iSheets

a spreadsheet-based machine learning development platform for data-driven process analytics

Farhad Amouzgar, Amin Beheshti*, Samira Ghodratnama, Boualem Benatallah, Jian Yang, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationService-Oriented Computing – ICSOC 2018 Workshops
Subtitle of host publicationADMS, ASOCA, ISYyCC, CloTS, DDBS, and NLS4IoT, Revised Selected Papers
EditorsXiao Liu, Michael Mrissa, Liang Zhang, Djamal Benslimane, Aditya Ghose, Zhongjie Wang, Antonio Bucchiarone, Wei Zhang, Ying Zou, Qi Yu
Place of PublicationSwitzerland
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Pages453-457
Number of pages5
ISBN (Electronic)9783030176426
ISBN (Print)9783030176419
DOIs
Publication statusPublished - 1 Jan 2019
Event16th International Conference on Service-Oriented Computing, ICSOC 2018 - Hangzhou, China
Duration: 12 Nov 201815 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11434 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Service-Oriented Computing, ICSOC 2018
CountryChina
CityHangzhou
Period12/11/1815/11/18

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Keywords

  • Data analytics
  • Machine learning
  • Process data science

Cite this

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 X. Liu, M. Mrissa, L. Zhang, D. Benslimane, A. Ghose, Z. Wang, A. Bucchiarone, W. Zhang, Y. Zou, ... Q. Yu (Eds.), Service-Oriented Computing – ICSOC 2018 Workshops: ADMS, ASOCA, ISYyCC, CloTS, DDBS, and NLS4IoT, Revised Selected Papers (pp. 453-457). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11434 LNCS). Switzerland: Springer-VDI-Verlag GmbH & Co. KG. https://doi.org/10.1007/978-3-030-17642-6_43