Value Of Data Research Articles

A comparative study on machine learning approaches for rock mass classification using drilling data

Current rock engineering design in drill and blast tunnelling primarily relies on engineers' observational assessments. Measure While Drilling (MWD) data, a high-resolution sensor dataset collected during tunnel excavation, is underutilised, mainly serving for geological visualisation. This study aims to automate the translation of MWD data into actionable metrics for rock engineering. It seeks to link data to specific engineering actions, thus providing critical decision support for geological challenges ahead of the tunnel face. Leveraging a large and geologically diverse dataset of ∼500,000 drillholes from 15 tunnels, the research introduces models for accurate rock mass quality classification in a real-world tunnelling context. Both conventional machine learning and image-based deep learning are explored to classify MWD data into Q-classes and Q-values—examples of metrics describing the stability of the rock mass—using both tabular- and image data. The results indicate that the K-nearest neighbours algorithm in an ensemble with tree-based models using tabular data effectively classifies rock mass quality. It achieves a cross-validated balanced accuracy of 0.86 in classifying rock mass into the Q-classes A, B, C, D, E1, E2, and 0.95 for a binary classification with E versus the rest. Classification using a CNN with MWD-images for each blasting round resulted in a balanced accuracy of 0.82 for binary classification. Regressing the Q-value from tabular MWD-data achieved cross-validated R2 and MSE scores of 0.80 and 0.18 for a similar ensemble model as in classification. High performance in regression and classification boosts confidence in automated rock mass assessment. Applying advanced modelling on a unique dataset demonstrates MWD data's value in improving rock mass classification accuracy and advancing data-driven rock engineering design, reducing manual intervention.

Why data about people are so hard to govern

AbstractHow data on individuals are gathered, analyzed, and stored remains largely ungoverned at both domestic and global levels. We address the unique governance problem posed by digital data to provide a framework for understanding why data governance remains elusive. Data are easily transferable and replicable, making them a useful tool. But this characteristic creates massive governance problems for all of us who want to have some agency and choice over how (or if) our data are collected and used. Moreover, data are co‐created: individuals are the object from which data are culled by an interested party. Yet, any data point has a marginal value of close to zero and thus individuals have little bargaining power when it comes to negotiating with data collectors. Relatedly, data follow the rule of winner take all—the parties that have the most can leverage that data for greater accuracy and utility, leading to natural oligopolies. Finally, data's value lies in combination with proprietary algorithms that analyze and predict the patterns. Given these characteristics, private governance solutions are ineffective. Public solutions will also likely be insufficient. The imbalance in market power between platforms that collect data and individuals will be reproduced in the political sphere. We conclude that some form of collective data governance is required. We examine the challenges to the data governance by looking a public effort, the EU's General Data Protection Regulation, a private effort, Apple's “privacy nutrition labels” in their App Store, and a collective effort, the First Nations Information Governance Centre in Canada.

Big Data Management and Analytics as a Cloud Service

Traditional database management systems are inadequate for handling uncertain data due to design limitations. Various methods address database uncertainty, often oversimplifying and restricting representable uncertainties. Rapid data growth from technologies like IoT, multimedia, and social media has led to massive diverse data, known as big data, categorized into structured, semi-structured, and unstructured types. Coping with big data's challenges is encapsulated in the "Vs Model" with volume, velocity, and variety dimensions. It aims to gather variable information for causal understanding and informed decisions. Big data offers competitive advantages: smart decisions (69%), operational control (54%), customer insights (52%), and cost cuts (47%). This research studies big data management and cloud computing, where data and apps are accessed via the internet, saving resources globally. Cloud's simplicity and power facilitate tasks and storage. Risks include bankruptcy or breaches, yet cloud remains valuable for preserving big data. Vital factors in big data's value creation involve converting IT costs to assets and connecting big data outcomes to performance. This abstract summarizes exploration of traditional limitations, big data's essence, cloud's potential, and value creation nuances.

Genomic Data Heterogeneity across Molecular Diagnostic Laboratories: A Real-World Connect Myeloid Disease Registry Perspective on Variabilities in Genomic Assay Methodology and Reporting

Genomic data variability from laboratory reports can impact clinical decisions and population-level analyses; however, the extent of this variability and the impact on the data's value are not well characterized. This pilot study used anonymized genetic and genomic test reports from the Connect Myeloid Disease Registry (NCT01688011), a multicenter, prospective, observational cohort study of patients with newly diagnosed myelodysplastic syndromes, acute myeloid leukemia, or idiopathic cytopenia of undetermined significance, to analyze laboratory test variabilities and limitations. Results for 56 randomly selected patients enrolled in the Registry were independently extracted and evaluated (data cutoff, January 2020). Ninety-five reports describing 113 assay results from these 56 patients were analyzed for discrepancies. Almost all assay results [101 (89%)] identified the sequencing technology applied, and 94 (83%) described the test limitations; 95 (84%) described the limits of detection, but none described the limit of blank for detecting false positives. RNA transcript identifiers were not provided for 20 (43%) variants analyzed by next-generation sequencing and reported by the same laboratory. Of 42 variants with variant allele frequencies ≥30%, 16 (38%) of the variants did not have report text indicating that the variants might be germline. Variabilities and lack of standardization present challenges for incorporating this information into clinical care and render data collation ineffective and unreliable for large-scale use in centralized databases for therapeutic discovery.

The value of data for environmental sustainability as perceived by the customers of a tissue‐paper supplier

AbstractTo manage their increasingly ambitious environmental agendas successfully, companies need better and more versatile data, yet the elements and mechanisms through which businesses and the environment benefit from data remain poorly understood. A single‐case‐study setting was designed for examining the perceived value of data for environmental sustainability as seen by the customers of a tissue‐paper supplier. The findings, from material collected during interviews with customers in 2022, showcase the multifaceted nature of data's value for business operations and associated ability to support decisions and activities that encourage environment‐informed choices and environmental improvements. Also, fully exploiting data's potential for environmental sustainability requires greater volume and detail of product‐specific data, transparency of value chains and environmental impacts, and data's better management and sharing. The emerging insight related to the value of data strengthens understanding of how companies could, with data, support their customers' environmental performance.

Abstract 719: Advocacy organizations as critical players in the research ecosystem: A case study in the value and use of real world evidence

Abstract Background: Patient-reported outcomes (PROs) are defined as critical data points in the overall assessment of healthcare quality. Coupled with advances in treatment and the evolution of patient-provider relationships, there is an increased emphasis on evaluating patient satisfaction and wellness, often most accurately captured using PROs to describe aspects of a patient’s health status, as directly reported by patients. The importance of direct patient reporting has been highlighted by both FDA and the European Medicines Agency (EMA). The Colorectal Cancer Alliance’s Never Too Young (N2Y) Survey provides accurate, accessible, and reusable data to show trends in young-onset colorectal cancer (YO-CRC) incidence rates, patient care, quality of life outcomes and research. Methods: To support the YO-CRC community and understand unmet needs, the Colorectal Cancer Alliance (Alliance) launched an annual N2Y survey in 2015. Survivors and caregivers played an indispensable role in the development of this survey. In 2022, the Alliance engaged in a comprehensive literature review to analyze how N2Y survey data and publications have been cited and utilized in research, showcasing survey data's value and global impact. Results: Data from our surveys produced 22 abstracts cited by more than 150 publications. The data provides healthcare providers and researchers with valuable information and metrics that did not previously exist from a YO-CRC patient and caregiver perspective. These reports are particularly important to patients with CRC due to the adverse effects of colorectal cancer treatments. Many citations reference the reports to emphasize the need to focus on YO-CRC for a national screening program and the importance of provider vigilance towards symptoms regardless of age., Additionally, our reports were used to elucidate the need for YO centers that comprehensively address the unique needs of patients with specific services selected based on PRO data from the Colorectal Cancer Alliance. Conclusion: Innovative advocacy efforts like PRO collection from organizations like the Colorectal Cancer Alliance using validated and systematic methodology can facilitate efficient, life-saving decision-making globally. With careful consideration and acknowledgment of PROs, positive care experiences can develop to better clinical outcomes overall. PROs indicate the importance of teams who actively communicate well with each other and with the patient to achieve positive outcomes through personalized, precision medicine and better position future research with implications for education and practice. As we advance, the patient and patient advocacy community should work collaboratively with researchers, industry, clinicians and investigators. Citation Format: Kimberly Lynn Newcomer, Andrea Goodman. Advocacy organizations as critical players in the research ecosystem: A case study in the value and use of real world evidence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 719.

Handling security issues by using homomorphic encryption in multi-cloud environment

Taking advantage of the high performance and powerful data processing capabilities of cloud computing technology, externalizing data to the cloud platform is considered as an inevitable trend in the digital field today. However, ensuring the security and privacy of data remains a major challenge. To overcome this drawback, a multi-cloud platform is proposed to improve privacy and high availability of data. A multi-cloud platform that integrates public, private, and managed clouds with a single user interface. Cloud-hosted data is distributed among different data centers in a multi-cloud environment based on cloud reliability and data sensitivity. In terms of security, current encryption algorithms are considered to be very efficient, but it requires a lot of resources to handle this, which is expensive and time consuming. In addition, they also make the data impossible to process without first decoding. To be specific, traditional public key encryption requires data to be decrypted before it can be analyzed or manipulated. In contrast, homomorphic encryption is an encryption method that allows data to be encrypted while it is being processed and manipulated. It allows user or a third party, which can be cloud provider, to apply functions on encrypted data without revealing the data's values. In this paper, we explore existing multi-cloud-based security solutions using homomorphic encryption to identify open issues and opportunities for further enhancement.

Four investment areas for ethical AI: Transdisciplinary opportunities to close the publication-to-practice gap

Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this Ethical Artificial Intelligence publication-to-practice gap, maximizing the positive impact Artificial Intelligence and Big Data have on society. For Ethical Artificial Intelligence to become a reality, I argue that these areas need to be addressed holistically in a way that acknowledges their interdependencies. Progress will require iteration, compromise, and transdisciplinary collaboration, but the result of our investments will be the realization of Artificial Intelligence's and Big Data's tremendous potential for social good, in practice rather than in just our hopes and aspirations.

Data-Trading Standard's Potential to Uncover the Value of Data Reserve

People say data is the new dominant raw material replacing oil, to emphasize its economic significance. Many companies have unused data, and collectively, there is a massive amount of unutilized or residual data, which could enable new business models. On the other hand, not all businesses have a complete set of data to implement their ultimate goals. Therefore, the data's values could increase if freely shared or traded, especially because the data can be used multiple times for various purposes, often without decreasing the data's value. However, data trading is not widely accessible at this point, and we are underutilizing the existing data reserve.

Artificial Intelligence and the Weaponization of Genetic Data

Advancements in genetics have the ability to rapidly improve medicine, with a number of factors converging to push the integration of genomics into mainstream healthcare. As technologies that use genetic data begin to expand, so does exposure to risks. The cost and harms from the misuse of genetic data can be latent. The immutability, uniqueness, and information-rich nature of DNA renders it a high-value target. Knowledge and control asymmetries exist between individuals, industry, and governments. Genetic data's value as an asset is mirrored in the potential degree of harm if abused. This article highlights the critical threats and vulnerabilities associated with genetic data risk, from identification and profiling, to exposure of health and medical conditions and susceptibility, as well as to the broader social welfare risk associated with bio-warfare. All of these threats are rapidly actualizing from the advancement of artificial intelligence (AI). Here we outline the ways in which data science is improving genetics and how that can ultimately lead to its weaponization.

Real-time stream processing for Big Data

Abstract With the rise of the web 2.0 and the Internet of things, it has become feasible to track all kinds of information over time, in particular fine-grained user activities and sensor data on their environment and even their biometrics. However, while efficiency remains mandatory for any application trying to cope with huge amounts of data, only part of the potential of today's Big Data repositories can be exploited using traditional batch-oriented approaches as the value of data often decays quickly and high latency becomes unacceptable in some applications. In the last couple of years, several distributed data processing systems have emerged that deviate from the batch-oriented approach and tackle data items as they arrive, thus acknowledging the growing importance of timeliness and velocity in Big Data analytics. In this article, we give an overview over the state of the art of stream processors for low-latency Big Data analytics and conduct a qualitative comparison of the most popular contenders, namely Storm and its abstraction layer Trident, Samza and Spark Streaming. We describe their respective underlying rationales, the guarantees they provide and discuss the trade-offs that come with selecting one of them for a particular task.

Certification of Coroners cases by pathologists would improve the completeness of death registration in Jamaica

ObjectivesDescribe the completeness and quality of Jamaica's 2008 vital registration mortality database. Study Design and SettingMultiple sources (hospitals, police, forensic pathologists, Coroners courts) were used to validate deaths registered as occurring in 2008. A 10% random sample was examined to evaluate the quality of certification and coding. Jamaica, a middle-income country of 2.7 million, began vital registration in 1877; however, the mortality database was considered of limited use, and the study was commissioned to understand the problem. ResultsOf 19,286 deaths identified, 76% were registered by 31.12.2009 for inclusion among 2008 demographic returns. Registration was highest among deaths not requiring autopsy (94%) and lowest among Coroners cases (22%) with only 41% of deaths among 15–44 year-olds registered. The leading causes of death were cerebrovascular disease, diabetes mellitus, and homicide. Fifteen percent were coded to ill-defined causes of death. Recoding the sample increased mortality from prematurity, pregnancy complications, homicide, selected cardiovascular disorders, and human immunodeficiency virus/AIDS. ConclusionDelays registering Coroners cases, certification and coding errors, introduced biases into the disease profile limiting the data's value in informing clinical care. Issuance of medical certificates by pathologists who investigate Coroners cases and training physicians and coders would eliminate most underreporting and improve data quality.

The Research of Algorithm to Obtain High Precision of Angle Sensor

This paper introduces how to use the testing circuit board of measuring accuracy ofthe sensor, testing circuit board is made by the SCA100T - D01 biaxialanglesensor, ATmega8 MCU, ADS8344AD converter, MAX232 and other related components.Through the three axis turntable to rotate a precise angle, which makes thesensor in the testing circuit board has the output value, the output value indata processing, to find out the best coefficient, so as to achieve therequirements of high precision.

Between Objectivity and Openness—The Mediality of Data for Journalism

A number of recent high profile news events have emphasised the importance of data as a journalistic resource. But with no definitive definition for what constitutes data in journalism, it is difficult to determine what the implications of collecting, analysing, and disseminating data are for journalism, particularly in terms of objectivity in journalism. Drawing selectively from theories of mediation and research in journalism studies we critically examine how data is incorporated into journalistic practice. In the first half of the paper, we argue that data's value for journalism is constructed through mediatic dimensions that unevenly evoke different socio-technical contexts including scientific research and computing. We develop three key dimensions related to data's mediality within journalism: the problem of scale, transparency work, and the provision of access to data as 'openness'. Having developed this first approach, we turn to a journalism studies perspective of journalism's longstanding "regime of objectivity", a regime that encompasses interacting news production practices, epistemological assumptions, and institutional arrangements, in order to consider how data is incorporated into journalism's own established procedures for producing objectivity. At first sight, working with data promises to challenge the regime, in part by taking a more conventionalist or interpretivist epistemological position with regard to the representation of truth. However, we argue that how journalists and other actors choose to work with data may in some ways deepen the regime's epistemological stance. We conclude by outlining a set of questions for future research into the relationship between data, objectivity and journalism.

Between Objectivity and Openness—The Mediality of Data for Journalism

A number of recent high profile news events have emphasised the importance of <em>data </em>as a journalistic resource. But with no definitive definition for what constitutes data in journalism, it is difficult to determine what the implications of collecting, analysing, and disseminating data are for journalism, particularly in terms of objectivity in journalism. Drawing selectively from theories of mediation and research in journalism studies we critically examine how data is incorporated into journalistic practice. In the first half of the paper, we argue that data's value for journalism is constructed through mediatic dimensions that unevenly evoke different socio-technical contexts including scientific research and computing. We develop three key dimensions related to data's mediality within journalism: the problem of scale, transparency work, and the provision of access to data as 'openness'. Having developed this first approach, we turn to a journalism studies perspective of journalism's longstanding "regime of objectivity", a regime that encompasses interacting news production practices, epistemological assumptions, and institutional arrangements, in order to consider how data is incorporated into journalism's own established procedures for producing objectivity. At first sight, working with data promises to challenge the regime, in part by taking a more conventionalist or interpretivist epistemological position with regard to the representation of truth. However, we argue that how journalists and other actors choose to work with data may in some ways deepen the regime's epistemological stance. We conclude by outlining a set of questions for future research into the relationship between data, objectivity and journalism.

사례분석을 통한 지방행정의 빅데이터 활용 전략

빅데이터의 가치가 인식되고 정부 3.0이 발표되면서 빅데이터에 대한 관심이 증가하고 있다. 그러나 각 부처나 지방자치단체에 구체적인 추진 대안이나 전략이 취약한 상황에서 빅데이터를 체계적으로 활용하고 성과를 낸다는 것은 그리 쉬운 일이 아닐 것이다. 이에 본 연구는 지방자치단체의 빅데이터 활용 영역을 정리한 다음, 빅데이터 활용 전략을 제안하고자 하였다. 연구 결과 지방행정의 빅데이터 활용 영역은 크게 이상 현상 감지 및 대응, 가까운 미래 예측 및 대응, 분석된 상황 대응 및 새로운 정책(행정 서비스) 개발, 시민 맞춤형 서비스 등 네 가지로 구분되었다. 또한 빅데이터 활용 전략은 단계적 접근, 사용자의 요구분석, 주요성공요소 기반 추진, 시범사업, 성과평가, 성과에 따른 인센티브, 공통기반 구축 등으로 정리하였다. As Big Data's value is perceived and Government 3.0 is announced, there is a growing interest in Big Data. However, it won't be easy for each public institute or local government to apply Big Data systematically and make a successful achievement despite lacking of specific alternative plan or strategy. So, this study tried to suggest strategies to use Big Data after arranging the area which local government utilize it in. As a result, utilization areas of local administration's Big Data are divided into four areas; recognizing and corresponding the abnormal phenomenon, predicting and corresponding the close future, corresponding analyzed situation and developing new policy(administration service), and citizen customized service. In addition, strategies about how to use Big Data are suggested; stepwise approach, user's requirements analysis, critical success factors based implementation, pilot project, result evaluation, performance based incentive, building common infrastructure.

Databases and the silification of health

Developments in databases and computing are helping to create a revolution in health and in biomedical research. Many aspects of medicine are increasingly data-centric, from basics such as record-keeping to diagnosis and biological discovery. Drivers of change include massive curated biological data sets, consolidations of medical knowledge into systematised online repositories, innovations in data linkage, change of practice in hospitals, and new diagnostic technologies. However, the volumes of data means that new database and computational innovations are required if the data's value is to be fully exploited. This talk reviews the biomedical mechanisms that are creating data and explores achievements and challenges for database researchers in future health.

Valuing Information Assets for Security Risk Management

Information security professionals are often asked by management to balance the cost of controls against the value of the information assets that the controls protect. Valuing information for this purpose differs significantly from valuing information for accounting purposes. In most cases, the organization is not trying to sell its data to others and has not established a marketplace in which the data's value could be tested. Cost-based valuations (often used to determine a “book” value for intellectual property) also fall short for risk management purposes. Nonetheless, relying on opinion averaging or similar highly subjective techniques rarely results in defensible valuations. This article explores the reasons for valuation in a security risk management context. From these reasons, it is possible to discover effective and defensible techniques of both a quantitative and a qualitative nature.