BioStar models of clinical and genomic data for biomedical data warehouse design

Zhang, Aidong; Ramanathan, Murali

doi:10.1504/ijbra.2005.006903

Cited by 26 publications

(21 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [20] propose models for three gene expression data spaces (sample, annotation, and gene expression) based on star and snowflake schemata. A more exhaustive study of the challenges and requirements of a biomedical multidimensional model is presented in [27], along with a new schema called BioStar. The authors claim that through this schema they can address typical challenges of the genomics domain, such as fast evolving structures, imprecise and incomplete data, etc.…”

Section: Related Workmentioning

confidence: 99%

Golam

Baldacci

Golfarelli

Graziani

et al. 2014

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

View full text Add to dashboard Cite

The emerging medical models aim at leveraging on highthroughput genome sequencing technologies to better target drugs to patients' personal profiles so as to increase their effectiveness. However, the huge amount of data made available by these technologies calls for sophisticated and automated analysis techniques. In this direction we present GOLAM, a framework for OLAP analysis and mining of matches between genomic regions extracted from ENCODE, a worldwide-available collection of shared genomic data. The goal of GOLAM is to overcome the current limitations of genome analysis methods, that are normally based on browsing. This is done by partially automating and speeding-up the analysis process on the one hand, by making it more flexible and introducing a multi-resolution view of data on the other. The framework has been partially implemented so far; in this paper we focus on conveying its potential and on describing its functional architecture and the underlying data models.

show abstract

Section: Related Workmentioning

confidence: 99%

Golam

Baldacci

Golfarelli

Graziani

et al. 2014

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

View full text Add to dashboard Cite

show abstract

“…This approach has sought to solve some of the limitations of classical OLAP through providing a new way to summarize biological data for non-numerical domains. Another approach is called BIOSTAR, and aims to move away from the fact schema to a quadruple schema [29]. …”

Section: Fundamental Concepts and Related Workmentioning

confidence: 99%

Methodological guidelines for reducing the complexity of data warehouse development for transactional blood bank systems

Takecian

Oikawa

Braghetto

et al. 2013

Decision Support Systems

View full text Add to dashboard Cite

Over time, data warehouse (DW) systems have become more difficult to develop because of the growing heterogeneity of data sources. Despite advances in research and technology, DW projects are still too slow for pragmatic results to be generated. Here, we address the following question: how can the complexity of DW development for integration of heterogeneous transactional information systems be reduced? To answer this, we proposed methodological guidelines based on cycles of conceptual modeling and data analysis, to drive construction of a modular DW system. These guidelines were applied to the blood donation domain, successfully reducing the complexity of DW development.

show abstract

“…Designers must be aware of these incomplete fact-dimension relationships, because they appear in several real-world situations, e.g., the inherent uncertainty about the function of some genes in MD models for the biological domain [23] or the heterogeneous facts related to surgical processes that can be found in biomedical data warehouses [11]. Otherwise, data analysis tools will present incorrect results.…”

Section: Incomplete Fact-dimension Relationshipsmentioning

confidence: 99%

Solving summarizability problems in fact-dimension relationships for multidimensional models

Mazón

Lechtenbörger

Trujillo

2008

Proceedings of the ACM 11th International Workshop on Data Warehousing and OLAP

View full text Add to dashboard Cite

Multidimensional analysis allows decision makers to efficiently and effectively use data analysis tools, which mainly depend on multidimensional (MD) structures of a data warehouse such as facts and dimension hierarchies to explore the information and aggregate it at different levels of detail in an accurate way. A conceptual model of such MD structures serves as abstract basis of the subsequent implementation according to one specific technology. However, there is a semantic gap between a conceptual model and its implementation which complicates an adequate treatment of summarizability issues, which in turn may lead to erroneous results of data analysis tools and cause the failure of the whole data warehouse project. To bridge this gap for relationships between facts and dimension, we present an approach at the conceptual level for (i) identifying problematic situations in fact-dimension relationships, (ii) defining these relationships in a conceptual MD model, and (iii) applying a normalization process to transform this conceptual MD model into a summarizability-compliant model that avoids erroneous analysis of data. Furthermore, we also describe our Eclipsebased implementation of this normalization process.

show abstract

BioStar models of clinical and genomic data for biomedical data warehouse design

Cited by 26 publications

References 13 publications

Golam

Golam

Methodological guidelines for reducing the complexity of data warehouse development for transactional blood bank systems

Solving summarizability problems in fact-dimension relationships for multidimensional models

Contact Info

Product

Resources

About