research-article

Big Data Service Request Prediction Based on Historical Behavior Time Series

Authors:

Bo ZhuAuthors Info & Claims

ICBDT '19: Proceedings of the 2nd International Conference on Big Data Technologies

Pages 77 - 81

https://doi.org/10.1145/3358528.3358584

Published: 28 August 2019 Publication History

Abstract

Big data analysis service has been used widely in our society. For example, in financial field, users often use big data analysis services to analyze stocks, assets, and accounts in real-time investment decision-making. Therefore, real-time service response is very important from the perspective of user experience. Caching data and analysis results have been used widely in industrial practice. But these caches generally are passive, rigid and inefficient. Proactive caching approach for time-consuming data services is a worthwhile research problem. In addition, we have encountered this problem in a practical enterprise application. In this paper, we propose a data service request prediction approach based on historical user behavior time series analysis. Results show that this approach can improve the response speed of backend data services effectively.

References

[1]

Borisov, A., Wardenaar, M., Markov, I., and de Rijke, M. 2018. A click sequence model for web search. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (Ann Arbor, MI, USA --- July 08-12, 2018). SIGIR ' 18. ACM New York, NY, 45--54. DOI=http://doi.acm.org/10.1145/3209978.3210004.

Digital Library

[2]

Sisodia, D. S., Khandal, V., and Singhal, R. 2018. Fast prediction of web user browsing behaviours using most interesting patterns. Journal of Information Science. 44,1 (February. 2018), 74--90. DOI=http://doi.org/10.1177/0165551516673293

Digital Library

[3]

Wang, S., Ma, Y., Cheng, B., Yang, F., and Chang, R. N. 2016. Multi-dimensional QoS prediction for service recommendations. IEEE Transactions on Services Computing. 12,1 (June. 2016), 47--57. DOI=http://doi.org/10.1109/TSC.2016.2584058

[4]

Gopalakrishnan, T., Sengottuvelan, P., Bharathi, A., and Lokeshkumar, R. 2018. An approach to webpage prediction method using variable order Markov model in recommendation systems. Journal of Internet Technology. 19,2 (March. 2018), 415--424. DOI=http://doi.org/10.3966/160792642018031902010

[5]

Xu, G., Shen, C., Liu, M., Zhang, F., and Shen, W. 2017. A user behavior prediction model based on parallel neural network and k-nearest neighbor algorithms. Cluster Computing. 20,2 (June. 2017), 1703--1715. DOI=http://doi.org/10.1007/s10586-017-0749-z

Digital Library

[6]

Cacheda, F., Blanco, R., and Barbieri, N. 2018. Characterizing and Predicting Users' Behavior on Local Search Queries. ACM Transactions on the Web. 12,2 (June. 2018), 11. DOI=http://doi.org/10.1145/3157059

Digital Library

[7]

Kompan, M., Kassak, O., and Bielikova, M. 2019. The Short-term User Modeling for Predictive Applications. Journal on Data Semantics. 8,1 (March. 2019), 21--37. DOI=http://doi.org/10.1007/s13740-018-0095-1

[8]

Ye, L., and Keogh, E. 2009. Time series shapelets: a new primitive for data mining. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (Paris, France --- June 28 - July 01, 2009). KDD '09. ACM New York, NY, 947--956. DOI=http://doi.org/10.1145/1557019.1557122

Digital Library

[9]

Paparrizos, J., and Gravano, L. 2017. Fast and accurate time-series clustering. ACM Transactions on Database Systems. 42,2 (June. 2017), 8. DOI=http://doi.org/10.1145/3044711

Digital Library

[10]

Ji, C., Zhao, C., Pan, L., Liu, S., Yang, C., and Wu, L. 2017. A fast shapelet discovery algorithm based on important data points. International Journal of Web Services Research. 14,2 (April. 2017), 67--80. DOI=http://doi.org/10.4018/IJWSR.2017040104

Digital Library

[11]

Renard, X., Rifqi, M., Erray, W., and Detyniecki, M. 2015. Random-shapelet: an algorithm for fast shapelet discovery. In 2015 IEEE International Conference on Data Science and Advanced Analytics. (October. 2015), 1--10. DOI=http://doi.org/10.1109/DSAA.2015.7344782

[12]

Hörmann, S., Kokoszka, P., and Nisol, G. 2018. Testing for periodicity in functional time series. The annals of statistics. 46,6A (September. 2018), 2960--2984. DOI=http://doi.org/10.1214/17-AOS1645

[13]

Lines, J., Taylor, S., and Bagnall, A. 2018. Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles. ACM Transactions on Knowledge Discovery from Data. 12,5 (July. 2018), 52. ACM New York, NY, 52. DOI=http://doi.org/10.1145/3182382

Digital Library

[14]

Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P. A. 2019. Deep learning for time series classification: a review. Data Mining and Knowledge Discovery. 33,4 (July. 2019), 917--963. DOI=http://doi.org/10.1007/s10618-019-00619-1

Digital Library

[15]

Guo, C. M., Xu, L. Y., Liu, H. F., Wang, L., Yu, X., and Han, B. 2015. The Financial Data of Anomaly Detection Research Based on Time Series. In 2015 International Conference on Computer Science and Applications. (November. 2015), 86--89. DOI=http://doi.org/10.1109/CSA.2015.42

[16]

Du, L., Meng, X., Liu, S., and Wu, L. 2012. A service response selection algorithm based on service provider benefits. Journal of xi'An Jiaotong University. 46,11 (November. 2012), 112--119.

[17]

Gao, Y., Zhang, B., Wang, S., and Ma, A. 2019. DBN Based Cloud Service Response Time Prediction Method. In 2019 21st International Conference on Advanced Communication Technology. (2019, February), 42--46. DOI=http://doi.org/10.23919/ICACT.2019.870192

Cited By

Hu XZhana X(2022)A Novel Method for Product Quality Control Based on Production IoT Data2022 5th International Conference on Computing and Big Data (ICCBD)10.1109/ICCBD56965.2022.10080209(39-43)Online publication date: 16-Dec-2022
https://doi.org/10.1109/ICCBD56965.2022.10080209
Zhao ZDing ZCao Y(2020)A Method of Emergency Prediction Based on Spatiotemporal Context Time SeriesSpatial Data and Intelligence10.1007/978-3-030-69873-7_2(14-28)Online publication date: 8-May-2020
https://dl.acm.org/doi/10.1007/978-3-030-69873-7_2

Index Terms

Big Data Service Request Prediction Based on Historical Behavior Time Series
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

An Analytic Model of Atomic Service for Services Descriptions
ICSS '10: Proceedings of the 2010 International Conference on Service Sciences

The key question with Service Science is to be how to formulate a theoretic foundation for service description, service discovery and service composition in the context of basic service system development and the building of service architecture. ...
Catalog-based service request management

To manage the delivery of services competitively on a large, global scale, an IT (information technology) service provider must efficiently use service delivery resources-in particular, skilled service delivery teams. Service requests form a large and ...
Service Composition Framework for Big Data Service
FICLOUD '14: Proceedings of the 2014 International Conference on Future Internet of Things and Cloud

Demand for big data analysis service has been increasing recently. In addition, the related business and market are also growing. However, big data service development requires a substantial amount of time and human resources. In this paper, we suggest ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICBDT '19: Proceedings of the 2nd International Conference on Big Data Technologies

August 2019

382 pages

ISBN:9781450371926

DOI:10.1145/3358528

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Shandong Univ.: Shandong University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 August 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICBDT2019

ICBDT2019: 2019 2nd International Conference on Big Data Technologies

August 28 - 30, 2019

Jinan, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
95
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hu XZhana X(2022)A Novel Method for Product Quality Control Based on Production IoT Data2022 5th International Conference on Computing and Big Data (ICCBD)10.1109/ICCBD56965.2022.10080209(39-43)Online publication date: 16-Dec-2022
https://doi.org/10.1109/ICCBD56965.2022.10080209
Zhao ZDing ZCao Y(2020)A Method of Emergency Prediction Based on Spatiotemporal Context Time SeriesSpatial Data and Intelligence10.1007/978-3-030-69873-7_2(14-28)Online publication date: 8-May-2020
https://dl.acm.org/doi/10.1007/978-3-030-69873-7_2

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents