Conceptual models of heterogeneous data representation

2010 ◽  
Author(s):  
Oleg A. Garipov ◽  
Sergey V. Zykov
Keyword(s):  
Conceptual Models ◽  
Data Representation ◽  
Heterogeneous Data

scholarly journals Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation (Preprint)

2019 ◽  
Author(s):  
Helmut Spengler ◽  
Claudia Lang ◽  
Tanmaya Mahapatra ◽  
Ingrid Gatz ◽  
Klaus A Kuhn ◽  
...  
Keyword(s):  
Laboratory Data ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Use Cases ◽  
Iterative Refinement ◽  
Cloud Infrastructure ◽  
Specific Domain ◽  
Wide Range ◽  
Data Loading ◽  
Time Required

BACKGROUND Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. OBJECTIVE Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. METHODS We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. RESULTS The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. CONCLUSIONS Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation. CLINICALTRIAL

Web Retrieval of XML Documents

2011 ◽  
pp. 170-199
Author(s):  
Barbara Catania ◽  
Elena Ferrari
Keyword(s):  
Expressive Power ◽  
Data Representation ◽  
Query Languages ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Xml Data ◽  
Web Documents ◽  
Web Retrieval ◽  
Heterogeneous Data Sources ◽  
The Web

Web is characterized by a huge amount of very heterogeneous data sources, that differ both in media support and format representation. In this scenario, there is the need of an integrating approach for querying heterogeneous Web documents. To this purpose, XML can play an important role since it is becoming a standard for data representation and exchange over the Web. Due to its flexibility, XML is currently being used as an interface language over the Web, by which (part of) document sources are represented and exported. Under this assumption, the problem of querying heterogeneous sources can be reduced to the problem of querying XML data sources. In this chapter, we first survey the most relevant query languages for XML data proposed both by the scientific community and by standardization committees, e.g., W3C, mainly focusing on their expressive power. Then, we investigate how typical Information Retrieval concepts, such as ranking, similarity-based search, and profile-based search, can be applied to XML query languages. Commercial products based on the considered approaches are then briefly surveyed. Finally, we conclude the chapter by providing an overview of the most promising research trends in the fields.

Study of Data Integration Model Based on Network Technology

2011 ◽  
Vol 268-270 ◽  
pp. 1868-1873
Author(s):  
Li Jun Yang
Keyword(s):  
Data Model ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Structured Data ◽  
Common Data Model ◽  
Network Technology ◽  
Interaction Technique ◽  
Integration Model ◽  
Heterogeneous Data Sources ◽  
Meaningful Research

The existence of heterogeneous data sources brings great inconvenience to realize the exchange visits to data between different information systems. Therefore, it becomes a meaningful research topic to solve the problem of realizing convenient and flexible exchange visits. This paper combines the data representation format of XML generally used in current network with an interaction technique of WebService, and constructs a UDM data model, which can implement structured data of relational type as well as describe unstructured data and self-describing semi-structured data. So UDM data model can be used as a common data model integrated by heterogeneous data to integrate these heterogeneous data.

scholarly journals Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important

2018 ◽  
Author(s):  
Michele Donini ◽  
Joao M. Monteiro ◽  
Massimiliano Pontil ◽  
Tim Hahn ◽  
Andreas J. Fallgatter ◽  
...  
Keyword(s):  
Feature Selection ◽  
Learning Algorithm ◽  
Multiple Kernel Learning ◽  
Clinical Information ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Healthy Controls ◽  
Sources Of Information ◽  
Heterogeneous Data Sources ◽  
Selection Methodology

Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e.g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods.

scholarly journals Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Metabolites ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 243 ◽  
Author(s):  
Ulf W. Liebal ◽  
An N. T. Phan ◽  
Malvika Sudhakar ◽  
Karthik Raman ◽  
Lars M. Blank
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Data Analysis ◽  
Metabolic Engineering ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

scholarly journals Semantic Association Analysis in Ontology-Based Information Retrieval

2009 ◽  
pp. 131-141
Author(s):  
Payam M. Barnaghi ◽  
Wei Wang ◽  
Jayan C. Kurian
Keyword(s):  
Semantic Web ◽  
Information Search ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Ontology Development ◽  
Semantic Association ◽  
Information Search And Retrieval ◽  
Association Discovery ◽  
Meaningful Relationships ◽  
Search And Retrieval

The Semantic Web is an extension to the current Web in which information is provided in machine-processable format. It allows interoperable data representation and expression of meaningful relationships between the information resources. In other words, it is envisaged with the supremacy of deduction capabilities on the Web, that being one of the limitations of the current Web. In a Semantic Web framework, an ontology provides a knowledge sharing structure. The research on Semantic Web in the past few years has offered an opportunity for conventional information search and retrieval systems to migrate from keyword to semantics-based methods. The fundamental difference is that the Semantic Web is not a Web of interlinked documents; rather, it is a Web of relations between resources denoting real world objects, together with well-defined metadata attached to those resources. In this chapter, we first investigate various approaches towards ontology development, ontology population from heterogeneous data sources, semantic association discovery, semantic association ranking and presentation, and social network analysis, and then we present our methodology for an ontology-based information search and retrieval. In particular, we are interested in developing efficient algorithms to resolve the semantic association discovery and analysis issues.

Information Visualization Techniques for Big Data

2016 ◽  
pp. 1677-1692
Author(s):  
William H. Hsu
Keyword(s):  
Information Visualization ◽  
Scale Up ◽  
Analytical Techniques ◽  
Large Data ◽  
Visual Display ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Visualization Techniques ◽  
Answering Questions

This chapter presents challenges and recommended practices for visualizing data about phenomena that are observed or simulated across space and time. Some data may be collected for the express purpose of answering questions through quantitative analysis and simulation, especially about future occurrences or continuations of the phenomena – that is, prediction. In this case, analytical computations may serve two purposes: to prepare the data for presentation and to answer questions by producing information, especially an informative model, that can also be visualized. These purposes may have significant overlap. Thus, the focus of the chapter is about analytical techniques for visual display of quantitative data and information that scale up to large data sets. It begins by surveying trends in educational and scientific use of visualization and reviewing taxonomies of data to be visualized. Next, it reviews aspects of spatiotemporal data that pose challenges, such as heterogeneity and scale, along with techniques for dealing specifically with geospatial data and text. An exploration of concrete applications then follows. Finally, tenets of information visualization design, put forward by Tufte and other experts on data representation and presentation, are considered in the context of analytical applications for heterogeneous data in spatiotemporal domains.

scholarly journals Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

10.2196/15918 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e15918
Author(s):  
Helmut Spengler ◽  
Claudia Lang ◽  
Tanmaya Mahapatra ◽  
Ingrid Gatz ◽  
Klaus A Kuhn ◽  
...  
Keyword(s):  
Laboratory Data ◽  
Data Representation ◽  
Heterogeneous Data ◽  
Use Cases ◽  
Iterative Refinement ◽  
Cloud Infrastructure ◽  
Specific Domain ◽  
Wide Range ◽  
Data Loading ◽  
Time Required

Background Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. Objective Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. Methods We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. Results The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. Conclusions Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.

An Eligibility Criteria Query Language for Heterogeneous Data Warehouses

2015 ◽  
Vol 54 (01) ◽  
pp. 41-44 ◽  
Author(s):  
A. Taweel ◽  
S. Miles ◽  
B. C. Delaney ◽  
R. Bache
Keyword(s):  
Clinical Data ◽  
Query Language ◽  
Data Representation ◽  
Query Languages ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Data Warehouses ◽  
Eligibility Criteria ◽  
Strong Basis ◽  
Temporal Semantics

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.Objectives: The increasing availability of electronic clinical data provides great potential for finding eligible patients for clinical research. However, data heterogeneity makes it difficult for clinical researchers to interrogate sources consistently. Existing standard query languages are often not sufficient to query across diverse representations. Thus, a higher- level domain language is needed so that queries become data-representation agnostic. To this end, we define a clinician-readable computational language for querying whether patients meet eligibility criteria (ECs) from clinical trials. This language is capable of implementing the temporal semantics required by many ECs, and can be automatically evaluated on heterogeneous data sources.Methods: By reference to standards and examples of existing ECs, a clinician-readable query language was developed. Using a model-based approach, it was implemented to transform captured ECs into queries that interrogate heterogeneous data warehouses. The query language was evaluated on two types of data sources, each different in structure and content.Results: The query language abstracts the level of expressivity so that researchers construct their ECs with no prior knowledge of the data sources. It was evaluated on two types of semantically and structurally diverse data warehouses. This query language is now used to express ECs in the EHR4CR project. A survey shows that it was perceived by the majority of users to be useful, easy to understand and unambiguous.Discussion: An EC-specific language enables clinical researchers to express their ECs as a query such that the user is isolated from complexities of different heterogeneous clinical data sets. More generally, the approach demonstrates that a domain query language has potential for overcoming the problems of semantic interoperability and is applicable where the nature of the queries is well understood and the data is conceptually similar but in different representations.Conclusions: Our language provides a strong basis for use across different clinical domains for expressing ECs by overcoming the heterogeneous nature of electronic clinical data whilst maintaining semantic consistency. It is readily comprehensible by target users. This demonstrates that a domain query language can be both usable and interoperable.

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