Bridging Workflow and Data Provenance Using Strong Links

GNSS Data Provenance Traceability Research

Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017) ◽

10.33012/2017.15318 ◽

2017 ◽

Author(s):

Yi Qu ◽

Haitao Wu ◽

Ting Liu

Keyword(s):

Data Provenance ◽

Gnss Data

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Data Provenance in a Scientific Workflow Service Framework Integrated with Object Deputy Database

Chinese Journal of Computers ◽

10.3724/sp.j.1016.2008.00721 ◽

2009 ◽

Vol 31 (5) ◽

pp. 721-732 ◽

Cited By ~ 1

Author(s):

Li-Wei WANG ◽

Ze-Qian HUANG ◽

Min LUO ◽

Zhi-Yong PENG

Keyword(s):

Scientific Workflow ◽

Data Provenance ◽

Service Framework

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ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

Bioinformatics ◽

10.1093/bioinformatics/btab179 ◽

2021 ◽

Author(s):

Irzam Sarfraz ◽

Muhammad Asif ◽

Joshua D Campbell

Keyword(s):

Single Cell ◽

R Package ◽

Poor Quality ◽

Data Matrix ◽

Supplementary Information ◽

Data Provenance ◽

Rna Seq ◽

Efficient Management ◽

The Matrix ◽

The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

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Data Provenance for Complex Event Processing Invoking Composition of Services

2020 IEEE International Conference on Services Computing (SCC) ◽

10.1109/scc49832.2020.00027 ◽

2020 ◽

Author(s):

Malik Khalfallah ◽

Parisa Ghodous

Keyword(s):

Complex Event Processing ◽

Event Processing ◽

Data Provenance

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The effect of data provenance on estimates of gestation length in African and Asian colobines

American Journal of Physical Anthropology ◽

10.1002/ajpa.24370 ◽

2021 ◽

Author(s):

Carola Borries ◽

Jeroen B. Smaers ◽

Carrie S. Mongle ◽

Andreas Koenig

Keyword(s):

Data Provenance ◽

Gestation Length

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Secure Data Provenance in Internet of Things using Hybrid Attribute based Crypt Technique

Wireless Personal Communications ◽

10.1007/s11277-021-08157-0 ◽

2021 ◽

Author(s):

S. Porkodi ◽

D. Kesavaraja

Keyword(s):

Internet Of Things ◽

Data Provenance ◽

Secure Data

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Proof-of-PUF Enabled Blockchain: Concurrent Data and Device Security for Internet-of-Energy

Sensors ◽

10.3390/s21010028 ◽

2020 ◽

Vol 21 (1) ◽

pp. 28

Author(s):

Rameez Asif ◽

Kinan Ghanem ◽

James Irvine

Keyword(s):

Hybrid Approach ◽

Future Research ◽

Data Provenance ◽

Data Generation ◽

Cyber Attack ◽

Design Development ◽

Seamless Integration ◽

Device Identification ◽

Physical Unclonable Functions ◽

History Of

A detailed review on the technological aspects of Blockchain and Physical Unclonable Functions (PUFs) is presented in this article. It stipulates an emerging concept of Blockchain that integrates hardware security primitives via PUFs to solve bandwidth, integration, scalability, latency, and energy requirements for the Internet-of-Energy (IoE) systems. This hybrid approach, hereinafter termed as PUFChain, provides device and data provenance which records data origins, history of data generation and processing, and clone-proof device identification and authentication, thus possible to track the sources and reasons of any cyber attack. In addition to this, we review the key areas of design, development, and implementation, which will give us the insight on seamless integration with legacy IoE systems, reliability, cyber resilience, and future research challenges.

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