scholarly journals Data at Scale

2018 ◽  
pp. 11-17
Author(s):  
Alberto Traverso ◽  
Frank J. W. M. Dankers ◽  
Leonard Wee ◽  
Sander M. J. van Kuijk
Keyword(s):  
Big Data ◽  
Clinical Data ◽  
Data Exchange ◽  
Learning Objectives ◽  
Data Sources ◽  
Basic Knowledge

AbstractPre-requisites to better understand the chapter: basic knowledge of major sources of clinical data.Logical position of the chapter with respect to the previous chapter: in the previous chapter, you have learned what the major sources of clinical data are. In this chapter, we will dive into the main characteristics of presented data sources. In particular, we will learn how to distinguish and classify data according to its scale.Learning objectives: you will learn the major differences between data sources presented in previous chapters; how clinical data can be classified according to its scale. You will get familiar with the concept of ‘big’ clinical data; you will learn which are the major concerns limiting ‘big’ data exchange.

scholarly journals BioPaxCOMP: an efficient system for integrating, compressing, and querying BioPAX

2016 ◽  
Author(s):  
Giuseppe Agapito ◽  
Andrea Greco ◽  
Mario Cannataro
Keyword(s):  
Big Data ◽  
Biological Networks ◽  
Data Exchange ◽  
Homo Sapiens ◽  
Point Of View ◽  
Data Sources ◽  
Efficient System ◽  
Ontology Language ◽  
Pathway Data ◽  
Size Growth

Abstract truncated at 3,000 characters - the full version is available in the pdf file. Biological networks and, in particular, biological pathways are composed of thousands of nodes and edges, posing several challenge regarding analysis and storage. The primary format used to represent pathways data is BioPAX (http://biopax.org.) BioPAX is a standard language that aims to enable integration, exchange, visualization and analysis of biological pathway data. BioPAX is an open and collaborative effort made by the community of researchers, software developers, and institutions and it specifically supports data exchange between pathway data groups. BioPAX is defined in OWL and is represented in the RDF/XML format. OWL (Web Ontology Language) is a W3C standard and is designed for use by applications that need to process the content of information instead of just presenting information to humans. RDF is a standard model for data interchange on the Web. Although OWL allows a standard representation of pathways, since it is based on XML, it is a verbose and redundant language, so the storage of pathways may be very huge, preventing an efficient transmission and sharing of this data. The typical size of a pathway is related to the organism, for example, the size of Homo Sapiens pathways (from Reactome database) is near to 200 MB on disk. Moreover, integrating pathways data coming from different data sources may require GBytes of space. A second problem with pathways is related to the possibility to integrate information coming from different data sources to have updated information in a centralized way. There exist several different databases for pathways data that emphasizes different aspect of the same pathway, thus, it could be useful to integrate and annotate together pathways coming from different databases to obtain a centralized and more informative pathway data. The principal obstacle for integrating, storing and exchanging such data is the extreme size growth when several pathways data are merged together, posing several challenges from the computational and archiving point of view. Pathways data can be easily classified as big data, because they meet all the 5V (Volume, Velocity, Variety, Veracity, Value) characteristics typical of Big Data, thus, the necessity to efficiently integrate and compress pathways data arises. The methodology for pathways data integration is based on the following steps: i) aggregation and validation locally of data coming from several pathway databases, ii) identification and normalization of compounds and reactions identifier and iii) integration. Integration occurs at the level of physical entities, such as proteins and small molecules. This is accomplished by linking interaction and pathway records together if they use the same physical entities (such as from UniProt for proteins) and by adding annotation data from UniProt or GeneOntology.

scholarly journals BioPaxCOMP: an efficient system for integrating, compressing, and querying BioPAX

2016 ◽  
Author(s):  
Giuseppe Agapito ◽  
Andrea Greco ◽  
Mario Cannataro
Keyword(s):  
Big Data ◽  
Biological Networks ◽  
Data Exchange ◽  
Homo Sapiens ◽  
Point Of View ◽  
Data Sources ◽  
Efficient System ◽  
Ontology Language ◽  
Pathway Data ◽  
Size Growth

Abstract truncated at 3,000 characters - the full version is available in the pdf file. Biological networks and, in particular, biological pathways are composed of thousands of nodes and edges, posing several challenge regarding analysis and storage. The primary format used to represent pathways data is BioPAX (http://biopax.org.) BioPAX is a standard language that aims to enable integration, exchange, visualization and analysis of biological pathway data. BioPAX is an open and collaborative effort made by the community of researchers, software developers, and institutions and it specifically supports data exchange between pathway data groups. BioPAX is defined in OWL and is represented in the RDF/XML format. OWL (Web Ontology Language) is a W3C standard and is designed for use by applications that need to process the content of information instead of just presenting information to humans. RDF is a standard model for data interchange on the Web. Although OWL allows a standard representation of pathways, since it is based on XML, it is a verbose and redundant language, so the storage of pathways may be very huge, preventing an efficient transmission and sharing of this data. The typical size of a pathway is related to the organism, for example, the size of Homo Sapiens pathways (from Reactome database) is near to 200 MB on disk. Moreover, integrating pathways data coming from different data sources may require GBytes of space. A second problem with pathways is related to the possibility to integrate information coming from different data sources to have updated information in a centralized way. There exist several different databases for pathways data that emphasizes different aspect of the same pathway, thus, it could be useful to integrate and annotate together pathways coming from different databases to obtain a centralized and more informative pathway data. The principal obstacle for integrating, storing and exchanging such data is the extreme size growth when several pathways data are merged together, posing several challenges from the computational and archiving point of view. Pathways data can be easily classified as big data, because they meet all the 5V (Volume, Velocity, Variety, Veracity, Value) characteristics typical of Big Data, thus, the necessity to efficiently integrate and compress pathways data arises. The methodology for pathways data integration is based on the following steps: i) aggregation and validation locally of data coming from several pathway databases, ii) identification and normalization of compounds and reactions identifier and iii) integration. Integration occurs at the level of physical entities, such as proteins and small molecules. This is accomplished by linking interaction and pathway records together if they use the same physical entities (such as from UniProt for proteins) and by adding annotation data from UniProt or GeneOntology.

Big Data Driven Clinical Informatics & Surveillance (BDD_CIS) – A Multimodal Database Focused Clinical, Community, and Multi-Omics Surveillance Plan for COVID-19: A study Protocol (Preprint)

2020 ◽  
Author(s):  
Bankole Olatosi ◽  
Jiajia Zhang ◽  
Sharon Weissman ◽  
Zhenlong Li ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Big Data ◽  
South Carolina ◽  
Data Science ◽  
Age Groups ◽  
The Elderly ◽  
The United States ◽  
Data Sources ◽  
Patient Registries ◽  
Multiple Partner ◽  
Multimodal Data

BACKGROUND The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) remains a serious global pandemic. Currently, all age groups are at risk for infection but the elderly and persons with underlying health conditions are at higher risk of severe complications. In the United States (US), the pandemic curve is rapidly changing with over 6,786,352 cases and 199,024 deaths reported. South Carolina (SC) as of 9/21/2020 reported 138,624 cases and 3,212 deaths across the state. OBJECTIVE The growing availability of COVID-19 data provides a basis for deploying Big Data science to leverage multitudinal and multimodal data sources for incremental learning. Doing this requires the acquisition and collation of multiple data sources at the individual and county level. METHODS The population for the comprehensive database comes from statewide COVID-19 testing surveillance data (March 2020- till present) for all SC COVID-19 patients (N≈140,000). This project will 1) connect multiple partner data sources for prediction and intelligence gathering, 2) build a REDCap database that links de-identified multitudinal and multimodal data sources useful for machine learning and deep learning algorithms to enable further studies. Additional data will include hospital based COVID-19 patient registries, Health Sciences South Carolina (HSSC) data, data from the office of Revenue and Fiscal Affairs (RFA), and Area Health Resource Files (AHRF). RESULTS The project was funded as of June 2020 by the National Institutes for Health. CONCLUSIONS The development of such a linked and integrated database will allow for the identification of important predictors of short- and long-term clinical outcomes for SC COVID-19 patients using data science.

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

Mapping the United Nations Fundamental Principles of Official Statistics against new and big data sources

2021 ◽  
Vol 37 (1) ◽  
pp. 161-169
Author(s):  
Dominik Rozkrut ◽  
Olga Świerkot-Strużewska ◽  
Gemma Van Halderen
Keyword(s):  
Big Data ◽  
Public Information ◽  
Fundamental Principle ◽  
Data Sources ◽  
Official Statistics ◽  
Development Agenda ◽  
Data Gaps ◽  
Data Source ◽  
Exciting Time ◽  
Statistical Systems

Never has there been a more exciting time to be an official statistician. The data revolution is responding to the demands of the CoVID-19 pandemic and a complex sustainable development agenda to improve how data is produced and used, to close data gaps to prevent discrimination, to build capacity and data literacy, to modernize data collection systems and to liberate data to promote transparency and accountability. But can all data be liberated in the production and communication of official statistics? This paper explores the UN Fundamental Principles of Official Statistics in the context of eight new and big data sources. The paper concludes each data source can be used for the production of official statistics in adherence with the Fundamental Principles and argues these data sources should be used if National Statistical Systems are to adhere to the first Fundamental Principle of compiling and making available official statistics that honor citizen’s entitlement to public information.

scholarly journals Section 1: Health and Clinical Management: Leveraging Information Technology to Improve Quality and Safety

2007 ◽  
Vol 16 (01) ◽  
pp. 22-29
Author(s):  
D. W. Bates ◽  
J. S. Einbinder
Keyword(s):  
Information Technology ◽  
Patient Safety ◽  
Clinical Data ◽  
Data Exchange ◽  
Personal Health Records ◽  
Personal Health ◽  
Health Records ◽  
Provider Behavior ◽  
Published Research ◽  
Improve Patient

SummaryTo examine five areas that we will be central to informatics research in the years to come: changing provider behavior and improving outcomes, secondary uses of clinical data, using health information technology to improve patient safety, personal health records, and clinical data exchange.Potential articles were identified through Medline and Internet searches and were selected for inclusion in this review by the authors.We review highlights from the literature in these areas over the past year, drawing attention to key points and opportunities for future work.Informatics may be a key tool for helping to improve patient care quality, safety, and efficiency. However, questions remain about how best to use existing technologies, deploy new ones, and to evaluate the effects. A great deal of research has been done on changing provider behavior, but most work to date has shown that process benefits are easier to achieve than outcomes benefits, especially for chronic diseases. Use of secondary data (data warehouses and disease registries) has enormous potential, though published research is scarce. It is now clear in most nations that one of the key tools for improving patient safety will be information technology— many more studies of different approaches are needed in this area. Finally, both personal health records and clinical data exchange appear to be potentially transformative developments, but much of the published research to date on these topics appears to be taking place in the U.S.— more research from other nations is needed.

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu
Keyword(s):  
Big Data ◽  
Portfolio Optimization ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Novel Approach

scholarly journals Big Data sources and methods for social and economic analyses

2018 ◽  
Vol 130 ◽  
pp. 99-113 ◽  
Author(s):  
Desamparados Blazquez ◽  
Josep Domenech
Keyword(s):  
Big Data ◽  
Data Sources ◽  
Economic Analyses
Sign in / Sign up

Export Citation Format

Share Document