Abstract
In the era of big data, people are dealing with data all the time. Data collection is the first step and foundation for many other downstream applications. Meanwhile, we observe that data collection is often entity-oriented, i.e., people usually collect data related to a specific entity. In most cases, people achieve entity-oriented data collection by manual query and filtering based on search engines or news applications. However, these methods are not very efficient and effective. In this paper, we consider designing reasonable process rules and integrating artificial intelligence algorithms to help people efficiently and effectively collect the target data related to the specific entity. Concretely, we propose an active workflow method to achieve this goal. The whole workflow method is composed of four processes: task modeling for data collection, Internet data collection, crowdsourcing data collection and multi-source data aggregation.
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Acknowledgment
This work was supported in part by the National Key Research and Development Program of China (No. 2017YFC0820402), the Intelligent Manufacturing Comprehensive Standardization and New Pattern Application Project of Ministry of Industry and Information Technology (Experimental validation of key technical standards for trusted services in industrial Internet), and the National Natural Science Foundation of China (No. 61373023).
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Guo, G. (2018). An Active Workflow Method for Entity-Oriented Data Collection. In: Woo, C., Lu, J., Li, Z., Ling, T., Li, G., Lee, M. (eds) Advances in Conceptual Modeling. ER 2018. Lecture Notes in Computer Science(), vol 11158. Springer, Cham. https://doi.org/10.1007/978-3-030-01391-2_15
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DOI: https://doi.org/10.1007/978-3-030-01391-2_15
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