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iMiner: Mining Inventory Data for Intelligent Management

Published: 03 November 2014 Publication History

Abstract

Inventory management refers to tracing inventory levels, orders and sales of a retailing business. In the current retailing market, a tremendous amount of data regarding stocked goods (items) in an inventory will be generated everyday. Due to the increasing volume of transaction data and the correlated relations of items, it is often a non-trivial task to efficiently and effectively manage stocked goods. In this demo, we present an intelligent system, called iMiner, to ease the management of enormous inventory data. We utilize distributed computing resources to process the huge volume of inventory data, and incorporate the latest advances of data mining technologies into the system to perform the tasks of inventory management, e.g., forecasting inventory, detecting abnormal items, and analyzing inventory aging. Since 2014, iMiner has been deployed as the major inventory management platform of ChangHong Electric Co., Ltd, one of the world's largest TV selling companies in China.

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Cited By

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  • (2024)AI in Managing Perishable Goods InventoryModern Management Science Practices in the Age of AI10.4018/979-8-3693-6720-9.ch002(29-70)Online publication date: 30-Aug-2024
  • (2019)Joint prediction of time series data in inventory managementKnowledge and Information Systems10.1007/s10115-018-1302-y61:2(905-929)Online publication date: 1-Nov-2019
  • (2017)An Advanced Inventory Data Mining System for Business Intelligence2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)10.1109/BigDataService.2017.36(210-217)Online publication date: Apr-2017
  • Show More Cited By

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    Published In

    cover image ACM Conferences
    CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management
    November 2014
    2152 pages
    ISBN:9781450325981
    DOI:10.1145/2661829
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 03 November 2014

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    Author Tags

    1. anomaly detection
    2. inventory aging
    3. inventory forecasting
    4. inventory management

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    Acceptance Rates

    CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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    Cited By

    View all
    • (2024)AI in Managing Perishable Goods InventoryModern Management Science Practices in the Age of AI10.4018/979-8-3693-6720-9.ch002(29-70)Online publication date: 30-Aug-2024
    • (2019)Joint prediction of time series data in inventory managementKnowledge and Information Systems10.1007/s10115-018-1302-y61:2(905-929)Online publication date: 1-Nov-2019
    • (2017)An Advanced Inventory Data Mining System for Business Intelligence2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)10.1109/BigDataService.2017.36(210-217)Online publication date: Apr-2017
    • (2017)FIU-Miner (a fast, integrated, and user-friendly system for data mining) and its applicationsKnowledge and Information Systems10.1007/s10115-016-1014-052:2(411-443)Online publication date: 1-Aug-2017
    • (2015)A Two-Step Dynamic Inventory Forecasting Model for Large Manufacturing2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2015.93(749-753)Online publication date: Dec-2015

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