Academic Journal
PECJ: Stream Window Join on Disorder Data Streams with Proactive Error Compensation
| العنوان: | PECJ: Stream Window Join on Disorder Data Streams with Proactive Error Compensation |
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| المؤلفون: | Zeng, Xianzhi, Zhang, Shuhao, Zhong, Hongbin, Zhang, Hao, Lu, Mian, Zheng, Zhao, Chen, Yuqiang |
| المساهمون: | Nanyang Technological University, Ministry of Education - Singapore |
| المصدر: | Proceedings of the ACM on Management of Data ; volume 2, issue 1, page 1-24 ; ISSN 2836-6573 |
| بيانات النشر: | Association for Computing Machinery (ACM) |
| سنة النشر: | 2024 |
| الوصف: | Stream Window Join (SWJ), a vital operation in stream analytics, struggles with achieving a balance between accuracy and latency due to out-of-order data arrivals. Existing methods predominantly rely on adaptive buffering, but often fall short in performance, thereby constraining practical applications. We introduce PECJ, a solution that proactively incorporates unobserved data to enhance accuracy while reducing latency, thus requiring robust predictive modeling of stream oscillation. At the heart of PECJ lies a mathematical formulation of the posterior distribution approximation (PDA) problem using variational inference (VI). This approach circumvents error propagation while meeting the low-latency demands of SWJ. We detail the implementation of PECJ, striking a balance between complexity and generality, and discuss both analytical and learning-based approaches. Experimental evaluations reveal PECJ's superior performance. The successful integration of PECJ into a multi-threaded SWJ benchmark testbed further establishes its practical value, demonstrating promising advancements in enhancing data stream processing capabilities amidst out-of-order data. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| DOI: | 10.1145/3639268 |
| الاتاحة: | http://dx.doi.org/10.1145/3639268 https://dl.acm.org/doi/pdf/10.1145/3639268 |
| رقم الانضمام: | edsbas.FB85703D |
| قاعدة البيانات: | BASE |
| DOI: | 10.1145/3639268 |
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