VIG: Data scaling for OBDA benchmarks

Abstract

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.

Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV

“…To answer these questions, we setup the following experimental studies: Datasets and queries. The GTFS-Madrid Benchmark 9 consists of an ontology, an initial dataset of the metro system of Madrid following the GTFS model, a set of mappings in several specifications, a set of queries according to the ontology that cover relevant features of the SPARQL query language, and a data generator based on a state of the art proposal [20]. We select the tabular sources of this benchmark (i.e., the CSV files) and we scale up the original data in several instances, we use the scale factors 10, 100 and 1000.…”

Handling qualitative preferences in SPARQL over virtual ontology-based data access

“…Figure 2(c) reports the average execution time in seconds of the query Q 1 only with skyline preferences. It was executed 5 times using the state-of-the-art tools [29,47] over our motivating dataset transformed into RDF by means of SDM-RDFizer [27] and scaled-up to a 10.000 scale value by using VIG [35]. These state-of-the-art tools evaluate preferences on top of triplestores [29,47].…”

“…iii) Gas Stations: Patel-Schneider and colleagues [41] presented a running example for gas stations over 18 instances. We have manually converted this data to CSV and then scaled it with VIG [35]. We have created an equivalent table and we have loaded this CSV file into mysql.…”

A collaborative framework for tweaking properties in a synthetic dataset

“…In [25], the authors propose a dataset scaling problem for RDF data and provide a solution RBench that scales the original input dataset by preserving 4 features: resource identity (resource name, resource type, resource degree), relationship patterns (subgraphs with only relationship edges), predicate dictionary (frequency counts of the words) and attribute stars (frequency counts of the star structure). In [21], the authors lift the scaling approach from the pure database level to the OBDA level, where the domain information of ontologies and mappings are also taken into account as well. VIG [21] maintains the similarity for OBDA data by preserving the following features: size of columns clusters and disjointness, schema dependencies and column-based duplicates and NULL Ratios.…”

“…In [21], the authors lift the scaling approach from the pure database level to the OBDA level, where the domain information of ontologies and mappings are also taken into account as well. VIG [21] maintains the similarity for OBDA data by preserving the following features: size of columns clusters and disjointness, schema dependencies and column-based duplicates and NULL Ratios. However, VIG only supports dataset where each table has at most one foreign key only.…”