Big Data Life Cycle Research Articles

A Process-based Approach to Informational Privacy and the Case of Big Medical Data

Abstract Data protection law has a linear logic, in that it purports to trace the lifecycle of personal data from creation to collection, processing, transfer, and ultimately its demise, and to regulate each step so as to promote the data subject’s control thereof. Big data defies this linear logic, in that it decontextualizes data from its original environment and conducts an algorithmic nonlinear mix, match, and mine analysis. Applying data protection law to the processing of big data does not work well, to say the least. This Article examines the case of big medical data. A survey of emerging research practices indicates that studies either ignore data protection law altogether or assume an ex post position, namely that because they are conducted after the data has already been created in the course of providing medical care, and they use de-identified data, they go under the radar of data protection law. These studies focus on the end-point of the lifecycle of big data: if sufficiently anonymous at publication, the previous steps are overlooked, on the claim that they enjoy immunity. I argue that this answer is too crude. To portray data protection law in its best light, we should view it as a process-based attempt to equip data subjects with some power to control personal data about them, in all phases of data processing. Such control reflects the underlying justification of data protection law as an implementation of human dignity. The process-based approach fits current legal practices and is justified by reflecting dignitarian conceptions of informational privacy.

Optimizing the Electronic Health Records Through Big Data Analytics: A Knowledge-Based View

Many hospitals are suffering from ineffective use of big data analytics with electronic health records (EHRs) to generate high quality insights for their clinical practices. Organizational learning has been a key role in improving the use of big data analytics with EHRs. Drawing on the knowledge-based view and big data lifecycle, we investigate how the three modes of knowledge can achieve meaningful use of big data analytics with EHRs. To test the associations in the proposed research model, we surveyed 580 nurses of a large hospital in China in 2019. Structural equation modelling was used to examine relationships between knowledge mode of EHRs and meaningful use of EHRs. The results reveal that know-what about EHRs utilization, know-how EHRs storage and utilization, and know-why storage and utilization can improve nurses’ meaningful use of big data analytics with EHRs. This study contributes to the existing digital health and big data literature by exploring the proper adaptation of analytical tools to EHRs from the different knowledge mode in order to shape meaningful use of big data analytics with EHRs.

A Privacy Security Risk Analysis Method for Medical Big Data in Urban Computing

As an emerging computing mode, urban computing is mainly used to integrate, analyze and reuse urban resources by using perceptual computing, data mining and intelligent extraction to eliminate the phenomenon of data islands and provide wisdom for people to make decisions. But in the era of big data, the security and privacy leakage of users has become a major obstacle in urban computing. Taking medical big data as an example, this paper analyzed the risk of security and privacy leakage in the collection, transmission, storage, use and sharing of medical big data, and established a medical big data security and privacy leakage risk indicator system with 4 primary indicators and 35 secondary indicators. In addition, the weight of each indicator was calculated by GI method and entropy weight method. Then the fuzzy comprehensive evaluation model was established to verify the risk of medical big data security and privacy disclosure in urban computing. The results show that the risk of medical big data security and privacy leakage in the Grade II Level A hospitals is higher than that in the Grade III Level A hospitals, and in the life cycle of medical big data, the two stages of data storage, data use and sharing may cause more prominent problems of data security and privacy disclosure, while the data collection and data transmission are slightly less. Finally, the comparison of performance further proved the scientificity and effectiveness of this method.

Review of the Complexity of Managing Big Data of the Internet of Things

There is a growing awareness that the complexity of managing Big Data is one of the main challenges in the developing field of the Internet of Things (IoT). Complexity arises from several aspects of the Big Data life cycle, such as gathering data, storing them onto cloud servers, cleaning and integrating the data, a process involving the last advances in ontologies, such as Extensible Markup Language (XML) and Resource Description Framework (RDF), and the application of machine learning methods to carry out classifications, predictions, and visualizations. In this review, the state of the art of all the aforementioned aspects of Big Data in the context of the Internet of Things is exposed. The most novel technologies in machine learning, deep learning, and data mining on Big Data are discussed as well. Finally, we also point the reader to the state‐of‐the‐art literature for further in‐depth studies, and we present the major trends for the future.

Big data security challenges and strategies

Big data, a recently popular term that refers to a massive collection of very large and complex data sets, is facing serious security and privacy challenges. Due to the typical characteristics of big data, namely velocity, volume and variety associated with large-scale cloud infrastructures and the Internet of Things (IoT), traditional security and privacy mechanisms are inadequate and unable to cope with the rapid data explosion in such a complex distributed computing environment. With big data analytics being widely used by businesses and government for decision making, security risk mitigation plays an important role in big data infrastructures worldwide. Traditional security mechanisms have failed to cope with the scalability, interoperability and adaptability of contemporary technologies that are required for big data. This paper takes an exploratory initial step using first principles to address this gap in literature. Firstly, we establish the current trends in big data comprehensively by identifying eleven Vs as important dimensions of big data, which form the contributing factors having an impact on the impending security problem. Next, we map the eleven Vs to the three phases of big data life cycle in order to unearth the major security and privacy challenges of big data. Finally, the paper provides four practical strategies adapted from contemporary technologies such as data provenance, encryption and access control, data mining and blockchain, identifying their associated real implementation examples. This work would pave way for future research investigations in this important big data security arena.

An Improved Security Threat Model for Big Data Life Cycle

Big data is a huge amount of data created by individuals related to their medical, internet activity, social networking sites, energy usage communication patterns etc. From these sources, data is being collected and processed by various survey organizations, national statistical agencies, medical centres, and other companies etc. There are many security challenges which occur during data transactions, such as un-authentication, phishing, Vishing, data mining based attacks, etc. From a security point of view the biggest challenge for big data is the protection of user’s privacy. Yazan et.al, have presented big data lifecycle threat model. This paper does a critical review of the work. An Improved Security Threat Model for Big Data Life Cycle has been proposed as a main contribution of the paper. A new phase i.e. data creation phase has been added to the life cycle and it is claimed that the phase is very important one with respect to security and privacy. To justify the claim theoretical and statistical evidences have been provided.

Security model for Big Healthcare Data Lifecycle

Big data is a concept that aimed at collecting, storing, processing and transforming large amount of data into value using new combination of strategies and technologies. Big data is characterized by data that have a large volume, massive velocity, numerous variety, useful value, and veracity. Big Data Analytics offers tremendous insights to different organizations especially in healthcare. Currently, Big healthcare data has the highest potential for improving patient outcomes, gaining valuable insights, predicting outbreaks of epidemics, avoiding preventable diseases and effectively minimizing the cost of healthcare delivery.However, the dynamic nature of health data presents various conceptual, technical, legal and ethical challenges associated with the data processing and analysis activities. The big data security and privacy concepts are some of the most pertinent issues and have become increasingly significant associated with big healthcare data in the modern world.In this paper, we give an overview of big data characteristics and challenges in healthcare and present big healthcare data lifecycle integrated with security threats and attacks to provide encompass policies and mechanisms that aim at solving the various security challenges in each step of big data lifecycle. The focus is also placed on the description of the recently proposed techniques related to authentication, encryption, anonymization, access control, and privacy. We finally propose an approach to secure threat model for big healthcare data lifecycle as a main contribution of this paper.

A Process-Based Approach to Informational Privacy and the Case of Big Medical Data

Data protection law has a linear logic, in that it purports to trace the lifecycle of personal data from creation to collection, processing, transfer, and ultimately its demise, and to regulate each step so as to promote the data subject’s control thereof. Big data defies this linear logic, in that it decontextualizes data from its original environment and conducts an algorithmic nonlinear mix, match, and mine analysis. Applying data protection law to the processing of big data does not work well, to say the least. This Article examines the case of big medical data. A survey of emerging research practices indicates that studies either ignore data protection law altogether or assume an ex post position, namely that because they are conducted after the data has already been created in the course of providing medical care, and they use de-identified data, they go under the radar of data protection law. These studies focus on the end-point of the lifecycle of big data: if sufficiently anonymous at publication, the previous steps are overlooked, on the claim that they enjoy immunity. I argue that this answer is too crude. To portray data protection law in its best light, we should view it as a process-based attempt to equip data subjects with some power to control personal data about them, in all phases of data processing. Such control reflects the underlying justification of data protection law as an implementation of human dignity. The process-based approach fits current legal practices and is justified by reflecting dignitarian conceptions of informational privacy.

Big data life cycle: security issues, challenges, threat and security model

Today the technologies of big data are completely bringing a vast change in the entire conventional technology discipline and it’s successfully applying the required latest security design methods to state the upcoming security provocations. Big Data Architecture is a “Data” centric architecture in which security can be included in all the levels. Data is collected from different sources and Data generation is done, the next step it undergoes is Data Processing, the next step is Data storage and the last step is Data analysis. At all the levels Data plays a vital role. It aims to give basic investigation regarding most of the security risks and Big Data provocation and bought out new provocations, complication to the conventional protective domains and also for conventional trends. This deals with the definition of big data and the characteristics that effect most of the data preservation, such as 3V’s, dynamicity. It analyses the original changes and new challenges to Data security. It also provides pitch for real time practice of security infrastructure peripherals which allows extend trusted non-local virtualized processing environment. This research focus on all levels of Big Data where and when the security services and techniques can be included to acquire accurate results.

Big Data information governance by accountants

PurposeBig Data has become increasingly important to multiple facets of the accounting profession, but accountants have little understanding of the steps necessary to convert Big Data into useful information. This limited understanding creates a gap between what accountants can do and what accountants should do to assist in Big Data information governance. The study aims to bridge this gap in two ways.Design/methodology/approachFirst, the study introduces a model of the Big Data life cycle to explain the process of converting Big Data into information. Knowledge of this life cycle is a first step toward enabling accountants to engage in Big Data information governance. Second, it highlights informational and control risks inherent to this life cycle, and identifies information governance activities and agents that can minimize these risks.FindingsBecause accountants have a strong ability to identify the informational and control needs of internal and external decision-makers, they should play a significant role in Big Data information governance.Originality/valueThis model of the Big Data life cycle and information governance provides a first attempt to formalize knowledge that accountants need in a new field of the accounting profession.

A survey on scholarly data: From big data perspective

Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform, with specific focus on directions for future research and maps them to the different phases of the big data lifecycle.

Big Data in Health: New Challenges and New Solutions in Data Management (A Lifecycle Review)

Background: Collecting and analyzing large volumes of disparate datasets are of major challenges arisen from the emergence of Big Data in the field of health. But the technology of Big Data is also associated with promising opportunities which can provide improvement of performance and facilitation of innovation in organizations. Objective: Since determining the lifetime is a practical approach regarding recognition of a phenomenon and its management, this paper aimed at identifying the challenges and opportunities of managing Big Data in the area of health at different stages of the lifecycle of Big Data. Methodology: This article is a structured review. After an initial review, 6 phases was detected in the lifecycle of Big Data, then the processes of traditional data were briefly reviewed in each phase, and the challenges associated with the emergence of Big Data and the solutions for their optimal management were discussed. Results: This study offers a broad over view of the advent of Big Data in the health sector, and provides a clear and accurate picture of the processes before and after its emergence through a comparative-based survey in each phase. The article points out innovations and modern methods of collection, pre-processing, and analysis of Big Data as well as the process of data extracting. It also describes cloud computing applications in the storage and release of Big Data. Conclusions: Our findings indicate that management of Big Data in health, based on its lifecycle, is resourceful for managers and policy-makers, in order to benefit from the technological features of Big Data with a managerial approach, to evaluate challenges, to apply innovative solutions at each phase of Big Data maturation, and to advance towards a new level of innovation, competitiveness, and productivity.

Big data privacy: a technological perspective and review

Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data. This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.

Managing Big, Linked, and Open Earth-Observation Data: Using the TELEIOS\/LEO software stack

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service.

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

Protection of Big Data Privacy

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data.

Foreword to the Special Issue on Big Data in Remote Sensing

The papers in this special issue focus on the deployment of Big Data applications for use in remote sensing. This issue is intended to introduce the latest techniques to manage, exploit, process, and analyze big data in remote sensing applications. It contains 11 papers that exhibit the latest advances in Big Data in Remote Sensing. To understand big data, usually three facets should be taken into account from owning data, data methods, and data applications, which contribute together to a single big data life cycle, including identification of applications, data collections, data processing, data analysis, data visualization, data evaluation, and so on.

E!DAL--a framework to store, share and publish research data.

BackgroundThe life-science community faces a major challenge in handling “big data”, highlighting the need for high quality infrastructures capable of sharing and publishing research data. Data preservation, analysis, and publication are the three pillars in the “big data life cycle”. The infrastructures currently available for managing and publishing data are often designed to meet domain-specific or project-specific requirements, resulting in the repeated development of proprietary solutions and lower quality data publication and preservation overall.Resultse!DAL is a lightweight software framework for publishing and sharing research data. Its main features are version tracking, metadata management, information retrieval, registration of persistent identifiers (DOI), an embedded HTTP(S) server for public data access, access as a network file system, and a scalable storage backend. e!DAL is available as an API for local non-shared storage and as a remote API featuring distributed applications. It can be deployed “out-of-the-box” as an on-site repository.Conclusionse!DAL was developed based on experiences coming from decades of research data management at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). Initially developed as a data publication and documentation infrastructure for the IPK’s role as a data center in the DataCite consortium, e!DAL has grown towards being a general data archiving and publication infrastructure. The e!DAL software has been deployed into the Maven Central Repository. Documentation and Software are also available at: http://edal.ipk-gatersleben.de.

Is “Big Data” creepy?

We now live in a world of Big Data, massive repositories of structured, unstructured or semi-structured data. This is seen as a valuable resource for organisations, given the potential to analyse and exploit that data to turn it into useful information. However, the cost and risk of continuing to hold that data can also make it a burden for many organisations.There are also a number of fetters to the exploitation of Big Data. The most significant is data privacy, which cuts across the whole of the Big Data lifecycle: collection, combination, analysis and use. This article considers the current framework for the regulation of Big Data, the Article 29 Working Party's opinion on Big Data and the proposed new General Data Protection Regulation. In particular, the article considers if current and proposed regulation strikes the right balance between the risks and benefits of Big Data.