In today's competitive and fast-evolving business environment, it is a critical time for organizations and individuals to rethink how to deal with career-related tasks (e.g., recruitment, development, incentive, and career planning) more scientifically. Indeed, the availability of large-scale career-related data provides unparalleled opportunities for business leaders and researchers to understand the scientific rules of careers. Career Science, an emerging interdisciplinary research direction, has increasingly attracted attention from data mining communities. As a result, extensive intelligent tools and models have been developed for effective and efficient decision-making in complex scenarios affecting talents, organizations, and the labor market. To this end, this workshop aims to bring together researchers and practitioners to discuss critical problems in career-related domains and potential data-driven solutions by leveraging state-of-the-art data mining technologies.
Program Sketch (December 1, 2023)
11:00-11:05 Opening Remarks
11:05-11:25 Industrial Invited Talk
11:25-11:45 Paper presentation 1: Exploring the Confounding Factors of Academic Career Success: An Empirical Study with Deep Predictive Modeling, Speaker: Chenguang Du
11:45-12:05 Paper presentation 2: Exploiting Time-Aware Spectral Neural Networks for Employee Turnover Analysis: An Organizational Change Perspective, Speaker: Chen Zhu
12:05-13:00 Lunch & Break
13:00-13:20 Paper presentation 3: Analyzing Hierarchical Organizational Structure Change: Strategic Planning Model from Personnel Perspective, Speaker: Shan Huang
13:20-13:40 Paper presentation 4: The Impact of AlGC on Organizational Knowledge Creation: From the Perspective of Adaptive Structuration Theory, Speaker: Xi Zhang
13:40-13:45 Closing Remarks
This workshop aims to bring together leading researchers and practitioners to exchange and share their experiences and the latest research/application results on all aspects of Career Science based on data mining technologies. It will provide a premier interdisciplinary forum to discuss the most recent trends, innovations, applications, real-world challenges encountered, and corresponding data-driven solutions in relevant domains.
Topics of interest include but not limited to:
We invite the submission of regular research papers (max 8 pages plus 2 extra pages), including all content and references. Submissions must be in PDF format, and formatted according to the new Standard IEEE Conference Proceedings Template.
Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Considering the practical characteristics of this workshop, to enrich the presentations, we strongly encourage the authors to submit their demonstrations, e.g., intelligent system for career analytics, which will also be evaluated during the review process.
All the papers are required to be submitted via the CyberChair system.
By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press.
For more questions about the workshop and submissions, please send email to ICDMW_AICS2023@googlegroups.com
● Workshop papers submission: September 15, 2023
● Notification of workshop papers acceptance to authors: September 24, 2023
● Camera-ready deadline and copyright form: October 15, 2023
● Workshop Day: December 1, 2023
Hengshu Zhu
Career Science Lab (CSL), BOSS Zhipin
Fuzhen Zhuang
Beihang University
Keli Xiao
Stony Brook university
Xi Zhang
Tianjin University
Yang Yang
Nanjing University of Science and Technology
Hui Xiong
The Hong Kong University of Science and Technology (Guangzhou)
Peking University
Abstract
We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a "partial evaluation and assembly" framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly.
Short Biography
Lei Zou received his BS degree and Ph.D. degree in Computer Science at Huazhong University of Science and Technology (HUST) in 2003 and 2009, respectively. He received a CCF (China Computer Federation) Doctoral Dissertation Nomination Award in 2009, won Second Class Prize of CCF Natural Science Award in 2014 and Second Class Prize of Natural Science of the Ministry of Education, China in 2017. Since September 2009, he joined Institute of Computer Science and Technology (ICST) of Peking University (PKU) as a faculty member. He has been a professor in PKU since August 2017. Before joining PKU, he visited Hong Kong University of Science and Technology in 2007 and University of Waterloo in 2008 as a visiting scholar. His recent research interests include graph databases, knowledge graph, particularly in graph-based RDF data management. He has published more than 50 papers, including more than 30 papers published in reputed journals and major international conferences, such as SIGMOD, VLDB, ICDE, TODS, TKDE, VLDB Journal. Lei Zou's research is supported by NSFC-Young Excellent Talent Project and National Key Research and Development Program of China.
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Abstract
Graph technology has been playing increasingly important roles in various machine learning, data analytics, and resource management domains, thus more and more companies have been adopting/utilizing graph platforms, either on cloud or on premise, to support their business. In this talk, we will investigate various factors that contribute to the success of a graph platform for enterprise use, ranging from graph data organization, runtime scheduling, analytics optimization, to some thoughts on recent graph deep learning frameworks. We will discuss through some concrete examples on how to effectively put together the above building blocks, so as to form a comprehensive graph platform delivering efficient end-to-end performance to meet the requirements in many industrial scenarios. At last, we will brief some recent activities in graph technology community, such as the discussions on its standardization, along with the summary of the challenges and opportunities.
Short Biography
Yinglong Xia is a chief architect at Huawei Research America, working on AI platforms and Graph Engine Service (GES, https://www.huaweicloud.com/en-us/product/ges.html). Prior to that, he was a technical leader and research staff member at IBM Watson Research Center, exploring graph database and reasoning framework, creating the IBM System G (http://systemg.mybluemix.net/) platform. He has solid experience in both industrial research and product development, already published 60+ technical papers and filed 30+ patents. He serves as a technical advisory committee (TAC) member in Linux Foundation, a board member of LDBC, and an associate editor of IEEE trans. Knowledge and Data Engineering (TKDE), and IEEE trans. Big Data (TBD); he is a general co-chair of IEEE HiPC'19, vice co-chair of IEEE BigData'19, TPC member of KDD'19, VLDB'19, and ICDE'19, etc.
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