Using Domain Ontologies as Semantic Dimensions in Data Warehouses

Anderlik, Stefan; Neumayr, Bernd; Schrefl, Michael

doi:10.1007/978-3-642-34002-4_7

Cited by 13 publications

(11 citation statements)

References 17 publications

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“…Four of them deal with semistructured data, and among these, two facilitate to some extent their inclusion in the DW. The exception to this is Neumayr et al [34], because they present an extension of the work (i.e., [35]) which adds the Exploratory part. As a general rule, when a work only deals with the schema, it allows High Expressiveness, but dealing with schemaless unstructured data requires to lower the Expressiveness to Medium in order to process the huge amount of data in a DW.…”

Section: Comparisonmentioning

confidence: 99%

“…Finally, Neumayr et al [34] and Anderlik et al [35] present a detailed layered approach consisting of a flat domain (i.e., with no MD meaning), a hierarchy domain (showing roll-up relationships) and an MD domain. By means of integrity constraints (Datalog rules without head) and Datalog inference capabilities, they guarantee that the asserted information does Thus, they propose to analyze traditional data by using external ontologies as semantic dimensions (former work in this line was proposed in [50], in which a domain ontology serves as a basis to define an MD schema for aggregating instance data).…”

Section: Ontologies For Semantic Annotationsmentioning

confidence: 99%

“…4 (i.e., [29], [30], [31], [32], [33], [34], [35]) neither provide support for tackling the system Extensibility in an automatic way nor deal with Semi-structured or Unstructured data. More specifically, Niemi et al [29], [30] and Priebe et al [31] have Low Expressiveness and thus yield Medium Computation (e.g., OWL 2.0 RL).…”

Section: Ontologies For Semantic Annotationsmentioning

confidence: 99%

See 2 more Smart Citations

Using Semantic Web Technologies for Exploratory OLAP: A Survey

Abelló

Romero

Pedersen

et al. 2015

IEEE Trans. Knowl. Data Eng.

130

104

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Abstract-This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), On-Line Analytical Processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the Web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the "passive" semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

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Section: Comparisonmentioning

confidence: 99%

Section: Ontologies For Semantic Annotationsmentioning

confidence: 99%

Section: Ontologies For Semantic Annotationsmentioning

confidence: 99%

See 1 more Smart Citation

Using Semantic Web Technologies for Exploratory OLAP: A Survey

Abelló

Romero

Pedersen

et al. 2015

IEEE Trans. Knowl. Data Eng.

130

104

View full text Add to dashboard Cite

show abstract

“…Ontologies were first introduced in DW design to facilitate the process of integrating sources, by exploiting their sharing and descriptive characteristics [5]. The use of ontologies was then extended to different design levels: they have been used for analyzing users' requirements [6], for facilitating the definition of DW schema [7][8][9][10], for automating the ETL process [11,12], and for documenting the integration process [13]. Semantic databases, which are sources containing their own ontology schema and data, have been involved in DW design as candidate sources [14].…”

Section: Dw Design From Internal and External Sourcesmentioning

confidence: 99%

LOD for Data Warehouses: Managing the Ecosystem Co-Evolution

Khouri

Bellatreche

2018

Information

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For more than 30 years, data warehouses (DWs) have attracted particular interest both in practice and in research. This success is explained by their ability to adapt to their evolving environment. One of the last challenges for DWs is their ability to open their frontiers to external data sources in addition to internal sources. The development of linked open data (LOD) as external sources is an excellent opportunity to create added value and enrich the analytical capabilities of DWs. However, the incorporation of LOD in the DW must be accompanied by careful management. In this paper, we are interested in managing the evolution of DW systems integrating internal and external LOD datasets. The particularity of LOD is that they contribute to evolving the DW at several levels: (i) source level, (ii) DW schema level, and (iii) DW design-cycle constructs. In this context, we have to ensure this co-evolution, as conventional evolution approaches are adapted neither to this new kind of source nor to semantic constructs underlying LOD sources. One way of tackling this co-evolution issue is to ensure the traceability of DW constructs for the whole design cycle. Our approach is tested using: the LUBM (Lehigh University BenchMark), different LOD datasets (DBepedia, YAGO, etc.), and Oracle 12c database management system (DBMS) used for the DW deployment.

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“…Information in the data warehouse is under the control of data warehouse users so that, even if the source system data is cleared over time, the information in the warehouse can't be stored safely for extended periods of time [8].…”

Section: Limitationsmentioning

confidence: 99%

A Review:Implementation Of OLAP Semantic Web Technologies For Business Analytic System Developement

Dhote¹,

Kapse²

2016

IJMTER

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Abstract-Business Intelligence (BI) tools provide fundamental supporting analyzing large volumes of information. Data Warehouses (DW) and Online Analytical Processing (OLAP) tools are used to store and analyze data. Nowadays more and more information is available on the Web in the form of Resource Description Framework (RDF) and BI tools have huge potential of achieving better results by integrating real-time data from web sources into the analysis process. We describe the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically)queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue knows the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. Next, we goes on to survey the use of SW Technologies for data modeling and data provisioning, including semantic data annotation and semanticaware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

show abstract

Using Domain Ontologies as Semantic Dimensions in Data Warehouses

Cited by 13 publications

References 17 publications

Using Semantic Web Technologies for Exploratory OLAP: A Survey

Using Semantic Web Technologies for Exploratory OLAP: A Survey

LOD for Data Warehouses: Managing the Ecosystem Co-Evolution

A Review:Implementation Of OLAP Semantic Web Technologies For Business Analytic System Developement

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