Proper Data Management Research Articles

An intelligent system for focused crawling from Big Data sources

Nowadays, the proper management of data is a key business enabler and booster for companies, so as to increase their competitiveness. Typically, companies hold massive amounts of data within their servers, which might include previously offered services, proposals, bids, and so on. They rely on their expert managers to manually analyse them in order to make strategic decisions. However, given the huge amount of information to be analysed and the necessity of making timely decisions, they often exploit a small amount of the available data, which often does not yield effective choices. For instance, this happens in the context of the e-procurement domain, where bids for new calls for tender are often formulated by looking at some past proposals from a company. Driven by an extensive experience on the e-procurement domain, in this paper we propose an intelligent system to support organisations in the focused crawling of artefacts (calls for tender, BIMs, equipment, policies, market trends, and so on) of interest from the web, semantically matching them against internal Big Data and knowledge sources, so as to let companies analysts make better strategic decisions. The novel contribution consists of a proper extension of the K-means algorithm used by a web crawler within the proposed system, and a semantic module exploiting search patterns to find relevant data within the crawled artefacts. The proposed solution has been implemented and extensively assessed in the e-procurement domain. It has been successively extended to other domains, such as robot programming, cloud providing, and several other domains. Since to the best of our knowledge in the literature do not exists similar systems, in order to prove its effectiveness we have compared its crawling component against similar crawlers, by plugging them within our system.

Identification of Business Intelligence in Managing Maintenance Management for Government Office Buildings in Putrajaya

A proper data management in maintenance practices are required to make the daily process smooth. With the ability of Information Technology (IT) will influence on maintenance management database system. However, this IT systems are challenged with massive increases in amount of data, the speed they are generated and the need to record, process and visualize those data in real time to the user. This rapid growth of information results in the pervasion of Big Data (BD) and Business Intelligence (BI). This research highlights Public Works Department or Jabatan Kerja Raya (JKR) as a key pillar in managing and store important data relating to maintenance management for each building in Putrajaya. JKR finds difficulties to create sustainable maintenance policy which require the right tools and equipment to achieve sustainable goals and objective. Last research attentions have been given to the use of big data and business intelligence in maintenance management industries particularly in government sector. Hence, this research presents an analysis of the complexities and requirement for maintenance that focuses on intelligent system that help to improve the intelligent management of maintenance in making informed decision and can be applied horizontally to address identified challenges in practices.

Text Mining and Grounded Theory for Appraising the Self-Management Indicators of Diabetes Mobile Apps

Background: Understanding diabetes mobile apps functionality is fundamental to diabetes self-management because of the reliance of many patients with diabetes on these apps.Objectives: The aim of this study is to perform a review of diabetes mobile apps to discover users’ sentiments and qualitatively examine the review comments to understand the perceptions of positive, neutral, and negative sentimental users of the apps.Method: A total of 2678 user review comments obtained from the google play store were analysed from 47 diabetes mobile apps to understand user sentiments following clinical Self-management Indicators (SMIs) shown in previous research. Pearson correlation analysis was conducted to determine the association between the SMIs present in the apps’ and user review indicators such as rating score, user sentiment and the number of downloads. The users’ review comments were thematically screened using grounded theory to establish the themes to describe their perception of the apps.Results: After evaluating SMIs such as weight tracking/BMI, sugar level monitoring, diet/Calories management, medication reminder, etc., 74.47% of the apps were found to have Sugar Level Monitoring(SLM) capabilities with 10.64% designed to track weight/BMI. There are 53.19% of the apps that can manage diet/calories and have data storage and security SMIs, however, less than 30% of them provide medication adherence, exercise management, doctor's appointment scheduling, and diabetes information repository. The number of the SMIs included in apps did not influence users’, but the value derived from the functionality of the apps.Conclusions: Users are satisfied with the apps that are easy to use, setup, provide good analytics for blood sugar monitoring and have uncrowded graphical outputs and user interface. Proper data management and contemporary information about diabetes are among the identified challenges of the apps that were found to crash relentlessly on downloading, uploading, installing, and setup.

Teaching data management and literacy to support course‐embedded research projects

Course-based undergraduate research experiences are an excellent approach to involve a large team of students in scientific study. These CUREs can generate lots of data that may be useful to the scholarship of the instructor and others. While the focus of many CUREs is on developing the scientific skills of students, embedding professional skills in these frameworks can enhance the quality and applicability of this training to a wider range of future careers. Data are ubiquitous in all careers and being able to make data accessible to others is an important professional skill. We saw with the novel coronavirus pandemic publicly accessible sequences and structures aided in the development of vaccines and potential therapeutics. Thus teaching students how to manage their data properly may help the progress of the field and aid the students in their future careers. I have developed an activity to inform students on data management practices and have them apply their lessons to a mock data set. In this activity, students take a poorly organized and formatted data set which students organize, reformat, then write a README file to describe the organization and contents of the data set to others. Students perform this activity on the Open Science Framework and have to physically organize the data as well as open the files to reformat and clean the data. Afterward, students reflect on the experience and how they may change their management of data. Later projects in the course include proper management of data as part of the assessment to encourage good practice and provide opportunities for the instructor to give feedback to the students. From this activity, students learn and demonstrate the practices of data management to enhance their course-embedded work and hopefully their professional careers.

Electronic crash reporting: Implementation of the Model Minimum Uniform crash Criteria (MMUCC) and crash record life cycle comparison

Electronic crash records have become the standard crash reporting method in most states, but crash report elements vary between states and there is little publicly available information detailing the data provenance and life cycles for these records. As crash report granularity increases, proper data management is critical to ensure report quality with minimal errors. States are encouraged to meet federal standards for minimum crash report elements detailed in the Model Minimum Uniform Crash Criteria. MMUCC standardization helps states manage long-term crash data, enables multi-state analysis, and promotes sound traffic safety policies. This paper details how the states of Alabama and Wisconsin manage crash reports by evaluating their compliance with MMUCC standards as well as tracking the life cycle of crash reports in the two states from provenance, through approval and validation processes, to preservation as official records. A web-based MMUCC Compliance Tool was developed using elements from Alabama and Wisconsin crash reports, and aids other states in mapping existing crash report elements to MMUCC standards. The tool uses an automated check to map crash report elements and flags missing elements. The output of the tool includes a report detailing changes needed for the crash report to more closely align with the MMUCC standards. Common crash report lifecycle elements in Alabama and Wisconsin were identified, including initial web service validations, supervisor review, transactional database backups, linkage to driver record systems, and processes in place to manage and fix void requests. To aid crash recording procedures nationwide, a set of best practices for crash report lifecycle management is synthesized from the analysis.

A Big Data & Business Intelligence in Government Office Buildings

Effective maintenance management requires proper data management for decision-making purposes. Big Data (BD) and Business Intelligence’s (BI) growing trend has created many challenges for government data management in particular. The government finds difficulties in integrating the massive volume of data with high-speed processing due to incapable database management in the current system, and the issues are not appropriately addressed. This paper contributes significantly, which focuses on an intelligent system that lets the government make an integral part of decision-making and can be applied horizontally to solve the problems in practice. Accordingly, an efficient data repository system with real-time analysis is proposed in this paper and it looks at a real case study highlighting the need for proper data management in government.

Ontology-based Process Reengineering To Support Digitalization Of MRO Operations: Application To An Aviation Industry Case

After sales services are characterized by interlinked service provider and customer operations. Furthermore, proper management of data related to the physical product and its lifecycle is essential for proposition of value-adding services. In this article, we present a novel ontology-based approach for improvement of maintenance, repair and overhaul (MRO) processes, where the ontology captures information of industry standards and product-related data. By clearly defining relations, the ontology supports the digitalization of operations and utilization of data in operational processes of different stakeholders. The approach was developed and demonstrated by investigating data of a case from aviation MRO industry.

Gestão de Dados de Pesquisa: um desafio para os Programas de Pós-Graduação em Enfermagem

Introduction: Open Science opens new horizons for the researcher and for academic and scientific activities. It has been growing in all areas of knowledge and comes in the nursing domain as a huge challenge to be faced. This article aims to show the path that the Nursing Postgraduate Programs at the Federal University of the Rio de Janeiro State are following to achieve a proper data management of their research, align to the scope of open science. Review and Discussion: For this purpose, it was necessary; contextualize the concept of open science emphasizing data management practices; presents the data life cycle, the FAIR principles and their application in the GO FAIR Brazil Health - Nursing network created with the purpose of elaborating strategies for the implementation of the FAIR principles in the nursing field, aiming the sharing and reuse of these data in new research. Final considerations: The coordination of the Graduate Nursing Programs at UNIRIO and the Alfredo Pinto Nursing School, from the GO FAIR Brazil Saude Network - Nursing represents an example of the adoption of open science practices within graduate courses. Therefore, it is expected that through the studies and events that begin within the scope of the GO FAIR Brazil Health – Nursing Network, carried out in 2020 and scheduled for the following years, answer the question asked in the introduction of this article and contribute to the promotion and implementation of training strategies for students and health professionals, in general, on the management of research data in the field of nursing.

Operational Improvement On Data Validation And Verification Of Health Insurance Assistance Beneficiaries In Indonesia: An Overview Of Accountability/Governance Perspective

Decent data management is a fundamental element in implementing government policies, especially in providing social assistance to the poor. Distribution of social assistance can only be done with the support of proper data management system that runs the data collection process, updating, validating according to the real condition, hence the distribution of social assistance is right on target. This study examines issues which concern on institutional, derivative function and authority in the distribution of social assistance, especially health insurance assistance for the poor and the needy in Indonesia. This study aims to review the whole implementation process of verification and validation on the distribution of health insurance beneficiaries (PBI-JK), starts from its procedures until it is implemented in the locations of MuBa Regency and Palembang City. To achieve the desired objective, this study uses a qualitative method by collecting data from documents, interviews and field observations. As the result, it appears that prelist data services for verification and validation purposes are carried out centrally at the Social Welfare Data and Information Center, Ministry of Social Affairs (PUSDATIN, KESSOS), and for verification and validation in the field are handled by other third parties but only for urban areas.

Automated System Testing for a Learning Management System

Over the last years software development life-cycles have continuously been shortened and new releases are being deployed at a more and more frequent level. In order to ensure the quality of those releases, a strong shift towards automated testing at all testing levels has become noticeable throughout the software devel-opment industry. At system testing level, the scope of testing is the developed product as a whole, tested in a test environment that has a very close resemblance to the production system. Because of this system-wide scope and the many po-tential sources for failures, the implementation of automated tests at this level is challenging. Exhaustive testing is neither feasible nor maintainable, therefore proper designed test cases that cover important functionality are essential. Due to increasing laws and regulations on data protection and data privacy, proper man-agement of test data used in automated testing is as important. This paper dis-cusses how automated system tests for TeachCenter 3.0, Graz University of Technology’s learning management system, were implemented.

Pentingnya Manajemen Data Pengeboran Untuk Meningkatkan Operasi Pengeboran Panas Bumi

Drilling is one of the major cost components in geothermal exploration and development. Effective and cost-efficient drilling significantly contribute to the success of geothermal development. Key factors in reducing drilling costs are optimising operations, utilising manpower to its fullest potential, and also benchmarking with other drilling activities to evaluate one’s performance objectively. This is possible if the information regarding the previous drilling activities is stored and easily gathered and analysed before making plans for the drilling campaign. The importance of drilling data analysis and drilling data management have been a subject of study and discussion since the 1980s, but it is still not that common in geothermal drilling, especially in Indonesia. The purpose of this paper is to summarise the definition and examples of drilling data management in a more well-established industry such as oil and gas from various studies in the past, assess the advantages of having a proper drilling database or data management system, and how can the data be used for potentially improving future drilling operation. A case study of converting legacy data from previous drilling campaign of two geothermal fields in Java into a database is also discussed to demonstrate how legacy drilling data can be used to evaluate drilling performance.

Ideas and perspectives: enhancing the impact of the FLUXNET network of eddy covariance sites

Abstract. In the last 20 years, the FLUXNET network provided unique measurements of CO2, energy and other greenhouse gas exchanges between ecosystems and atmosphere measured with the eddy covariance technique. These data have been widely used in different and heterogeneous applications, and FLUXNET became a reference source of information not only for ecological studies but also in modeling and remote sensing applications. The data are, in general, collected, processed and shared by regional networks or by single sites, and for this reason it is difficult for users interested in analyses involving multiple sites to easily access a coherent and standardized dataset. For this reason, periodic FLUXNET collections have been released in the last 15 years, every 5 to 10 years, with data standardized and shared under the same data use policy. However, the new tools available for data analysis and the need to constantly monitor the relations between ecosystem behavior and climate change require a reorganization of FLUXNET in order to increase the data interoperability, reduce the delay in the data sharing and facilitate the data use, all this while keeping in mind the great effort made by the site teams to collect these unique data and respecting the different regional and national network organizations and data policies. Here a proposal for a new organization of FLUXNET is presented with the aim of stimulating a discussion for the needed developments. In this new scheme, the regional and national networks become the pillars of the global initiative, organizing clusters and becoming responsible for the processing, preparation and distribution of datasets that users will be able to access in real time and with a machine-to-machine tool, obtaining always the most updated collection possible but keeping a high standardization and common data policy. This will also lead to an increase in the FAIRness (Findability, Accessibility, Interoperability and Reusability) of the FLUXNET data that will ensure a larger impact of the unique data produced and a proper data management and traceability.

Research Data Management in Universities: A Comparative Study from the Perspectives of Librarians and Management

With the research landscape tilting toward a more intensive research, propelled by the power of information and communication technologies, the research community is overwhelmed with tons of research data generated on a daily basis. Research data is hailed as the cornerstone for current and future science discoveries. Hence, key research stakeholders especially research funders and publishers demand proper research data management (RDM) practices. The burden to manage research data has been placed on academic libraries. In light of this, the current study employs questionnaires and interviews to explore the status of RDM at two public university libraries in Malawi. The study unveils that RDM remains a new and emerging concept in the country. However, with librarians involved in some very basic RDM activities, the future is promising. Key challenges interrupting RDM initiatives are exposed and their remedies suggested. Generally, university affiliation has no effect on the key factors affecting RDM initiatives.

Occurrence of Medical Cases and Utilisation among Enrollees of Private Health Insurance Programme in Kaduna, Nigeria

Aims: This study examines the occurrence of various medical cases presented by enrollees that have subscribed to access healthcare from a network of healthcare providers (HCPs) managed by a Health Maintenance Organisation (HMO) under its Private Health Insurance Programme (PHIP).
 Study Design: A descriptive cross-sectional design was employed.
 Methodology: Secondary data from collected or submitted medical encounters in form of bills of registered enrollees (principals and their dependants) who have visited and received treatment from their chosen healthcare providers in Kaduna State between the month of January and December 2019 were purposively compiled and analysed. Cases were classified using the National Health Insurance Scheme (NHIS) Operational Guideline. Frequency tables, charts, percentages and Chi-Square analysis were used with the aid of Statistical Package for Social Sciences (SPSS) 22 at P=.05 level of significance.
 Results: A total of 11,156 medical cases were recorded after attrition, 9,525 (85.38%) primary cases and 1,632 (14.62%) secondary cases. Malaria (41.23%) and Respiratory Tract Infection (11.98%) led the primary case table while Hypertension (3.83%) Urology related cases (2.49%) and Diabetes (0.79%) were among the leading secondary cases. Female enrollees had slightly more cases and therefore higher tendencies to seek medical treatment than their male counterpart even though there was no significant relation between gender and type of case.
 Conclusion: The study concludes that the awareness and utilization of healthcare services are gradually growing among enrollees under the Private Health Insurance Programme (PHIP). In ensuring that there is an improvement in the health sector of Nigeria and achieving universal health coverage, focus should be on the primary healthcare services with high consideration for research, proper data management and periodic sharing of trends, observations and outcome of researches with the growing health community.

Cyber Physical Systems and IoT: Architectural Practices, Interoperability, and Transformation

In this present era, internet and cyber technologies have revolutionized and improved people's lifestyles completely at a remarkable rate. Having significant benefits, there are certain challenges faced by current technologies. In this perspective, one of the main challenges is not the production and acquisition of data; rather, it is the proper management, of it, including storage and analysis of data, has become a major concern. In this article, we conduct a study to discuss the two most widely used data centric technologies, i.e., Cyber Physical Systems (CPSs) and Internet of Things (IoT). The goal is to identify the architectural similarities and differences to achieve an interoperable solution that can manage data more efficiently. Our analysis revealed that a possible transformation of IoT to CPS and vice versa is possible when certain transformation steps are performed.

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

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.

Indoor environmental quality and learning outcomes: protocol on large-scale sensor deployment in schools

IntroductionExposure to poor environmental conditions has been associated with deterioration of physical and mental health, and with reduction of cognitive performance. Environmental conditions may also influence cognitive development of children,...

Tree Ecosystem Services, for Everyone? A Compositional Analysis Approach to Assess the Distribution of Urban Trees as an Indicator of Environmental Justice

Trees provide a broad amount of ecosystem services in urban areas. Although it is well documented that trees are essential for the well-being and livability of cities, trees are often not evenly distributed. Studies have found that urban residents with a deprived socioeconomic status are associated with a lower coverage and access to urban trees in their communities, yet a fair distribution of trees contributes to the sustainability and resilience of cities. In this context, the environmental justice movement seeks to ensure equal distribution of green infrastructure and its benefits throughout a territory. The objective of this study is threefold: (i) to determine whether urban trees in Guadalajara, Mexico, are distributed equally; (ii) to assess the association between urban trees and socioeconomic status; and (iii) to introduce compositional data analysis to the existing literature. Due to the compositional nature of the data, compositional analysis techniques are applied. We believe this novel approach will help define the proper management of data used in the literature. The outcomes provide insights for urban planners working towards the Sustainable Development Goals to help eradicate the uneven distribution of urban trees in cities.

From a research vision to a state-of-the-art research strategy: UNESCO experts’ meeting in the Karst and River Reka Basin Biosphere Reserve

UNESCO´s Lima Action Plan (LAP) explicitly calls on biosphere reserves (BRs) to build partnerships with universities and other research institutions to establish research, training and practical learning opportunities. For a BR, a research strategy is an essential framework that provides insights into the work carried out in the area, available data and needs for future research studies. It is the roadmap for adaptive management in a BR. It was therefore a major concern of the Karst and River Reka Basin BR (Škocjan, Slovenia) to develop a research strategy for the greater integration of science and research in the BR in a highly participatory process and with the support of international experts. Scientists, researchers and BR managers from eight countries shared their knowledge and experiences during a 3-day meeting in Škocjan (Slovenia), 23 to 25 October 2019. The importance of research in BRs, proper data management, involvement of local communities for monitoring and research, partnership and education were presented in case studies.

A Bioinformatics Primer to Data Science, with Examples for Metabolomics.

With the increasing importance of big data in biomedicine, skills in data science are a foundation for the individual career development and for the progress of science. This chapter is a practical guide to working with high-throughput biomedical data. It covers how to understand and set up the computing environment, to start a research project with proper and effective data management, and to perform common bioinformatics tasks such as data wrangling, quality control, statistical analysis, and visualization, with examples on metabolomics data. Concepts and tools related to coding and scripting are discussed. Version control, knitr and Jupyter notebooks are important to project management, collaboration, and research reproducibility. Overall, this chapter describes a core set of skills to work in bioinformatics, and can serve as a reference text at the level of a graduate course and interfacing with data science.