Fine-Tuning Data Structures for Analytical Query Processing

• URL: http://arxiv.org/abs/2112.13099v1
• Date: Fri, 24 Dec 2021 16:36:35 GMT
• Title: Fine-Tuning Data Structures for Analytical Query Processing
• Authors: Amir Shaikhha, Marios Kelepeshis, Mahdi Ghorbani
• Abstract summary: We introduce a framework for automatically choosing data structures to support efficient computation of analytical workloads. We introduce a novel low-level intermediate language that can express the algorithms behind various query processing paradigms. We show that the performance of the code generated by our framework either outperforms or is on par with the state-of-the-art analytical query engines.
• Score: 0.5156484100374058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a framework for automatically choosing data structures to support efficient computation of analytical workloads. Our contributions are twofold. First, we introduce a novel low-level intermediate language that can express the algorithms behind various query processing paradigms such as classical joins, groupjoin, and in-database machine learning engines. This language is designed around the notion of dictionaries, and allows for a more fine-grained choice of its low-level implementation. Second, the cost model for alternative implementations is automatically inferred by combining machine learning and program reasoning. The dictionary cost model is learned using a regression model trained over the profiling dataset of dictionary operations on a given hardware architecture. The program cost model is inferred using static program analysis. Our experimental results show the effectiveness of the trained cost model on micro benchmarks. Furthermore, we show that the performance of the code generated by our framework either outperforms or is on par with the state-of-the-art analytical query engines and a recent in-database machine learning framework.

Related papers

• UQE: A Query Engine for Unstructured Databases [71.49289088592842]
  We investigate the potential of Large Language Models to enable unstructured data analytics. We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
  arXiv  Detail & Related papers  (2024-06-23T06:58:55Z)
• Leveraging Reinforcement Learning and Large Language Models for Code Optimization [14.602997316032706]
  This paper introduces a new framework to decrease the complexity of code optimization. The proposed framework builds on large language models (LLMs) and reinforcement learning (RL) We run several experiments on the PIE dataset using a CodeT5 language model and RRHF, a new reinforcement learning algorithm.
  arXiv  Detail & Related papers  (2023-12-09T19:50:23Z)
• Context-Aware Ensemble Learning for Time Series [11.716677452529114]
  We introduce a new approach using a meta learner that effectively combines the base model predictions via using a superset of the features that is the union of the base models' feature vectors instead of the predictions themselves. Our model does not use the predictions of the base models as inputs to a machine learning algorithm, but choose the best possible combination at each time step based on the state of the problem.
  arXiv  Detail & Related papers  (2022-11-30T10:36:13Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• Efficient Sub-structured Knowledge Distillation [52.5931565465661]
  We propose an approach that is much simpler in its formulation and far more efficient for training than existing approaches. We transfer the knowledge from a teacher model to its student model by locally matching their predictions on all sub-structures, instead of the whole output space.
  arXiv  Detail & Related papers  (2022-03-09T15:56:49Z)
• AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection [0.0]
  We present a novel framework for automated machine learning that incorporates advances in dynamic ensemble selection. Our approach is the first in the field of AutoML to search and optimize ensemble strategies. In comparison experiments, our method outperforms the state-of-the-art automated machine learning frameworks with the same CPU time.
  arXiv  Detail & Related papers  (2022-01-01T15:17:07Z)
• Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval [12.514666775853598]
  We propose a novel framework to leverage the advantages of interactive and non-interactive models. We introduce semi-interactive mechanism, which builds our model upon non-interactive architecture but encodes each document together with its associated multilingual queries. Our methods significantly boost the retrieval accuracy while maintaining the computational efficiency.
  arXiv  Detail & Related papers  (2021-11-03T03:03:19Z)
• Learning to Synthesize Data for Semantic Parsing [57.190817162674875]
  We propose a generative model which models the composition of programs and maps a program to an utterance. Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand. We evaluate our method in both in-domain and out-of-domain settings of text-to-Query parsing on the standard benchmarks of GeoQuery and Spider.
  arXiv  Detail & Related papers  (2021-04-12T21:24:02Z)
• Comparative Code Structure Analysis using Deep Learning for Performance Prediction [18.226950022938954]
  This paper aims to assess the feasibility of using purely static information (e.g., abstract syntax tree or AST) of applications to predict performance change based on the change in code structure. Our evaluations of several deep embedding learning methods demonstrate that tree-based Long Short-Term Memory (LSTM) models can leverage the hierarchical structure of source-code to discover latent representations and achieve up to 84% (individual problem) and 73% (combined dataset with multiple of problems) accuracy in predicting the change in performance.
  arXiv  Detail & Related papers  (2021-02-12T16:59:12Z)
• StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics [4.237343083490243]
  In machine learning (ML), ensemble methods such as bagging, boosting, and stacking are widely-established approaches. StackGenVis is a visual analytics system for stacked generalization.
  arXiv  Detail & Related papers  (2020-05-04T15:43:55Z)
• Multi-layer Optimizations for End-to-End Data Analytics [71.05611866288196]
  We introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language. We show that a Scala implementation of IFAQ can outperform mlpack, Scikit, and specialization by several orders of magnitude for linear regression and regression tree models over several relational datasets.
  arXiv  Detail & Related papers  (2020-01-10T16:14:44Z)

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.