Intellectual property can be an important asset for academic institutions. Good data management practices are important for capture, development and protection of intellectual property assets. Selected issues focused on the relationship between data management and intellectual property are reviewed and a thesis that academic institutions and scientists should honor their obligations to responsibly manage data.

Data management raises a number of issues, both regulatory and non-regulatory. Researchers should understand how data are defined by their particular institutions and regulatory authorities. Data are the bases of scientific communication and provide a strong defense against allegations of scientific misconduct. Authorization is often necessary before collection of data can commence. Proper handling, retention, and storage of data, especially that involving humans, are crucial for the researcher. Data ownership by the institution leads to (...) a responsibility by the institution to educate all its researchers in responsible data management practices. (shrink)

The basic components of data management including data ownership, collection, selection, recording, analysis, storage, retention, destruction, and sharing. A number of important principles underlie best practices for each of these components; these include recording details such that another can repeat the experiment, keeping the data safe, managing storage in such a way as to facilitate easy retrieval for the period of time required by regulatory agencies and establishing data sharing principles with colleagues before collaborations begin. Experience (...) as practicing scientists and teachers has aided in developing helpful strategies and approaches for communicating these principles, policies and practices to trainees and colleagues. We recommend didactic instruction focused by discipline, combined with the use of teachable moments in a student’s career, as well as teaching principles versus rules, because changing methods of data collection and storage have implications for data management practices. (shrink)

This paper presents reports on discussions among an international group of science and technology studies scholars who convened at the US National Science Foundation to think about data sharing and open STS. The first report, which reflects discussions among members of the Society for Social Studies of Science, relates the potential benefits of data sharing and open science for STS. The second report, which reflects discussions among scholars from many professional STS societies, focuses on practical and conceptual issues (...) related to managing, storing, and curating STS data. As is the case for all reports of such open discussions, a scholar’s presence at the meeting does not necessarily mean that they agree with all aspects of the text to follow. (shrink)

With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first (...) introduce the background of sports big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions. (shrink)

Low levels of public trust in data practices have led to growing calls for changes to data-driven systems, and in the EU, the General Data Protection Regulation provides a legal motivation for such changes. Data management is a vital component of data-driven systems, but what constitutes ‘good’ data management is not straightforward. Academic attention is turning to the question of what ‘good data’ might look like more generally, but public views are absent from (...) these debates. This paper addresses this gap, reporting on a survey of the public on their views of data management approaches, undertaken by the authors and administered in the UK, where departure from the EU makes future data legislation uncertain. The survey found that respondents dislike the current approach in which commercial organizations control their personal data and prefer approaches that give them control over their data, that include oversight from regulatory bodies or that enable them to opt out of data gathering. Variations of data trusts – that is, structures that provide independent stewardship of data – were also preferable to the current approach, but not as widely preferred as control, oversight and opt out options. These features therefore constitute ‘good data management’ for survey respondents. These findings align only in part with principles of good data identified by policy experts and researchers. Our findings nuance understandings of good data as a concept and of good data management as a practice and point to where further research and policy action are needed. (shrink)

With the powerful analyses and resources they enable, digital humanities tools have captivated researchers from many different fields who want to use them to study science. Digital tools, as well as funding agencies, research communities, and academic administrators, require researchers to think carefully about how they conceptualize, manage, and store data, and about what they plan to do with that data once a given project is over. The difficulties of developing strategies to address these problems can prevent new (...) researchers from sticking with digital tools and flummox even experienced researchers. To help overcome the data hurdle, we present five principles to help researchers, novice and experienced alike, conceptualize and plan for their data. We illustrate the use of those principles with two digital projects from the history of science, the Embryo Project and the Marine Biological Laboratory History Project, and their associated HPS Repository for data. The principles apply beyond the digital realm, so those who collect and manage data by more traditional means will also find them useful. (shrink)

The main purpose of this study is to examine the impact of the big data management capabilities on the performance of manufacturing firms in the Asian Economy during coronavirus disease 2019. In addition to this, this study is also planned to examine the mediating role of organizational agility in the relationship between the big data management capabilities and the performance of Chinese manufacturing firms during COVID-19. Last, this study has examined the moderating role of information technology capability in (...) the relationship between the big data management capabilities and performance of Chinese manufacturing firms during COVID-19. This study adopted the quantitative method of research with a cross-sectional technique. This study employed a questionnaire to gather the data as a research instrument. This study has used the purposive sampling method by keeping in mind the context of this study. Employees of the Chinese SMEs that were at least 10 years old were the population of this study. The research model was being analyzed by employing the “partial least squares” technique through statistical software the Smart PLS version 3. The results are in line with the proposed hypothesis. This study contributed to the literature by suggesting characteristics that promote or prevent the organization from successfully implementing big data and pointed out that showing resistance in information management system implementation may have different effects on the organization. Besides, the study also discussed the relationship between such information systems and the organization. Findings of these two factors provide insights for the practitioners and researchers in assessing the success or failure of organizations for using big data. (shrink)

What comprises ‘data’ varies from one institution to another based on the information which is deemed important by individual institutions. To effectively and efficiently produce, collect, and retain data, an organization develops specific defining characteristics of data to meet its informational needs. Procedures to maintain and retain knowledge among laboratory members and principal investigators will allow for improved efficiency of data collection. Optimization of communication, maintenance of inventories, record keeping, and updating relevant training programs are all (...) critical to supporting the quality and integrity of a particular organization’s data. Concurrent revisions to such procedures will ensure that the definition of data as well as the means by which it is collected and maintained remain appropriate to the needs of the individual organization. (shrink)

BackgroundInstitutions, funding agencies and publishers are placing increasing emphasis on good research data management (RDM). RDM lapses in medical science can result in questionable data and cause the public’s confidence in the scientific community to crumble. A fledgling medical school in a young university in Singapore has mandated every funded research project to have a data management plan (DMP). However, researchers’ adherence to their DMPs was unknown until the school embarked on routine data auditing. We hypothesize (...) that research data auditing improves RDM awareness, compliance and reception in the school.MethodsWe conducted surveys with research PIs and researchers before and after data auditing to evaluate differences in self-reported RDM awareness, compliance and reception. As it is mandatory to deposit research data in a central data repository system in the school, we tracked data deposition by each laboratory from 2 weeks before to 3 months after data auditing as a marker of actual RDM compliance.ResultsResearch data auditing had an overall positive effect on self-reported RDM awareness, compliance and reception for both research PIs and researchers. Research PIs agreed more that RDM was important to scientific reproducibility, were more aware of proper RDM, had higher RDM strength in their laboratories and were more compliant with the DMP. Both research PIs and researchers believed data auditing helped them to be more compliant with data deposition in the repository. However, data auditing had no significant impact on laboratories’ data deposition rates over time, which could be due to the short sampling period.ConclusionsResearch PIs and researchers generally felt that data auditing was effective in improving RDM practices. It helped to evaluate their RDM practices objectively, propose corrective actions for RDM lapses and spread awareness of the university’s data management policies. Our findings corroborated other studies in medical research, geosciences, engineering and ethics that data auditing promotes good RDM practices. Hence, we recommend research institutions worldwide to adopt data auditing as a tool to reinforce research integrity. (shrink)

BackgroundThe widespread sharing of biological and biomedical data is recognised as a key element in facilitating translation of scientific discoveries into novel clinical applications and services. At the same time, twenty-first century states are increasingly concerned that this data could also be used for purposes of bioterrorism. There is thus a tension between the desire to promote the sharing of data, as encapsulated by the Open Data movement, and the desire to prevent this data from (...) ‘falling into the wrong hands’ as represented by ‘dual use’ policies. Both frameworks posit a moral duty for life sciences researchers with respect to how they should make their data available. However, Open data and dual use concerns are rarely discussed in concert and their implementation can present scientists with potentially conflicting ethical requirements.DiscussionBoth dual use and Open data policies frame scientific data and data dissemination in particular, though different, ways. As such they contain implicit models for how data is translated. Both approaches are limited by a focus on abstract conceptions of data and data sharing. This works to impede consensus-building between the two ethical frameworks. As an alternative, this paper proposes that an ethics of responsible management of scientific data should be based on a more nuanced understanding of the everyday data practices of life scientists. Responsibility for these ‘micromovements’ of data must consider the needs and duties of scientists as individuals and as collectively-organised groups.SummaryResearchers in the life sciences are faced with conflicting ethical responsibilities to share data as widely as possible, but prevent it being used for bioterrorist purposes. In order to reconcile the responsibilities posed by the Open Data and dual use frameworks, approaches should focus more on the everyday practices of laboratory scientists and less on abstract conceptions of data. (shrink)

We send messages as much in how we communicate as by what we communicate. Learning best practices, such as those for data management proposed in the accompanying article, are components of becoming a responsible and contributing member of the community of scholars. Not only must we teach the principles underlying best practices, we should model and teach approaches for implementing those practices and help students come to view them within the larger context of becoming members of a professional community. (...) How to collaborate across differences and how to have disputes professionally are skills all professionals need, and they should be taught along with the content itself. (shrink)

The problem that I present in this paper concerns the issue of ethical evaluation of algorithms, especially those used in social media and which create profiles of users of these media and new technologies that have recently emerged and are intended to change the functioning of technologies used in data management. Systems such as Overton, SambaNova or Snorkel were created to help engineers create data management models, but they are based on different assumptions than the previous approach in (...) machine learning and deep learning. There is a need to analyze both deep learning algorithms and new technologies in database management in terms of their actions towards a person who leaves their digital footprints, on which these technologies work. Then, the possibilities of applying the existing deep learning technology and new Big Data systems in the economy will be shown. The opportunities offered by the systems mentioned above seem to be promising for many companies and—if implemented on a larger scale—they will affect the functioning of the free market. (shrink)

The framers of the U.S. Constitution believed that intellectual property rights were crucial to scientific advancement. Yet, the framers also recognized the need to balance innovation, privatization, and public use. The courts’ expansion of patent protection for biotechnology innovations in the last 30 years raises the question whether the patent system effectively balances these concerns. While the question is not new, only through a thorough and thoughtful examination of these issues can the current system be evaluated. It is then a (...) policy decision for Congress if any change is necessary. (shrink)

Background There has been considerable investment and strategic planning to introduce genomic testing into Australia’s public health system. As more patients’ genomic data is being held by the public health system, there will be increased requests from researchers to access this data. It is important that public policy reflects public expectations for how genomic data that is generated from clinical tests is used. To inform public policy and discussions around genomic data sharing, we sought public opinions (...) on using genomic data contained in medical records for research purposes in the Australian state of Queensland. Methods A total of 1494 participants completed an online questionnaire between February and May 2019. Participants were adults living in Australia. The questionnaire explored participant preferences for sharing genomic data or biological samples with researchers, and concerns about genomic data sharing. Results Most participants wanted to be given the choice to have their genomic data from medical records used in research. Their expectations on whether and how often they needed to be approached for permission on using their genomic data, depended on whether the data was identifiable or anonymous. Their willingness to sharing data for research purposes depended on the type of information being shared, what type of research would be undertaken and who would be doing the research. Participants were most concerned with genomics data sharing that could lead to discrimination, data being used for marketing, data security, or commercial use. Conclusions Most participants were willing to share their genomic data from medical records with researchers, as long as permission for use was sought. However, the existing policies related to this process in Queensland do not reflect participant expectations for how this is achieved, particularly with anonymous genomics data. This inconsistency may be addressed by process changes, such as inclusion of research in addition to clinical consent or general research data consent programs. (shrink)

In 1927, Walter Lippmann published The Phantom Public, denouncing the ‘mystical fallacy of democracy.’ Decrying romantic democratic models that privilege self-governance, he writes: “I have not happened to meet anybody, from a President of the United States to a professor of political science, who came anywhere near to embodying the accepted ideal of the sovereign and omnicompetent citizen.” Almost 90 years later, Lippmann’s pragmatism is as relevant as ever, and should be applied in new contexts where similar self-governance concerns persist. (...) This paper does just that, repurposing Lippmann’s argument in the context of the ongoing debate over the role of the digital citizen in Big Data management. It is argued that proposals by the Federal Trade Commission, the White House and the US Congress, championing failed notice and choice privacy policy, perpetuate a self-governance fallacy comparable to Lippmann’s, referred to here as the fallacy of data privacy self-management. Even if the digital citizen had the faculties and the system for data privacy self-management, the digital citizen has little time for data governance. We desire the freedom to pursue the ends of digital production, without being inhibited by the means. We want privacy, and safety, but cannot complete all that is required for its protection. If it is true that the fallacy of democracy is similar to the fallacy of data privacy self-management, then perhaps the pragmatic solution is representative data management: a combination of non/for-profit digital dossier management via infomediaries that can ensure the protection of personal data, while freeing individuals from what Lippmann referred to as an ‘unattainable ideal.’. (shrink)

A course focusing on ethical issues in physics has been taught to undergraduate students at Eastern Michigan University since 1988. The course covers both responsible conduct of research and ethical issues associated with how physicists interact with the rest of society. Since most undergraduate physics majors will not have a career in academia, it is important that a course such as this address issues that will be relevant to physicists in a wide range of job situations. There is a wealth (...) of published work that can be drawn on for reading assignments. (shrink)

Biobanking in Europe has made major steps towards harmonization and shared standards for the collection and processing of data and samples stored in biobanks. Still, biobanks and researchers face substantial legal difficulties in the field of data protection and sample management. Data protection law was harmonized almost 15 years ago while rights in samples fall under the competence of the Member States of the EU. Despite the Data Protection Directive the field of data protection shows (...) a substantial degree of deviation as public health was excluded from the harmonization. Biobanks seem to have substantially fewer difficulties to cope with in terms of the legal requirements in the field of sample management. This paper discusses the legal framework, experiences of different biobanks in Europe and potential ways forward. It also highlights the need for a health economic analysis of the costs and benefits of privacy protection in Europe. At the moment, policymakers seem to build their decisions on an insufficient evidence base which underestimates the potential value of biobanks for European public health. Within the past few years little progress has been achieved with regards to the development of a unified legal framework in Europe, The diversity in the legal system is also reflected in the different approaches of ethics committees towards biobanking. To secure the responsible and effective use of data and samples, more efforts are needed to come up with pathways for a solution. (shrink)

Current formal dialectical models postulate normative rules that enable discussants to conduct dialogical interactions without committing fallacies. Though the rules for conducting a dialogue are supposed to apply to interactions between actual arguers, they are without exception theoretically motivated. This creates a gap between model and reality, because dialogue participants typically leave important content-related elements implicit. Therefore, analysts cannot readily relate normative rules to actual debates in ways that will be empirically confirmable. This paper details a new, data-driven method (...) for describing discussants’ actual reply structures, wherein corpus studies serve to acknowledge the complexity of natural argumentation. Rather than refer exclusively to propositional content as an indicator of arguing pro/contra a given claim, the proposed approach to dialogue structure tracks the sequence of dialogical moves itself. This arguably improves the applicability of theoretical dialectical models to empirical data, and thus advances the study of dialogue systems. (shrink)

Nowadays, the modern management is promoted to resolve the issue of unreliable information transmission and to provide work efficiency. The basic aim of the modern management is to be more effective in the role of the school to train talents and serve the society. This article focuses on the application of data mining (DM) in the development of information management system (IMS) in universities and colleges. DM provides powerful approaches for a variety of educational areas. Due to the large (...) amount of student information that can be used to design valuable patterns relevant to student learning behavior, research in the field of education is continuously expanding. Educational data mining can be used by educational institutions to assess student performance, assisting the institution in recognizing the student’s accomplishments. In DM, classification is a well-known technique that has been regularly used to determine student achievement. In this study, the process of DM and the application research of association rules is introduced in the development of IMS in universities and colleges. The results show that the curriculum covers the whole field and the minimum transaction support count be 2, minconf = 70%. The results also suggested that students who choose one course also tend to choose the other course. The application of DM theory in university information will greatly upsurge the data analysis capability of administrators and improve the management level. (shrink)

Biobanking in Europe has made major steps towards harmonization and shared standards for the collection and processing of data and samples stored in biobanks. Still, biobanks and researchers face substantial legal difficulties in the field of data protection and sample management. Data protection law was harmonized almost 15 years ago while rights in samples fall under the competence of the Member States of the EU. Despite the Data Protection Directive the field of data protection shows (...) a substantial degree of deviation as public health was excluded from the harmonization. Biobanks seem to have substantially fewer difficulties to cope with in terms of the legal requirements in the field of sample management. This paper discusses the legal framework, experiences of different biobanks in Europe and potential ways forward. It also highlights the need for a health economic analysis of the costs and benefits of privacy protection in Europe. At the moment, policymakers seem to build their decisions on an insufficient evidence base which underestimates the potential value of biobanks for European public health. Within the past few years little progress has been achieved with regards to the development of a unified legal framework in Europe, The diversity in the legal system is also reflected in the different approaches of ethics committees towards biobanking. To secure the responsible and effective use of data and samples, more efforts are needed to come up with pathways for a solution. (shrink)

Following the rediscovery of Mendelian genetics, food supply pressures and the rapid expansion of crop varieties with defined performance characteristics, international systems were set up throughout the 20 C to regulate the trade of seed, the protection of intellectual property and the sale of productive varieties of key agricultural crops. These systems are a highly connected but largely linear set of processes. System changes are slow to be adopted due to the cascade of effects that structural alteration would have globally. (...) Multi-omic technologies and the subsequent proliferation of data types used within modern breeding, offer the possibility to gain deeper insights into the performance characteristics of varieties. Current integration of data, standards and ownership structures limit their applications for wider purposes, both private and public. We explore how data within and between breeding programmes and the varietal approval and monitoring processes could be made FAIR. We examine what role expanded or aligned programmes of data collection and expanded trait evaluation at the point of varietal registration and evaluation, as well as on farm could have in ensuring the best linkage of public and private data to address some of the challenges society faces over the next 30 years with the required, rapid transition to sustainable agricultural systems. (shrink)

Responsible data management is a multifaceted topic involving standards within the research community regarding research design and the sharing of data as well as the collection, selection, analysis and interpretation of data. Transparency in the manipulation of images is increasingly important in order to avoid misrepresentation of research findings, and research oversight is also critical in helping to assure the integrity of the research process. Intellectual property issues both unite and divide academe and industry in their approaches (...) to data management. Central to the realization and promulgation of responsible data management is clear and careful communication of standards and expectations within the research community to trainees as well as among colleagues. These topics are examined and explored in depth in a special issue of Science and Engineering Ethics on responsible data management. (shrink)

A multitude of legislation impacts the use of samples and data for research in South Africa. With the coming into effect of the Protection of Personal Information Act No. 4 of 2013 in July 2021, recent attention has been given to safeguarding research participants’ personal information. The protection of participants’ privacy in research is essential, but it is not the only risk at stake in the use and sharing of personal information. Other rights and interests that must also be (...) considered and safeguarded include the right to non-discrimination, the right to dignity, and the right to enjoy the benefits of scientific progress. This can be achieved by ensuring that the ethical values and universal principles that underpin the research landscape, namely equity, reciprocity, justice and solidarity, are embedded in the regulatory framework for the management and use of personal information. This paper advocates an integrated bioethics approach to the use of personal information for research in SA. A key feature in embedding such an approach is a Data Transfer Agreement that appropriately integrates the safeguards and protections set out by our regulatory framework with bioethical rules and procedures when personal information is used and shared for research purposes. This paper thus calls for the development of a national DTA that is guided by key ethical principles when data are shared for research purposes. This paper will be followed by a webinar on 23 June 2022 hosted by the Department of Science and Innovation and the SA Medical Research Council, where the recommendations and suggestions set out herein will be unpacked, discussed and debated by relevant stakeholders. (shrink)

Note?takers in early modern Europe mixed a number of scribal practices. Not only did they write down extracts of texts, they also collected data from observation or from accounting. Practices such as commonplacing were part of sometimes communal, rather informal personal practices that laid the foundations for personal diaries. Other note?taking was prescriptive, fact?establishing technical data entry. Yet both the personal, sentimental and technical forms of note?taking were interrelated. It was during this period that merchants, administrators, scholars and (...) scientists sought methods to transform raw information such as notes into formalized knowledge for practical use. Notes had formed a part of larger writing and archival projects since the dawn of writing and literacy, but during the seventeenth and eighteenth centuries, figures such as Bacon, Boyle and Pepys not only mixed note?taking practices from both scholarly and mercantile traditions; they also thought about the very act of note?taking and how to transform it into a tool for the management of large?scale government, industry and research. Most of all, in the seventeenth century, scholars, merchants and naturalists began thinking in formal terms about how to use notes as part of larger information systems which they called collections, compilations and even archives. (shrink)

Data mining is a new technology developed in recent years. Through data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. This paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect student development and the various (...) relationships between these factors. Predictive assessment of knowledge and personalized teaching decision-making provide the basis. First, the general situation of genetic algorithm and fuzzy genetic algorithm is introduced, and then, an improved genetic fuzzy clustering algorithm is proposed. Compared with traditional clustering algorithm and improved genetic fuzzy clustering algorithm, the effectiveness of the algorithm proposed in this paper is proved. Based on the analysis system development related tools and methods, in response to the needs of the student information management system, a simple student information management system is designed and implemented, which provides a platform and data source for the next application of clustering algorithm for performance analysis. Finally, clustering the students’ scores with a clustering algorithm based on fuzzy genetic algorithm, the experimental results show that this method can better analyze the students’ scores and help relevant teachers and departments make decisions. (shrink)

As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource management scheme. (...) As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines into two pools: a hot pool and a warm pool. Each PM is configured with a resource search engine that finds an available virtual machine for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm is presented to minimize the system cost and optimize the sleep parameter. (shrink)

Proliferation of data processing and data storage devices in the Internet of Things poses significant privacy risks. At the same time, faster and faster use-cycles and obsolescence of devices with electronic components causes environmental problems. Some of the solutions to the environmental challenges of e-waste include mandatory recycling schemes as well as informal second hand markets. However, the data security and privacy implications of these green policies are as yet badly understood. This paper argues that based on (...) the experience with second hand markets in desktop computers, it is very likely that data that was legitimately collected under the household exception of the Data Protection Directive will “leak” into public spheres. Operators of large recycling schemes may find themselves inadvertently and unknowingly to be data controller for the purpose of Data Protection law, private resale of electronic devices can expose the prior owner to significant privacy risks. (shrink)

This paper investigates a distributed energy management strategy for the port power system under false data injection attacks. The attacker can tamper with the interaction information of energy equipment, penetrate the boundary between port information system and port power system, and cause serious operation failure of port energy equipment. Firstly, a hierarchical topology is proposed to allocate the security resources of the port power system. Secondly, by reconstructing the topological structure of the port information system, the robustness of the (...) information system is improved, and the impact of false data injection attacks on the port power system is reduced, which realizes secure distributed energy management of the port power system. Finally, the simulation results show the effectiveness of the proposed strategy, and the defense capability of the port power system is improved. (shrink)

Risk management is a well-known method to face technological challenges through a win–win combination of protective and proactive approaches, fostering the collaboration of operators, researchers, regulators, and industries for the exploitation of new markets. In the field of autonomous and unmanned aerial systems, or UAS, a considerable amount of work has been devoted to risk analysis, the generation of ground risk maps, and ground risk assessment by estimating the fatality rate. The paper aims to expand this approach with a tool (...) for managing data protection risks raised by drones through the design of flight maps. The tool should allow UAS operators choosing the best air corridor for their drones based on the so-called privacy by design principle pursuant to Article 25 of the EU data protection regulation, the GDPR. Among the manifold applications of this approach, the design of fly zones for drones can be tailored for public authorities in the phase of authorization of new operations, much as for national Data Protection authorities that have to control the lawfulness of personal data processing by UAS operations. The overall aim is to present the first win–win approach to data protection issues, aerospace engineering challenges, and risk management methods for the threats posed by this technology. (shrink)

Risk management is a well-known method to face technological challenges through a win–win combination of protective and proactive approaches, fostering the collaboration of operators, researchers, regulators, and industries for the exploitation of new markets. In the field of autonomous and unmanned aerial systems, or UAS, a considerable amount of work has been devoted to risk analysis, the generation of ground risk maps, and ground risk assessment by estimating the fatality rate. The paper aims to expand this approach with a tool (...) for managing data protection risks raised by drones through the design of flight maps. The tool should allow UAS operators choosing the best air corridor for their drones based on the so-called privacy by design principle pursuant to Article 25 of the EU data protection regulation, the GDPR. Among the manifold applications of this approach, the design of fly zones for drones can be tailored for public authorities in the phase of authorization of new operations, much as for national Data Protection authorities that have to control the lawfulness of personal data processing by UAS operations. The overall aim is to present the first win–win approach to data protection issues, aerospace engineering challenges, and risk management methods for the threats posed by this technology. (shrink)

Risk management is a well-known method to face technological challenges through a win–win combination of protective and proactive approaches, fostering the collaboration of operators, researchers, regulators, and industries for the exploitation of new markets. In the field of autonomous and unmanned aerial systems, or UAS, a considerable amount of work has been devoted to risk analysis, the generation of ground risk maps, and ground risk assessment by estimating the fatality rate. The paper aims to expand this approach with a tool (...) for managing data protection risks raised by drones through the design of flight maps. The tool should allow UAS operators choosing the best air corridor for their drones based on the so-called privacy by design principle pursuant to Article 25 of the EU data protection regulation, the GDPR. Among the manifold applications of this approach, the design of fly zones for drones can be tailored for public authorities in the phase of authorization of new operations, much as for national Data Protection authorities that have to control the lawfulness of personal data processing by UAS operations. The overall aim is to present the first win–win approach to data protection issues, aerospace engineering challenges, and risk management methods for the threats posed by this technology. (shrink)

Power provision is coming to be the most important constraint to data center development. The efficient management of power consumption according to the loads of the data center is urgent. As the load for every application hosted in every server node of the data center and corresponding Service Level Agreement requirements can be quite different, it is hard to deploy a power strategy at application. The asynchronies and abruptness characteristics of workload fluctuation make power management policymaking using (...) periodic resource scheduling method invalid. In this article, the design and implementation of the request–response distributed power management scheme is elaborated. Bound by linear time complexity, the method proposed integrates dynamic voltage/frequency scaling, power-on–power-off, and virtual machine migration mechanisms and dynamically optimizes the power consumption of a cloud data center. The significant advantage of the scheme is that it does not need synchronous scheduling between all SNs. Simulation results showed that the scheme could effectively decrease the power consumption of the data center, with a tiny reduction in performance as centralized control methods. (shrink)

With the rapid development of the Internet information age, social networks, mobile Internet, and e-commerce have expanded the scope of Internet applications. The “big data” era is a challenge and chance for companies and has a great impact on social economy, politics, culture, and people’s lives. An accurate marketing system is developed based on J2EE, and the architecture is selected from the user layer, business logic layer, and data layer and the B/S3 layer application, including three layers of (...) crip-dm and semma. And, other process methods are used. Data-mining-based marketing system information solutions consist of several parts, such as requirement analysis, design, implementation, and testing. This paper introduces data mining technology to the marketing business based on the practical use and design IT solutions for precision marketing, attribute selection tools, attribute analysis tools, modeling prediction tools, and others. This paper introduces a precision marketing system based on data mining technology. The system passes the actual test and the deployment and the operation of this system are confirmed. The system, which can improve marketing activity, is tested, and the development and operation of this system markedly increase the company’s earnings. (shrink)