IR Design for Application-Specific Natural Language: A Case Study on Traffic Data
In the realm of software applications in the transportation industry, Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their ease of use and various other benefits. With the ceaseless progress in computer performance and the rapid development of large-scale models, the possib...
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| creator | Hu, Wei Wang, Xuhong Wang, Ding Yao, Shengyue Mao, Zuqiu Li, Li Wang, Fei-Yue Lin, Yilun |
| description | In the realm of software applications in the transportation industry,
Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their
ease of use and various other benefits. With the ceaseless progress in computer
performance and the rapid development of large-scale models, the possibility of
programming using natural language in specified applications - referred to as
Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits
greater flexibility and freedom, which, in turn, leads to an increase in
computational complexity for parsing and a decrease in processing performance.
To tackle this issue, our paper advances a design for an intermediate
representation (IR) that caters to ASNL and can uniformly process
transportation data into graph data format, improving data processing
performance. Experimental comparisons reveal that in standard data query
operations, our proposed IR design can achieve a speed improvement of over
forty times compared to direct usage of standard XML format data. |
| doi_str_mv | 10.48550/arxiv.2307.06983 |
| format | Article |
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Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their
ease of use and various other benefits. With the ceaseless progress in computer
performance and the rapid development of large-scale models, the possibility of
programming using natural language in specified applications - referred to as
Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits
greater flexibility and freedom, which, in turn, leads to an increase in
computational complexity for parsing and a decrease in processing performance.
To tackle this issue, our paper advances a design for an intermediate
representation (IR) that caters to ASNL and can uniformly process
transportation data into graph data format, improving data processing
performance. Experimental comparisons reveal that in standard data query
operations, our proposed IR design can achieve a speed improvement of over
forty times compared to direct usage of standard XML format data.</description><identifier>DOI: 10.48550/arxiv.2307.06983</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Programming Languages ; Computer Science - Software Engineering</subject><creationdate>2023-07</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,777,882</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2307.06983$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2307.06983$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Hu, Wei</creatorcontrib><creatorcontrib>Wang, Xuhong</creatorcontrib><creatorcontrib>Wang, Ding</creatorcontrib><creatorcontrib>Yao, Shengyue</creatorcontrib><creatorcontrib>Mao, Zuqiu</creatorcontrib><creatorcontrib>Li, Li</creatorcontrib><creatorcontrib>Wang, Fei-Yue</creatorcontrib><creatorcontrib>Lin, Yilun</creatorcontrib><title>IR Design for Application-Specific Natural Language: A Case Study on Traffic Data</title><description>In the realm of software applications in the transportation industry,
Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their
ease of use and various other benefits. With the ceaseless progress in computer
performance and the rapid development of large-scale models, the possibility of
programming using natural language in specified applications - referred to as
Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits
greater flexibility and freedom, which, in turn, leads to an increase in
computational complexity for parsing and a decrease in processing performance.
To tackle this issue, our paper advances a design for an intermediate
representation (IR) that caters to ASNL and can uniformly process
transportation data into graph data format, improving data processing
performance. Experimental comparisons reveal that in standard data query
operations, our proposed IR design can achieve a speed improvement of over
forty times compared to direct usage of standard XML format data.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Programming Languages</subject><subject>Computer Science - Software Engineering</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tOhDAYRrtxYUYfwJV9AbCl9II7wniZhGh02JN_-rekCQIpYJy3d2Z0dTbfd5JDyB1naW6kZA8Qf8J3mgmmU6YKI67Jx-6Tbt0cuoH6MdJymvpgYQnjkOwnZ4MPlr7BskboaQ1Dt0LnHmlJK5gd3S8rHuk40CaCPy-3sMANufLQz-72nxvSPD811WtSv7_sqrJOQGmRaFvgIctywcUB88LbQjnMDJ7IeGa5FFZL43yOiEJxaaTSjBuUaKVgp9-G3P9pL03tFMMXxGN7bmsvbeIXfwtH4g</recordid><startdate>20230713</startdate><enddate>20230713</enddate><creator>Hu, Wei</creator><creator>Wang, Xuhong</creator><creator>Wang, Ding</creator><creator>Yao, Shengyue</creator><creator>Mao, Zuqiu</creator><creator>Li, Li</creator><creator>Wang, Fei-Yue</creator><creator>Lin, Yilun</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230713</creationdate><title>IR Design for Application-Specific Natural Language: A Case Study on Traffic Data</title><author>Hu, Wei ; Wang, Xuhong ; Wang, Ding ; Yao, Shengyue ; Mao, Zuqiu ; Li, Li ; Wang, Fei-Yue ; Lin, Yilun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a673-7c9db224313bd49fc96ed28dc96012c153c758ef4ddd36158567018d5dc530243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Programming Languages</topic><topic>Computer Science - Software Engineering</topic><toplevel>online_resources</toplevel><creatorcontrib>Hu, Wei</creatorcontrib><creatorcontrib>Wang, Xuhong</creatorcontrib><creatorcontrib>Wang, Ding</creatorcontrib><creatorcontrib>Yao, Shengyue</creatorcontrib><creatorcontrib>Mao, Zuqiu</creatorcontrib><creatorcontrib>Li, Li</creatorcontrib><creatorcontrib>Wang, Fei-Yue</creatorcontrib><creatorcontrib>Lin, Yilun</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hu, Wei</au><au>Wang, Xuhong</au><au>Wang, Ding</au><au>Yao, Shengyue</au><au>Mao, Zuqiu</au><au>Li, Li</au><au>Wang, Fei-Yue</au><au>Lin, Yilun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>IR Design for Application-Specific Natural Language: A Case Study on Traffic Data</atitle><date>2023-07-13</date><risdate>2023</risdate><abstract>In the realm of software applications in the transportation industry,
Domain-Specific Languages (DSLs) have enjoyed widespread adoption due to their
ease of use and various other benefits. With the ceaseless progress in computer
performance and the rapid development of large-scale models, the possibility of
programming using natural language in specified applications - referred to as
Application-Specific Natural Language (ASNL) - has emerged. ASNL exhibits
greater flexibility and freedom, which, in turn, leads to an increase in
computational complexity for parsing and a decrease in processing performance.
To tackle this issue, our paper advances a design for an intermediate
representation (IR) that caters to ASNL and can uniformly process
transportation data into graph data format, improving data processing
performance. Experimental comparisons reveal that in standard data query
operations, our proposed IR design can achieve a speed improvement of over
forty times compared to direct usage of standard XML format data.</abstract><doi>10.48550/arxiv.2307.06983</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Programming Languages Computer Science - Software Engineering |
| title | IR Design for Application-Specific Natural Language: A Case Study on Traffic Data |
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