In spatial networks, clustering adjacent data to disk pages is highly likely to reduce the number of disk page accesses made by the aggregate network operations during query processing.

Our study introduces a novel distributed query plan refinement phase in an enhanced architecture of distributed query processing engine (DQPE). Query plan refinement generates potentially efficient distributed query plan by reusable aggregate query shipping (RAQS) approach. The approach improves response time at the cost of …

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 …

View a PDF of the paper titled Online Aggregation based Approximate Query Processing: A Literature Survey, by Pritom Saha Akash and 2 other authors. In the current world, OLAP (Online Analytical Processing) is used intensively by modern …

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 …

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 ...

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 …

In the past, the database community has proposed two separate ideas, sampling-based approximate query processing (AQP) and aggregate precomputation (AggPre) such as …

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 …

In the past, the database community has proposed two separate ideas, sampling-based approximate query processing (AQP) and aggregate precomputation (AggPre) such as data cubes, to address this challenge. In this paper, we argue for the need to connect these two separate ideas for interactive analytics. We propose AQP++, …

By applying aggregate functions later in the query process, only relevant data is aggregated, improving efficiency and reducing resource consumption. To implement late aggregation, use subqueries or common table expressions (CTEs) to perform filtering and joining operations first, then apply aggregate functions on the reduced dataset.

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 …

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 query …

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 ...

Sampling based approximate query processing and aggregate precomputation are the two methods proposed in the past to try to solve this problem. During interactive queries, the database will produce a large number of materialized views, and the queries on certain data sets are usually concentrated in practice, which makes …

Impact of Applying Aggregate Query Processing in Mobile Commerce: 10.4018/jbdcn.2012040101: With the increased usage of mobile devices, society is seeing more and more users doing transactions wirelessly. Often, 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 …

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, …

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.

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 …

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 ...

Sample-based approximate query processing (AQP) suffers from many pitfalls such as the inability to answer very selective queries and unreliable confidence …

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 …

A range aggregate query (RA query) on certain data returns summarized information about objects satisfying a given query range, such as the total num-ber of qualified objects [19]. This type of query is important since users may be ... query processing techniques are presented in Section 3 and Section 4, respec-tively. In Section 5, the ...

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 …

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 …

aggregate query processing algorithms focus on optimizing various query operations but give less importance to communication cost overhead (Two-phase algorithm). However, …

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 …

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 ...