Abstract
Data analysis often involves
comparing
subsets of data across many dimensions for finding unusual trends and patterns. While the comparison between subsets of data can be expressed using SQL, they tend to be complex to write, and suffer from poor performance over large and high-dimensional datasets. In this paper, we propose a new logical operator COMPARE for relational databases that concisely captures the enumeration and comparison between subsets of data and greatly simplifies the expressing of a large class of comparative queries. We extend the database engine with optimization techniques that exploit the semantics of COMPARE to significantly improve the performance of such queries. We have implemented these extensions inside Microsoft SQL Server, a commercial DBMS engine. Our extensive evaluation on synthetic and real-world datasets shows that COMPARE results in a significant speedup over existing approaches, including physical plans generated by today's database systems, user-defined functions (UDFs), as well as middleware solutions that compare subsets outside the databases.
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Cited by
2 articles.
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1. Recursive SQL for Data Mining;34th International Conference on Scientific and Statistical Database Management;2022-07-06
2. Suggesting assess queries for interactive analysis of multidimensional data;IEEE Transactions on Knowledge and Data Engineering;2022