Budget-Constrained Runtime Allocation of Linked Data Services in Stream Processing
Abstract Stream processing requires integrating with background knowledge in order to become rich knowledge. As a promising approach, it is getting important to combine streaming data with linked open data. However, since linked data change dynamically, it is impossible to synchronize their distribu...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Data Science and Engineering |
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| Online Access: | https://doi.org/10.1007/s41019-024-00277-4 |
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| _version_ | 1849725086695686144 |
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| author | Jungkyu Han Sejin Chun |
| author_facet | Jungkyu Han Sejin Chun |
| author_sort | Jungkyu Han |
| collection | DOAJ |
| description | Abstract Stream processing requires integrating with background knowledge in order to become rich knowledge. As a promising approach, it is getting important to combine streaming data with linked open data. However, since linked data change dynamically, it is impossible to synchronize their distributed data sources perfectly and seamlessly. To reduce the high cost of the synchronization, the materialized views (or views) that store local copies of remote sources are used but may degrade the accuracy of stream processing. To balance response time against accuracy, recent works manage a refresh budget—that is, the limited cost allocated for updating views over remote sources. However, they fail to allocate a refresh budget and produce a low accuracy when a tight deadline is given. To solve the problem, we propose an efficient method of allocating a refresh budget to view updates. The proposed method updates views both in the background and on demand. Experimental results with real and synthetic data sets show that the proposed method achieves superiority in terms of answer staleness, resource utilization, and refresh budget usage. |
| format | Article |
| id | doaj-art-d79eaae5ac654826b59b44bc26742589 |
| institution | DOAJ |
| issn | 2364-1185 2364-1541 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Data Science and Engineering |
| spelling | doaj-art-d79eaae5ac654826b59b44bc267425892025-08-20T03:10:34ZengSpringerOpenData Science and Engineering2364-11852364-15412025-02-0110227729510.1007/s41019-024-00277-4Budget-Constrained Runtime Allocation of Linked Data Services in Stream ProcessingJungkyu Han0Sejin Chun1Department of Computer Engineering, Dong-A UniversityDepartment of Computer Engineering, Dong-A UniversityAbstract Stream processing requires integrating with background knowledge in order to become rich knowledge. As a promising approach, it is getting important to combine streaming data with linked open data. However, since linked data change dynamically, it is impossible to synchronize their distributed data sources perfectly and seamlessly. To reduce the high cost of the synchronization, the materialized views (or views) that store local copies of remote sources are used but may degrade the accuracy of stream processing. To balance response time against accuracy, recent works manage a refresh budget—that is, the limited cost allocated for updating views over remote sources. However, they fail to allocate a refresh budget and produce a low accuracy when a tight deadline is given. To solve the problem, we propose an efficient method of allocating a refresh budget to view updates. The proposed method updates views both in the background and on demand. Experimental results with real and synthetic data sets show that the proposed method achieves superiority in terms of answer staleness, resource utilization, and refresh budget usage.https://doi.org/10.1007/s41019-024-00277-4Stream reasoningUpdate budgetLinked dataStream processing |
| spellingShingle | Jungkyu Han Sejin Chun Budget-Constrained Runtime Allocation of Linked Data Services in Stream Processing Data Science and Engineering Stream reasoning Update budget Linked data Stream processing |
| title | Budget-Constrained Runtime Allocation of Linked Data Services in Stream Processing |
| title_full | Budget-Constrained Runtime Allocation of Linked Data Services in Stream Processing |
| title_fullStr | Budget-Constrained Runtime Allocation of Linked Data Services in Stream Processing |
| title_full_unstemmed | Budget-Constrained Runtime Allocation of Linked Data Services in Stream Processing |
| title_short | Budget-Constrained Runtime Allocation of Linked Data Services in Stream Processing |
| title_sort | budget constrained runtime allocation of linked data services in stream processing |
| topic | Stream reasoning Update budget Linked data Stream processing |
| url | https://doi.org/10.1007/s41019-024-00277-4 |
| work_keys_str_mv | AT jungkyuhan budgetconstrainedruntimeallocationoflinkeddataservicesinstreamprocessing AT sejinchun budgetconstrainedruntimeallocationoflinkeddataservicesinstreamprocessing |