An open-source representation for 2-DE-centric proteomics and support infrastructure for data storage and analysis

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

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A Study on Digital Forensics in Hadoop

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v4.i2.pp473-478 ◽

2016 ◽

Vol 4 (2) ◽

pp. 473 ◽

Cited By ~ 2

Author(s):

Sachin Arun Thanekar ◽

K. Subrahmanyam ◽

A.B. Bagwan

Keyword(s):

Big Data ◽

Open Source ◽

Data Storage ◽

High Velocity ◽

Digital Forensics ◽

Short Description ◽

Open Source Tool ◽

Automated Tool ◽

Data Forensics

<p>Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Hadoop is a framework which can be used for tremendous data storage and faster processing. It is freely available, easy to use and implement. Big data forensic is one of the challenges of big data. For this it is very important to know the internal details of the Hadoop. Different files are generated by Hadoop during its process. Same can be used for forensics. In our paper our focus is on digital forensics and different files generated during different processes. We have given the short description on different files generated in Hadoop. With the help of an open source tool ‘Autopsy’ we demonstrated that how we can perform digital forensics using automated tool and thus big data forensics can be done efficiently.</p>

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Possible Combinations of Electronic Data Capture and Randomization Systems

Methods of Information in Medicine ◽

10.3414/me13-01-0074 ◽

2014 ◽

Vol 53 (03) ◽

pp. 202-207 ◽

Cited By ~ 7

Author(s):

M. Haag ◽

L. R. Pilz ◽

D. Schrimpf

Keyword(s):

Open Source ◽

Data Storage ◽

Data Entry ◽

Data Capture ◽

Electronic Systems ◽

Electronic Data Capture ◽

Electronic Data ◽

Training Costs ◽

Pros And Cons ◽

General Data

SummaryBackground: Clinical trials (CT) are in a wider sense experiments to prove and establish clinical benefit of treatments. Nowadays electronic data capture systems (EDCS) are used more often bringing a better data management and higher data quality into clinical practice. Also electronic systems for the randomization are used to assign the patients to the treatments.Objectives: If the mentioned randomization system (RS) and EDCS are used, possibly identical data are collected in both, especially by stratified randomization. This separated data storage may lead to data inconsistency and in general data samples have to be aligned. The article discusses solutions to combine RS and EDCS. In detail one approach is realized and introduced.Methods: Different possible settings of combination of EDCS and RS are determined and the pros and cons for each solution are worked out. For the combination of two independent applications the necessary interfaces for the communication are defined. Thereby, existing standards are considered. An example realization is implemented with the help of open-source applications and state-of-the-art software development procedures.Results: Three possibilities of separate usage or combination of EDCS and RS are pre -sented and assessed: i) the complete independent usage of both systems; ii) realization of one system with both functions; and iii) two separate systems, which communicate via defined interfaces. In addition a realization of our preferred approach, the combination of both systems, is introduced using the open source tools RANDI2 and Open-Clinica.Conclusion: The advantage of a flexible independent development of EDCS and RS is shown based on the fact that these tool are very different featured. In our opinion the combination of both systems via defined interfaces fulfills the requirements of randomization and electronic data capture and is feasible in practice. In addition, the use of such a setting can reduce the training costs and the error-prone duplicated data entry.

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Data Mining Application using Association Rule Mining ECLAT Algorithm Based on SPMF

MATEC Web of Conferences ◽

10.1051/matecconf/201816401019 ◽

2018 ◽

Vol 164 ◽

pp. 01019

Author(s):

Jason Reynaldo ◽

David Boy Tonara

Keyword(s):

Data Mining ◽

Open Source ◽

Data Storage ◽

Association Rule ◽

Association Rule Mining ◽

Search Space ◽

Rule Mining ◽

Master Data ◽

Research Domain ◽

Data Mining Application

Data mining is an important research domain that currently focused on knowledge discovery database. Where data from the database are mined so that information can be generated and used effectively and efficiently by humans. Mining can be applied to the market analysis. Association Rule Mining (ARM) has become the core of data mining. The search space is exponential in the number of database attributes and with millions of database objects the problem of I/O minimization becomes paramount. To get the information and the data such as, observation of the master data storage systems and interviews were done. Then, ECLAT algorithm is applied to the open-source library SPMF. In this project, this application can perform data mining assisted by open source SPMF with determined writing format of transaction data. It successfully displayed data with 100 % success rate. The application can generate a new easier knowledge which can be used for marketing the product.

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The Cambridge Cognitive and Psychiatric Assessment Kit (CamCOPS): A Secure Open-Source Client–Server System for Mobile Research and Clinical Data Capture

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.578298 ◽

2021 ◽

Vol 12 ◽

Author(s):

Rudolf N. Cardinal ◽

Martin Burchell

Keyword(s):

Open Source ◽

Data Storage ◽

Data Entry ◽

Data Access ◽

Data Capture ◽

Third Party ◽

Record System ◽

Client Server ◽

Network Connection ◽

Server System

CamCOPS is a free, open-source client–server system for secure data capture in the domain of psychiatry, psychology, and the clinical neurosciences. The client is a cross-platform C++ application, suitable for mobile and offline (disconnected) use. It allows touchscreen data entry by subjects/patients, researchers/clinicians, or both together. It implements a large and extensible range of tasks, from simple questionnaires to complex animated tasks. The client uses encrypted data storage and sends data via an encrypted network connection to a CamCOPS server. Individual institutional users set up and run their own CamCOPS server, so no data is transferred outside the hosting institution's control. The server, written in Python, provides clinically oriented and research-oriented views of tasks, including the tracking of changes over time. It provides an audit trail, export facilities (such as to an institution's primary electronic health record system), and full structured data access subject to authorization. A single CamCOPS server can support multiple research/clinical groups, each having its own identity policy (e.g., fully identifiable for clinical use; de-identified/pseudonymised for research use). Intellectual property rules regarding third-party tasks vary and CamCOPS has several mechanisms to support compliance, including for tasks that may be permitted to some institutions but not others. CamCOPS supports task scheduling and home testing via a simplified user interface. We describe the software, report local information governance approvals within part of the UK National Health Service, and describe illustrative clinical and research uses.

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Acquisition System for Photovoltaic Panels with Data Storage in Remote Server Platforms Using Open Source Platforms Raspberry Pi and Arduino

J of Electrical Engineering ◽

10.17265/2328-2223/2017.03.006 ◽

2017 ◽

Vol 5 (3) ◽

Cited By ~ 1

Author(s):

José Ilton de Oliveira Filho ◽

Wilk Maia Coelho ◽

Marcos Eduardo Do Prado Villarroel Zurita ◽

Mateus de Melo Araújo ◽

Yago Borges Moreira

Keyword(s):

Open Source ◽

Data Storage ◽

Acquisition System ◽

Raspberry Pi ◽

Photovoltaic Panels ◽

Remote Server

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Challenges for the NoSQL systems

International Journal of Sustainable Economies Management ◽

10.4018/ijsem.2013010106 ◽

2013 ◽

Vol 2 (1) ◽

pp. 55-64

Author(s):

George Tudorica Bogdan

Keyword(s):

Open Source ◽

Data Storage ◽

Relational Databases ◽

Query Interface ◽

Academic World ◽

Definition Of ◽

Storage Structures ◽

Sql Query

The concept described by the term NoSQL (Not Only SQL) is a database that is distributed, may not require fixed table schemas, usually avoids join operations and is typically horizontally scalable, it does not offer SQL query interface and is available in most cases as open source - some bibliographic sources use the term to refer to a completely unrelated system. This concept is also assimilated by sources in the academic world as a structured form of storage. The two terms seem not to be entirely equivalent; relational databases, for example, also meet the official definition of data storage structures, but they are somewhat opposite qualities to the concept of NoSQL. The aim of this paper is to discuss the challenges met by the NoSQL solutions and to propose solutions for these challenges.

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EVALUASI DAN PERUMUSAN REKOMENDASI SENAYAN LIBRARY MANAGEMENT SYSTEMS PERPUSTAKAAN MENGGUNAKAN FRAMEWORK COBIT4.1

Jurnal Ilmiah SINUS ◽

10.30646/sinus.v17i1.386 ◽

2019 ◽

Vol 17 (1) ◽

pp. 59

Author(s):

Lilis Aminawati ◽

Sri Siswanti ◽

Setiyowati Setiyowati

Keyword(s):

Data Processing ◽

Open Source ◽

Data Storage ◽

Management Systems ◽

System Software ◽

Maturity Level ◽

Literature Study ◽

Library Management ◽

System Recommendation ◽

Library Management System

Senayan Library Management Systems (SLiMS) is a licensed open source library management system software under the GPL v3. Evaluation of the system process needs to be done especially on the data storage and data processing since both are important in managing data in the library of STIE AUB of Surakarta. The purpose of this study is to determine the maturity level of Senayan Library Management Systems (SLiMS) and provide system recommendation using the domain of Delivery and Support 1 (DS1) and Delivery Support 11 (DS11) with COBIT Framework 4.1. The methods used were observation, interview, literature study and questionnaire. The questionnaires were given directly to the respondents related to the system users, and the performance of the system was carried out by using maturity levels to produce recommendations at the library of STIE AUB Surakarta.

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Making WAVES in Breedbase: An Integrated Spectral Data Storage and Analysis Pipeline for Plant Breeding Programs

10.1101/2020.09.18.278549 ◽

2020 ◽

Author(s):

Jenna Hershberger ◽

Nicolas Morales ◽

Christiano C. Simoes ◽

Bryan Ellerbrock ◽

Guillaume Bauchet ◽

...

Keyword(s):

Plant Breeding ◽

Open Source ◽

Spectral Data ◽

Data Storage ◽

Cross Validation ◽

R Package ◽

List Type ◽

Breeding Programs ◽

Wide Range ◽

Prediction Functions

ABSTRACTVisible and near-infrared (vis-NIRS) spectroscopy is a promising tool for increasing phenotyping throughput in plant breeding programs, but existing analysis software packages are not optimized for a breeding context. Additionally, commercial software options are often outside of budget constraints for some breeding and research programs. To that end, we developed an open-source R package, waves, for the streamlined analysis of spectral data with several cross-validation schemes to assess prediction accuracy. Waves is compatible with a wide range of spectrometer models and performs visualization, filtering, aggregation, cross-validation set formation, model training, and prediction functions for the association of vis-NIRS spectra with reference measurements. Furthermore, we have integrated this package into the Breedbase family of open-source databases, expanding the analysis capabilities of this growing digital ecosystem to a number of crop species. Taken together, the standalone and Breedbase versions of waves enhance the accessibility of tools for the analysis of spectral data during the plant breeding process.Core ideaswaves is an open-source R package for spectral data analysis in plant breedingBreeding relevant cross-validation schemes to evaluate predictive accuracy of modelsExtension of Breedbase—an open-source database—to support spectral data storageGraphical user interface developed for implementation of waves in Breedbase

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ANALISIS KINERJA ISCSI TARGET PADA WIRELESS LAN MEMAKAI STANDAR LIO

SINERGI ◽

10.22441/sinergi.2015.1.005 ◽

2015 ◽

Vol 19 (1) ◽

pp. 25

Author(s):

Rizal Bahaweres ◽

Tjetjep Rony Budiman ◽

Andi Adriansyah

Keyword(s):

Open Source ◽

Data Storage ◽

Data Center ◽

Wireless Lan ◽

Fibre Channel ◽

Ieee 1394 ◽

Scsi Target

Semakin banyaknya kebutuhan data center maupun laboratorium komputer di Indonesia dipengaruhi oleh semakin banyaknya pengguna yang memanfaatkan komputer baik untuk bisnis maupun pendidikan. Salah satu kebutuhan utama yang tidak bisa dilepaskan dari pemakaian komputer adalah tempat penyimpanan baik berupa USB Flash Disk, HD Eksternal, HD Internal sampai HD untuk kebutuhan skala besar untuk komputer server yang berada di data center, laboratorium atau jaringan komputer. Ruang penyimpanan data atau data storage semakin berkembang dengan munculnya teknologi komputer jaringan yang memunculkan alternatif data storage berupa DAS, NAS, FC, FcoE dan iSCSI. iSCSI menggunakan standard TCP/IP protocol over Ethernet untuk menyediakan penyimpanan berbasis block. Saat ini ada 2 jenis multiprotocol SCSI Target utama di industri yaitu LIO dan COMSTAR yang menggantikan teknologi sebelumnya yaitu iET, SCST dan STGT. LIO (linux-iscsi.org) merupakan standard open source iSCSI Target untuk berbagi ruang penyimpanan di Linux. LIO mendukung storage fabrics, yaitu Fibre Channel (QLogic), FCoE, iEEE 1394, iSCSI, iSER (Mellanox InfiniBand), SRP (Mellanox InfiniBand), USB, vHost, dan lain-lain.

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