ReX: Extrapolating Relational Data in a Representative Way

Buda, Teodora Sandra; Cerqueus, Thomas; Murphy, John; Kristiansen, Morten

doi:10.1007/978-3-319-20424-6_10

Cited by 8 publications

(10 citation statements)

References 16 publications

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“…This default behavior, however, is not applied with fixed-domain columns, which are columns whose content does not depend on the size of the database instance. An example of fixeddomain column is the column active in the table exploration_wellbore, because depending on the value of active, the elements of id are partitioned into a fixed number of types, corresponding to classes in the ontology 3 . VIG analyzes the mappings to detect such cases of fixed-domain columns, and additional fixed-domain columns can be manually specified by the user.…”

Section: Similarity Measures For Obda and Their Rationalementioning

confidence: 99%

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VIG: Data scaling for OBDA benchmarks

Lanti

Xiao

Calvanese

2019

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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.

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Section: Similarity Measures For Obda and Their Rationalementioning

confidence: 99%

“…Moreover, generating values for the foreign keys requires reading of previously generated values, which is not required in VIG. ReX [3] is an approach closely related to UpSizeR. This system improves the handling of non-key columns through the use of dictionaries.…”

Section: Related Workmentioning

confidence: 99%

VIG: Data scaling for OBDA benchmarks

Lanti

Xiao

Calvanese

2019

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“…Size-scalers are orthogonal to enforcing features in the final dataset D. Our experiments show that ASPECT is able to generate datasets with small errors for three different sizescalers, DSCALER [36], ReX [8] and Rand, described below:…”

Section: B Size-scalermentioning

confidence: 99%

“…ReX is an automated representative extrapolation technique [8]. It scales all tables by the same ratio.…”

Section: B Size-scalermentioning

confidence: 99%

“…U pSizeR [33] is the first solution to DSP, which uses attribute correlation extracted from an empirical dataset to generate a synthetic dataset. ReX [8] is a later work that scales up the original dataset by an integer factor s, using an automated representative extrapolation technique. Chronos [17] scales the streaming data by focusing on capturing and simulating streaming data with both column correlation and temporal correlation.…”

Section: Related Workmentioning

confidence: 99%

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A collaborative framework for tweaking properties in a synthetic dataset

2018

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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.

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