Data Creator Research Articles

Cartography & geovisual analytics in personal contexts: designing for the data creator

ABSTRACT With the rise of the Quantified-Self movement and widespread adoption of self tracking technologies, 'personal' has become a new dimension in the map and geovisualization design process. Despite a rich cartographic literature on how to map movement data, and an equally extensive geovisual analytics literature on how to make sense of complex visual representations of movement data at scale, opportunities exist to adapt and create new map and interaction design paradigms to meet the disparate needs of an ever-growing number of data creators. This paper presents a cross-disciplinary review of literature that reports on the design and evaluation of personal geovisualizations. Specifically, this review paper: contextualizes individual movement data types and provides a short synthesis of cartographic and geovisual analytics approaches that have been employed to explore such data in non-personal contexts; surveys a growing yet disconnected body of foundational work beyond the GIScience discipline; presents a detailed discussion on a collection of works that exemplifies the potential of applying a geographic perspective to personal visualization; and outlines key challenges and opportunities for advancing the state-of-the-art & science in the design of maps and geovisual analytics applications constructed from personal data and that benefit the data creator.

A Blockchain-Based Dynamic Consent Architecture to Support Clinical Genomic Data Sharing (ConsentChain): Proof-of-Concept Study.

BackgroundIn clinical genomics, sharing of rare genetic disease information between genetic databases and laboratories is essential to determine the pathogenic significance of variants to enable the diagnosis of rare genetic diseases. Significant concerns regarding data governance and security have reduced this sharing in practice. Blockchain could provide a secure method for sharing genomic data between involved parties and thus help overcome some of these issues.ObjectiveThis study aims to contribute to the growing knowledge of the potential role of blockchain technology in supporting the sharing of clinical genomic data by describing blockchain-based dynamic consent architecture to support clinical genomic data sharing and provide a proof-of-concept implementation, called ConsentChain, for the architecture to explore its performance.MethodsThe ConsentChain requirements were captured from a patient forum to identify security and consent concerns. The ConsentChain was developed on the Ethereum platform, in which smart contracts were used to model the actions of patients, who may provide or withdraw consent to share their data; the data creator, who collects and stores patient data; and the data requester, who needs to query and access the patient data. A detailed analysis was undertaken of the ConsentChain performance as a function of the number of transactions processed by the system.ResultsWe describe ConsentChain, a blockchain-based system that provides a web portal interface to support clinical genomic sharing. ConsentChain allows patients to grant or withdraw data requester access and allows data requesters to query and submit access to data stored in a secure off-chain database. We also developed an ontology model to represent patient consent elements into machine-readable codes to automate the consent and data access processes.ConclusionsBlockchains and smart contracts can provide an efficient and scalable mechanism to support dynamic consent functionality and address some of the barriers that inhibit genomic data sharing. However, they are not a complete answer, and a number of issues still need to be addressed before such systems can be deployed in practice, particularly in relation to verifying user credentials.

The role of personal data value, culture and self-construal in online privacy behaviour.

Personal data is ubiquitous in the digital world, can be highly valuable in aggregate, and can lead to unintended intrusions for the data creator. However, individuals’ expressions of concern about exposure of their personal information are generally not matched by their behavioural caution. One reason for this mismatch could be the varied and intangible value of personal data. We present three studies investigating the potential association between personal data value and privacy behaviour, assessing both individual and cross-cultural differences in personal data valuation, comparing collectivist and individualistic cultures. Study 1a, using a representative UK sample, found no relationship between personal data value and privacy behaviour. However, Study 1b found Indian (collectivist) participants’ privacy behaviour was sensitive to personal data value, unlike US (individualist) participants. Study 2 showed that in a UK sample, privacy behaviour was sensitive to personal data value but only for individuals who think of themselves as more similar to others (i.e., self-construe as similar, rather than different). We suggest those who prioritise group memberships are more sensitive to unintentional disclosure harm and therefore behave in accordance with personal data valuations—which informs the privacy concern-behaviour relationship. Our findings can suggest approaches to encourage privacy behaviours.

A funder-imposed data publication requirement seldom inspired data sharing.

Growth of the open science movement has drawn significant attention to data sharing and availability across the scientific community. In this study, we tested the ability to recover data collected under a particular funder-imposed requirement of public availability. We assessed overall data recovery success, tested whether characteristics of the data or data creator were indicators of recovery success, and identified hurdles to data recovery. Overall the majority of data were not recovered (26% recovery of 315 data projects), a similar result to journal-driven efforts to recover data. Field of research was the most important indicator of recovery success, but neither home agency sector nor age of data were determinants of recovery. While we did not find a relationship between recovery of data and age of data, age did predict whether we could find contact information for the grantee. The main hurdles to data recovery included those associated with communication with the researcher; loss of contact with the data creator accounted for half (50%) of unrecoverable datasets, and unavailability of contact information accounted for 35% of unrecoverable datasets. Overall, our results suggest that funding agencies and journals face similar challenges to enforcement of data requirements. We advocate that funding agencies could improve the availability of the data they fund by dedicating more resources to enforcing compliance with data requirements, providing data-sharing tools and technical support to awardees, and administering stricter consequences for those who ignore data sharing preconditions.

Systematic survey of big data and data mining in internet of things

In recent years, the Internet of Things (IoT) has emerged as a new opportunity. Thus, all devices such as smartphones, transportation facilities, public services, and home appliances are used as data creator devices. All the electronic devices around us help our daily life. Devices such as wrist watches, emergency alarms, and garage doors and home appliances such as refrigerators, microwaves, air conditioning, and water heaters are connected to an IoT network and controlled remotely. Methods such as big data and data mining can be used to improve the efficiency of IoT and storage challenges of a large data volume and the transmission, analysis, and processing of the data volume on the IoT. The aim of this study is to investigate the research done on IoT using big data as well as data mining methods to identify subjects that must be emphasized more in current and future research paths. This article tries to achieve the goal by following the conference and journal articles published on IoT-big data and also IoT-data mining areas between 2010 and August 2017. In order to examine these articles, the combination of Systematic Mapping and literature review was used to create an intended review article. In this research, 44 articles were studied. These articles are divided into three categories: Architecture & Platform, framework, and application. In this research, a summary of the methods used in the area of IoT-big data and IoT-data mining is presented in three categories to provide a starting point for researchers in the future.

Responsible practices for data sharing.

Research transparency, reproducibility, and data sharing uphold core principles of science at a time when the integrity of scientific research is being questioned. This article discusses how research data in psychology can be made accessible for reproducibility and reanalysis by describing practical ways to overcome barriers to data sharing. We examine key issues surrounding the sharing of data such as who owns research data, how to protect the confidentiality of the research participant, how to give appropriate credit to the data creator, how to deal with metadata and codebooks, how to address provenance, and other specifics such as versioning and file formats. The protection of research subjects is a fundamental obligation, and we explain frameworks and procedures designed to protect against the harms that may result from disclosure of confidential information. We also advocate greater recognition for data creators and the authors of program code used in the management and analysis of data. We argue that research data and program code are important scientific contributions that should be cited in the same way as publications. (PsycINFO Database Record

Evaluating Utilization of Cloud Computing for IoT Big Data Systems

The Internet of Things (IoT) has arisen as a novel prospect in the recent years. This has presented the notion that all devices such as smartphones, public services, conveyance facilities, and home appliances can be viewed as data creator devices. The Cloud Computing framework for IoT highlighted in this work has potential to act as a data storage system supporting IoT devices utilized to improve data processing efficiency and offer a huge competitive advantage to the IoT applications. The purpose of this study is to examine the work done on IoT using big data as well as data mining methods to recognize focuses that must be highlighted further. Some focus is made on data mining technologies integrated with IoT technologies for decision making support and system optimization as data mining involves discovering novel, interesting, and potentially useful patterns from data and applying algorithms to the extraction of hidden information.

Keystroke and mouse movement profiling for data loss prevention

Data leakage is a serious problem for many large organizations. In order to provide the user with information about confidential data, many prevalent data leakage prevention (DLP) solutions rely on scanning the content of the relevant files. This approach requires the capability to parse various file formats. However, risks of data breach persist for unsupported file formats. To address this issue, we propose in this paper an active behavior-based DLP model that hooks the keyboard and mouse application programming interfaces (APIs) to track and profile user behavior. This model has two major advantages: (1) it can help discover sensitive data without parsing file formats, and (2) a data creator can be identified according to his/her keystroke and mouse movement behavior. Since this model is based on profiling user behavior, it eliminates the risk of data leakage from unsupported file formats and can identify the creator of a file. The experiments showcase the effectiveness of the proposed model with data creator identification method yields an accuracy rate of 92.64%, which is promising considering that the features of keystroke and mouse movement behavior are dealing together.

Modeling Resource Description Tasks in RDA

This study aims to model the resource description tasks that are performed by catalogers. First, a model of authority data creator tasks is derived from Functional Requirements for Authority Data (FRAD), since FRAD partially includes such task modeling. Second, modeling of resource description tasks in line with Resource Description and Access (RDA) is conducted to cover the whole resource description tasks including those on descriptive data as well as those on authority data, and also to reflect RDA instructions as much as possible. Third, the modeling of resource description tasks is compared with that of user tasks in order to understand their differences.

A DCT-Domain System for Digital Watermarking

Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia data in a networked environment. It makes possible to tightly associated to a digital document a code allowing the identification of the data creator, owner, authorized consumer, and so on. In this paper a new DCT-domain system for digital watermarking algorithm for digital images is presented: the method, which operates in the frequency domain, embeds a pseudo-random sequence of scrambled image in a selected set of DCT coefficients. After embedding, the watermark is adapted to the image by exploiting the masking characteristics of the human visual system, thus ensuring the watermark invisibility. By exploiting the statistical properties of the embedded sequence, the mark can be reliably extracted without resorting to the original uncorrupted image. Experimental results demonstrate that the watermark is robust to several signal processing techniques, including JPEG compression, cut, fuzzy, addition of noise, and sharpen.

The Library as Research Partner and Data Creator: The Don Valley Historical Mapping Project

The emergence of new academic disciplines that embrace geospatial analysis allow GIS librarians to be involved in collaboration and outreach work that promotes the academic library and the subject and technical expertise of GIS librarians. The Don River Valley Historical Mapping Project (DVHMO) is an example of such a scholarly collaborative project. At the University of Toronto, the map and GIS librarian, Marcel Fortin, collaborated with PhD history student Jennifer Bonnell (currently a history postdoctoral fellow at the University of Guelph) in a historical mapping project that would promote and breathe new life into library resources. By drawing on paper-based, historical geographic resources, such as fire insurance plans, topographic maps, county atlases, and planning and conservation reports, they built several geospatial historical data sets of shorelines, industries, and landownership data. The digital data sets are now available free online, and intended to be used by researchers and curious citizens alike. Through projects like these, GIS librarians develop services that draw on their longstanding expertise of maintaining collections and promoting free and open access to information. At the same time, they demonstrate how librarians can become the architects of new information. They thereby assert the skills of academic librarians as research partners and data creators.

Product data quality validation system for product development processes in high-tech industry

Among various product data, 3D CAD data plays a key role in current product design and manufacturing processes including industrial design, detail design, CAE, inspection, mould-making, production, and so on. If 3D CAD data has geometrical or topological errors by user mistakes or modelling software bugs and those errors are not cleaned by the data creator in an early stage, the data will be transferred to the downstream operators and they have to fix the errors before starting their own work. Because 3D data is quite complex, it is very difficult to recognise the data errors manually in a modelling system before a big problem is encountered which blocks the next operation. In this case, it generally causes time delay and high cost for data correction and the effect will be bigger when the process is close to the back-end. In this research, we develop a fully automated product data quality validation and management system to support the product development processes of high-tech products like televisions, camcorders, mobile telephones, home appliances, etc. The system automatically validates the 3D data in real time and gives 3D error reports to the creator to correct modelling failures in their steps. Also, project managers can check or control the data delivery based on the data quality for each step.

Copyright protection of digital images by embedded unperceivable marks

Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia documents in a networked environment. It makes it possible to tightly associate a code to an image, allowing the identification of the data creator, owner and so on. In this paper, after a review of some important watermarking techniques, a new watermarking algorithm for digital images is presented: the method, which enhances a previous technique proposed by Cox et al. [1] [I.J. Cox, J. Kilian, T. Leighton, T. Shamoon, Secure spread spectrum watermarking for multimedia, NEC Research Institute Technical Report 95-10], operates in the frequency domain, by embedding in a selected set of DCT coefficients a sequence of randomly generated real numbers. By exploiting their statistical characteristics, it is possible to reliably extract the embedded sequences without resorting to the original unmarked image. Experimental results demonstrate that the watermark is robust to several image transformations.

A DCT-domain system for robust image watermarking

Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia data in a networked environment. It makes possible to tightly associate to a digital document a code allowing the identification of the data creator, owner, authorized consumer, and so on. In this paper a new watermarking algorithm for digital images is presented: the method, which operates in the frequency domain, embeds a pseudo-random sequence of real numbers in a selected set of DCT coefficients. After embedding, the watermark is adapted to the image by exploiting the masking characteristics of the human visual system, thus ensuring the watermark invisibility. By exploiting the statistical properties of the embedded sequence, the mark can be reliably extracted without resorting to the original uncorrupted image. Experimental results demonstrate that the watermark is robust to several signal processing techniques, including JPEG compression, low pass and median filtering, histogram equalization and stretching, dithering, addition of Gaussian noise, resizing, and multiple watermarking.