Data management pipeline for plant phenotyping in a multisite project

In plant breeding, plants have to be characterised precisely, consistently and rapidly by different people at several field sites within defined time spans. For a meaningful data evaluation and statistical analysis, standardised data storage is required. Data access must be provided on a long-term basis and be independent of organisational barriers without endangering data integrity or intellectual property rights. We discuss the associated technical challenges and demonstrate adequate solutions exemplified in a data management pipeline for a project to identify markers for drought tolerance in potato. This project involves 11 groups from academia and breeding companies, 11 sites and four analytical platforms. Our data warehouse concept combines central data storage in databases and a file server and integrates existing and specialised database solutions for particular data types with new, project-specific databases. The strict use of controlled vocabularies and the application of web-access technologies proved vital to the successful data exchange between diverse institutes and data management concepts and infrastructures. By presenting our data management system and making the software available, we aim to support related phenotyping projects.

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Database Considerations for an ICU Patient Data Management System

ASME 1991 Computers in Engineering Conference: Volume 2 — Finite Elements/Computational Geometry; Computers in Education; Robotics and Controls ◽

10.1115/cie1991-0174 ◽

1991 ◽

Author(s):

N. Fumai ◽

C. Collet ◽

M. Petroni ◽

K. Roger ◽

E. Saab ◽

...

Keyword(s):

Data Management ◽

Real Time ◽

Data Storage ◽

Relational Database ◽

Management System ◽

Patient Data ◽

Patient Data Management System ◽

Data Management System ◽

Data Types ◽

Management Structure

Abstract A Patient Data Management System (PDMS) is being developed for use in the Intensive Care Unit (ICU) of the Montreal Children’s Hospital. The PDMS acquires real-time patient data from a network of physiological bedside monitors and facilitates the review and interpretation of this data by presenting it as graphical trends, charts and plots on a color video display. Due to the large amounts of data involved, the data storage and data management processes are an important task of the PDMS. The data management structure must integrate varied data types and provide database support for different applications, while preserving the real-time acquisition of network data. This paper outlines a new data management structure which is based primarily on OS/2’s Extended Edition relational database. The relational database design is expected to solve the query shortcomings of the previous data management structure, as well as offer support for security and concurrency. The discussion will also highlight future advantages available from a network implementation.

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The BNLBox Cloud Storage Service

EPJ Web of Conferences ◽

10.1051/epjconf/202024504011 ◽

2020 ◽

Vol 245 ◽

pp. 04011

Author(s):

Ofer Rind ◽

Hironori Ito ◽

Guangwei Che ◽

Tim Chou ◽

Robert Hancock ◽

...

Keyword(s):

Data Management ◽

Data Storage ◽

Cloud Storage ◽

Storage System ◽

Data Access ◽

Scientific Data ◽

Data Management System ◽

Easy Access ◽

Cloud Storage Service ◽

Storage Service

Large scientific data centers have recently begun providing a number of different types of data storage in order to satisfy the various needs of their users. Users with interactive accounts, for example, might want a POSIX interface for easy access to the data from their interactive machines. Grid computing sites, on the other hand, likely need to provide an X509-based storage protocol, like SRM and GridFTP, since the data management system is built upon them. Meanwhile, an experiment producing large amounts of data typically demands a service that provides archival storage for the safe keeping of their unique data. To access these various types of data, users must use specific sets of commands tailored to their respective storage, making access to their data complex and difficult. BNLBox is an attempt to provide a unified and easy to use storage service for all BNL users, to store their important documents, code and data. It is a cloud storage system with an intuitive web interface for novice users. It provides an automated synchronization feature that enables users to upload data to their cloud storage without manual intervention, freeing them to focus on analysis rather than data management software. It provides a POSIX interface for local interactive users, which simplifies data access from batch jobs as well. At the same time, it also provides users with a straightforward mechanism for archiving large data sets for later processing. The storage space can be used for both code and data within the compute job environment. This paper will describe various aspects of the BNLBox storage service.

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Sensor Grid Enhancement with Data Management System for Ubiquitous Healthcare Computing

Advances in Healthcare Information Systems and Administration - Interoperability in Healthcare Information Systems ◽

10.4018/978-1-4666-3000-0.ch007 ◽

2013 ◽

pp. 184-210

Author(s):

Nikolaos Preve

Keyword(s):

Data Management ◽

Network Architecture ◽

Management System ◽

Data Access ◽

Sensor Nodes ◽

Data Management System ◽

Biomedical Sensors ◽

Communication Ability ◽

Open Geospatial Consortium ◽

Network Technologies

A Wireless Sensor Network (WSN) can be deployed to monitor the health of patients suffering from critical diseases. A wireless network consisting of biomedical sensors can also be implanted into the patient's body and can monitor the patients' conditions. These sensor devices, apart from having an enormous capability of collecting data from their physical surroundings, are also resource constraint in nature with a limited processing and communication ability. Therefore, it is necessary to integrate them with the Grid technology in order to process and store the collected data by the sensor nodes. This chapter proposes the SEnsor Grid Enhancement Data Management system, called SEGEDMA, ensuring the integration of different network technologies and the continuous data access to system users. The main contribution of this work is to achieve the interoperability of both technologies through a novel network architecture ensuring also the interoperability of Open Geospatial Consortium (OGC) and HL7 standards. According to the results SEGEDMA can be applied successfully in a decentralized healthcare environment.

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Implementing a new data acquisition system for the advanced integrated atmospheric observation system KITcube

10.5194/egusphere-egu2020-10431 ◽

2020 ◽

Author(s):

Martin Kohler ◽

Mahnaz Fekri ◽

Andreas Wieser ◽

Jan Handwerker

Keyword(s):

Time Series ◽

Data Acquisition ◽

Real Time ◽

Binary Data ◽

Data Exchange ◽

Data Access ◽

Series Data ◽

Observation System ◽

Observation Data ◽

File Server

KITcube (Kalthoff et al, 2013) is a mobile advanced integrated observation system for the measurement of meteorological processes within a volume of 10x10x10 km3. A large variety of different instruments from in-situ sensors to scanning remote sensing devices are deployed during campaigns. The simultaneous operation and real time instrument control needed for maximum instrument synergy requires a real-time data management designed to cover the various user needs: Save data acquisition, fast loading, compressed storage, easy data access, monitoring and data exchange. Large volumes of data such as raw and semi-processed data of various data types, from simple ASCII time series to high frequency multi-dimensional binary data provide abundant information, but makes the integration and efficient management of such data volumes to a challenge. Our data processing architecture is based on open source technologies and involves the following five sections: 1) Transferring: Data and metadata collected during a campaign are stored on a file server. 2) Populating the database: A relational database is used for time series data and a hybrid database model for very large, complex, unstructured data. 3) Quality control: Automated checks for data acceptance and data consistency. 4) Monitoring: Data visualization in a web-application. 5) Data exchange: Allows the exchange of observation data and metadata in specified data formats with external users. The implemented data architecture and workflow is illustrated in this presentation using data from the MOSES project (http://moses.eskp.de/home).References:KITcube - A mobile observation platform for convection studies deployed during HyMeX&#160;. Kalthoff, N.; Adler, B.; Wieser, A.; Kohler, M.; Tr&#228;umner, K.; Handwerker, J.; Corsmeier, U.; Khodayar, S.; Lambert, D.; Kopmann, A.; Kunka, N.; Dick, G.; Ramatschi, M.; Wickert, J.; Kottmeier, C. 2013. Meteorologische Zeitschrift, 22 (6), 633&#8211;647. doi:10.1127/0941-2948/2013/0542&#160;

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Penerapan Network Attached Storage (NAS) berbasis Raspberry Pi di LP3SDM AZRA Palembang

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2019631416 ◽

2019 ◽

Vol 6 (3) ◽

pp. 309

Author(s):

R.M. Nasrul Halim

Keyword(s):

Data Storage ◽

Data Exchange ◽

Storage System ◽

Local Network ◽

Raspberry Pi ◽

Storage Device ◽

File Server ◽

Data Storage System ◽

E Mail ◽

Network Attached Storage

Kebutuhan penyimpanan data yang semakin besar seiring dengan berkembangnya teknologi informasi, dibutuhkanlah suatu sistem penyimpanan data yang dapat melayani kebutuhan data secara cepat serta dapat diakses melalui jaringan lokal, salah satunya menggunakan Network Attached Storage (NAS). NAS adalah perangkat penyimpanan yang tersambung ke jaringan yang memungkinkan penyimpanan dan pengambilan data dari lokasi terpusat untuk pengguna jaringan. Sistem NAS fleksibel dan dapat disesuaikan jika memerlukan penyimpanan tambahan. Proses penyimpanan data di LP3SDM AZRA Palembang selama ini masih menggunakan media flashdisk dan e-mail untuk pertukaran data sedangkan penyimpanan data dilakukan di laptop masing-masing pegawai dan disimpan di flashdisk. Penggunaan Raspberry pi dalam penelitian ini berfungsi sebagai NAS Server sebagai pengganti PC ataupun server dedicated, dikarenakan harga Raspberry yang cukup murah dibanding PC dan Raspberry tidak membutuhkan spesifikasi perangkat keras yang tinggi serta tidak membutuhkan lisensi perangkat lunak. Hasil dari penelitian ini berupa sistem penyimpanan data menggunakan Raspberry Pi sebagai file server pengganti PC, yang dapat melayani proses penyimpanan dan pertukaran data di LP3SDM AZRA Palembang sehingga dapat memudahkan pekerjaan karyawannya. Abstract The need for data storage is increasing along with the development of information technology, it is necessary to a data storage system is needed that can serve data needs quickly that can serve the needs of data quickly and can be accessed through the local network, which one of them using Network Attached Storage (NAS). NAS is a network-connected storage device that allows storage and retrieval of data from a centralized location for network users. The NAS system is flexible and customizable if it requires additional storage. The process of data storage in LP3SDM AZRA Palembang so far still uses flash disk and e-mail for data exchange while data storage is done in each employee laptop and uses flash disk. The use of raspberry pi in this research serves as a NAS Server as a replacement for PC or dedicated server, because the price of raspberry is quite cheap compared to PC and Raspberry does not require high hardware specifications and does not require software licenses. The result of this research is data storage system using Raspberry Pi as a PC replacement file server, that can serve the storage and data exchange process in LP3SDM AZRA Palembang so it can facilitates the work of its employees.

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Patient Health Record Systems Scope and Functionalities: Literature Review and Future Directions (Preprint)

10.2196/preprints.8073 ◽

2017 ◽

Author(s):

Lina Bouayad ◽

Anna Ialynytchev ◽

Balaji Padmanabhan

Keyword(s):

Health Care ◽

Data Storage ◽

Time Series Data ◽

Data Access ◽

Series Data ◽

Health Record ◽

Review Of The Literature ◽

Data Types ◽

Future Directions ◽

Patient Health

BACKGROUND A new generation of user-centric information systems is emerging in health care as patient health record (PHR) systems. These systems create a platform supporting the new vision of health services that empowers patients and enables patient-provider communication, with the goal of improving health outcomes and reducing costs. This evolution has generated new sets of data and capabilities, providing opportunities and challenges at the user, system, and industry levels. OBJECTIVE The objective of our study was to assess PHR data types and functionalities through a review of the literature to inform the health care informatics community, and to provide recommendations for PHR design, research, and practice. METHODS We conducted a review of the literature to assess PHR data types and functionalities. We searched PubMed, Embase, and MEDLINE databases from 1966 to 2015 for studies of PHRs, resulting in 1822 articles, from which we selected a total of 106 articles for a detailed review of PHR data content. RESULTS We present several key findings related to the scope and functionalities in PHR systems. We also present a functional taxonomy and chronological analysis of PHR data types and functionalities, to improve understanding and provide insights for future directions. Functional taxonomy analysis of the extracted data revealed the presence of new PHR data sources such as tracking devices and data types such as time-series data. Chronological data analysis showed an evolution of PHR system functionalities over time, from simple data access to data modification and, more recently, automated assessment, prediction, and recommendation. CONCLUSIONS Efforts are needed to improve (1) PHR data quality through patient-centered user interface design and standardized patient-generated data guidelines, (2) data integrity through consolidation of various types and sources, (3) PHR functionality through application of new data analytics methods, and (4) metrics to evaluate clinical outcomes associated with automated PHR system use, and costs associated with PHR data storage and analytics.

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Blockchain and novel coronavirus: Towards preventing COVID-19 and future pandemics

Iberoamerican Journal of Medicine ◽

10.53986/ibjm.2020.0037 ◽

2020 ◽

Vol 2 (3) ◽

pp. 215-218

Author(s):

Amirul Azim ◽

◽

Muhammad Nazrul Islam ◽

Paul E. Spranger ◽

◽

...

Keyword(s):

Data Management ◽

Data Storage ◽

Storage System ◽

Data Management System ◽

The Novel ◽

Centralized Control ◽

Novel Technology ◽

Distributed Ledger ◽

Novel Coronavirus ◽

Present World

The present world has observed the SARS-CoV2 or COVID-19 spreading rapidly with a rising death toll and transmission rates with an absence of proper data management and information sharing. The current traditional database storage system has the limitations of a centralized control system and tampering of data, particularly when it is being shared with others. The Novel technology known as “Blockchain” is a distributed ledger technology that acts as a shared database, keeping all its copies synced and verified. The objective of this article is to study the concept of a Blockchain based pandemic data management system that would ensure unified patients’ data storage and reliable data management to trackdown coronavirus to combat against this and future pandemics.

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The Implementation of the Largest Geological and Production Corporate Database in Ukraine to Accelerate Digital Transformation

10.2118/208530-ms ◽

2021 ◽

Author(s):

Vadym Reshetniak ◽

Arsene Akono ◽

Rana Sherif ◽

Amine Boumehdi ◽

Sid Ahmed Morsli ◽

...

Keyword(s):

Data Quality ◽

Data Storage ◽

Data Access ◽

Development Planning ◽

Digital Transformation ◽

Data Repository ◽

Production Data ◽

Business Systems ◽

Data Types ◽

Field Development

Abstract Two years ago, geoscientists of the leading East European gas producers were still using paper logs, unknown data quality sources, and many data versions, stored on individual local disks for their interpretation jobs. To overcome this challenge, the deployment of a geological and production database was initiated in the framework of the digital transformation programme. The key objectives were to build a single data repository for all company assets and integrate it with the production and drilling business systems. The development of the corporate data repository started with an extensive data assessment. A report of available data types, business processes, recommended data management, and business rules were produced. Loading and quality control procedures were designed to load over 40 different data types, including geology, geophysics, production, and drilling. Standardisation of data available in non-industry formats was necessary, e.g. for Lithology data. To enable reporting of drilling and production data stored in the business systems, complex integration and synchronisation between different Database Management Systems were developed. Data delivery to petrotechnical applications was a key to productivity. By implementing this centralised and unique Corporate Data Storage, digitalization and loading of the well, log, seismic, drilling, and production data with proper quality were enabled. Petrotechnical experts can now use one data access point to retrieve data into their applications quickly and efficiently using just an integrated web browser. Searching information within SEGY or DLIS files was previously a difficult process that has been facilitated through an application user interface displayed in the local language. Thousands of well logs, documents, and reports have been digitised and made available in the system. The interpretation results and knowledge are now captured and reused in future field development planning. All the company data including drilling and production data synched from business systems are now available in a single place and accurate reporting can be facilitated. The system allowed the reduction of the time spent by the users searching and data quality checks.

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Modified Data Storage and Replication Mechanism with Frequent Use-Case Based Indexing

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9413 ◽

2020 ◽

Vol 17 (12) ◽

pp. 5229-5237

Author(s):

P. Selvaraj ◽

Venkatesh Kannan ◽

Bruno Voisin

Keyword(s):

Health Care ◽

Data Management ◽

Query Processing ◽

Data Storage ◽

Data Modeling ◽

Data Access ◽

Use Case ◽

Replication Mechanism ◽

Logical Data ◽

Case Based

The real time applications demands high speed and reliable data access from the remote database. An effective logical data management strategy that handles simultaneous connections with better performance negotiation is inevitable. This work considers an e-health care application that proposes MongoDB based modified indexing and performance tuning methods. To cope with certain high frequency use case and its performance mandates, a flexible and efficient logical data management may be preferred. By analysing the data dependency, data decomposition concerns and the performance requirements of the specific use case of the medical application, a logical schema may be customized on an ala-carte basis. This work focused on the flexible logical data modeling schemes and its performance factors of the NoSql DB. The efficiency of unstructured data base management in storing and retrieving the e-health care data was analysed with a web based tool. To enable faster data retrieval and query processing over the distributed nodes, a Spark based storage engine was built on top of the MongoDB based data storage management. With Spark tool, the database has been made distributed as master–slave structures with suitable data replication mechanisms. In such distributed database the fail-over also implemented with the suitable replication mechanism. This work considered MongoDB based flexible schema modeling and Spark based distributed computation with multiple chunks of data. The flexible data modeling scheme with MongoDB with the on-demand Spark based computation framework was proposed. To facilitate the eventual consistency, scalability aspects of the e-health care applications, use case based indexing was proposed. With the effective data management, faster query processing the horizontal scalability has been increased. The overall efficiency and scalability of the proposed logical data management approach was analysed. Through the simulation studies, the proposed approach has been claimed to boost the performance of the bigdata based application to a considerable extent.

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Analysis and Design of Data Management Information System for the Enterprise of Wooden Door

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.480-481.800 ◽

2011 ◽

Vol 480-481 ◽

pp. 800-804

Author(s):

Dong Fen Ye ◽

Wei Fan ◽

Lin Jing Lin

Keyword(s):

Information System ◽

Data Management ◽

Management Information System ◽

Data Exchange ◽

Data Management System ◽

Practical Significance ◽

Management Information ◽

Analysis And Design ◽

Head Office ◽

Paperless Office

In some companies, there are a lot of data exchange between head office and branch offices. Based on the situation and the actual requirements of a wooden door enterprise, a data management information system is researched and designed for the enterprise, which adopts B/S and C/S combined architecture as well as is developed on .net platform of Microsoft. The head office can monitor purchase, inventory and sales of products in branch offices in real time through the system to improve the efficiency of information management and to achieve the supervision with paperless office. It is of practical significance for the promotion of internal data management system of enterprise.

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