Estimating Characteristic Sets for RDF Dataset Profiles Based on Sampling
| Autor: | Maribel Acosta, Lars Heling |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Structure (mathematical logic)
Computer science 010401 analytical chemistry Sampling (statistics) 02 engineering and technology computer.file_format computer.software_genre Query optimization 01 natural sciences 0104 chemical sciences Query plan Projection (relational algebra) Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) 020201 artificial intelligence & image processing Data mining RDF computer |
| Zdroj: | The Semantic Web ISBN: 9783030494605 ESWC |
| Popis: | RDF dataset profiles provide a formal representation of a dataset’s characteristics (features). These profiles may cover various aspects of the data represented in the dataset as well as statistical descriptors of the data distribution. In this work, we focus on the characteristic sets profile feature summarizing the characteristic sets contained in an RDF graph. As this type of feature provides detailed information on both the structure and semantics of RDF graphs, they can be very beneficial in query optimization. However, in decentralized query processing, computing them is challenging as it is difficult and/or costly to access and process all datasets. To overcome this shortcoming, we propose the concept of a profile feature estimation. We present sampling methods and projection functions to generate estimations which aim to be as similar as possible to the original characteristic sets profile feature. In our evaluation, we investigate the feasibility of the proposed methods on four RDF graphs. Our results show that samples containing \(0.5\%\) of the entities in the graph allow for good estimations and may be used by downstream tasks such as query plan optimization in decentralized querying. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|