Strategy for Data Stream Processing Based on Measurement Metadata: An Outpatient Monitoring Scenario
Mario Diván, Luis Olsina, Silvia Gordillo
DOI: 10.4236/jsea.2011.412077   PDF   HTML     4,745 Downloads   7,706 Views   Citations


In this work we discuss SDSPbMM, an integrated Strategy for Data Stream Processing based on Measurement Metadata, applied to an outpatient monitoring scenario. The measures associated to the attributes of the patient (entity) under monitoring, come from heterogeneous data sources as data streams, together with metadata associated with the formal definition of a measurement and evaluation project. Such metadata supports the patient analysis and monitoring in a more consistent way, facilitating for instance: i) The early detection of problems typical of data such as missing values, outliers, among others; and ii) The risk anticipation by means of on-line classification models adapted to the patient. We also performed a simulation using a prototype developed for outpatient monitoring, in order to analyze empirically processing times and variable scalability, which shed light on the feasibility of applying the prototype to real situations. In addition, we analyze statistically the results of the simulation, in order to detect the components which incorporate more variability to the system.

Share and Cite:

M. Diván, L. Olsina and S. Gordillo, "Strategy for Data Stream Processing Based on Measurement Metadata: An Outpatient Monitoring Scenario," Journal of Software Engineering and Applications, Vol. 4 No. 12, 2011, pp. 653-665. doi: 10.4236/jsea.2011.412077.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. Namit, J. Gehrke and H. Balakrishan, “Towards a Streaming SQL Standard,” Proceedings of the VLDB Endowment, Vol. 1, No. 2, 2008, pp. 1379-1390.
[2] H. Molina and L. Olsina, “Towards the Support of Contextual Information to a Measurement and Evaluation Framework,” International Conference on Quality of Information and Communications Technology, Lisbon, 12-14 September 2007, pp. 154-166.
[3] L. Olsina, F. Papa and H. Molina, “How to Measure and Evaluate Web Applications in a Consistent Way,” In: G. Rossi, O. Pastor, D. Schwabe and L. Olsina, Eds., Web Engineering: Modelling and Implementing Web Applications, Springer Book, London, 2008, pp. 385-420.
[4] M. Diván and L. Olsina, “Integrated Strategy for the Data Stream Processing: A Scenario of Use,” Proceeding of Iberoamerican Conference in “Software Engineering”, Medellín, 2009, pp. 374-387.
[5] M. Diván, L. Olsina and S. Gordillo, “Data Stream Processing Enriched with Measurement Metadata: A Statistical Analysis,” Proceeding of Iberoamerican Conference in “Software Engineering” (CIbSE), Rio de Janeiro, 2011, p. 29.
[6] M. Wei, W. Rundensteiner and M. Mani, “Utility-Driven Load Shedding for XML Stream Processing,” International Conference on World Wide Web, Beijing, 21-25 April 2008, pp. 855-864.
[7] C. Marrocco, R. Duin and F. Tortorella, “Maximizing the Area under the ROC Curve by Pairwise Feature Combination,” ACM Pattern Recognition, Vol. 41, No. 6, 2008, pp. 1961-1974. doi:10.1016/j.patcog.2007.11.017
[8] R. Software Foundation, “The R Foundation for Statistical Computing,” 2010.
[9] S. Babu and J. Widom, “Continuous Queries over Data Streams,” ACM SIGMOD Record, Vol. 30, No. 3, 2001, pp. 109-120. doi:10.1145/603867.603884
[10] D. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack, J. Hwang, W. Lindner, A. Maskey, A. Rasin, E. Ryvkina, N. Tatbul, Y. Xing and S. Zdonik, “The Design of the Borealis Stream Processing Engine,” Conference on Innovative Data Systems Research (CIDR), Asilomar, 2005, pp. 277-289.
[11] The Stream Group, “STREAM: The Stanford Stream Data Manager,” Stanford, 2003.
[12] S. Krishnamurthy, S. Chandrasekaran, O. Cooper, A. Deshpande, M. Franklin, J. Hellerstein, W. Hong, S. Madden, F. Reiss and M. Shah, “Telegraph CQ: An Architectural Status Report,” IEEE Data Engineering Bulletin, Vol. 26, No. 2, 2003, pp. 11-18.
[13] S. Chakravarthy and Q. Jiang, “Stream Data Processing: A Quality of Service Perspective,” Springer Book, New York, 2009.
[14] M. Ali, W. Aref, R. Bose, A. Elmagarmid, A. Helal, I. Kamel and M. Mokbel, “NILE-PDT: A Phenomenon Detection and Tracking Framework for Data Stream Management Systems,” Very Large Database, Trondheim, 2005, pp. 1295-1298.
[15] S. Singh, P. Vajirkar and Y. Lee, “Context-Aware Data Mining Framework for Wireless Medical Application,” LNCS of Springer, Vol. 2736, 2003, pp. 381-391.
[16] S. Singh, P. Vajirkar and Y. Lee, “Context-Based Data Mining Using Ontologies,” LNCS of Springer, Vol. 2813. 2003, pp. 405-418.
[17] Y. Huang, H. Zheng, C. Nugent, P. McCullagh, N. Black, K. Vowles and L. McCracken, “Feature Selection and Classification in Supporting Report Based Self Management for People with Chronic Pain,” IEEE Transactions on Information Technology in Biomedicine, Vol. 15, No. 1, 2011, pp. 54-61.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.