Data Management Support Research Articles

A Blockchain-Based Trust Model for Supporting Collaborative Healthcare Data Management

The development of information technology allows the collaborative business process to be run across multiple enterprises in a larger market environment. However, while collaborative business expands the realm of businesses, it also causes various hazards in collaborative Interaction, such as data falsification, inconstancy, and misuse. To solve these issues, a blockchain-based collaborative business modeling approach was proposed and analyzed. However, the existing studies lack the blockchain risk problem-solving specification, and there is no verification technique to examine the process. Consequently, it is difficult to confirm the appropriateness of the approach. Thus, here, we propose and build a blockchain-based trust model to strengthen and verify the integrity and security of the collaborative business process; Integrity and security address the validity of collaborative interactions in terms of a trust, and we construct a blockchain pattern based on trust elements to meet the required the characteristics. Specifically, a trust model can be applied to the healthcare data-sharing process, and then the achievement of the trust-based safe data-sharing process can be proven. Our model can be used as a trust-building guidance tool or for integrity and security verification with the collaborative business process in a distributed environment with blockchain.

Is C&RL Ready for a Data Sharing Policy?

In the summer of 2020, C&RL received a request from the ACRL Board of Directors to establish a registered report submission track as a major step to ensure C&RL ’s high standards of rigorous methods. The request letter was signed by a group of ACRL members, led by Amy Riegelman, who later published an editorial on this topic (Amy Riegelman, 2021), calling C&RL to be more proactive in supporting open research practices. In order to increase C&RL ’s rigor in supporting and implementing open research practices, it was recognized that both access to research data and transparency of research methods are necessary. From this line of thought, the C&RL Editorial Board, former Editor Wendi Arant Kaspar and Editor Kristen Totleben, have been engaged in an ongoing conversation on the possibility and the journal’s capacity to implement a data sharing policy. For the past three years, Editorial Board member Minglu Wang has been researching academic journals’ data sharing policies and reaching out to journal editors and editorial board members for consultation. Her efforts culminated in fall 2022 when she, Totleben, and Editorial Board member Adrian Ho conducted a survey (see Appendix) requesting input from colleagues in academic libraries regarding their perceptions of a data sharing policy and what types of data management support they would need or recommend.

The Environmental Data Initiative: Connecting the past to the future through data reuse.

The Environmental Data Initiative (EDI) is a trustworthy, stable data repository, and data management support organization for the environmental scientist. In a bottom-up community process, EDI was built with the premise that freely and easily available data are necessary to advance the understanding of complex environmental processes and change, to improve transparency of research results, and to democratize ecological research. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, we analyze metadata to provide a general description of EDI's collection of data and its data management philosophy and placement in the repository landscape. We discuss how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, we review measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.

The Influence of Big Data Management on Organizational Performance in Organizations: The Role of Electronic Records Management System Potentiality

Aim/Purpose: The use of digital technology, such as an electronic records management system (ERMS), has prompted widespread changes across organizations. The organization needs to support its operations with an automation system to improve production performance. This study investigates ERMS’s potentiality to enhance organizational performance in the oil and gas industry. Background: Oil and gas organizations generate enormous electronic records that lead to difficulties in managing them without any system or digitalization procedure. The need to use a system to manage big data and records affects information security and creates several problems. This study supports decision-makers in oil and gas organizations to use ERMS to enhance organizational performance. Methodology: We used a quantitative method by integrating the typical partial least squares (SEM-PLS) approach, including measurement items, respondents’ demographics, sampling and collection of data, and data analysis. The SEM-PLS approach uses a measurement and structural model assessment to analyze data. Contribution: This study contributes significantly to theory and practice by providing advancements in identity theory in the context of big data management and electronic records management. This study is a foundation for further research on the role of ERMS in operations performance and Big Data Management (BDM). This research makes a theoretical contribution by studying a theory-driven framework that may serve as an essential lens to evaluate the role of ERMS in performance and increase its potentiality in the future. This research also evaluated the combined impacts of general technology acceptance theory elements and identity theory in the context of ERMS to support data management. Findings: This study provides an empirically tested model that helps organizations to adopt ERMS based on the influence of big data management. The current study’s findings looked at the concerns of oil and gas organizations about integrating new technologies to support organizational performance. The results demonstrated that individual characteristics of users in oil and gas organizations, in conjunction with administrative features, are robust predictors of ERMS. The results show that ERMS potentiality significantly influences the organizational performance of oil and gas organizations. The research results fit the big ideas about how big data management and ERMS affect respondents to adopt new technologies. Recommendations for Practitioners: This study contributes significantly to the theory and practice of ERMS potentiality and BDM by developing and validating a new framework for adopting ERMS to support the performance and production of oil and gas organizations. The current study adds a new framework to identity theory in the context of ERMS and BDM. It increases the perceived benefits of using ERMS in protecting the credibility and authenticity of electronic records in oil and gas organizations. Recommendation for Researchers: This study serves as a foundation for future research into the function and influence of big data management on ERMS that support the organizational performance. Researchers can examine the framework of this study in other nations in the future, and they will be able to analyze this research framework to compare various results in other countries and expand ERMS generalizability and efficacy. Impact on Society: ERMS and its impact on BDM is still a developing field, and readers of this article can assist in gaining a better understanding of the literature’s dissemination of ERMS adoption in the oil and gas industry. This study presents an experimentally validated model of ERMS adoption with the effect of BDM in the oil and gas industry. Future Research: In the future, researchers may be able to examine the impact of BDM and user technology fit as critical factors in adopting ERMS by using different theories or locations. Furthermore, researchers may include the moderating impact of demographical parameters such as age, gender, wealth, and experience into this study model to make it even more robust and comprehensive. In addition, future research may examine the significant direct correlations between human traits, organizational features, and individual perceptions of BDM that are directly related to ERMS potentiality and operational performance in the future.

General practice management of COPD patients following acute exacerbations: a qualitative study.

Exacerbations are the strongest risk factor for future exacerbations for patients living with chronic obstructive pulmonary disease (COPD). The period immediately following exacerbation is a high-risk period for recurrence and hospital admission, and is a critical time to intervene. GPs are ideally positioned to deliver this care. To explore perceptions of GPs regarding the care of patients following exacerbations of COPD and to identify factors affecting the provision of evidence-based care. A descriptive qualitative study was undertaken involving semi-structured, in-depth interviews with Australian GPs who volunteered to participate following a national survey of general practice care for COPD patients following exacerbations. Interviews were conducted via the Zoom video conference platform, which were audio-recorded and transcribed verbatim. QSR NVivo was used to support data management, coding, and inductive thematic analysis. Eighteen GPs completed interviews. Six key themes were identified: 1) GPs' perceptions and knowledge in the management of COPD patients following exacerbation and admission to hospital; 2) pharmacological management; 3) consultation time; 4) communication between healthcare professionals; 5) access to other health services; and 6) patient compliance. Delivery of post-exacerbation care to COPD patients is affected by GPs, patients, and health service-related factors. The care of COPD patients may be further improved by supporting GPs to overcome identified barriers.

National Blood Foundation 2021 Research and Development summit: Discovery, innovation, and challenges in advancing blood and biotherapies.

National Blood Foundation 2021 Research and Development summit: Discovery, innovation, and challenges in advancing blood and biotherapies.

"Using MongoDB to Understand the Underlying Methods Techniques Encryption in NoSQL database"

There is a need for appropriate encryption methods, which must be adhered by the concerned parties, in order to provide a high level of security for the secret data. In this study, an examination of the different encryption algorithms and their performance in the management of private data with authentication, access control, secure configuration, and data encryption is presented. It includes enhancing MongoDB's level-based access protected model and adding privacy keys for security and monitoring purposes respectively. The NoSQL data stores, also known as highly compressed data on non-relational database management systems, which provide support for data management of internet user programs, do not now offer this service. Data security, encryption techniques, MongoDB, and NoSQL are some of the keywords that come to mind.The proper encryption methods used to various crucial data fields provide data protection without adversely affecting the database's performance in terms of speed or memory usage. Due to the use of appropriate encryption techniques, this is achievable. Since document-oriented and unstructured data are generally kept in databases, it is frequently used to handle various sorts of data. Examples of such databases include MongoDB, Cassandra, CouchDB, Redis, Hyper-table, and others. Since they are open source, there is a critical necessity to offer good security and protect the user's private information when it is in transit or at rest. There is a need for a single solution that can improve the security of data transfer by providing an improved encryption method that speeds up processes and uses less memory when maintaining databases.

Privacy protection throughout the research data life cycle

Introduction. The sharing and reuse of research data is gradually becoming best practice in research. However, multiple frictions exist between realising stakeholders’ ambitions for research and research data sharing and addressing legal, social and cultural imperatives for protecting data subjects’ privacy. Through identifying and addressing frictions between personal privacy and research, our paper offers advice to research data management services on how to approach personal privacy in research data, sharing using the research data life cycle as the context. Method. A three-phase Delphi study on a population comprising twenty-four stakeholders involved in research data curation in Norway. Data were collected during three consecutive rounds over fourteen months. Analysis. The data were analysed qualitatively using themes following exploratory sequential design methods. After three rounds of data collection, the entire corpus of data were connected and analysed thematically according to integrated analysis. Results. The findings show multiple tensions between maintaining research subjects’ right to privacy and advancing research through data sharing. This paper identifies and analyses three particular sources of tension: 1) maintaining trust with the research participants, 2) managing divergent views of privacy in international and intercultural research collaborations and 3) interpreting and applying policy. The divergent motivations and perspectives on privacy held by different stakeholders complicate these tensions. Conclusions. Researchers, research data management support staff and data organizations must reconcile these motivations and resolve tensions throughout the data life cycle, from collection to archiving and eventual sharing. Through dialogue and negotiation, all stakeholders involved in data sharing should aim to respect the research subjects’ own understandings of privacy.

Knowledge Management Competencies in Health and Social Care

Knowledge management literature has thoroughly discussed the aims and objectives of managing knowledge, recognized critical success factors of knowledge processes, analyzed knowledge creation, transfer and sharing, and even some indications of the effects of knowledge management have been evidenced. However, despite skills, competences, and employees’ knowledge and experience are among the recognized success factors, discussions on competence needs of effective knowledge management are lacking. To contribute to this knowledge gap, this article studies knowledge management competences with an aim to elaborate this critical component of organizational performance and renewal. The special focus of the article is on the management of health and social care and the article brings together two distinct disciplines, knowledge management and health informatics, to suggest a novel way to approach KM capabilities and competences in the given context. Based on the literature and empirical data gathered with two surveys targeted to health informatics and knowledge management practitioners in health and social care the article concludes that: 1) It is difficult to separate the discussion on competence needs from the discussions concerning the information content and tools of data management. The latter aspects dominate the debate and determine and limit in many ways the discussions on knowledge management, its nature, content, and people participating to these discussions, and 2) knowledge management in health and social care focuses extensively on issues of data management and decision-making support.

Abstract LB149: Luna Pathology: An integrated open source platform for computational pathology research

Abstract Pathologic review of tissue samples is a crucial step in cancer diagnosis and treatment planning. In recent years, quantitative analysis, including artificial intelligence (AI) techniques, have been applied to facilitate the evaluation of histopathologic images. Research in computational pathology comes with numerous engineering challenges: from management of whole slide images (WSIs) and their metadata, collection of expert annotations, data manipulation and feature extraction, to AI model training and evaluation of the results. These challenges span multiple departments and sets of expertise; there is a lack of an efficient system that helps manage and process histologic images and supports AI analysis. In particular, computational oncology requires significant compute resources. The need for an integrated platform with systematic processes to manage the job parameters and complex workflows that interface with multidisciplinary teams is critical for successful experiments and reproducible research. To address these challenges, we developed Luna Pathology, an open-source library and platform for computational pathology research. With flexibility, scalability, and FAIR data principles in mind, Luna Pathology was designed to provide 1) reusable workflows and modular tools for end-to-end pathology analysis 2) FAIR datasets for reproducible research and 3) a collaborative open-source research platform. Namely, Luna Pathology features support data management, annotation support, image processing, image and annotation visualization, feature extraction, and human-in-the-loop machine learning. A description of the Luna Pathology platform, its supporting computational infrastructure, and its integration within the standard computational pathology will be presented. Luna Pathology meets data processing and management needs, as well data analysis experiments and reproducible AI research on a flexible data platform. Its workflows have been deployed in colorectal, neuroblastoma and ovarian cancer projects at Memorial Sloan Kettering Cancer Center (MSK) to derive new insights from ~10,000 WSIs. We additionally present a view of the Luna Pathology platform as part of the institutional effort to bridge computational research and clinical care at MSK. The integrated open platform brings together a multidisciplinary team of pathologists, computational biologists, and bioinformatics engineers to more effectively collaborate on iterative large-scale computational pathology research. Citation Format: Doori Rose, Andrew Aukerman, Druv Patel, Arfath Pasha, Armaan Kohli, Ignacio Vázquez-García, Kevin Boehm, Rami Vanguri, Matthew Shabet, Vassiliki Mancoridis, Elizabeth Zakszewski, Benjamin Gross, Christopher Fong, Pegah Khosravi, Sohrab Shah, JianJiong Gao. Luna Pathology: An integrated open source platform for computational pathology research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB149.

Virtual Teaching and Training Models in Pediatric Oncology: A Retrospective Study from an LMIC

Abstract Introduction A multidisciplinary approach is essential for success in pediatric oncology treatment. Updated protocols, quality nursing care, psychosocial support, safe and standardized preparation of chemotherapy, infection control, and effective data management are key shareholders for the effective management of childhood cancer. The Department of Pediatric Oncology at Indus Hospital and Health Network (IHHN) initiated consistent teaching and trainings with the help of the My Child Matters Grant from Sanofi Espoir Foundation. These courses were conducted in person starting in 2019 and had to be postponed and restructured due to coronavirus (COVID-19) pandemic in early 2020. Objectives The aim of this study was to determine the impact of virtual teaching models for healthcare workers employed in pediatric hematology/oncology departments in low-resource settings. Materials and Methods After in-person courses in 2019, courses for all six disciplines (physicians, nursing, infection control, pharmacy, psychosocial care, and cancer registry) were conducted virtually starting December 2020, open to all and free of cost. A total of 878 registrations were obtained and 267 certifications given. Lectures with Q&A sessions were conducted via zoom and recordings shared through email. Each course was conducted by the relevant department at IHHN with pre- and postassessment conducted through Google Forms. Session feedback was taken through zoom polls and a comprehensive course feedback conducted after completion; e-certificates were awarded to successful participants according to a predetermined criterion. Results A total of 434 physicians' registrations were done from around Pakistan and countries like Saudi Arabia, Malaysia, Jordan, and Canada for the online physicians' course, of which 110 received certifications after completing post-test and attendance criteria of 55%. Pharmacy, infection control, psychosocial care, and cancer registry courses saw participation and certification of 51, 41, 24, and 14 participants, respectively. Online sessions received positive feedback in terms of instructors, course content, convenience, and access from over 90% participants. Conclusion Due to the ease in coordinating hectic schedules and cost-effectiveness of online lectures, this virtual teaching model will persist despite the trajectory of the COVID-19 pandemic. Similar ventures aimed at pediatric oncology teaching and training are needed in a widespread manner to improve outcomes of childhood cancer.

0008 University-wide chronotyping shows late-type students have lower grades, shorter sleep, and more absenteeism

Abstract Introduction A person’s preferred timing of nocturnal sleep (chronotype) has important implications for cognitive performance. Students who prefer to sleep late may have a selective learning disadvantage for morning classes due to inadequate sleep and circadian desynchrony. We tested whether late-type students perform worse only for morning classes, and we investigated factors that may contribute to their poorer academic achievement. Methods Chronotype was determined objectively in 33,645 university students (early, n = 3,965; intermediate, n = 23,787; late, n = 5,893) by analysing the diurnal distribution of their logins on the university’s Learning Management System (LMS). Linear mixed models were used to test for differences between chronotype groups in grade point average (n = 33,645), actigraphy-estimated sleep behaviour (n = 261), and class attendance estimated using Wi-Fi connection data (n = 17,356). Results Late-type students had lower grades than their peers for courses held at all different times of day, and during semesters when they had no morning classes. Actigraphy studies confirmed LMS-derived chronotype was associated with students’ sleep patterns. Nocturnal sleep on school days was shortest in late-type students because they went to bed later than the other chronotype groups and woke up earlier compared with non-school days. Wi-Fi connection logs for classrooms revealed that late-type students had lower lecture attendance than their peers for both morning and afternoon classes. Conclusion Large university-archived datasets can be used to assess relationships between chronotype and academic performance. Late-type students had lower grades, shorter sleep, and were more likely to miss classes. Shifting classes later may improve sleep and circadian synchrony in late-type students. However, this probably will not eliminate the performance gap because they still had lower grades when they only had afternoon classes. Interventions that focus on improving students’ well-being and learning strategies may be important for addressing the late-type academic disadvantage. Support (If Any) Data storage and management were supported by the NUS Office of the Senior Deputy President & Provost and ALSET. The work was funded by the Ministry of Education, Singapore (MOE2019-T2-2-074) and the National Research Foundation, Singapore (NRF2016-SOL002-001).

0137 Phase response curve for effects of school start times on students’ diurnal learning rhythm

Abstract Introduction School start times impose constraints on sleep-wake behaviour that can result in social jet lag. Here, we used university archived datasets (1) to test whether social jet lag was associated with lower grades, and (2) to assess the phase resetting effects of school start times on students’ diurnal learning behaviour. Methods Social jet lag was estimated in 33,645 university students by measuring the phase shift in their Learning Management System (LMS) login rhythm on school days relative to non-school days (LMS social jet lag). ANCOVA was used to test the association between LMS social jet lag and grade point average, adjusting for demographic variables. We constructed a phase response curve by plotting phase shifts on school days (LMS social jet lag) against the initial phase when students’ first class of the day took place. The initial phase was expressed relative to students’ LMS login rhythm on non-school days (LMS chronotype). Results Students with greater LMS social jet lag had a lower grade point average (ANCOVA: F9,32269 = 44.8, P < 0.001). Social jet lag was larger in students with a later LMS login rhythm on non-school days (later chronotype) and for earlier school start times. The phase response curve revealed that the direction and magnitude of social jet lag were strongly dependent on the phase of students’ diurnal rhythm when their first class of the day took place. Phase shifts of up to 12 h were observed when school start times occurred out of phase with students’ diurnal rhythm. Conclusion School start times have a profound impact on students’ diurnal behaviour. Students whose diurnal patterns of LMS logins were similar on both school days and non-school days obtained better grades than their peers with LMS social jet lag. Universities can potentially improve learning by scheduling classes at times that are better aligned with students’ diurnal learning rhythm. Support (If Any) Data storage and management were supported by the NUS Office of the Senior Deputy President & Provost and ALSET. The work was funded by the Ministry of Education, Singapore (MOE2019-T2-2-074) and the National Research Foundation, Singapore (NRF2016-SOL002-001).

Heterogeneous Social Linked Data Integration and Sharing for Public Transportation

Solid (social linked data) technology has made significant progress in social web applications developed, such as Facebook, Twitter, and Wikipedia. Solid is based on semantic web and RDF (Resource Description Framework) technologies. Solid platforms can provide decentralized authentication, data management, and developer support in the form of libraries and web applications. However, thus far, little research has been conducted on understanding the problems involved in sharing public transportation data through Solid technology. It is challenging to provide personalized and adaptable public transportation services for citizens because the public transportation data originate from different devices and are heterogeneous in nature. A novel approach is proposed in this study, in order to provide personalized sharing of public transportation data between different users through integrating and sharing these heterogeneous data. This approach not only integrates diverse data types into a uniform data type using the semantic web but also stores these data in a personal online data store and retrieves data through SPARQL on the Solid platform; these data are visualized on the web pages using Google Maps. To the best of our knowledge, we are the first to apply Solid in public transportation. Furthermore, we conduct performance tests of the new C2RMF (CSV to RDF Mapping File) algorithm and functional and non-functional tests to demonstrate the stability and effectiveness of the approach. Our results indicate the feasibility of the proposed approach in facilitating public transportation data integration and sharing through Solid and semantic web technologies.

A Modular Tool to Support Data Management for LCA in Industry: Methodology, Application and Potentialities

Life Cycle Assessment (LCA) computes potential environmental impacts of a product or process. However, LCAs in the industrial sector are generally delivered through static yearly analyses which cannot capture any temporal dynamics of inventory data. Moreover, LCA must deal with differences across background models, Life Cycle Impact Assessment (LCIA) methods and specific rules of environmental labels, together with their developments over time and the difficulty of the non-expert organization staff to effectively interpret LCA results. A case study which discusses how to manage these barriers and their relevance is currently lacking. Here, we fill this gap by proposing a general methodology to develop a modular tool which integrates spreadsheets, LCA software, coding and visualization modules that can be independently modified while leaving the architecture unchanged. We test the tool within the ORI Martin secondary steelmaking plant, finding that it can manage (i) a high amount of primary foreground data to build a dynamic LCA; (ii) different background models, LCIA methods and environmental labels rules; (iii) interactive visualizations. Then, we outline the relevance of these capabilities since (i) temporal dynamics of foreground inventory data affect monthly LCA results, which may vary by ±14% around the yearly value; (ii) background datasets, LCIA methods and environmental label rules may alter LCA results by 20%; (iii) more than 105 LCA values can be clearly visualized through dynamically updated dashboards. Our work paves the way towards near-real-time LCA monitoring of single product batches, while contextualizing the company sustainability targets within global environmental trends.

A Decision Support System for Marine Regulation

The implementation of marine spatial data management and decision support based on GIS provides a powerful tool for marine regulation. This study constructed a three-dimensional spatiotemporal data model of marine resources that is hierarchical, partitioned, and classified according to the management principle of using an underground resource layer, surface matrix layer, surface overburden layer, and management layer, taking the marine resource entity as the unit. Key research topics include marine multi-source information integration, visual expression, and spatiotemporal topology analysis for typical application scenarios such as sea area use, island development and utilization, and marine environmental monitoring and protection. The study developed a marine information decision support system with comprehensive integration and visual analysis of information on maritime islands, early warning and monitoring, the marine economy, and the marine environment. The system was applied to dynamic regulation of sea area use, spatiotemporal characteristic analysis of marine resources, the regulation of human activities in marine protected areas, and the monitoring of sea level rise. Practice has proved that a three-dimensional spatiotemporal data model of marine resources can meet the application requirements of “One Map”, the basic land and spatial information platform.

Rural Transportation Infrastructure in Low- and Middle-Income Countries: A Review of Impacts, Implications, and Interventions

The rural transport infrastructure sector is a critical force for sustainable development that is interwoven with many other sectors. Rural transportation is an underlying driver of many of the Sustainable Development Goals (SDGs) and a crucial contributor to many socioeconomic benefits for rural people around the world. This review paper expands upon, enhances, and cross-references the perspectives outlined in previous rural infrastructure-focused review papers. Firstly, this work gives a thorough look into the progress of the rural transportation sector in recent years by focusing on the thematic relationships between infrastructure and other components of sustainable development, namely, economics and agriculture, policy and governance, health, gender, education, and climate change and the environment. Secondly, several strategies, approaches, and tools employed by governments and practitioners within the rural transport sector are analyzed and discussed for their contributions to the wellbeing of rural dwellers in low- and middle-income countries (LMICs). These include rural roads, bridges, maintenance, and non-infrastructural approaches that include concepts such as advanced technological innovations, intermediate modes of transport (IMTs), and transport services. This paper concludes that enhancement, improvement, and extension of rural transportation infrastructure brings significant benefits to rural dwellers. However, this paper also calls for additional integration of the sector and increased usage of systems approaches that view rural transport as an active part of many other sectors and a key leverage point within rural development as a whole. Further, this paper notes areas for future research and investigation, including increased investigation of the relationship between rural transportation infrastructure and education, improved data collection and management in support of improved policymaking, improved prioritization of interventions and institutionalization of maintenance, and expansion of pro-poor transportation strategies and interventions.

Integrating Citizen Experiences in Cultural Heritage Archives: Requirements, State of the Art, and Challenges

Digital archives of memory institutions are typically concerned with the cataloguing of artefacts of artistic, historical, and cultural value. Recently, new forms of citizen participation in cultural heritage have emerged, producing a wealth of material spanning from visitors’ experiential feedback on exhibitions and cultural artefacts to digitally mediated interactions like the ones happening on social media platforms. Citizen curation is proposed in the context of the European project SPICE (Social Participation, Cohesion, and Inclusion through Cultural Engagement) as a methodology for producing, collecting, interpreting, and archiving people’s responses to cultural objects, with the aim of favouring the emergence of multiple, sometimes conflicting, viewpoints and motivating users and memory institutions to reflect upon them. We argue that citizen curation urges to rethink the nature of computational infrastructures supporting data management of memory institutions, bringing novel challenges that include issues of distribution, authoritativeness, interdependence, privacy, and rights management. To approach these issues, we survey relevant literature toward a distributed, Linked Data infrastructure, with a focus on identifying the roles and requirements involved in such an infrastructure. We show how existing research can contribute significantly in facing the challenges raised by citizen curation and discuss challenges and opportunities from the socio-technical standpoint.

Obrada podataka prikupljenih bespilotnim aerofotogrametrijskim sistemima

The paper describes the method of application of the platform for processing and managing spatial data collected by drones, as unmanned aerial photogrammetry systems. Software support for data processing and management, as well as model generation for photogrammetry purposes, is provided using the Agisoft Metashape software as an example. The data used for processing is related to the recording of the primary military standard for large lengths and angles, which is located in Kovin. The filming was carried out by a WingtraOne drone. The results of the work confirm the quality of the collected-recorded and processed-output data. The research confirmed the fact that the WingtraOne unmanned aerial photogrammetry system can be a good solution for collecting spatial data and creating some geotopographical materials for the needs of the Military Geographical Institute -"General Stevan Bošković".

A Machine Learning and Multi-Agent Model to Automate Big Data Analytics in Smart Cities

The objective of this paper is to present an architecture to improve the process of automating big data analytics using a multi-agent system and machine learning techniques, to support the processing of real time big data streams and to enhance the process of decision-making for urban planning and management. With the rapidly evolving information technologies, and their utilization in many areas such as smart cities, social networks, urban management and planning, massive data streams are generated and need an efficient approach to deal with. The proposition in this paper adopts the concept of smart data which focuses on the value aspect from big data. The proposed architecture is composed of three layers: data acquisition and storage, data management and processing and the service layer, based on a multi-agent system to automate the big data analytics; the proposed model describe the functionalities of the system and the collaboration between agents, these autonomous entities receive data streams in real time, they perform operations of preprocessing, big data analytics and storage into a Hadoop cluster. The techniques of machine learning are also used to enhance the process of decision making, such the use of classification algorithms to predict habitat type based on the characteristics of a population to help making efficient urban planning decisions. The proposed system can serve as a platform to support data management and to conduct effective decision-making in smart cities.