{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:34:43Z","timestamp":1776976483241,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>This study proposes a Lakehouse Collaborative Adaptive Transformer Semantic Representation Algorithm (LC-ATSR) to address the core challenge of semantic retrieval of unstructured data in a Lakehouse integrated data platform. The algorithm uses a transformer as its backbone and incorporates a dual-dimensional adaptive mechanism of storage attributes and data types to construct an integrated \"preprocessing-representation-retrieval\" framework. The core designs include a storage attribute-enhanced attention mechanism that integrates Lake storage tags into self-attention computation to adapt to heterogeneous storage characteristics; a lightweight semantic compression and multi-modal alignment module that reduces vector dimensions (from 768 to 256) while constructing a unified semantic space; and a Lakehouse incremental semantic indexing mechanism that dynamically updates the index based on LSH hashing. The experimental results show that the LC-ATSR algorithm achieves a P@10 score of 89.7% and an F1 score of 88.2%, significantly outperforming mainstream algorithms such as BERT, DPR, and RoBERTa. The single-retrieval latency was 18.3ms, the incremental index construction time was reduced by 68.4% compared with the full-data approach, and the accuracy fluctuation was only 3.2% in heterogeneous data scenarios. The retrieval system built based on this algorithm achieves a 99.8% functional pass rate, a throughput of 286 QPS with 500 concurrent connections, and a response time of 21.3ms, meeting the engineering requirements of enterprise data platforms and providing technical support for the value mining of unstructured data.<\/jats:p>","DOI":"10.31449\/inf.v50i11.13673","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:37:48Z","timestamp":1776973068000},"source":"Crossref","is-referenced-by-count":0,"title":["LC-ATSR: A Transformer-Based Approach for Semantic Retrieval in Lakehouse Data Platforms"],"prefix":"10.31449","volume":"50","author":[{"given":"Youfang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,4,23]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/13673\/6663","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/13673\/6663","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T19:37:49Z","timestamp":1776973069000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/13673"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":0,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,4,23]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i11.13673","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4,23]]}}}