AI-enabled legacy data integration with privacy protection: a case study on regional cloud arbitration court
Abstract This paper presents an interesting case study on Legacy Data Integration (LDI for short) for a Regional Cloud Arbitration Court. Due to the inconsistent structure and presentation, legacy arbitration cases can hardly integrate into the Cloud Court unless processed manually. In this study, w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-10-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-023-00500-z |
| _version_ | 1797556413627629568 |
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| author | Jie Song Haifei Fu Tianzhe Jiao Dongqi Wang |
| author_facet | Jie Song Haifei Fu Tianzhe Jiao Dongqi Wang |
| author_sort | Jie Song |
| collection | DOAJ |
| description | Abstract This paper presents an interesting case study on Legacy Data Integration (LDI for short) for a Regional Cloud Arbitration Court. Due to the inconsistent structure and presentation, legacy arbitration cases can hardly integrate into the Cloud Court unless processed manually. In this study, we propose an AI-enabled LDI method to replace the costly manual approach and ensure privacy protection during the process. We trained AI models to replace tasks such as reading and understanding legacy cases, removing privacy information, composing new case records, and inputting them through the system interfaces. Our approach employs Optical Character Recognition (OCR), text classification, and Named Entity Recognition (NER) to transform legacy data into a system format. We applied our method to a Cloud Arbitration Court in Liaoning Province, China, and achieved a comparable privacy filtering effect while retaining the maximum amount of information. Our method demonstrated similar effectiveness as the manual LDI, but with greater efficiency, saving 90% of the workforce and achieving a 60%-70% information extraction rate compared to manual work. With the increasing development of informationalization and intelligentization in judgment and arbitration, many courts are adopting ABC technologies, namely Artificial intelligence, Big data, and Cloud computing, to build the court system. Our method provides a practical reference for integrating legal data into the system. |
| first_indexed | 2024-03-10T17:01:31Z |
| format | Article |
| id | doaj.art-0409777857c6409895a8d681e63c9b60 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-03-10T17:01:31Z |
| publishDate | 2023-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-0409777857c6409895a8d681e63c9b602023-11-20T10:56:13ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2023-10-0112111810.1186/s13677-023-00500-zAI-enabled legacy data integration with privacy protection: a case study on regional cloud arbitration courtJie Song0Haifei Fu1Tianzhe Jiao2Dongqi Wang3Software College, Northeastern UniversitySoftware College, Northeastern UniversitySoftware College, Northeastern UniversitySoftware College, Northeastern UniversityAbstract This paper presents an interesting case study on Legacy Data Integration (LDI for short) for a Regional Cloud Arbitration Court. Due to the inconsistent structure and presentation, legacy arbitration cases can hardly integrate into the Cloud Court unless processed manually. In this study, we propose an AI-enabled LDI method to replace the costly manual approach and ensure privacy protection during the process. We trained AI models to replace tasks such as reading and understanding legacy cases, removing privacy information, composing new case records, and inputting them through the system interfaces. Our approach employs Optical Character Recognition (OCR), text classification, and Named Entity Recognition (NER) to transform legacy data into a system format. We applied our method to a Cloud Arbitration Court in Liaoning Province, China, and achieved a comparable privacy filtering effect while retaining the maximum amount of information. Our method demonstrated similar effectiveness as the manual LDI, but with greater efficiency, saving 90% of the workforce and achieving a 60%-70% information extraction rate compared to manual work. With the increasing development of informationalization and intelligentization in judgment and arbitration, many courts are adopting ABC technologies, namely Artificial intelligence, Big data, and Cloud computing, to build the court system. Our method provides a practical reference for integrating legal data into the system.https://doi.org/10.1186/s13677-023-00500-zLegacy data integrationPrivacy filteringAI-enabledCloud courtNatural language processing |
| spellingShingle | Jie Song Haifei Fu Tianzhe Jiao Dongqi Wang AI-enabled legacy data integration with privacy protection: a case study on regional cloud arbitration court Journal of Cloud Computing: Advances, Systems and Applications Legacy data integration Privacy filtering AI-enabled Cloud court Natural language processing |
| title | AI-enabled legacy data integration with privacy protection: a case study on regional cloud arbitration court |
| title_full | AI-enabled legacy data integration with privacy protection: a case study on regional cloud arbitration court |
| title_fullStr | AI-enabled legacy data integration with privacy protection: a case study on regional cloud arbitration court |
| title_full_unstemmed | AI-enabled legacy data integration with privacy protection: a case study on regional cloud arbitration court |
| title_short | AI-enabled legacy data integration with privacy protection: a case study on regional cloud arbitration court |
| title_sort | ai enabled legacy data integration with privacy protection a case study on regional cloud arbitration court |
| topic | Legacy data integration Privacy filtering AI-enabled Cloud court Natural language processing |
| url | https://doi.org/10.1186/s13677-023-00500-z |
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