ReX: Representative extrapolating relational databases. (July 2017)

• Record Type: Journal Article
• Authors: Buda, Teodora Sandra; Cerqueus, Thomas; Grava, Cristian; Murphy, John
• Is Part Of: Information systems. Volume 67(2017)
• Page Start: 83
• Page End: 99
• Publication Date: 2017-07
• Subjects: Relational database -- Extrapolation -- Sampling -- Representative -- Random; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2017.03.001
• Languages: English
• ISSNs: 0306-4379

Abstract

Highlights: An automated database extrapolation system that generates a representative extrapolated database of an original database, given a rational scaling rate. An automated sampling method, called RVFDS, that aims to generate a representative sample of the original database. This method is used in addition to the extrapolation method to handle rational scaling rates. A comprehensive experimental comparison, using both real and synthetic databases, between a state-of-the-art scaling technique, namely UpSizeR, and our proposed representative scaling system, ReX. Abstract: Generating synthetic data is useful in multiple application areas (e.g., database testing, software testing). Nevertheless, existing synthetic data generators are either limited to generating data that only respect the database schema constraints, or they are not accurate in terms of representativeness, unless a complex set of inputs are given from the user (such as the data characteristics of the desired generated data). In this paper, we present an extension of a prior representative extrapolation technique, namely ReX [20], limited to natural scaling rates. The objective is to produce in an automated and efficient way a representative extrapolated database, given an original database O and a rational scaling rate, s ∈ Q . In the extended version, the ReX system can handle rational scaling rates by combining existing efficient sampling and extrapolation techniques. Furthermore, we propose a novel sampling technique, RVFDS for handling positive rational values for the desired size of the generated database. We evaluate ReX in comparison with a realistic scaling method, namely UpSizeR [43], on both real and synthetic databases. We show that our solution statistically and significantly outperforms the compared method for rational scaling rates in terms of representativeness.