & Tekinerdogan, B., 2005. Early aspects: The current landscape. Technical Notes, CMU/SEI and Lancaster University.
  2. Arrango, G., 1994. Domain Analysis Methods in Software Reusability. Schäfer, R. Prieto-Díaz, and M. Matsumoto (Eds.), Ellis Horwood, New York, New York, pp. 17-49.
  3. Ballard, C., Compert, C., Jesionowski, T., Milman, I., Plants, B., Rosen, B., & Smith, H., 2014. Information Governance Principles and Practices for a Big Data Landscape, IBM Redbooks.
  4. Chapelle, D., 2013. Big Data & Analytics Reference Architecture, An Oracle White Paper.
  5. Czarnecki, K., Hwan, C., Kim, P., & Kalleberg, K. T., 2006. Feature models are views on ontologies. In Software Product Line Conference, 2006 10th International (pp. 41-51). IEEE.
  6. Geerdink, B., 2013. A reference architecture for big data solutions introducing a model to perform predictive analytics using big data technology. In Internet Technology and Secured Transactions (ICITST), 2013 8th International Conference for (pp. 71-76). IEEE.
  7. Harsu, M., 2002. A survey on domain engineering. Tampere University of Technology.
  8. Kang, K. C., Cohen, S. G., Hess, J. A., Novak, W. E., & Peterson, A. S., 1990. Feature-oriented domain analysis (FODA) feasibility study (No. CMU/SEI-90- TR-21). Carnegie-Mellon Univ. Pittsburgh Pa. Software Engineering Inst.
  9. Laney, D., 2001. 3D Data Management: Controlling Data Volume, Velocity and Variety. Meta-Group Report #949.
  10. Lee, K., Kang, K. C., & Lee, J. (2002). Concepts and guidelines of feature modeling for product line software engineering. In Software Reuse: Methods, Techniques, and Tools (pp. 62-77). Springer Berlin Heidelberg.
  11. Maier, M., Serebrenik, A., & Vanderfeesten, I. T. P., 2013. Towards a Big Data Reference Architecture.
  12. Marz, N., & Warren, J., 2015. Big Data: Principles and Best Practices of Scalable Realtime Data Systems, Manning Publications Co..
  13. May, W., 2014. Draft NIST Big Data Interoperability Framework: Volume 6 Reference Architecture.
  14. Mishne, G., Dalton, J., Li, Z., Sharma, A., & Lin, J. 2013. Fast data in the era of big data: Twitter's real-time related query suggestion architecture. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data (pp. 1147-1158). ACM.
  15. Oracle, 2013. Information Management and Big Data A Reference Architecture, An Oracle White Paper.
  16. Pääkkönen, P., & Pakkala, D., 2015. Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems. Big Data Research.
  17. Sinnema, M., & Deelstra, S., 2007. Classifying variability modeling techniques. Information and Software Technology, 49(7), 717-739.
  18. Soares, S., 2012. Big Data Governance. Information Asset, LLC.
  19. Tekinerdogan, B., & Aksit, M., 2001. Classifying and Evaluating Architecture Design Methods, in Software Architectures and Component Technology: The State of the Art in Research and Practice. M. Aksit (Ed.), Boston:Kluwer Academic Publishers, pp. 3 - 27.
  20. Tekinerdogan, B., Bilir, S., & Abatlevi, C. (2005). Integrating platform selection rules in the model driven architecture approach. In Model Driven Architecture(pp. 159-173). Springer Berlin Heidelberg.
  21. Tekinerdogan, B., & Öztürk, K. (2013). Feature-Driven Design of SaaS Architectures. In Software Engineering Frameworks for the Cloud Computing Paradigm (pp. 189-212). Springer London.
  22. Thusoo, A., Shao, Z., Anthony, S., Borthakur, D., Jain, N., Sen Sarma, J., Murthy, R. and Liu, H., 2010, June. Data warehousing and analytics infrastructure at facebook. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data (pp. 1013-1020). ACM.
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Paper Citation


in Harvard Style

Salma C., Tekinerdogan B. and Athanasiadis I. (2016). Feature Driven Survey of Big Data Systems . In Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD, ISBN 978-989-758-183-0, pages 348-355. DOI: 10.5220/0005877503480355


in Bibtex Style

@conference{iotbd16,
author={Cigdem Avci Salma and Bedir Tekinerdogan and Ioannis N. Athanasiadis},
title={Feature Driven Survey of Big Data Systems},
booktitle={Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,},
year={2016},
pages={348-355},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005877503480355},
isbn={978-989-758-183-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,
TI - Feature Driven Survey of Big Data Systems
SN - 978-989-758-183-0
AU - Salma C.
AU - Tekinerdogan B.
AU - Athanasiadis I.
PY - 2016
SP - 348
EP - 355
DO - 10.5220/0005877503480355