Data Lifecycle Model Research Articles

The ripple effect of dataset reuse: Contextualising the data lifecycle for machine learning data sets and social impact

Although there exists a rich literature on data lifecycle, a common framework for data lifecycle depicts reuse as the last stage. However, this framework fails to explain the complex lifecycle of machine learning (ML) data sets, which can have many different afterlives. Data sets for ML can be expanded to supplement previous research, and researchers can concatenate multiple data sets to develop new models. This study discusses ML dataset reuse through the lens of the data–information–knowledge–wisdom pyramid. In social science research, researchers might reuse data to analyse a new research question that is still in the context of the data domain. By contrast, research practices in ML, where researchers layer multiple data sets for training purposes, require us to ask whether the existing data lifecycle model, ending with ‘reuse’, is appropriate for explaining such an iterative and layered lifecycle. This study introduces one case of merging computer vision data set and natural language processing data set and two cases of applying ML models from outside of the ML community (hate speech detection and politeness detection) to justify a framework for a ML dataset lifecycle. Last but not least, this study proposes a ML dataset lifecycle and provides case examples to describe each stage.

Quality Assessment for Research Data Management in Research Projects

In the context of research data management (RDM), researchers are confronted with a multitude of new tasks and responsibilities. The totality of all tasks to ensure the re-use of data, long-term archiving, and access to data through data management planning, further data documentation, and provinces of data collection and analysis are described as research data management [1]. Often, the process of RDM is represented with data life cycle models, which include the basic phases of planning, data collection, analysis, archiving, access, and reuse [2].

Secure Data Management Life Cycle for Government Big-Data Ecosystem: Design and Development Perspective

The rapid generation of data from various sources by the public sector, private corporations, business associations, and local communities is referred to as big data. This large and complex dataset is often regarded as the ‘new oil’ by public administrations (PAs), and data-driven approaches are employed to transform it into valuable insights that can improve governance, transparency, digital services, and public engagement. The government’s big-data ecosystem (GBDE) is a result of this initiative. Effective data management is the first step towards large-scale data analysis, which yields insights that benefit your work and your customers. However, managing big data throughout its life cycle is a daunting challenge for public agencies. Despite its widespread use, big data management is still a significant obstacle. To address this issue, this study proposes a hybrid approach to secure the data management life cycle for GBDE. Specifically, we use a combination of the ECC algorithm with AES 128 BITS encryption to ensure that the data remain confidential and secure. We identified and analyzed various data life cycle models through a systematic literature review to create a data management life cycle for data-driven governments. This approach enhances the security and privacy of data management and addresses the challenges faced by public agencies.

Workflow for health-related and brain data lifecycle.

Poor lifestyle leads potentially to chronic diseases and low-grade physical and mental fitness. However, ahead of time, we can measure and analyze multiple aspects of physical and mental health, such as body parameters, health risk factors, degrees of motivation, and the overall willingness to change the current lifestyle. In conjunction with data representing human brain activity, we can obtain and identify human health problems resulting from a long-term lifestyle more precisely and, where appropriate, improve the quality and length of human life. Currently, brain and physical health-related data are not commonly collected and evaluated together. However, doing that is supposed to be an interesting and viable concept, especially when followed by a more detailed definition and description of their whole processing lifecycle. Moreover, when best practices are used to store, annotate, analyze, and evaluate such data collections, the necessary infrastructure development and more intense cooperation among scientific teams and laboratories are facilitated. This approach also improves the reproducibility of experimental work. As a result, large collections of physical and brain health-related data could provide a robust basis for better interpretation of a person's overall health. This work aims to overview and reflect some best practices used within global communities to ensure the reproducibility of experiments, collected datasets and related workflows. These best practices concern, e.g., data lifecycle models, FAIR principles, and definitions and implementations of terminologies and ontologies. Then, an example of how an automated workflow system could be created to support the collection, annotation, storage, analysis, and publication of findings is shown. The Body in Numbers pilot system, also utilizing software engineering best practices, was developed to implement the concept of such an automated workflow system. It is unique just due to the combination of the processing and evaluation of physical and brain (electrophysiological) data. Its implementation is explored in greater detail, and opportunities to use the gained findings and results throughout various application domains are discussed.

Research on Personal Data Privacy Security in the Era of Big Data

Big data privacy security has become a hot research topic in contemporary society. Based on the data relevance and life-cycle in the era of big data, this paper analyzes the causes of security problems in China’s data privacy. It puts forward suggestions from three aspects to provide references for subsequent research. Based on the current research progress, this paper first sorts out the definitions of data privacy and data privacy protection, then summarizes the causes of privacy security from the perspectives of technology and management and reveals the consequences of data privacy security issues. The demonstrated results trigger an insight into the solution strategy of data privacy problems and offer suggestions for solving problems from the perspective of management based on the data life-cycle model. Finally, starting from other stages of the data life-cycle and the application scenarios of big data, this paper looks forward to the future research direction. This study found that the present study needs to focus on the combination of system and technology, the improvement of laws and regulations, and the data life-cycle model in both technical and institutional management.

How is Big Data Changing Economic Research Paradigms?

Technological aspect of Big Data is the domain of computer science. Managers and economists are interested, however, not in the methods of implementation of the systems, but in possibilities what such technology allows. For several years, the research has confirmed that Big Data have positive effect on companies' performance. This paper proposes a novel Big Data architecture designed for nowcasting and forecasting social and economic changes. The main contribution of the paper is the proposal of a new data lifecycle model that encompasses all the processes related to working with Big Data, and an architecture for a Big Data system able to integrate, process and analyze data from different sources with the objective to forecast economic and social changes. In order to frame the data analysis in an organizational perspective and allow its management in a robust and flexible architecture, a Data Lifecycle approach for the management of data in the organization has been taken. Economic and social aspects were presented as a complement to the technological aspect, which is usually cited as the primary one in the context of Big Data.

Process-driven quality improvement for scientific data based on information product map

PurposeThis paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data.Design/methodology/approachFirst, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers’ experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward.FindingsThe scientific data manufacturing process and data stakeholders’ responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data.Research limitations/implicationsAs for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders.Practical implicationsThe process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues.Originality/valueThis research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.

Construction of Shipping Linked Data Lifecycle Model and Its Application in Semantic Navigation

As one of the most important government data assets, shipping data has attracted more and more attention from stakeholders. The term linked data refers to a set of best practices for publishing and linking structured data on the web. The application of linked data technology to open the shipping data can promote the transparency and reusability of data. Many shipping data have been placed on the network as linked data. However, the existing construction of shipping linked data does not consider the concept of lifecycle, and the concept of lifecycle naturally lies in the link data itself. Therefore, this paper attempts to propose a linked data lifecycle model to assist in publishing shipping data as linked data on the Web. We first conduct a systematic analysis of the typical lifecycle model. On this basis, a new linked data lifecycle model is proposed, and the role of each step of the model is also explained. In particular, we take domain modeling as the first step of the model and discuss the key role of it in publishing linked data. Taking the voyage data as an example, we show in detail how to use the proposed model to construct shipping linked data. Then, a specific semantic navigation case based on the above data is given to prove the added value of the linked data. The results show that the lifecycle model proposed in this paper is suitable for the publication of shipping data and is convenient for future expansion and maintenance.

Data Lifecycle Management in Precision Agriculture Supported by Information and Communication Technology

The role of agriculture in environmental degradation and climate change has been at the center of a long-lasting and controversial debate. This situation combined with the expected growth in crop demand and the increasing prices of fertilizers and pesticides has made the need for a more resource-efficient and environmentally sustainable agriculture more evident than ever. Precision agriculture (PA), as a relatively new farming management concept, aims to improve crop performance as well as to reduce the environmental footprint by utilizing information about the temporal and the spatial variability of crops. Information and communication technology (ICT) systems have influenced and shaped every part of modern life, and PA is no exception. The current paper conducts a literature review of prominent ICT solutions, focusing on their role in supporting different phases of the lifecycle of PA-related data. In addition to this, a data lifecycle model was developed as part of a novel categorization approach for the analyzed solutions.

The scientific approach as a transparency enabler throughout the data life-cycle1

As societies evolve through the data revolution, it is important that National Statistical Offices (NSOs) continue to devote efforts to fine-tune their approaches to maintain the vital trust they need to operate successfully. To do this, they must constantly re-invent themselves to remain relevant to new data needs and keep up with their high-quality standards. With new sources of information surfacing both in the public and private spheres, options are multiplying for NSOs to design new ways to gather and grow the data into information. As this is happening, new practices and new issues are emerging throughout the data life-cycle process. Operating beyond the sample survey paradigm, NSOs see themselves confronted with the need to anchor their new approaches in solidly defined and defendable frameworks. Further, as new data themes, methods and sources are considered, transparency becomes a central issue. Using Statistics Canada’s data life-cycle management model, this paper illustrates how the scientific approach can be leveraged to make transparency more explicit both in projects and management.

Towards an Enhanced Data- and Knowledge Management Capability: A Data Life Cycle Model Proposition for Integrated Vehicle Health Management

The creation, capturing, using and sharing of knowledge is based on data. The rate of data creation, collection, and elicitation through wide range experiments, simulations and measurements is rapidly increasing within Integrated Vehicle Health Management (IVHM). In addition, Knowledge Management (KM), data abstraction, analyses, storage and accessibility challenges persist, resulting in loss of knowledge and increased costs. This growth in the creation of research data, algorithms, technical papers, reports and logs, requires both a strategy and tool to address these challenges. A Data Life Cycle Model (DLCM) ensures the efficient and effective abstraction and management of both data and knowledge outputs. IVHM which depend heavily on high-quality data to perform data-driven, model-based and hybrid computational analysis of asset health. IVHM Centre does not yet have a systematic and coherent approach to its data management. The absence of a DLCM means that valuable knowledge might be lost or is difficult to find. Data visualization is fragmented and done on a project by project basis leading to increased costs. There is insufficient algorithm documentation and communication for easy transition between subsequent researchers and personnel. A systematic review of DLCMs, frameworks, standards and process models pertaining to data- and KM in the context of IVHM, found that there is no DLCM that is consistent with IVHM data and knowledge management requirements. Specifically, there is a need to develop a DLCM based on Open System Architecture for Condition-Based Maintenance framework.

Research Data Management Practices: Synergies and Discords between Researchers and Institutions

The aim of this study was to explore the synergies and discords in attitudes towards research data management (RDM) drivers and barriers for both researchers and institutions. Previous work has studied RDM from a single perspective, but not compared researchers’ and institutions’ perspectives. We carried out qualitative interviews with researchers as well as institutional representatives to identify drivers and barriers, and to explore synergies and discords of both towards RDM. We mapped these to a data lifecycle model and found that the contradictions occur at early stages in the lifecycle of data and the synergies occur at the later stages. This means that for future successful RDM, the points of discord at the start of the data lifecycle must be overcome. Finally, we conclude by proposing key recommendations that could help institutions when addressing both researcher and institutional RDM needs.

Developing a Knowledge Management System for Integrated Vehicle Health Management Using a Data Life Cycle Model

Data forms the foundation on which knowledge is created, captured, used and shared. The lack of an approach consistent with technological changes and needs can facilitate loss of knowledge and increased costs. Integrated Vehicle Health Management (IVHM) is characterized by prognostics and diagnostics which depend heavily on high quality data to perform data-driven, model-based and hybrid computational analysis of asset health. As a result, managing data and knowledge for Integrated Vehicle Health Management (IVHM) requires a data life cycle model that adopts the OSA-CBM data model and integrate with other approaches. This project will propose such a model and use it to support the development of an IVHM knowledge management system.

A study of e-Research and its relation with research data life cycle: a literature perspective

PurposeThe traditional means of pursuing research by having all the parameters and processes under one roof has given way to collaborative mechanisms of performing the same task. Collaborative work increases the quality of research and it is a big contributing factor to augment the growth of the scientific knowledge. This process leads to training of new and well-informed academicians and scientists. e-Research (Electronic Research) has gained significant amount of traction as technology serves as the backbone for undertaking collaborative research. The purpose of this paper is to provide a synoptic view of existing research surrounding e-Research and suggest a data lifecycle model that can improve the outcome of collaborative research.Design/methodology/approachSystematic literature review methodology has been employed to undertake this study. Using the outcome of the literature review and the analysis of the existing data lifecycle models, an improvised version of the data lifecycle model has been suggested.FindingsThis study has brought a conceptual model for data lifecycle for collaborative research. The literature review in the domain of e-Research has shown that the focus of these papers was on the following stages of data lifecycle model: concept and design, data collection, data processing, sharing and distribution of data and data analysis.Research limitations/implicationsIn this paper, only journal papers have been considered and conference proceedings have not been included for literature review.Originality/valueThis paper suggests a conceptual model for the data lifecycle for collaborative research. This study can be useful for academic and research institutions to design their data lifecycle model.

Towards improving existing online social networks' privacy policies

The privacy policies of online social network (OSN) service providers are criticised as falling short of satisfying their users' privacy expectations letting huge quantities of their personally identifiable information (PII) exposed to unknown audiences. The purpose of this paper is twofold: to assess the conformance of the privacy policies applied in the five topmost leading OSNs to an internationally acknowledged benchmark such as the ISO 29100:2011 standard, and to propose improvements based on the findings of the assessment. Further, as serious mismatches between these privacy policies and the adherence criteria set out in the ISO 29100:2011 standard were identified, a data lifecycle model is proposed as the basis for an improved OSN privacy policy. A restructuring of the existing policies according to the data lifecycle model will allow them to enjoy characteristics that are known to be important in forming users' perceptions.

The Evolution, Approval and Implementation of the U.S. Geological Survey Science Data Lifecycle Model

The Evolution, Approval and Implementation of the U.S. Geological Survey Science Data Lifecycle Model

Revisiting the Data Lifecycle with Big Data Curation

As science becomes more data-intensive and collaborative, researchers increasingly use larger and more complex data to answer research questions. The capacity of storage infrastructure, the increased sophistication and deployment of sensors, the ubiquitous availability of computer clusters, the development of new analysis techniques, and larger collaborations allow researchers to address grand societal challenges in a way that is unprecedented. In parallel, research data repositories have been built to host research data in response to the requirements of sponsors that research data be publicly available. Libraries are re-inventing themselves to respond to a growing demand to manage, store, curate and preserve the data produced in the course of publicly funded research. As librarians and data managers are developing the tools and knowledge they need to meet these new expectations, they inevitably encounter conversations around Big Data. This paper explores definitions of Big Data that have coalesced in the last decade around four commonly mentioned characteristics: volume, variety, velocity, and veracity. We highlight the issues associated with each characteristic, particularly their impact on data management and curation. We use the methodological framework of the data life cycle model, assessing two models developed in the context of Big Data projects and find them lacking. We propose a Big Data life cycle model that includes activities focused on Big Data and more closely integrates curation with the research life cycle. These activities include planning, acquiring, preparing, analyzing, preserving, and discovering, with describing the data and assuring quality being an integral part of each activity. We discuss the relationship between institutional data curation repositories and new long-term data resources associated with high performance computing centers, and reproducibility in computational science. We apply this model by mapping the four characteristics of Big Data outlined above to each of the activities in the model. This mapping produces a set of questions that practitioners should be asking in a Big Data project

Lifecycle models of data-centric systems and domains

The Semantic Web, especially in the light of the current focus on its nature as a Web of Data, is a data-centric system, and arguably the largest such system in existence. Data is being created, published, exported, imported, used, transformed and re-used, by different parties and for different purposes. Together, these actions form a lifecycle of data on the Semantic Web. Understanding this lifecycle will help to better understand the nature of data on the SW, to explain paradigm shifts, to compare the functionality of different platforms, to aid the integration of previously disparate implementation efforts or to position various actors on the SW and relate them to each other. However, while conceptualisations of many aspects of the SW exist, no exhaustive data lifecycle has been proposed.This article proposes a data lifecycle model for the Semantic Web by first looking outward, and performing a survey of lifecycle models in other data-centric domains, such as digital libraries, multimedia, eLearning, knowledge and Web content management or ontology development. For each domain, an extensive list of models is taken from the literature, and then described and analysed in terms of its different phases, actor roles and other characteristics. By contrasting and comparing the existing models, a meta vocabulary of lifecycle models for data-centric systems ---the Abstract Data Lifecycle Model, or ADLM ---is developed. In particular, a common set of lifecycle phases, lifecycle features and lifecycle roles is established, as well as additional actor features and generic features of data and metadata. This vocabulary now provides a tool to describe each individual model, relate them to each other, determine similarities and overlaps and eventually establish a new such model for the Semantic Web.

Managing Organizational Data Resources

Little guidance has been available to organizations interested in addressing the necessary dimensions of data resources management to ensure data quality in increasingly encountered situations when data usage crosses system boundaries. The basic concept of metadata quality as a foundation for data quality engineering is proposed, as well as an extended data life cycle model consisting of eight phases: metadata creation, metadata structuring, metadata refinement, data creation, data utilization, data assessment, data refinement, and data manipulation. This extended model will enable further development of life cycle phase-specific data quality engineering methods. The paper also expands the concept of applicable data quality dimensions, presenting data quality as a function of four distinct components: data value quality; data representation quality; data model quality; and data architecture quality. Each of these, in turn, is described in terms of specific data quality attributes.