UpSizeR: Synthetically scaling an empirical relational database

Tay, Y. C.; Dai, Bing Tian; Wang, Daniel T.; Sun, Eldora Y.; Lin, Yong Loo; Lin, Yu-Ting

doi:10.1016/j.is.2013.07.004

Cited by 20 publications

(26 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, the measured execution times for the evaluation of the benchmark queries over D 0 should be close to the measured execution times for the evaluation of the same queries over D 1 . In [21], the authors do not consider benchmark queries to be available to the generator, since their goal is broader than benchmarking over a pre-defined set of queries. In OBDA benchmarking, however, the (SQL) workload for the database can be estimated from the mapping component.…”

Section: Data Scaling For Obda Benchmarks: the Vig Approachmentioning

confidence: 99%

“…Without loss of generality, we assume that the intervals generated by VIG satisfying the constraints above are I E = [2,11] and I S = [12,21]. These intervals are assigned to columns ew.id and sw.id, respectively.…”

Section: Analysis Phasementioning

confidence: 99%

“…Data scaling [21] is a recent approach that tries to overcome this problem by automatically tuning the generation parameters through statistics collected over an initial data instance. Hence, the same generator can be reused in different contexts, as long as an initial data instance is available.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

VIG: Data scaling for OBDA benchmarks

Lanti

Xiao

Calvanese

2019

View full text Add to dashboard Cite

In this paper we describe VIG, a data scaler for Ontology-Based Data Access (OBDA) benchmarks. Data scaling is a relatively recent approach, proposed in the database community, that allows for quickly scaling an input data instance to s times its size, while preserving certain application-specific characteristics. The advantages of the scaling approach are that the same generator is general, in the sense that it can be re-used on different database schemas, and that users are not required to manually input the data characteristics. In the VIG system, we lift the scaling approach from the pure database level to the OBDA level, where the domain information of ontologies and mappings has to be taken into account as well. VIG is efficient and notably each tuple is generated in constant time. To evaluate VIG, we have carried out an extensive set of experiments with three datasets (BSBM, DBLP, and NPD), using two OBDA systems (Ontop and D2RQ), backed by two relational database engines (MySQL and PostgreSQL), and compared with real-world data, ad-hoc data generators, and random data generators. The encouraging results show that the data scaling performed by VIG is efficient and that the scaled data are suitable for benchmarking OBDA systems.

show abstract

Section: Data Scaling For Obda Benchmarks: the Vig Approachmentioning

confidence: 99%

Section: Analysis Phasementioning

confidence: 99%

See 1 more Smart Citation

VIG: Data scaling for OBDA benchmarks

Lanti

Xiao

Calvanese

2019

View full text Add to dashboard Cite

show abstract

“…To ensure D is similar to D, previous work [17], [24], [33], [36] typically follows the framework in Fig.1. Each algorithm extracts a fixed set of features F = {F 1 , F 2 , .…”

Section: A Existing Approach and The Limitationsmentioning

confidence: 99%

“…Given this outlook, a tool that scales an empirical dataset D to a synthetic and similar D will be very appealing. This generation of artificial data is necessary if D is larger, and helpful if D is smaller or equal in size [24], [33]. For all cases: D must be similar to D. Moreover, the similarity definition should be application-specific.…”

Section: Introductionmentioning

confidence: 99%

A collaborative framework for tweaking properties in a synthetic dataset

2018

View full text Add to dashboard Cite

Researchers and developers use benchmarks to compare their algorithms and products. A database benchmark must have a dataset D. To be application-specific, this dataset D should be empirical. However, D may be too small, or too large, for the benchmarking experiments. D must, therefore, be scaled to the desired size.To ensure the scaled D is similar to D, previous work typically specifies or extracts a fixed set of features F = {F1, F2, . . . , Fn} from D, then uses F to generate synthetic data for D. However, this approach (D → F → D ) becomes increasingly intractable as F gets larger, so a new solution is necessary.Different from existing approaches, this paper proposes AS-PECT to scale D to enforce similarity. ASPECT first uses a size-scaler (S0) to scale D to D. Then the user selects a set of desired features F1, . . . , Fn. For each desired feature F k , there is a tweaking tool T k that tweaks D to make sure D has the required feature F k . ASPECT coordinates the tweaking of T1, . . . , Tn to D, so Tn(· · · (T1( D)) · · · ) has the required features F1, . . . , Fn.By shifting from D → F → D to D → D → F, data scaling becomes flexible. The user can customise the scaled dataset with their own interested features. Extensive experiments on real datasets show that ASPECT can enforce similarity in the dataset effectively and efficiently.

show abstract

ReX: Extrapolating Relational Data in a Representative Way

et al. 2015

View full text Add to dashboard Cite

Generating synthetic data is useful in multiple application areas (e.g., database testing, software testing). Nevertheless, existing synthetic data generators generally lack the necessary mechanism to produce realistic data, unless a complex set of inputs are given from the user, such as the characteristics of the desired data. An automated and e cient technique is needed for generating realistic data. In this paper, we propose ReX, a novel extrapolation system targeting relational databases that aims to produce a representative extrapolated database given an original one and a natural scaling rate. Furthermore, we evaluate our system in comparison with an existing realistic scaling method, UpSizeR, by measuring the representativeness of the extrapolated database to the original one, the accuracy for approximate query answering, the database size, and their performance. Results show that our solution significantly outperforms the compared method for all considered dimensions.

show abstract

UpSizeR: Synthetically scaling an empirical relational database

Cited by 20 publications

References 24 publications

VIG: Data scaling for OBDA benchmarks

VIG: Data scaling for OBDA benchmarks

A collaborative framework for tweaking properties in a synthetic dataset

ReX: Extrapolating Relational Data in a Representative Way

Contact Info

Product

Resources

About