Serving Deep Learning Model in Relational Databases

• URL: http://arxiv.org/abs/2310.04696v3
• Date: Thu, 26 Sep 2024 04:33:35 GMT
• Title: Serving Deep Learning Model in Relational Databases
• Authors: Lixi Zhou, Qi Lin, Kanchan Chowdhury, Saif Masood, Alexandre Eichenberger, Hong Min, Alexander Sim, Jie Wang, Yida Wang, Kesheng Wu, Binhang Yuan, Jia Zou,
• Abstract summary: Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
• Score: 70.53282490832189
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains, sparking growing interest recently. In this visionary paper, we embark on a comprehensive exploration of representative architectures to address the requirement. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS). The potential relation-centric architecture aims to represent a large-scale tensor computation through relational operators. While each of these architectures demonstrates promise in specific use scenarios, we identify urgent requirements for seamless integration of these architectures and the middle ground in-between these architectures. We delve into the gaps that impede the integration and explore innovative strategies to close them. We present a pathway to establish a novel RDBMS for enabling a broad class of data-intensive DL inference applications.

Related papers

• A Pre-training Framework for Relational Data with Information-theoretic Principles [57.93973948947743]
  We introduce Task Vector Estimation (TVE), a novel pre-training framework that constructs supervisory signals via set-based aggregation over relational graphs.<n>TVE consistently outperforms traditional pre-training baselines.<n>Our findings advocate for pre-training objectives that encode task heterogeneity and temporal structure as design principles for predictive modeling on relational databases.
  arXiv  Detail & Related papers  (2025-07-14T00:17:21Z)
• REDELEX: A Framework for Relational Deep Learning Exploration [0.0]
  Recently, Deep Deep Learning has emerged as a novel paradigm wherein RDBs are conceptualized as graph structures.<n>There is a lack of analysis into the relationships between various RDL models and the characteristics of the underlying RDBs.<n>We present REDELEX$-$a comprehensive exploration framework for evaluating RDL models of varying complexity on the most diverse collection of over 70 RDBs.
  arXiv  Detail & Related papers  (2025-06-27T13:05:15Z)
• Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures [50.46688111973999]
  Graph machine learning has led to a significant increase in the capabilities of models that learn on arbitrary graph-structured data.<n>We present a new blueprint that enables end-to-end representation of'relational entity graphs' without traditional engineering feature.<n>We discuss key challenges including large-scale multi-table integration and the complexities of modeling temporal dynamics and heterogeneous data.
  arXiv  Detail & Related papers  (2025-06-19T23:51:38Z)
• Foundation Models for Tabular Data within Systemic Contexts Need Grounding [9.820997523824676]
  We introduce the concept of Semantically Linked Tables (SLT), recognizing that tables are inherently connected to both declarative and procedural operational knowledge.<n>We propose Foundation Models for Semantically Linked Tables (FMSLT), which integrate these components to ground data within its true operational context.
  arXiv  Detail & Related papers  (2025-05-26T11:02:51Z)
• Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey [20.360136850102833]
  This survey aims to provide a broader optimization space for more free resource-performance tradeoffs. By consolidating problems and techniques scattered over diverse levels, we aim to help readers understand their connections and stimulate further discussions.
  arXiv  Detail & Related papers  (2023-09-27T08:04:24Z)
• ADMUS: A Progressive Question Answering Framework Adaptable to Multiple Knowledge Sources [9.484792817869671]
  We present ADMUS, a progressive knowledge base question answering framework designed to accommodate a wide variety of datasets. Our framework supports the seamless integration of new datasets with minimal effort, only requiring creating a dataset-related micro-service at a negligible cost.
  arXiv  Detail & Related papers  (2023-08-09T08:46:39Z)
• Graph-based Neural Modules to Inspect Attention-based Architectures: A Position Paper [0.0]
  encoder-decoder models offer an exciting opportunity for visualization and editing by humans of the knowledge implicitly represented in model weights. In this work, we explore ways to create an abstraction for segments of the network as a two-way graph-based representation. Such two-way graph representation enables new neuro-symbolic systems by leveraging the pattern recognition capabilities of the encoder-decoder along with symbolic reasoning carried out on the graphs.
  arXiv  Detail & Related papers  (2022-10-13T15:52:12Z)
• Federated Learning with Heterogeneous Architectures using Graph HyperNetworks [154.60662664160333]
  We propose a new FL framework that accommodates heterogeneous client architecture by adopting a graph hypernetwork for parameter sharing. Unlike existing solutions, our framework does not limit the clients to share the same architecture type, makes no use of external data and does not require clients to disclose their model architecture.
  arXiv  Detail & Related papers  (2022-01-20T21:36:25Z)
• HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression [53.90578309960526]
  Large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. We propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information.
  arXiv  Detail & Related papers  (2021-10-16T11:23:02Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)
• Automated Search for Resource-Efficient Branched Multi-Task Networks [81.48051635183916]
  We propose a principled approach, rooted in differentiable neural architecture search, to automatically define branching structures in a multi-task neural network. We show that our approach consistently finds high-performing branching structures within limited resource budgets.
  arXiv  Detail & Related papers  (2020-08-24T09:49:19Z)
• Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition [49.463864096615254]
  We analyze the influence of learning complexity, including the input complexity and model complexity. We propose a recurrent convolutional network (RCN) to explore the shallower-architecture and lower-resolution input data. We develop three parameter-free modules to integrate with RCN without increasing any learnable parameters.
  arXiv  Detail & Related papers  (2020-06-17T06:19:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.