AI-Enabled Processes: The Age of Artificial Intelligence and Big Data

被引:2
|
作者
Beheshti, Amin [1 ]
Benatallah, Boualem [2 ]
Sheng, Quan Z. [1 ]
Casati, Fabio [3 ]
Nezhad, Hamid-Reza Motahari [1 ]
Yang, Jian [1 ]
Ghose, Aditya [4 ]
机构
[1] Macquarie Univ, Sydney, NSW, Australia
[2] Univ New South Wales, Sydney, NSW, Australia
[3] Servicenow, Santa Clara, CA USA
[4] Univ Wollongong, Wollongong, NSW, Australia
关键词
Business process management; Process data science; AI-enabled processes; Artificial intelligence;
D O I
10.1007/978-3-031-14135-5_29
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Business processes, i.e., a set of coordinated tasks and activities carried out manually/automatically to achieve a business objective or goal, are central to the operation of public and private enterprises. Modern processes are often highly complex, data-driven, and knowledge-intensive. In such processes, it is not sufficient to focus on data storage/analysis; and the knowledge workers will need to collect, understand, and relate the big data (from open, private, social, and IoT data islands) to process analysis. Today, the advancement in Artificial Intelligence (AI) and Data Science can transform business processes in fundamental ways; by assisting knowledge workers in communicating analysis findings, supporting evidence, and making decisions. This tutorial gives an overview of services in organizations, businesses, and society. We introduce notions of Data Lake as a Service and Knowledge Lake as a Service and discuss their role in analyzing data-centric and knowledge-intensive processes in the age of Artificial Intelligence and Big Data. We introduce the novel notion of AI-enabled Processes and discuss methods for building intelligent Data Lakes and Knowledge Lakes as the foundation for Process Automation and Cognitive Augmentation in Business Process Management. The tutorial also points out challenges and research opportunities.
引用
收藏
页码:321 / 335
页数:15
相关论文
共 50 条
  • [21] AI-enabled future crime
    Caldwell, M.
    Andrews, J. T. A.
    Tanay, T.
    Griffin, L. D.
    [J]. CRIME SCIENCE, 2020, 9 (01)
  • [22] Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data
    Quentin André
    Ziv Carmon
    Klaus Wertenbroch
    Alia Crum
    Douglas Frank
    William Goldstein
    Joel Huber
    Leaf van Boven
    Bernd Weber
    Haiyang Yang
    [J]. Customer Needs and Solutions, 2018, 5 (1-2) : 28 - 37
  • [23] AI-Enabled Emotion Communication
    Li, Yujie
    Jiang, Yinging
    Tian, Daxin
    Hu, Long
    Lu, Huimin
    Yuan, Zhiyong
    [J]. IEEE NETWORK, 2019, 33 (06): : 15 - 21
  • [24] AI-Enabled EW Systems
    不详
    [J]. MICROWAVE JOURNAL, 2024, 67 (06)
  • [25] Design and performance of an AI-enabled threat intelligence framework for IoT-enabled autonomous vehicles
    Akhunzada, Adnan
    Al-Shamayleh, Ahmad Sami
    Zeadally, Sherali
    Almogren, Ahmad
    Abu-Shareha, Ahmad Adel
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2024, 119
  • [26] A scalable and transparent data pipeline for AI-enabled health data ecosystems
    Namli, Tuncay
    Sinaci, Ali Anil
    Gonul, Suat
    Herguido, Cristina Ruiz
    Garcia-Canadilla, Patricia
    Munoz, Adriana Modrego
    Esteve, Arnau Valls
    Erturkmen, Goekce Banu Laleci
    [J]. FRONTIERS IN MEDICINE, 2024, 11
  • [27] Data Security Challenges in AI-Enabled Medical Device Software
    Jayaneththi, Buddhika
    McCaffery, Fergal
    Regan, Gilbert
    [J]. 2023 31ST IRISH CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COGNITIVE SCIENCE, AICS, 2023,
  • [28] Editorial: AI-Enabled Data Science for COVID-19
    Yan, Da
    Qin, Hong
    Wu, Hsiang-Yun
    Chen, Jake Y.
    [J]. FRONTIERS IN BIG DATA, 2021, 4
  • [29] Taming Data Quality in AI-Enabled Industrial Internet of Things
    Sen, Sagar
    Husom, Erik Johannes
    Goknil, Arda
    Tverdal, Simeon
    Phu Nguyen
    Mancisidor, Iker
    [J]. IEEE SOFTWARE, 2022, 39 (06) : 35 - 42
  • [30] Consumer private data collection strategies for AI-enabled products
    Yang, Zhaojun
    Li, Yinmeng
    Sun, Jun
    Hu, Xu
    Zhang, Yali
    [J]. Electronic Commerce Research and Applications, 2024, 68