BioDWH: A Data Warehouse Kit for Life Science Data Integration

Töpel, Thoralf; Kormeier, Benjamin; Klassen, A.; Hofestädt, Ralf

doi:10.1515/jib-2008-93

Cited by 31 publications

(17 citation statements)

References 6 publications

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“…Thereby, the majority of systems focuses on supporting downstream analysis tasks leveraging the data integration features of database systems such as Atlas [19], BioWarehouse [9], and BioDWH [20]. These systems provide tools to extract, transform, and load data into an integrated database, but do not integrate analysis tasks such as variant calling.…”

Section: Related Workmentioning

confidence: 99%

Toward Efficient Variant Calling Inside Main-Memory Database Systems

Dorok

Breß

Saake

2014

2014 25th International Workshop on Database and Expert Systems Applications

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Abstract-Mutations in genomes indicate predisposition for diseases or effects on efficacy of drugs. A variant calling algorithm determines possible mutations in sample genomes. Afterwards, scientists have to decide about the impact of these mutations. Certainly, many different variant calling algorithms exist that generate different outputs due to different sequence alignments as input and parameterizations of variant calling algorithms. Thus, a combination of variant calling results is necessary to provide a more complete set of mutations than single algorithm runs can provide. Therefore, a system is required that facilitates the integration and parameterization of different variant calling algorithms and processing of different sequence alignments. Moreover, against the backdrop of ever increasing amounts of available genome sequencing data, such a system must provide matured database management capabilities to enable flexible and efficient analyses while keeping data consistent. In this paper, we present a first approach to integrate variant calling into a main-memory database management system that allows for calling variants via SQL.

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Section: Related Workmentioning

confidence: 99%

Toward Efficient Variant Calling Inside Main-Memory Database Systems

Dorok

Breß

Saake

2014

2014 25th International Workshop on Database and Expert Systems Applications

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“…BioDWH [68] is another toolkit for biomedical data warehouse development. One important feature of this system is that it maintains the data always updated, monitoring the original datasources, and updating the warehouse when necessary, following some update strategies.…”

Section: The Main Advantage Of Informationmentioning

confidence: 99%

Data Integration in Genomic Medicine: Trends and Applications

Seoane¹,

Dorado²,

Aguiar‐Pulido³

2012

Yearb Med Inform

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SummaryObjectives: In a near future, each person will incorporate his/her own sequenced genome in his/her electronic health record. In that precise moment, genomic medicine will be fundamental for clinical practice, as an essential key of personalized medicine. All the genomic data, as well as other 'omics' and clinical data necessary for personalized medicine, are stored in several distributed databases. Research and patient care require each time more biomedical data integration of several distributed heterogeneous datasources. Methods: This work develops a comprehensive review of the most relevant works in biomedical data integration, specifically in genomic medical data, analyzing the evolution of architecture and integration techniques during the last 20 years, and its usage. Conclusion: Most of these solutions, based on cross-linking, data warehouse or federated approaches, are suitable for specific domains. However, none of the models found in the literature is completely appropriate for a general biomedical data integration problem. KeywordsGenetic database, integrated advanced information management systems, information storage and retrieval, federated systems, data warehouse

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“…The use of this mass of information to improve care and patient safety is still very limited. Literature offers different approaches to address some www.ijacsa.thesai.org of the issues raised above, including: data warehouse approach as in BioWarehouse [16] and BioDWH [17] projects, the integration by views approach in Hemsys [18] and Tambis [19] projects or the so-called mashup in SRS [20] and Integr8 [21] projects. These different approaches offer methods and techniques to solve problems related to access to information regardless of its informative content, ie their semantics.…”

Section: A the Main Approachesmentioning

confidence: 99%

Enhancing eHealth Information Systems for chronic diseases remote monitoring systems

Hassani¹

2012

IJACSA

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Abstract-Statistics and demographics for the aging population in Europe are compelling. The stakes are then in terms of disability and chronic diseases whose proportions will increase because of increased life expectancy. Heart failure (HF), a serious chronic disease, induces frequent re-hospitalizations, some of which can be prevented by up-stream actions. Managing HF is quite a complex process: long, often difficult and expensive. In France, nearly one million people suffer from HF and 120,000 new cases are diagnosed every year. Managing such patients, a telemedicine system tools associated with motivation and education can significantly reduce the number of hospital days that believes therefore that the patient is hospitalized for acute HF. The current development projects are fully in prevention, human security, and remote monitoring of people in their living day-to-day spaces, from the perspective of health and wellness. These projects encompass gathering, organizing, structuring and sharing medical information. They also have to take into account the main aspects of interoperability. A different approach has been used to capitalize on such information: data warehouse approach, mediation approach (or integration by views) or integration approach by link (or so-called mashup).In this paper, we will focus on ontologies that take a central place in the Semantic Web: on one hand, they rely on modeling from conceptual representations of the areas concerned and, on the other hand, they allow programs to make inferences over them.

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BioDWH: A Data Warehouse Kit for Life Science Data Integration

Cited by 31 publications

References 6 publications

Toward Efficient Variant Calling Inside Main-Memory Database Systems

Toward Efficient Variant Calling Inside Main-Memory Database Systems

Data Integration in Genomic Medicine: Trends and Applications

Enhancing eHealth Information Systems for chronic diseases remote monitoring systems

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