scholarly journals Parallel Generative Topographic Mapping: An Efficient Approach for Big Data Handling

2020 ◽  
Vol 39 (12) ◽  
pp. 2000009
Author(s):  
Arkadii Lin ◽  
Igor I. Baskin ◽  
Gilles Marcou ◽  
Dragos Horvath ◽  
Bernd Beck ◽  
...  
Keyword(s):  
Big Data ◽  
Topographic Mapping ◽  
Data Handling ◽  
Generative Topographic Mapping ◽  
Efficient Approach

Chemical Data Visualization and Analysis with Incremental Generative Topographic Mapping: Big Data Challenge

2014 ◽  
Vol 55 (1) ◽  
pp. 84-94 ◽  
Author(s):  
Héléna A. Gaspar ◽  
Igor I. Baskin ◽  
Gilles Marcou ◽  
Dragos Horvath ◽  
Alexandre Varnek
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Chemical Data ◽  
Topographic Mapping ◽  
Generative Topographic Mapping

scholarly journals IoT Based Decision Making System to Improve Veracity of Big Data

2018 ◽  
Vol 7 (3.1) ◽  
pp. 63 ◽  
Author(s):  
R Revathy ◽  
R Aroul Canessane
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Internet Of Things ◽  
Data Streams ◽  
Data Cleaning ◽  
Data Handling ◽  
Secure Data ◽  
Decision Making System ◽  
Data Investigation ◽  
Impact Data

Data are vital to help decision making. On the off chance that data have low veracity, choices are not liable to be sound. Internet of Things (IoT) quality rates big data with error, irregularity, deficiency, trickery, and model guess. Improving data veracity is critical to address these difficulties. In this article, we condense the key qualities and difficulties of IoT, which impact data handling and decision making. We audit the scene of estimating and upgrading data veracity and mining indeterminate data streams. Also, we propose five suggestions for future advancement of veracious big IoT data investigation that are identified with the heterogeneous and appropriated nature of IoT data, self-governing basic leadership, setting mindful and area streamlined philosophies, data cleaning and handling procedures for IoT edge gadgets, and protection safeguarding, customized, and secure data administration.  

scholarly journals Biomedical Cyber-Physical Systems in the Light of Database as a Service (DBaaS) Paradigm

2020 ◽  
Vol 4 (3) ◽  
pp. 577-577
Author(s):  
Vania V Estrela
Keyword(s):  
Big Data ◽  
Data Warehouse ◽  
Query Language ◽  
Treatment Decisions ◽  
Cyber Physical Systems ◽  
Detailed Knowledge ◽  
Data Handling ◽  
Search Query ◽  
Physical Systems ◽  
Database As A Service

Background: A database (DB) to store indexed information about drug delivery, test, and their temporal behavior is paramount in new Biomedical Cyber-Physical Systems (BCPSs). The term Database as a Service (DBaaS) means that a corporation delivers the hardware, software, and other infrastructure required by companies to operate their databases according to their demands instead of keeping an internal data warehouse. Methods: BCPSs attributes are presented and discussed.  One needs to retrieve detailed knowledge reliably to make adequate healthcare treatment decisions. Furthermore, these DBs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. There are Search Query Language (SQL), and NoSQL DBs.  Results: This work investigates how to retrieve biomedical-related knowledge reliably to make adequate healthcare treatment decisions. Furthermore, Biomedical DBaaSs store, organize, manipulate, and retrieve the necessary data from an ocean of Big Data (BD) associated processes. Conclusion: A NoSQL DB allows more flexibility with changes while the BCPSs are running, which allows for queries and data handling according to the context and situation. A DBaaS must be adaptive and permit the DB management within an extensive variety of distinctive sources, modalities, dimensionalities, and data handling according to conventional ways.

scholarly journals An Efficient Approach for Storing and Accessing Small Files with Big Data Technology

2016 ◽  
Vol 146 (1) ◽  
pp. 36-39 ◽  
Author(s):  
Bharti Gupta ◽  
Rajender Nath ◽  
Girdhar Gopal ◽  
Kartik K
Keyword(s):  
Big Data ◽  
Efficient Approach ◽  
Big Data Technology

Developments of the generative topographic mapping

1998 ◽  
Vol 21 (1-3) ◽  
pp. 203-224 ◽  
Author(s):  
Christopher M. Bishop ◽  
Markus Svensén ◽  
Christopher K.I. Williams
Keyword(s):  
Topographic Mapping ◽  
Generative Topographic Mapping

The Acquisition and Management of Healthcare Data, Within a Hospital Infrastructure

2020 ◽  
Author(s):  
James Darby-Taylor ◽  
Fernando Luís-Ferreira ◽  
João Sarraipa ◽  
Ricardo Jardim-Goncalves
Keyword(s):  
Big Data ◽  
Clinical Practice ◽  
Big Data Analysis ◽  
Data Handling ◽  
Healthcare Data ◽  
Health And Wellbeing ◽  
Clinical Procedures ◽  
Legal Constraints ◽  
Hospital Practices

Abstract The quality of care provided to citizens by professionals and institutions depends on the quality and availability of information. Early commencement of treatment and medication, and the decisions on how to proceed, depend a lot on patients’ data in the different modalities available. It is also important to notice that large pools of data help inform health and wellbeing parameters for the largest possible community. To make that possible it is necessary both to have the best hospital practices but also to get consent and collaboration from patients. In order to accomplish such a goal, it is necessary to use practices, which adhere to legal constraints and are transparent while handling data and also to transmit those practices and protocols to professionals and patients. The present document aims to provide a framework envisaging the seamless application of the clinical procedures, following legal guidance and making the process known, secure and trustworthy. It aims to contribute to clinical practice, and clinical research, thereby contributing to big data analysis by ensuring trust and best clinical data handling.

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