Semantic Data Warehouse Design: From ETL to Deployment à la Carte

Bellatreche, Ladjel; Khouri, Selma; Berkani, Nabila

doi:10.1007/978-3-642-37450-0_5

Cited by 31 publications

(24 citation statements)

References 19 publications

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“…Even though we notice the lower requirement for the cleanness and constraintness of output data in the next generation BI setting (see Table 2), which would typically result with lower complexity of a data flow, today's BI applications do require rather complex data analytics, which are typically not supported in the schema mapping approaches. Some approaches try to extend this by automating the flow design (e.g., [79,5]), but still with very limited and predefined operation sets (i.e., ETL & ETO). Therefore, automating the creation of more complex data-intensive flows (e.g., machine learning algorithms), by means of exploiting different input data characteristics or using metadata mechanisms is still lacking.…”

Section: Discussionmentioning

confidence: 99%

“…On the one hand, it should raise the usability of the BI system, for a broader set of business users. This module should provide a business-oriented view over the included data sources (e.g., by means of domain ontologies like in [79,50,5]). On the other hand, the Query Assistance module should also facilitate the low coupledness of data sources and be able to efficiently connect to a plethora of external data source repositories.…”

Section: Overall Discussionmentioning

confidence: 99%

“…Both DW and ETL conceptual designs are then generated to satisfy information requirements posed in the domain vocabulary (i.e., ontology). Finally, [5] entirely rely on an ontology, both for describing source and target data stores, and corresponding mappings among them. Integration processes (ETL) are then also derived at the ontological level based on the type of mappings between source and target concepts (e.g., equality, containment).…”

Section: Automationmentioning

confidence: 99%

See 2 more Smart Citations

A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

Jovanovic

Romero

Abelló

2016

Lecture Notes in Computer Science

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Abstract. Data-intensive flows are central processes in today's business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysisready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of dataintensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today's research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.

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Section: Discussionmentioning

confidence: 99%

Section: Overall Discussionmentioning

confidence: 99%

Section: Automationmentioning

confidence: 99%

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A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

Jovanovic

Romero

Abelló

2016

Lecture Notes in Computer Science

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“…For example, in [10] the authors use an ontology of the data sources and their functional dependencies, together with business queries, to semiautomatically generate the ETL steps and the data warehouse multi-dimentional model at a conceptual level. Similarly, [2] proposes an approach where the domain model along with user-requirements are modelled on the ontological level and subsequently, an ETL process is produced, also modelled as an implementation-independent ontology.…”

Section: Functionality-based Designmentioning

confidence: 99%

A Framework for User-Centered Declarative ETL

Theodorou

Abelló

Thiele

et al. 2014

Proceedings of the 17th International Workshop on Data Warehousing and OLAP

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As business requirements evolve with increasing information density and velocity, there is a growing need for efficiency and automation of Extract-Transform-Load (ETL) processes. Current approaches for the modeling and optimization of ETL processes provide platform-independent optimization solutions for the (semi-)automated transition among different abstraction levels, focusing on cost and performance. However, the suggested representations are not abstract enough to communicate business requirements and the role of the process quality in a user-centered perspective has not yet been adequately examined. In this paper, we introduce a novel methodology for the end-to-end design of ETL processes that takes under consideration both functional and non-functional requirements. Based on existing work, we raise the level of abstraction for the conceptual representation of ETL operations and we show how process quality characteristics can generate specific patterns on the process design.

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“…As a final remark, CoAl works at the logical level and is therefore applicable to a variety of approaches that generate logical data flows from information requirements expressed either as high level business objects (e.g., [9], [10], [11]) or in engine specific languages (e.g., [12]). …”

Section: Introductionmentioning

confidence: 99%

Incremental Consolidation of Data-Intensive Multi-Flows

Jovanovic

Romero

Simitsis

et al. 2016

IEEE Trans. Knowl. Data Eng.

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Abstract-Business intelligence (BI) systems depend on efficient integration of disparate and often heterogeneous data. The integration of data is governed by data-intensive flows and is driven by a set of information requirements. Designing such flows is in general a complex process, which due to the complexity of business environments is hard to be done manually. In this paper, we deal with the challenge of efficient design and maintenance of data-intensive flows and propose an incremental approach, namely CoAl, for semi-automatically consolidating data-intensive flows satisfying a given set of information requirements. CoAl works at the logical level and consolidates data flows from either high-level information requirements or platform-specific programs. As CoAl integrates a new data flow, it opts for maximal reuse of existing flows and applies a customizable cost model tuned for minimizing the overall cost of a unified solution. We demonstrate the efficiency and effectiveness of our approach through an experimental evaluation using our implemented prototype.

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Semantic Data Warehouse Design: From ETL to Deployment à la Carte

Cited by 31 publications

References 19 publications

A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

A Framework for User-Centered Declarative ETL

Incremental Consolidation of Data-Intensive Multi-Flows

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