文章基本信息

标题：Contextual crowd intelligence
本地全文：下载
作者：Beng Chin Ooi ; Kian Lee Tan ; Quoc Trung Tran 等
期刊名称：SIGKDD Explorations
印刷版ISSN：1931-0145
出版年度：2014
卷号：16
期号：1
页码：39-46
DOI：10.1145/2674026.2674032
出版社：Association for Computing Machinery
摘要：Most data analytics applications are industry/domain specific, e.g., predicting patients at high risk of being admitted to intensive care unit in the healthcare sector or predicting malicious SMSs in the telecommunication sector. Existing solutions are based on "best practices", i.e., the systems' decisions are knowledge-driven and/or data-driven . However, there are rules and exceptional cases that can only be precisely formulated and identified by subject-matter experts (SMEs) who have accumulated many years of experience. This paper envisions a more intelligent database management system (DBMS) that captures such knowledge to effectively address the industry/domain specific applications. At the core, the system is a hybrid human-machine database engine where the machine interacts with the SMEs as part of a feedback loop to gather, infer, ascertain and enhance the database knowledge and processing. We discuss the challenges towards building such a system through examples in healthcare predictive analysis -- a popular area for big data analytics.