Trust and Big Data: A Roadmap for Research

Sänger, Johannes; Richthammer, Christian; Hassan, Sabri; Pernul, Günther

doi:10.1109/dexa.2014.63

Cited by 25 publications

(7 citation statements)

References 17 publications

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“…Diftong could be included in a more robust information life cycle management framework as described in [35]. Similarly by ensuring data quality, big data analytics [36] can be applied with more trust [37].…”

Section: Resultsmentioning

confidence: 99%

Diftong: a tool for validating big data workflows

et al. 2019

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Data validation is about verifying the correctness of data. When organisations update and refine their data transformations to meet evolving requirements, it is imperative to ensure that the new version of a workflow still produces the correct output. We motivate the need for workflows and describe the implementation of a validation tool called Diftong. This tool compares two tabular databases resulting from different versions of a workflow to detect and prevent potential unwanted alterations. Row-based and column-based statistics are used to quantify the results of the database comparison. Diftong was shown to provide accurate results in test scenarios, bringing benefits to companies that need to validate the outputs of their workflows. By automating this process, the risk of human error is also eliminated. Compared to the more labourintensive manual alternative, it has the added benefit of improved turnaround time for the validation process. Together this allows for a more agile way of updating data transformation workflows.

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Section: Resultsmentioning

confidence: 99%

Diftong: a tool for validating big data workflows

et al. 2019

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“…Data is aggregated, processed and then circulated through different sites and practices [8,58]. Data is not innately neutral, there are a multitude of judgements, biases, limitations, assumptions and uncertainties within [27,42,99,134,151]. Given these complexities, the application of data science to any area is challenging [102,121].…”

Section: Background 21 Environmental Data Sciencementioning

confidence: 99%

Fifty Shades of Grey

Thornton

Knowles

Blair

2021

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

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Environmental data science is uniquely placed to respond to essentially complex and fantastically worthy challenges related to arresting planetary destruction. Trust is needed for facilitating collaboration between scientists who may share datasets and algorithms, and for crafting appropriate science-based policies. Achieving this trust is particularly challenging because of the numerous complexities, multi-scale variables, interdependencies and multi-level uncertainties inherent in environmental data science. Virtual Labs-easily accessible online environments provisioning access to datasets, analysis and visualisations-are socio-technical systems which, if carefully designed, might address these challenges and promote trust in a variety of ways. In addition to various system properties that can be utilised in support of effective collaboration, certain features which are commonly seen to benefit trust-transparency and provenance in particular-appear applicable to promoting trust in and through Virtual Labs. Attempting to realise these features in their design reveals, however, that their implementation is more nuanced and complex than it would appear. Using the lens of affordances, we argue for the need to carefully articulate these features, with consideration of multiple stakeholder needs on balance, so that these Virtual Labs do in fact promote trust. We argue that these features not be conceived as widgets that can be imported into a given context to promote trust; rather, whether they promote trust is a function of how systematically designers consider various (potentially conflicting) stakeholder trust needs. CCS CONCEPTS• Human-centered computing → Computer supported cooperative work; HCI theory, concepts and models.

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“…Use of role mining in [28] can be extended to determine roles of users based on attributes. A research roadmap on trust and Big Data is presented in [34]. Trust based Data ingestion or processing can use models in [30].…”

Section: Related Workmentioning

confidence: 99%

Object-Tagged RBAC Model for the Hadoop Ecosystem

Gupta

Patwa

Sandhu

2017

Lecture Notes in Computer Science

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Hadoop ecosystem provides a highly scalable, fault-tolerant and cost-effective platform for storing and analyzing variety of data formats. Apache Ranger and Apache Sentry are two predominant frameworks used to provide authorization capabilities in Hadoop ecosystem. In this paper we present a formal multi-layer access control model (called HeAC) for Hadoop ecosystem, as an academic-style abstraction of Ranger, Sentry and native Apache Hadoop access-control capabilities. We further extend HeAC base model to provide a cohesive object-tagged role-based access control (OT-RBAC) model, consistent with generally accepted academic concepts of RBAC. Besides inheriting advantages of RBAC, OT-RBAC offers a novel method for combining RBAC with attributes (beyond NIST proposed strategies). Additionally, a proposed implementation approach for OT-RBAC in Apache Ranger, is presented. We further outline attribute-based extensions to OT-RBAC.

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Trust and Big Data: A Roadmap for Research

Cited by 25 publications

References 17 publications

Diftong: a tool for validating big data workflows

Diftong: a tool for validating big data workflows

Fifty Shades of Grey

Object-Tagged RBAC Model for the Hadoop Ecosystem

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