Data Integration Issues and Opportunities in Biological XML Data Management

2010 ◽  
pp. 263-286 ◽  
Author(s):  
Marco Mesiti ◽  
Ernesto Jiménez Ruiz ◽  
Ismael Sanz ◽  
Rafael Berlanga Llavori ◽  
Giorgio Valentini ◽  
...  
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Biological Data ◽  
Future Research ◽  
Data Types ◽  
Industrial Organizations ◽  
Heterogeneous Data Sources ◽  
Future Research Directions ◽  
Biological Data Integration ◽  
Xml Data Management

There is a proliferation of research and industrial organizations that produce sources of huge amounts of biological data issuing from experimentation with biological systems. In order to make these heterogeneous data sources easy to use, several efforts at data integration are currently being undertaken based mainly on XML. Starting from a discussion of the main biological data types and system interactions that need to be represented, the authors deal with the main approaches proposed for their modelling through XML. Then, they show the current efforts in biological data integration and how an increasing amount of Semantic information is required in terms of vocabulary control and ontologies. Finally, future research directions in biological data integration are discussed.

scholarly journals Methods for biological data integration: perspectives and challenges

2015 ◽  
Vol 12 (112) ◽  
pp. 20150571 ◽  
Author(s):  
Vladimir Gligorijević ◽  
Nataša Pržulj
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Biological Data ◽  
Data Types ◽  
Specific Data ◽  
Advantages And Disadvantages ◽  
Technological Advances ◽  
Biological Data Integration ◽  
Biological Entities ◽  
Further Development

Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biological entities. Standard network data analysis methods were shown to be limited in dealing with such heterogeneous networked data and consequently, new methods for integrative data analyses have been proposed. The integrative methods can collectively mine multiple types of biological data and produce more holistic, systems-level biological insights. We survey recent methods for collective mining ( integration ) of various types of networked biological data. We compare different state-of-the-art methods for data integration and highlight their advantages and disadvantages in addressing important biological problems. We identify the important computational challenges of these methods and provide a general guideline for which methods are suited for specific biological problems, or specific data types. Moreover, we propose that recent non-negative matrix factorization-based approaches may become the integration methodology of choice, as they are well suited and accurate in dealing with heterogeneous data and have many opportunities for further development.

scholarly journals Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

2021 ◽  
Vol 11 (17) ◽  
pp. 8275
Author(s):  
Ganesh Kumar ◽  
Shuib Basri ◽  
Abdullahi Abubakar Imam ◽  
Sunder Ali Khowaja ◽  
Luiz Fernando Capretz ◽  
...  
Keyword(s):  
Research Question ◽  
Heterogeneous Data ◽  
Future Research ◽  
Data Types ◽  
Data Harmonization ◽  
Heterogeneous Datasets ◽  
Challenging Tasks ◽  
Future Research Directions ◽  
And Performance ◽  
Academic Implications

As data size increases drastically, its variety also increases. Investigating such heterogeneous data is one of the most challenging tasks in information management and data analytics. The heterogeneity and decentralization of data sources affect data visualization and prediction, thereby influencing analytical results accordingly. Data harmonization (DH) corresponds to a field that unifies the representation of such a disparate nature of data. Over the years, multiple solutions have been developed to minimize the heterogeneity aspects and disparity in formats of big-data types. In this study, a systematic review of the literature was conducted to assess the state-of-the-art DH techniques. This study aimed to understand the issues faced due to heterogeneity, the need for DH and the techniques that deal with substantial heterogeneous textual datasets. The process produced 1355 articles, but among them, only 70 articles were found to be relevant through inclusion and exclusion criteria methods. The result shows that the heterogeneity of structured, semi-structured, and unstructured (SSU) data can be managed by using DH and its core techniques, such as text preprocessing, Natural Language Preprocessing (NLP), machine learning (ML), and deep learning (DL). These techniques are applied to many real-world applications centered on the information-retrieval domain. Several assessment criteria were implemented to measure the efficiency of these techniques, such as precision, recall, F-1, accuracy, and time. A detailed explanation of each research question, common techniques, and performance measures is also discussed. Lastly, we present readers with a detailed discussion of the existing work, contributions, and managerial and academic implications, along with the conclusion, limitations, and future research directions.

scholarly journals Integração, Relacionamento e Representação de Dados em Cidades Inteligentes: Uma Revisão de Literatura

2018 ◽  
Author(s):  
Larysse Silva ◽  
José Alex Lima ◽  
Nélio Cacho ◽  
Eiji Adachi ◽  
Frederico Lopes ◽  
...  
Keyword(s):  
Decision Making ◽  
Literature Review ◽  
Data Integration ◽  
Smart Cities ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Application Development ◽  
Continuous Integration ◽  
Heterogeneous Data Sources ◽  
Computational Systems

A notable characteristic of smart cities is the increase in the amount of available data generated by several devices and computational systems, thus augmenting the challenges related to the development of software that involves the integration of larges volumes of data. In this context, this paper presents a literature review aimed to identify the main strategies used in the development of solutions for data integration, relationship, and representation in smart cities. This study systematically selected and analyzed eleven studies published from 2015 to 2017. The achieved results reveal gaps regarding solutions for the continuous integration of heterogeneous data sources towards supporting application development and decision-making.

Analysis and Integration of Biological Data

2010 ◽  
pp. 287-314
Author(s):  
Diego Milone ◽  
Georgina Stegmayer ◽  
Matías Gerard ◽  
Laura Kamenetzky ◽  
Mariana López ◽  
...  
Keyword(s):  
Data Integration ◽  
New Technologies ◽  
Biological Significance ◽  
Neural Model ◽  
Biological Data ◽  
Data Sets ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Internal Coherence ◽  
Biological Data Integration

The volume of information derived from post genomic technologies is rapidly increasing. Due to the amount of involved data, novel computational methods are needed for the analysis and knowledge discovery into the massive data sets produced by these new technologies. Furthermore, data integration is also gaining attention for merging signals from different sources in order to discover unknown relations. This chapter presents a pipeline for biological data integration and discovery of a priori unknown relationships between gene expressions and metabolite accumulations. In this pipeline, two standard clustering methods are compared against a novel neural network approach. The neural model provides a simple visualization interface for identification of coordinated patterns variations, independently of the number of produced clusters. Several quality measurements have been defined for the evaluation of the clustering results obtained on a case study involving transcriptomic and metabolomic profiles from tomato fruits. Moreover, a method is proposed for the evaluation of the biological significance of the clusters found. The neural model has shown a high performance in most of the quality measures, with internal coherence in all the identified clusters and better visualization capabilities.

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