quaryprocessingmachine

  •  · Approximate Query Processing using Deep Generative Models. Data is generated at an unprecedented rate surpassing our ability to analyze them. The database community has pioneered many novel techniques for Approximate Query Processing (AQP) that could give approximate results in a fraction of time needed for computing

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  • CS 186 Spring 2023 Parallel Query Processing 1 Introduction 2

    CS 186 Spring 2023 Parallel Query Processing 6 Partitioning Practice Questions Assume that we have 5 machines and a 1000 page students(sid, name, gpa) table. Initially, all of the pages start on one machine. Assume pages are 1KB. 1) How much network cost

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  • :Query ProcessingMachine LearningPublish Year:2021 · The use of big data technologies for machine learning-based predictive maintenance applications has been drawing attention over the last couple of years.

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  • Bao: Making Learned Query Optimization Practical | Proceedings

    Recent efforts applying machine learning techniques to query optimization have shown few practical gains due to substantive training overhead, inability to adapt to changes, and poor tail performance. Motivated by these difficulties, we introduce Bao (the

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  • :Machine LearningBig Data AnalysisQuery Processing and Optimization · Approximate Query Processing (AQP) is proposed to alleviate this issue. Although many researchers continuously improve the performance of AQP with the help

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  •  · It is done in the following steps: Step-1: Parser: During parse call, the database performs the following checks- Syntax check, Semantic check and Shared pool check, after converting the query into

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  • Zorba-The NoSQL Query Processor

    Zorba is a virtual machine for query processing. Two different syntaxes-XQuery and JSONiq-are featured by the same query compiler and query runtime. XQuery and JSONiq share the same type system, the same operations on atomic types, the same semantics of core expressions such that if-then-else expressions, FLWOR expressions, and the same

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  •  · Concretely, we offer a solution that can provide approximate answers to aggregate queries, relying on Machine Learning (ML), which is able to work alongside Cloud systems. Our developed lightweight ML-led system can be stored on an analyst's local machine or deployed as a service to instantly answer analytic queries, having low

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  •  · Querying on big data is a challenging task due to the rapid growth of data amount. Approximate query processing (AQP) is a way to meet the requirement of fast response. In this paper, we propose a arXivLabs: experimental projects with community collaborators

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  • Query Processing Engines with DBEst: Revisiting Approximate

    DBEst-LIMITATIONS AND CHALLENGES Models grow linearly with number of groups *increase query processing time?-parallelizable SOLUTION: create Model bundles to store model necessary for “High-cardinality” queries *still 10x as fast as sampling

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  • :Query ProcessingMachine Learning · DBEst is presented, a system based on Machine Learning models (regression models and probability density estimators) that can complement existing systems and substantiate its advantages using queries and data from the TPC-DS benchmark and real-life datasets, compared against state of the art AQP engines. In the

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  •  · We propose WCM, a weighted cost model that pays more attention to the balance between different cost factors and can evaluate query performance comprehensively. (2) We implement the rewrite of cost constants and operators for WCM, which makes the cost evaluation faster and more accurate. (3)

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  • Query Processing in DBMS-TutorialCup

    There are four phases in a typical Query Processing in DBMS. Parsing and Translation. This can also be represented in relational structures like tree and graphs as below: Measures of Query cost. Influence of Indexes

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  •  · In this study, we propose three k-nearest neighbor (k-NN) optimization techniques for a distributed, in-memory-based, high-dimensional indexing method to speed up content-based image retrieval. The proposed techniques perform distributed, in-memory, high-dimensional indexing-based k-NN query optimization: a density-based optimization

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  • ML-AQP: Query-Driven Approximate Query Processing based on

    A flexible vectorized representation for (SQL) queries, to be used by ML models; The first AQP engine (ML-AQP) that mines query logs (query-driven) and develops ML models meeting all above desiderata; Up to 5 orders of magnitude greater eficiency than the state of the art sampling-based techniques;

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  • :Query ProcessingMeifan Zhang, Hongzhi WangPublish Year:2020

    Query processing on tensor computation runtimes | Proceedings

    The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the

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  •  · The chapter is divided into three parts to cover different aspects of query processing: Part 1 (Section 3.1): This section serves as an introduction to query processing in PostgreSQL, providing an overview of the entire process. Part 2 (Sections 3.2-3.4): This part delves into the steps involved in obtaining the optimal execution plan for a

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  •  · Query: The above query would be divided into the following tokens: SELECT, emp_name, FROM, employee, WHERE, salary, >, 10000. The tokens (and hence the query) get validated for. The name of the queried table is looked into the data dictionary table. The name of the columns mentioned ( emp_name and salary) in the tokens are

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  •  · Online analytical processing (OLAP) is a core functionality in database systems. The performance of OLAP is crucial to make online decisions in many applications. However, it is rather costly to support OLAP on large datasets, especially big data, and the methods that compute exact answers cannot meet the high-performance requirement. To

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  • Accelerating Machine Learning Queries with Linear Algebra Query

    In this paper, we propose an operator fusing method based on GPU-accelerated linear algebraic evaluation of relational queries. Our method leverages linear algebra computation properties to merge operators in machine learning predictions and data pro-cessing, significantly accelerating predictive pipelines by up to 317x.

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  • Approximate Query Processing Using Machine Learning-Warwick

    3. We further improve DBEst by replacing classical machine learning models with deep learning networks and word embedding. This overcomes the drawbacks of queries with large groups, and query response time and space overheads are further reduced. We

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  •  · Exploiting available condition monitoring data of industrial machines for intelligent maintenance purposes has been attracting attention in various application fields. Machine learning algorithms for fault detection, diagnosis and prognosis are popular and easily accessible. However, our experience in working at the intersection of academia

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  • GitHub-intel/qpl: Intel® Query Processing Library (Intel® QPL)

    The Intel® Query Processing Library (Intel® QPL) is an open-source library to provide high-performance query processing operations on Intel CPUs. Intel® QPL is aimed to support capabilities of the new Intel® In-Memory Analytics Accelerator (Intel® IAA) available on Next Generation Intel® Xeon® Scalable processors, codenamed Sapphire

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  •  · The new generation of information technology is exchanging a huge data and these data should be process for analytical and visualization purposes. In most of our daily activities we are exchanging the data such as our mobile devices maintains call records, visited location records, exchanging messages etc., and these data are growing

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  • Query Processing (in Relational Databases) | SpringerLink

    Query processing denotes the compilation and execution of a query specification usually expressed in a declarative database query language such as the structured query language (SQL). Query processing consists of a compile-time phase and a runtime phase. At compile-time, the query compiler translates the query specification into an executable

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  • :Query ProcessingMeifan Zhang, Hongzhi WangPublish Year:2021 · Approximate query processing (AQP) is a way to meet the requirement of fast response. In this paper, we propose a learning-based AQP method called the LAQP.

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  • Manuscript version: Author’s Accepted Manuscript in WRAP is the

    DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models Qingzhi Ma University of Warwick [email protected] Peter Triantafillou University of Warwick [email protected] ABSTRACT In the era of big data, computing

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  • Query Processing Architecture Guide-SQL Server

    Applies to: SQL Server Azure SQL Database Azure SQL Managed Instance. The SQL Server Database Engine processes queries on various data storage architectures such as local tables, partitioned tables, and tables distributed across multiple servers. The following sections cover how SQL Server processes queries and optimizes query reuse through

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  • Approximate Query Processing Using Machine Learning | Guide

    Approximate query processing has emerged as a cost-effective approach for dealing with the huge data volumes and stringent response-time requirements of today's decision support systems (DSS). Most work in this area, however, has so far been limited in

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  • Extending Relational Query Processing with ML Inference

    Extending Relational Query Processing with ML Inference Konstantinos Karanasos1, Matteo Interlandi1, Doris Xin2, Fotis Psallidas1, Rathijit Sen1, Kwanghyun Park1, Ivan Popivanov1, Supun Nakandal3, Subru Krishnan1, Markus Weimer1, Yuan Yu1, Raghu Ramakrishnan1, Carlo Curino1

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