Abstract
In the era of big data, the quality of services any organization provides largely depends on the quality of their data-driven processes. In this context, the goal of process data science, is to enable innovative forms of information processing that enable enhanced insight and decision making. For example, consider the data-driven and knowledge-intensive processes in Australian government’s office of the e-Safety commissioner, where the goal is to empowering all citizens to have safer, more positive experiences online. An example process, is to analyze the large amount of data generated every second on social networks to understand patterns of suicidal thoughts, online bullying and criminal/exterimist behaviour. Current processes leverage machine learning systems, e.g., to perform automatic mental-health-disorders detection from social networks. This approach is challenging for knowledge workers (end-user analysts) who have little knowledge of computer science to use machine learning solutions in their data-driven processes. In this paper, we present a novel platform, namely iSheets, that makes it easy for knowledge workers of all skill levels to use machine learning technology, the way people use spreadsheet. We present and develop a Machine Learning (ML) as a service framework and a spreadsheet-based ML development platform to enable knowledge workers in data-driven processes engage with ML tasks and uncover hidden insights through learning in an easy way.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., Barham, P., Chen, et al.: Tensorflow: a system for large-scale machine learning. In: OSDI, vol. 16, pp. 265–283 (2016)
Beheshti, A., Benatallah, B., Nouri, R., Chhieng, V.M., Xiong, H., Zhao, X.: CoreDB: a data lake service. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, pp. 2451–2454, 06–10 November 2017
Beheshti, A., Benatallah, B., Nouri, R., Tabebordbar, A.: CoreKG: a knowledge lake service. PVLDB 11(12), 1942–1945 (2018)
Beheshti, A., et al.: iProcess: enabling IoT platforms in data-driven knowledge-intensive processes. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNBIP, vol. 329, pp. 108–126. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98651-7_7
Beheshti, S.M.: Organizing, querying, and analyzing ad-hoc processes’ data. Ph.D. thesis, University of New South Wales, Sydney, Australia (2012)
Beheshti, S., Benatallah, B., Motahari-Nezhad, H.R.: Galaxy: a platform for explorative analysis of open data sources. In: Proceedings of the 19th International Conference on Extending Database Technology EDBT, pp. 640–643 (2016)
Beheshti, S., et al.: Process Analytics - Concepts and Techniques for Querying and Analyzing Process Data. Springer, Switzerland (2016). https://doi.org/10.1007/978-3-319-25037-3
Beheshti, S., Tabebordbar, A., Benatallah, B., Nouri, R.: On automating basic data curation tasks. In: Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia, pp. 165–169, 3–7 April 2017
Beheshti, S.M.R., Venugopal, S., Ryu, S.H., Benatallah, B., Wang, W.: Big data and cross-document coreference resolution: Current state and future opportunities. arXiv preprint arXiv:1311.3987 (2013)
Maamar, Z., Sakr, S., Barnawi, A., Beheshti, S.-M.-R.: A framework of enriching business processes life-cycle with tagging information. In: Sharaf, M.A., Cheema, M.A., Qi, J. (eds.) ADC 2015. LNCS, vol. 9093, pp. 309–313. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19548-3_25
Pang, B., Lee, L., et al.: Opinion mining and sentiment analysis. Found. Trends® Inf. Retrieval 2(1–2), 1–135 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Amouzgar, F., Beheshti, A., Ghodratnama, S., Benatallah, B., Yang, J., Sheng, Q.Z. (2019). iSheets: A Spreadsheet-Based Machine Learning Development Platform for Data-Driven Process Analytics. In: Liu, X., et al. Service-Oriented Computing – ICSOC 2018 Workshops. ICSOC 2018. Lecture Notes in Computer Science(), vol 11434. Springer, Cham. https://doi.org/10.1007/978-3-030-17642-6_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-17642-6_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17641-9
Online ISBN: 978-3-030-17642-6
eBook Packages: Computer ScienceComputer Science (R0)