Керування даними часових рядів у системах розумного дому: баланс між аналітикою в реальному часі та зберіганням даних
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The article presents a novel approach to managing time-series data in smart home systems that balances real-time analytics with efficient historical data storage. A hybrid architecture combining Azure CosmosDB for real-time data processing and Azure Synapse for historical data analytics demonstrates significant performance advantages. The system achieves up to 165-fold acceleration in analytical query execution ( 3.3 seconds to 20 milliseconds for time-series aggregation queries) while reducing the number of read operations by 17. Implementing optimization strategies, such as state-based recording and interval-based storage, significantly reduces data volume while maintaining temporal data integrity. The system demonstrates linear scalability, handling up to 1 million write operations per second, and achieves compression ratios of up to 5:1 in Cosmos DB and 10:1 in Synapse.
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Талах М. В., Дворжак В. В., Ушенко Ю. О. Керування даними часових рядів у системах розумного дому: баланс між аналітикою в реальному часі та зберіганням даних // Оптико-електроннi iнформацiйно-енергетичнi технологiї. 2025. № 2. С. 54-61. URI: https://oeipt.vntu.edu.ua/index.php/oeipt/article/view/796.
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Список використаної літератури (12)
- Yu, T., & Wang, X. (2020). Real-Time Data Analytics in Internet of Things Systems. In H. X. Lin, A. Shoshani, & J. M. Wing (Eds.), Handbook of Real-Time Computing (pp. 1–28). Singapore: Springer. https://doi.org/10.1007/978-981-4585-87-3_25-1.
- Hu, C., Sun, Z., Li, C., Zhang, Y., & Xing, C. (2023). Survey of Time Series Data Generation in IoT. Sensors, 23(15), 6976. https://doi.org/10.3390/s23156976.
- Stojmenovic, I., & Wen, S. (2014). The Fog Computing Paradigm: Scenarios and Security Issues. In Proceedings of the 2014 Federated Conference on Computer Science and Information Systems (FedCSIS) (Vol. 2, pp. 1–8). Warsaw, Poland: IEEE. https://doi.org/10.15439/2014F500.
- Al-Ali, A. R., Zualkernan, I. A., Rashid, M., Gupta, R., & AliKarar, M. (2017). A Smart Home Energy Management System Using IoT and Big Data Analytics Approach. IEEE Transactions on Consumer Electronics, 63(4), 426–434. https://doi.org/10.1109/TCE.2017.015014.
- Cattell, R. (2011). Scalable SQL and NoSQL Data Stores. ACM SIGMOD Record, 39(4), 12–27. https://doi.org/10.1145/1978915.1978919.
- Han, J., Haihong, E., Le, G., & Du, J. (2011). Survey on NoSQL Database. In Proceedings of the 6th International Conference on Pervasive Computing and Applications (ICPCA) (pp. 363–366). Port Elizabeth, South Africa: IEEE. https://doi.org/10.1109/ICPCA.2011.6106531.
- Chaudhuri, S., & Dayal, U. (1997). An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD Record, 26(1), 65–74. https://doi.org/10.1145/248603.248616.
- Cai, L., & Zhu, Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14, 2. https://doi.org/10.5334/dsj-2015-002.
- Kang, Y. S., Park, I.-H., Rhee, J., & Lee, Y.-H. (2016). MongoDB-Based Repository Design for IoT-Generated RFID/Sensor Big Data. IEEE Sensors Journal, 16(2), 485–497. https://doi.org/10.1109/JSEN.2015.2483499.
- Rinaldi, S., Pasetti, M., Sisinni, E., Gentili, M., & Flammini, A. (2019). Impact of Data Model on Performance of Time Series Database for Internet of Things Applications. In 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1–6). Auckland, New Zealand: IEEE. https://doi.org/10.1109/I2MTC.2019.8826829.