Abstract
Medical data streams processing becomes increasingly important since it extracts critical information from a continuous flow of patient data. Various types of problems have been studied on medical data streams, such as classification, clustering, anomaly detection, etc.; however, efficient evaluation of cumulative frequency queries has not been well studied. The cumulative frequency of patients’ status can play an instrumental role in monitoring the patients’ health conditions. Up to now, efficiently processing cumulative frequency queries on medical data streams is still a challenging task due to the large size of the incoming data. Therefore, in this paper, we propose a novel framework for processing the cumulative frequency queries over medical data streams to support the online medical decision. The proposed framework includes two components: data summarisation and dynamic maintenance. For data summarisation, we propose a hybrid approach that combines two data structures and exploits a classification algorithm to select the more efficient data structure for computing the cumulative frequency. For dynamic maintenance, we propose an incremental maintenance approach for updating the cumulative frequencies when new data arrive. The experimental results on a real dataset demonstrate the efficiency of the proposed approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abbas, A.M., Bakar, A.A., Ahmad, M.Z.: Fast dynamic clustering SOAP messages based compression and aggregation model for enhanced performance of Web services. J. Netw. Comput. Appl. 41, 80–88 (2014)
Al-Shammari, A., Liu, C., Naseriparsa, M., Vo, B.Q., Anwar, T., Zhou, R.: A framework for clustering and dynamic maintenance of XML documents. In: Cong, G., Peng, W.-C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS, vol. 10604, pp. 399–412. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69179-4_28
Bille, P., Christiansen, A.R., Prezza, N., Skjoldjensen, F.R.: Succinct partial sums and Fenwick trees. In: Fici, G., Sciortino, M., Venturini, R. (eds.) SPIRE 2017. LNCS, vol. 10508, pp. 91–96. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67428-5_8
Chen, L., Gao, Y., Li, X., Jensen, C. S., Chen, G., Zheng, B.: Indexing metric uncertain data for range queries. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 951–965. ACM (2015)
Chen, L., Gao, Y., Zhong, A., Jensen, C.S., Chen, G., Zheng, B.: Indexing metric uncertain data for range queries and range joins. VLDB J. 26(4), 585–610 (2017)
Dima, M., Ceterchi, R.: Efficient range minimum queries using binary indexed trees. Olymp. Inform. 9, 39–44 (2015)
Fenwick, P.M.: A new data structure for cumulative frequency tables. Softw.: Pract. Exp. 24(3), 327–336 (1994)
Han, D., Xiao, C., Zhou, R., Wang, G., Huo, H., Hui, X.: Load shedding for window joins over streams. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds.) WAIM 2006. LNCS, vol. 4016, pp. 472–483. Springer, Heidelberg (2006). https://doi.org/10.1007/11775300_40
Hoplaros, D., Tari, Z., Khalil, I.: Data summarization for network traffic monitoring. J. Netw. Comput. Appl. 37, 194–205 (2014)
Jung, H., Kim, Y.S., Chung, Y.D.: QR-tree: an efficient and scalable method for evaluation of continuous range queries. Inf. Sci. 274, 156–176 (2014)
Mladenović, N., Urošević, D., Ilić, A.: A general variable neighborhood search for the one-commodity pickup-and-delivery travelling salesman problem. Eur. J. Oper. Res. 220(1), 270–285 (2012)
Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision tree for mining data streams. Inf. Sci. 266, 1–15 (2014)
Wang, C., Zhang, R., He, X., Zhou, G., Zhou, A.: Event phase extraction and summarization. In: Cellary, W., Mokbel, M.F., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2016. LNCS, vol. 10041, pp. 473–488. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48740-3_35
Wang, Y., Meliou, A., Miklau, G.: RC-Index: diversifying answers to range queries. Proc. VLDB Endow. 11(7), 773–786 (2018)
Zhu, H., Yang, X., Wang, B., Lee, W.-C.: Range-based nearest neighbor queries with complex-shaped obstacles. IEEE Trans. Knowl. Data Eng. 30(5), 963–977 (2018)
Acknowledgement
This work was partially supported by the ARC Discovery Project under Grant No. DP170104747 and DP180100212.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Al-Shammari, A., Zhou, R., Liu, C., Naseriparsa, M., Vo, B.Q. (2018). A Framework for Processing Cumulative Frequency Queries over Medical Data Streams. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-02925-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02924-1
Online ISBN: 978-3-030-02925-8
eBook Packages: Computer ScienceComputer Science (R0)