Ontario: Federated Query Processing Against a Semantic Data Lake

Endris, Kemele M.; Rohde, Philipp D.; Vidal, María-Esther; Auer, Sören

doi:10.1007/978-3-030-27615-7_29

Cited by 30 publications

(30 citation statements)

References 12 publications

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“…Because of the fragmentation of the data in many different tables, querying is expensive due to many join operations. Also, the mapping of DLs to semantic models has been considered in Endris et al (2019). They propose a framework that maps heterogeneous sources to a unified RDF graph and thereby allows federated query processing.…”

Section: Data Models and Semantics In Data Lakesmentioning

confidence: 99%

Data Lake

Quix¹,

Geisler²,

Hai³

2020

Encyclopedia of Big Data

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Data lakes (DL) have been proposed as a new concept for centralized data repositories. In contrast to data warehouses (DW), which usually require a complex and fine-tuned Extract-Transform-Load (ETL) process, DLs use a simpler model which just aims at loading the complete source data in its raw format into the DL. While a more complex ETL process with data transformation and aggregation increases the data quality, it might also come with some information loss as irregular or unstructured data not fitting into the integrated DW schema will not be loaded into the DW. Moreover, some data silos might not get connected to integrated data repositories at all due to the complexity of the data integration process. DLs address these problems: they should provide access to the source data in its original format without requiring an elaborated ETL process to ingest the data into the lake.

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Section: Data Models and Semantics In Data Lakesmentioning

confidence: 99%

Data Lake

Quix¹,

Geisler²,

Hai³

2020

Encyclopedia of Big Data

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show abstract

“…Endris K.M., Rohde P.D., Vidal ME., and Auer S. say that "To solve the problem of data integration, especially the heterogeneous data, a number of data lake systems are appeared, focusing more on data input and on metadata management." [4]. Llave [12] defines a data lake from a business perspective as "an ability to improve business effectiveness where you can get raw, unaltered data from various source systems."…”

Section: Data Lakementioning

confidence: 99%

“…Nevertheless, traditional data warehouses extract data and integrate data after converting it to a common static pattern. Thus, they are called centralized data stores [4]. Now that this way of data integration ultimately leads to severe information silos, companies require finding an effective method to link and structure the various flows from the information silos that help address specific issues flexibility [5].…”

Section: Introductionmentioning

confidence: 99%

Extracting knowledge patterns in a data lake for management effectiveness

Cheng

Wang

2020

E3S Web Conf.

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With the correlation collision between different types of data becomes more and more intense, a meaningful and far-reaching data revolution has arrived. Enterprises urgently require a hybrid data platform that can effectively break data silos, and unify data aggregation and sharing. Once the data lake was born, it has been a promising method for enterprises to profoundly improve their Business Intelligence. In this paper, we combine principle component analysis (PCA) with a network-based approach to extract a visual knowledge pattern from data sources in data lake, so as to improve management effectiveness.

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“…[16] extends a column-store, whereas [1] extends a relational DBMS and Drill loads the data into NoSQL databases. Current Ontology Based Data Integration (OBDI) open source systems that take tabular data as input are Ontario [15] and Squerall [22]. Ontario is a federated query processing approach for heterogeneous data sources.…”

Section: Related Workmentioning

confidence: 99%

“…Implicit constraints over data sources are usually explicitly defined in mappings and tabular metadata, e.g., the W3C recommendation to annotate tabular data, CSVW [28]. This information has not been included in the majority of OBDA query translation engines [24,15], and those engines that have included it [22], are not fully documented for their use. Examples of these constraints are the standardization of the column format (e.g., dates), integrity constraints or datatypes.…”

Section: Introductionmentioning

confidence: 99%

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

Chaves-Fraga,

Ruckhaus,

Priyatna

et al. 2020

Preprint

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Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets (e.g., relational database, CSV, JSON), either by materializing integrated data into RDF or by performing on-the-fly integration via SPARQL-to-SQL query translation. In the specific case of tabular datasets comprised of several CSV or Excel files, query translation approaches have been applied taking as input a lightweight schema with table and column names, and considering each source as a single table that can be loaded into a relational database system (RDB). This naïve approach does not consider implicit constraints in this type of data, e.g., referential integrity among data sources, datatypes, or data integrity; thus, completeness and performance of query processing can be affected. Our work is focused on explicitly enforcing implicit constraints during OBDA query translation over tabular data. We propose Morph-CSV, a framework that enforces constraints and can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV resorts to both a Constraints component and a set of operators that apply each type of constraint to the input with the aim of enhancing query completeness and performance. We evaluate Morph-CSV against a set of real-world open tabular datasets in the domain of the public transport; Morph-CSV is compared with existing approaches in terms of query result completeness and performance.

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Ontario: Federated Query Processing Against a Semantic Data Lake

Cited by 30 publications

References 12 publications

Data Lake

Data Lake

Extracting knowledge patterns in a data lake for management effectiveness

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

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