PsiDB: A Framework for Batched Query Processing and Optimization

Eslami, Mehrad; Tu, Yi-Cheng; Charkhgard, Hadi; Xu, Zichen; Liu, Jiacheng

doi:10.1109/bigdata47090.2019.9005486

Cited by 4 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, common subexpressions are evaluated once. This approach was subsequently extended to include query result caches, materialized/cached views, intermediate query results, and query rewriting, which have been extensively studied for relational database systems [11], [14], [15], [28]- [31], [34], [35] and streaming processing systems [19]- [21]. Group processing algorithms have proven to be effective in multiple applications involving high-load conditions [7], [8], [15], [19]- [21], [27]- [31], [34]- [37], [52], [53].…”

Section: A Farthest Neighbor Search Algorithmsmentioning

confidence: 99%

Group Processing of Multiple k-Farthest Neighbor Queries in Road Networks

Cho

Attique

2020

IEEE Access

View full text Add to dashboard Cite

Advances in mobile technologies and map-based applications enables users to utilize sophisticated spatial queries, including k-nearest neighbor and shortest path queries. Often, location-based servers are used to handle multiple simultaneous queries because of the popularity of map-based applications. This study focuses on the efficient processing of multiple concurrent k-farthest neighbor (kFN) queries in road networks. For a positive integer k, query point q, and set of data points P , a kFN query returns k data points farthest from the query point q. For addressing multiple concurrent spatial queries, traditional locationbased servers based on one-query-at-a-time processing are unsuitable owing to high redundant computation costs. Therefore, we propose a group processing of multiple kFN (GMP) algorithm to process multiple kFN queries in road networks. The proposed GMP algorithm uses group computation to avoid the redundant computation of network distances between the query and data points. The experiments using real-world roadmaps demonstrate the proposed solution's effectiveness and efficiency. INDEX TERMS Spatial databases, group processing, multiple k-farthest neighbor query, road network.

show abstract

Section: A Farthest Neighbor Search Algorithmsmentioning

confidence: 99%

Group Processing of Multiple k-Farthest Neighbor Queries in Road Networks

Cho

Attique

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Therefore, the subexpressions are typically evaluated once. These multi-query optimization techniques later expanded to involve query rewriting, query result caches, materialized views, and intermediate query results for relational database systems [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] and streaming processing systems [ 37 , 38 , 39 ]. Many applications involving high-load conditions have proven that batch processing algorithms can significantly reduce the query processing time for multiple simultaneous queries [ 19 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ].…”

Section: Related Workmentioning

confidence: 99%

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

Cho¹

2021

Sensors

View full text Add to dashboard Cite

Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.

show abstract

Recursive SQL and GPU-support for in-database machine learning

Schüle¹,

Lang²,

Springer³

et al. 2022

Distrib Parallel Databases

View full text Add to dashboard Cite

In machine learning, continuously retraining a model guarantees accurate predictions based on the latest data as training input. But to retrieve the latest data from a database, time-consuming extraction is necessary as database systems have rarely been used for operations such as matrix algebra and gradient descent. In this work, we demonstrate that SQL with recursive tables makes it possible to express a complete machine learning pipeline out of data preprocessing, model training and its validation. To facilitate the specification of loss functions, we extend the code-generating database system Umbra by an operator for automatic differentiation for use within recursive tables: With the loss function expressed in SQL as a lambda function, Umbra generates machine code for each partial derivative. We further use automatic differentiation for a dedicated gradient descent operator, which generates LLVM code to train a user-specified model on GPUs. We fine-tune GPU kernels at hardware level to allow a higher throughput and propose non-blocking synchronisation of multiple units. In our evaluation, automatic differentiation accelerated the runtime by the number of cached subexpressions compared to compiling each derivative separately. Our GPU kernels with independent models allowed maximal throughput even for small batch sizes, making machine learning pipelines within SQL more competitive.

show abstract

PsiDB: A Framework for Batched Query Processing and Optimization

Abstract: Chapter 1: Introduction Chapter 2: Related Work 2.1 Background .

Cited by 4 publications

References 30 publications

Group Processing of Multiple k-Farthest Neighbor Queries in Road Networks

Group Processing of Multiple k-Farthest Neighbor Queries in Road Networks

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

Recursive SQL and GPU-support for in-database machine learning

Contact Info

Product

Resources

About