NeurDB: An AI-powered Autonomous Data System
Abstract: In the wake of rapid advancements in AI, we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.
- Database meets deep learning: Challenges and opportunities. SIGMOD Rec., 45(2):17–22, 2016.
- What’s really new with newsql? SIGMOD Rec., 45(2):45–55, 2016.
- Transaction Processing: Concepts and Techniques. Morgan Kaufmann, 1993.
- Michael L. Brodie. Future intelligent information systems: AI and database technologies working together. In AAAI, pages 844–845. AAAI Press / The MIT Press, 1988.
- Input selection for fast feature engineering. In ICDE, pages 577–588. IEEE Computer Society, 2016.
- Model slicing for supporting complex analytics with elastic inference cost and resource constraints. Proc. VLDB Endow., 13(2):86–99, 2019.
- Kinetica: naturalistic multi-touch data visualization. In CHI, pages 897–906. ACM, 2014.
- MB2: decomposed behavior modeling for self-driving database management systems. In SIGMOD Conference, pages 1248–1261. ACM, 2021.
- Big healthcare data analytics: Challenges and applications. In Handbook of Large-Scale Distributed Computing in Smart Healthcare, pages 11–41. Springer, 2017.
- MINT: detecting fraudulent behaviors from time-series relational data. Proc. VLDB Endow., 16(12):3610–3623, 2023.
- Apache SINGA. https://singa.apache.org/, 2024.
- SINGA-Easy: An easy-to-use framework for multimodal analysis. In ACM Multimedia, pages 1293–1302. ACM, 2021.
- Robust and transferable log-based anomaly detection. Proc. ACM Manag. Data, 1(1):64:1–64:26, 2023.
- Data Cube: A relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Discov., 1(1):29–53, 1997.
- Rafiki: machine learning as an analytics service system. Proc. VLDB Endow., 12(2):128–140, 2018.
- Clipper: A low-latency online prediction serving system. In NSDI, pages 613–627. USENIX Association, 2017.
- Deep residual learning for image recognition. In CVPR, pages 770–778. IEEE Computer Society, 2016.
- Learning transferable architectures for scalable image recognition. In CVPR, pages 8697–8710. Computer Vision Foundation / IEEE Computer Society, 2018.
- Incentive-aware decentralized data collaboration. Proc. ACM Manag. Data, 1(2):158:1–158:27, 2023.
- Falcon: A privacy-preserving and interpretable vertical federated learning system. Proc. VLDB Endow., 16(10):2471–2484, 2023.
- Privacy preserving vertical federated learning for tree-based models. Proc. VLDB Endow., 13(11):2090–2103, 2020.
- ForkBase: An efficient storage engine for blockchain and forkable applications. Proc. VLDB Endow., 11(10):1137–1150, 2018.
- TRACER: A framework for facilitating accurate and interpretable analytics for high stakes applications. In SIGMOD Conference, pages 1747–1763. ACM, 2020.
- ELDA: learning explicit dual-interactions for healthcare analytics. In ICDE, pages 393–406. IEEE, 2022.
- PACE: learning effective task decomposition for human-in-the-loop healthcare delivery. In SIGMOD Conference, pages 2156–2168. ACM, 2021.
- MLCask: Efficient management of component evolution in collaborative data analytics pipelines. In ICDE, pages 1655–1666. IEEE, 2021.
- Enabling secure and efficient data analytics pipeline evolution with trusted execution environment. Proc. VLDB Endow., 16(10):2485–2498, 2023.
- The shift from models to compound ai systems. https://bair.berkeley.edu/blog/2024/02/18/compound-ai-systems/, 2024.
- Large language models for business process management: Opportunities and challenges. In BPM (Forum), volume 490 of Lecture Notes in Business Information Processing, pages 107–123. Springer, 2023.
- IP8Value. https://webapp.ip8value.com/, 2024.
- Cerebro: A data system for optimized deep learning model selection. Proc. VLDB Endow., 13(11):2159–2173, 2020.
- Improving keyword spotting and language identification via neural architecture search at scale. In INTERSPEECH, pages 1278–1282. ISCA, 2019.
- Zero-cost proxies for lightweight NAS. In ICLR. OpenReview.net, 2021.
- Unifying and boosting gradient-based training-free neural architecture search. In NeurIPS, 2022.
- How powerful are performance predictors in neural architecture search? In NeurIPS, pages 28454–28469, 2021.
- MArk: Exploiting cloud services for cost-effective, slo-aware machine learning inference serving. In USENIX Annual Technical Conference, pages 1049–1062. USENIX Association, 2019.
- PIQL: success-tolerant query processing in the cloud. Proc. VLDB Endow., 5(3):181–192, 2011.
- Anytime neural architecture search on tabular data. CoRR, abs/2403.10318, 2024.
- Pruning neural networks without any data by iteratively conserving synaptic flow. In NeurIPS, 2020.
- Neural architecture search without training. In ICML, volume 139 of Proceedings of Machine Learning Research, pages 7588–7598. PMLR, 2021.
- Database native model selection: Harnessing deep neural networks in database systems. Proc. VLDB Endow., 17(5):1020–1033, 2024.
- Hierarchical representations for efficient architecture search. In ICLR (Poster). OpenReview.net, 2018.
- Neural factorization machines for sparse predictive analytics. In SIGIR, pages 355–364. ACM, 2017.
- Learning models over relational data using sparse tensors and functional dependencies. ACM Trans. Database Syst., 45(2):7:1–7:66, 2020.
- Powering in-database dynamic model slicing for structured data analytics. CoRR, abs/2405.00568, 2024.
- DyHealth: Making neural networks dynamic for effective healthcare analytics. Proc. VLDB Endow., 15(12):3445–3458, 2022.
- Effective multi-modal retrieval based on stacked auto-encoders. Proc. VLDB Endow., 7(8):649–660, 2014.
- SINGA: putting deep learning in the hands of multimedia users. In ACM Multimedia, pages 25–34. ACM, 2015.
- Index selection in a self-adaptive data base management system. In SIGMOD Conference, pages 1–8. ACM, 1976.
- The EXODUS optimizer generator. In SIGMOD Conference, pages 160–172. ACM Press, 1987.
- PQR: predicting query execution times for autonomous workload management. In ICAC, pages 13–22. IEEE Computer Society, 2008.
- VeriTxn: Verifiable transactions for cloud-native databases with storage disaggregation. Proc. ACM Manag. Data, 1(4):270:1–270:27, 2023.
- The indispensability of dispensable indexes. IEEE Trans. Knowl. Data Eng., 11(1):17–27, 1999.
- iDistance: an adaptive B++{}^{\mbox{+}}start_FLOATSUPERSCRIPT + end_FLOATSUPERSCRIPT-tree based indexing method for nearest neighbor search. ACM Trans. Database Syst., 30(2):364–397, 2005.
- Efficient cost models for spatial queries using r-trees. IEEE Trans. Knowl. Data Eng., 12(1):19–32, 2000.
- Database System Concepts, Seventh Edition. McGraw-Hill Book Company, 2020.
- Fast filter-and-refine algorithms for subsequence selection. In IDEAS, pages 243–255. IEEE Computer Society, 2002.
- Lero: A learning-to-rank query optimizer. Proc. VLDB Endow., 16(6):1466–1479, 2023.
- Bao: Making learned query optimization practical. In SIGMOD Conference, pages 1275–1288. ACM, 2021.
- Balsa: Learning a query optimizer without expert demonstrations. In SIGMOD Conference, pages 931–944. ACM, 2022.
- Kepler: Robust learning for parametric query optimization. Proc. ACM Manag. Data, 1(1):109:1–109:25, 2023.
- Neo: A learned query optimizer. Proc. VLDB Endow., 12(11):1705–1718, 2019.
- Hyper-decision transformer for efficient online policy adaptation. In ICLR. OpenReview.net, 2023.
- Decision Transformer: Reinforcement learning via sequence modeling. In NeurIPS, pages 15084–15097, 2021.
- Polyjuice: High-performance transactions via learned concurrency control. In OSDI, pages 198–216. USENIX Association, 2021.
- Bringing modular concurrency control to the next level. In SIGMOD Conference, pages 283–297. ACM, 2017.
- Toward coordination-free and reconfigurable mixed concurrency control. In USENIX Annual Technical Conference, pages 809–822. USENIX Association, 2018.
- SINGA: A distributed deep learning platform. In ACM Multimedia, pages 685–688. ACM, 2015.
- Secure and verifiable data collaboration with low-cost zero-knowledge proofs. Proc. VLDB Endow., 2024.
- Communication efficient and differentially private logistic regression under the distributed setting. In KDD, pages 69–79. ACM, 2023.
- Analyzing subgraph statistics from extended local views with decentralized differential privacy. In CCS, pages 703–717. ACM, 2019.
- GlassDB: An efficient verifiable ledger database system through transparency. Proc. VLDB Endow., 16(6):1359–1371, 2023.
- Concerto: A high concurrency key-value store with integrity. In SIGMOD Conference, pages 251–266. ACM, 2017.
- NeurDB. https://neurdb.com, 2024.
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