Optimizing Aggregate Query Processing in Cloud Data …
aggregate query processing algorithms focus on optimizing various query operations but give less importance to communication cost overhead (Two-phase algorithm). However, …
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Optimizing Performance of Aggregate Query Processing with Histogram
The time it takes to make these decisions is often to be critical. Since approximate query processing (AQP) can efficiently reduce the involved data and workload, it has becoming an effective technology to optimize massive data processing. Aggregate query is a method of deriving group and subgroup data by analysis of a set …
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Approximate Query Processing: What is New and Where to …
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 …
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Merged aggregate nearest neighbor query processing in …
Aggregate nearest neighbor (ANN) query returns a common interesting data object that minimizes an aggregate distance for multiple query points. In this paper, we propose a novel approach to efficiently process ANN queries in road networks. This approach ...
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Clustering spatial networks for aggregate query processing…
Recall that the cost of aggregate network query processing includes the costs of GaS and GSs operations and the cost of priority queue operations. Increasing P and the buffer size independently decrease the number of disk accesses in both models since the chance of assigning concurrently accessed records to the pages that are …
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Optimizing Aggregate Query Processing in Cloud Data …
Abstract. In this paper, we study and optimize the aggregate query processing in a highly distributed Cloud Data Warehouse, where each database stores a subset of relational …
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Combining Aggregation and Sampling (Nearly) Optimally for …
Sample-based approximate query processing (AQP) suffers from many pitfalls such as the inability to answer very selective queries and unreliable confidence …
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Relational aggregate query processing techniques for real …
In this thesis, a new query processing technique is proposed to cut down systematically the time involved in processing aggregate relational algebra queries in relational databases. We use data samples and statistical methods to construct an approximate response to a given aggregate query when there is not enough time to completely …
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Aggregate Query Processing on Incomplete Data
In this paper, we propose to directly estimate the aggregate query result on incomplete data, rather than imputing the missing values. An interval estimation, composed of the upper and lower bound of aggregate query results among all possible interpretation of missing values, are presented to the end-users.
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Aggregate data on Azure Databricks
Batch aggregation is the default behavior observed when running an ad hoc query in SQL or processing data with Apache Spark DataFrames. An aggregate query written against a table or data source computes the aggregate statistics for all records in the data source. Azure Databricks leverages optimizations and metadata whenever …
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Intelligent query processing details
The new APPROX_COUNT_DISTINCT aggregate function returns the approximate number of unique non-null values in a group. This feature is available starting with SQL Server 2019 (15.x), regardless of the compatibility level. ... Query processing feedback is a process by which the query processor in SQL Server, Azure SQL …
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APPROXIMATE QUERY PROCESSING TECHNIQUE FOR …
Approximate aggregate query processing techniques presented in [1,2] provide approximate results to a simple non-join aggregate query as depicted in Query 1, for Big Data queries. Here, aggregate() denotes the aggregate function such as: Sum, Average, Variance, Standard Deviation etc. Additionally, individual selection predicates
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A Sample-and-Clean Framework for Fast and Accurate …
quality answers to aggregate queries is di cult due to the challenges of processing and cleaning large, dirty data sets. To increase the speed of query processing, there has been a resurgence of interest in sampling-based approximate query processing (SAQP). In its usual formulation, however, SAQP does not address data cleaning at all, and in ...
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Aggregate Query in Salesforce
An aggregate query is a specialized type of SOQL query that performs calculations on sets of records and returns summarized results. ... eliminating the need for manual calculations or additional post-processing. Aggregate queries are commonly used in reporting and analytics within Salesforce, making it easier to retrieve high-level insights ...
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Combining Aggregation and Sampling (Nearly) Optimally for …
Sample-based approximate query processing (AQP) suffers from many pitfalls such as the inability to answer very selective queries and unreliable confidence intervals when sample sizes are small.Recent research presented an intriguing solution of combining materialized, pre-computed aggregates with sampling for accurate and more …
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Processing SPARQL Aggregate Queries with Web Preemption
Approximate query processing is a well-known approach to speedup aggregate query processing . The approach relies on sampling, synopses or sketches techniques to approximate results with bounded errors. The sampling approach proposed in scales with large knowledge graphs, and overcomes quotas but computes approximate …
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Processing Complex Aggregate Queries over Data Streams
sults to user queries while looking at the relevant data items only once and in a fixed order (determined by the stream-arrival pattern). Two key parameters for query processing …
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Optimal Linear Aggregate Query Processing under …
This paper points out that under differential privacy, the optimal strategy for answering an arbitrary batch of linear aggregate queries can be found, rather surprisingly, by solving a simple and elegant convex optimization program and proposes a efficient algorithm based on Newton's method, which proves t always converge to the optimal …
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Query Processing in SQL
Query processing in a distributed database management system requires the transmission of data between the computers in a network. A distribution strategy for a query is the ordering of data transmissions and local data processing in a database system. Generally, a query in Distributed DBMS requires data from multiple sites, and …
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Clustering spatial networks for aggregate query processing: …
A link-based storage scheme for efficient aggregate query processing on clustered road networks. 2010, Information Systems. Citation Excerpt : The partitioning constraint corresponds to enforcing the page size limit on the record-to-page allocation. As shown in [13], the partitioning objective corresponds to minimizing the total number of …
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Aggregate data on Databricks | Databricks on Google Cloud
Batch aggregation is the default behavior observed when running an ad hoc query in SQL or processing data with Apache Spark DataFrames. An aggregate query written against a table or data source computes the aggregate statistics for all records in the data source. Databricks leverages optimizations and metadata whenever possible to optimize ...
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SAMPLING BASED JOIN-AGGREGATE QUERY …
achieve better query-execution efficiency such as: approximate query processing [1,2], which provide approximate answers to aggregate queries. 1.2. Addressed Open Issues SELECT aggregate (𝑅.𝐴) FROM 𝑅 Query 1: Simple Aggregate query (SQL) e-ISSN : …
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Aggregate-Query Processing in Data Warehousing …
TLDR. This work proposes a query based aggregate cache of 'multidimensional objects' which allows the combination of several aggregates to derive a single query using …
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Aggregate Query Processing on Incomplete Data
Our primary focus is answering aggregate numerical queries of the form: SELECT AGG(attr) FROM table WHERE predicate. Predicate attributes are attributes involved in the predicate condition of the aggregate query, and aggregate attributes are attributes involved in the aggregate computation (AGG). AGG could be SUM, COUNT, …
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Query Processing Architecture Guide
The methods used to compute calculations, and how to filter, aggregate, and sort data from each table. ... Query processing enhancements on partitioned tables and indexes. SQL Server 2008 (10.0.x) improved query processing performance on partitioned tables for many parallel plans, changes the way parallel and serial plans are represented, and ...
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Approximate Query Processing using Deep Generative …
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 exact results. In this work, we explore the usage of deep learning (DL) for …
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SAMPLING BASED JOIN-AGGREGATE QUERY PROCESSING …
In [1], the presented Approximation Query Processing Technique (AQPT) addresses four aggregate operations: Sum, Average, Variance and Standa rd Deviation. This AQPT is designed using Map Reduce
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A link-based storage scheme for efficient aggregate query processing on
We introduced the link-based storage scheme for efficient aggregate query processing on clustered road networks. In this storage scheme, each record stores the data associated with a link together with the link's connectivity information. We introduced a clustering hypergraph model for the link-based storage scheme to partition the network …
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Aggregate data on Databricks
Batch aggregation is the default behavior observed when running an ad hoc query in SQL or processing data with Apache Spark DataFrames. An aggregate query written against a table or data source computes the aggregate statistics for all records in the data source. Databricks leverages optimizations and metadata whenever possible to optimize ...
WhatsApp: +86 18221755073
SAMPLING BASED JOIN-AGGREGATE QUERY …
Approximate aggregate query processing techniques presented in [1,2] provide approximate results to a simple non-join aggregate query as depicted in Query 1, for Big Data queries.
WhatsApp: +86 18221755073