Scientific user behavior and data-sharing trends in a petascale file system

Lim, Seung-Hwan; Sim, Hyogi; Gunasekaran, Raghul; Vazhkudai, Sudharshan S.

doi:10.1145/3126908.3126924

Cited by 20 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The OakRidge Leadership Computing Facility, which exploits logs in petascale file systems, has been developing since 2017, profiling usage behavior trends and the sharing trends of users, and it invites other HPC centers to develop a similar approach [29] with new methodologies [30] for efficient web mining taxonomy.…”

Section: Availabilitymentioning

confidence: 99%

A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP

et al. 2023

View full text Add to dashboard Cite

The digital support for scientific reasoning presents contrasting results. Bibliometric services are improving, but not academic assessment; no service for scholars relies on logs of web usage to base query strategies for relevance judgments (or assessor shifts). Our Scientific Knowledge Graph GRAPHYP innovates with interpretable patterns of web usage, providing scientific reasoning with conceptual fingerprints and helping identify eligible hypotheses. In a previous article, we showed how usage log data, in the form of ‘documentary tracks’, help determine distinct cognitive communities (called adversarial cliques) within sub-graphs. A typology of these documentary tracks through a triplet of measurements from logs (intensity, variety and attention) describes the potential approaches to a (research) question. GRAPHYP assists interpretation as a classifier, with possibilistic graphical modeling. This paper (Paper 2) shows what this approach can bring to scientific reasoning; it involves visualizing complete interpretable pathways, in a multi-hop assessor shift, which users can then explore toward the ‘best possible solution’—the one that is most consistent with their hypotheses. Applying the Leibnizian paradigm of scientific reasoning, GRAPHYP highlights infinitesimal learning pathways, as a ‘multiverse’ geometric graph in modeling possible search strategies answering research questions.

show abstract

Section: Availabilitymentioning

confidence: 99%

A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP

et al. 2023

View full text Add to dashboard Cite

show abstract

“…As the adoption of tools like Dropbox 1 , Google Drive 2 , and Box 3 suggests, file systems offer a very popular approach to data sharing across applications [13] and across systems [2].…”

Section: Introductionmentioning

confidence: 99%

DLPFS: The Data Leakage Prevention FileSystem

Braghin¹,

Simioni²,

Sinn³

2021

Preprint

View full text Add to dashboard Cite

Shared folders are still a common practice for granting third parties access to data files, regardless of the advances in data sharing technologies. Services like Google Drive, Dropbox, Box, and others, provide infrastructures and interfaces to manage file sharing. The human factor is the weakest link and data leaks caused by human error are regrettable common news. This takes place as both mishandled data, for example stored to the wrong directory, or via misconfigured or failing applications dumping data incorrectly. We present Data Leakage Prevention FileSystem (DLPFS), a first attempt to systematically protect against data leakage caused by misconfigured application or human error. This filesystem interface provides a privacy protection layer on top of the POSIX filesystem interface, allowing for seamless integration with existing infrastructures and applications, simply augmenting existing security controls. At the same time, DLPFS allows data administrators to protect files shared within an organisation by preventing unauthorised parties to access potentially sensitive content. DLPFS achieves this by transparently integrating with existing access control mechanisms. We empirically evaluate the impact of DLPFS on system's performances to demonstrate the feasibility of the proposed solution.

show abstract

“…Moreover, the collaboration workspace should support advanced data discovery services, e.g., attribute-based file search queries, to effectively retrieve desired datasets and avoid unnecessary data transfers. In addition, it is common that a scientist participates in multiple collaborations [23]. To the best of our knowledge, none of existing systems directly support multiple collaborations, which we address via providing template namespace.…”

Section: Introductionmentioning

confidence: 99%