Evaluation of the Level-of-Detail Generator for Visual Analysis of the ATLAS Computing Metadata

Grigorieva, Maria; Titov, Mikhail; Alekseev, A. A.; Artamonov, Alexey; Klimentov, A.; Korchuganova, Tatiana; Milman, I.; Galkin, Timofei; Pilyugin, V.V.

doi:10.1134/s199508021911012x

Cited by 1 publication

(1 citation statement)

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“…The data sample for real-time exploration of PanDA [4] computing payload starts from thousands of jobs and may exceed hundreds of thousands, depending on the filtering conditions and selected time range. The implementation of the Level-of-Detail Generator (LoD) [5] was initiated to avoid visualization of such a huge amount of data objects in one scene. Initially, LoD provided clustering of the initial data sample using the MiniBatchKMeans 2 algorithm only and returned clusters (i.e., groups) as aggregated data objects.…”

Section: Level-of-detail Methodsmentioning

confidence: 99%

Enhancements in Functionality of the Interactive Visual Explorer for ATLAS Computing Metadata

et al. 2020

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The development of the Interactive Visual Explorer (InVEx), a visual analytics tool for the computing metadata of the ATLAS experiment at LHC, includes research of various approaches for data handling both on server and client sides. InVEx is implemented as a web-based application which aims at the enhancing of analytical and visualization capabilities of the existing monitoring tools and facilitates the process of data analysis with the interactivity and human supervision. The current work is focused on the architecture enhancements of the InVEx application. First, we will describe the user-manageable data preparation stage for cluster analysis. Then, the Level-of-Detail approach for the interactive visual analysis will be presented. It starts with the low detailing, when all data records are grouped (by clustering algorithms or by categories) and aggregated. We provide users with means to look deeply into this data, incrementally increasing the level of detail. Finally, we demonstrate the development of data storage backend for InVEx, which is adapted for the Level-of-Detail method to keep all stages of data derivation sequence.

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Section: Level-of-detail Methodsmentioning

confidence: 99%