{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:16:35Z","timestamp":1773263795061,"version":"3.50.1"},"reference-count":59,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,12]]},"DOI":"10.1109\/icdmw69685.2025.00051","type":"proceedings-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T19:50:39Z","timestamp":1773172239000},"page":"392-400","source":"Crossref","is-referenced-by-count":0,"title":["Automated Indicator Mining and Forecast-Ready Feature Engineering via LLMs: Application in Colocation Data Center Supply Chains"],"prefix":"10.1109","author":[{"given":"Pegah","family":"Mavaie","sequence":"first","affiliation":[{"name":"Hitachi R&#x0026;D,SDSL Lab,Santa Clara,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neda","family":"Zarayeneh","sequence":"additional","affiliation":[{"name":"Hitachi R&#x0026;D,SDSL Lab,Santa Clara,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravigopal","family":"Vennelakanti","sequence":"additional","affiliation":[{"name":"Hitachi R&#x0026;D,SDSL Lab,Santa Clara,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.1955.0005"},{"key":"ref2","article-title":"Title of paper with only first word capitalized","author":"Nicole","journal-title":"J. Name Stand. Abbrev."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TJMJ.1987.4549593"},{"key":"ref4","volume-title":"The Technical Writer\u2019s Handbook","author":"Young","year":"1989"},{"key":"ref5","article-title":"Exploring the Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation","author":"Aghaei","year":"2025","journal-title":"arXiv preprint"},{"key":"ref6","article-title":"The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation","author":"Aghaei","year":"2025","journal-title":"arXiv preprint"},{"key":"ref7","author":"Ziegler","year":"2024","journal-title":"Automating Information Extraction from Financial Reports Using LLMs"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.2307\/2297968"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1093\/qje\/qjw024"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4076(98)00009-8"},{"key":"ref11","article-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"1970","journal-title":"Holden-Day"},{"key":"ref12","first-page":"1877","article-title":"Language Models are Few-Shot Learners","volume":"33","author":"Brown","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref13","article-title":"Knowledge Extraction from LLMs for Scalable Historical Data Annotation","author":"Cabrera","year":"2024","journal-title":"ResearchGate"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.2307\/41703503"},{"issue":"5","key":"ref15","first-page":"849","article-title":"Big data analytics in supply chain management","volume":"49","author":"Choi","year":"2018","journal-title":"Decision Sciences"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/vl\/N19-142"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/math10142437"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.2308\/ISYS-2023-047"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123477"},{"issue":"5","key":"ref20","first-page":"1","article-title":"Automated Feature Engineering: A Survey","volume":"53","author":"Kalyanaraman","year":"2020","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"ref21","first-page":"115","article-title":"Clinical Information Extraction with Large Language Models: A Case Study on Organ Procurement","volume-title":"AMIA Annual Symposium Proceedings","author":"Adam","year":"2025"},{"key":"ref22","first-page":"100","article-title":"Sentiment analysis for supply chain risk management: A deep learning approach","volume":"131","author":"Li","year":"2019","journal-title":"Computers & Industrial Engineering"},{"key":"ref23","article-title":"LLM-driven Interactive Planning for Supply Chain Optimization","volume-title":"International Conference on Operations Research and Management Science","author":"Li","year":"2024"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3737873"},{"key":"ref25","article-title":"The Memorization Problem: Can We Trust LLMs\u2019 Economic Forecasts?","author":"Ludwig","year":"2025","journal-title":"NBER Working Paper Series"},{"issue":"3","key":"ref26","first-page":"253","article-title":"Supply chain forecasting: A review and research agenda","volume":"66","author":"\u00d6zer","year":"2000","journal-title":"International Journal of Production Economics"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.11591\/ijece.v15i1.pp1027-1037"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-80775-6_13"},{"issue":"12","key":"ref29","first-page":"1935","article-title":"Feature engineering for machine learning","volume-title":"Proceedings of the VLDB Endowment","volume":"11","author":"Schelter","year":"2018"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.2307\/1912017"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"issue":"2","key":"ref32","first-page":"112","article-title":"Web Mining for Financial Market Prediction: A Survey","volume":"5","author":"Wang","year":"2019","journal-title":"Journal of Financial Data Science"},{"key":"ref33","article-title":"Introductory Econometrics: A Modern Approach","author":"Wooldridge","year":"2016","journal-title":"Cengage Learning"},{"key":"ref34","article-title":"A Survey of Generative Information Extraction","volume-title":"Proceedings of the 2025 Conference on Computational Linguistics (COLING)","author":"Xu","year":"2025"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.2004"},{"key":"ref36","article-title":"LLM-TSF: Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics","author":"Zhou","year":"2023","journal-title":"arXiv preprint"},{"key":"ref37","article-title":"GPT-4 Technical Report","volume-title":"arXiv preprint","year":"2023"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1410"},{"key":"ref39","article-title":"Text and Code Embeddings by OpenAI","volume-title":"OpenAI Blog","author":"Neelakantan","year":"2022"},{"key":"ref40","article-title":"A case study in the application of information retrieval technology","volume-title":"Proceedings of the Fifth Text REtrieval Conference (TREC-5)","author":"Gunning","year":"2005"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.25080\/majora-92bf1922-00a"},{"key":"ref42","volume-title":"Corporate Website","year":"2025"},{"key":"ref43","volume-title":"Corporate Website","year":"2025"},{"key":"ref44","volume-title":"Corporate Website","year":"2025"},{"key":"ref45","volume-title":"Corporate Website","year":"2025"},{"key":"ref46","volume-title":"Corporate Website","year":"2025"},{"key":"ref47","volume-title":"Federal Reserve Economic Data (FRED)","year":"2025"},{"key":"ref48","volume-title":"World Bank Open Data","year":"2025"},{"key":"ref49","volume-title":"Corporate Website","year":"2025"},{"key":"ref50","volume-title":"Corporate Website","year":"2025"},{"key":"ref51","volume-title":"Google Search API","year":"2025"},{"key":"ref52","article-title":"Nvidia\u2019s Data Center Revenue Rose 56% in Q2 2025","volume-title":"Wall Street Journal","year":"2025"},{"key":"ref53","article-title":"Nvidia Q2 2025 Earnings: Data Center Sales up 56% to $41.1B","volume-title":"A P News","year":"2025"},{"key":"ref54","volume-title":"Global Data Center Trends 2025","year":"2025"},{"key":"ref55","volume-title":"North America Data Center Trends H1 2025: AI and Hyperscaler Demand Lead to Record Low Vacancy","year":"2025"},{"key":"ref56","article-title":"Vacancy Rates in Top Data Center Markets Hit Record Low","volume-title":"DatacenterDynamics","author":"Chernicoff","year":"2025"},{"key":"ref57","article-title":"Employment Projections:2022\u20132032 Summary","volume-title":"U.S. Department of Labor","year":"2024"},{"key":"ref58","volume-title":"Data.gov. [Dataset Title]. U.S. Government"},{"key":"ref59","volume-title":"LLMChain Documentation","year":"2025"}],"event":{"name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","location":"Washington, DC, USA","start":{"date-parts":[[2025,11,12]]},"end":{"date-parts":[[2025,11,15]]}},"container-title":["2025 IEEE International Conference on Data Mining Workshops (ICDMW)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11415623\/11415713\/11415849.pdf?arnumber=11415849","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T05:16:23Z","timestamp":1773206183000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11415849\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,12]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/icdmw69685.2025.00051","relation":{},"subject":[],"published":{"date-parts":[[2025,11,12]]}}}