VisTrails: visualization meets data management

By Steven P. Callahan, Juliana Freire, E. Santos, Carlos Eduardo Scheidegger, Cláudio T. Silva, H. Vo. SIGMOD Conference, 2006

588 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/24d8a2c600076c878d05930b8501cf63d2100387

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Information Systems and Management

Citations in the corpus

Interactions with big data analytics (2012, methodology): “Inspired by tools like VisTrails [10], which today can remember both where a given data item came from and how users have changed their CoVer STorY Figure 2.”
Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline (2009, unknown): “VisTrails (Callahan et al., 2006) addresses the problem of visualization from a data management perspective where imaging data and meta-data are represented as a conjoint visualization product.”
Enterprise Data Analysis and Visualization: An Interview Study (2012, background): “Several researchers have articulated the importance of capturing provenance to manage analytic workflows [2, 11, 12, 15].”
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python (2011, background): “However, most current method sections do not have sufficient details to enable a researcher knowledgeable in the domain to reproduce the analysis process.”
Research directions in data wrangling: Visualizations and transformations for usable and credible data (2011, unknown): “These data could lead to better automatic suggestions.(56) Such a corpus would also be a valuable resource for studying data cleaning strategies and informing the iterative design of wrangling tools.”
State of the Art: Coordinated & Multiple Views in Exploratory Visualization (2007, background): “Research has started to look at the challenge of managing the user in this exploration (such as vistrails [13]) and provenance for visual exploration systems [28], but further research is required.”
SPADE: Support for Provenance Auditing in Distributed Environments (2012, unknown): “Several projects provide OPM-compliant provenance, including SPADEv2 [59], PASS [52], VisTrails [8], and Tupelo [62].”
Ground: A Data Context Service (2017, background): “Vistrails [7] is a scientific workflow and provenance management system that shares some of the same goals, with a focus on scientific reproducibility.”
Workflows and e-Science: An overview of workflow system features and capabilities (2009, unknown): “The VisTrails system (91) has an extensive”
Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow (2018, methodology): “Dataflow graphs arise in diverse domains: distributed systems [15, 32, 42], databases [41], user-interface development [20], visualization [14, 46, 52] and engineering [4].”
A survey on provenance: What for? What form? What from? (2017, background): “Example applications leveraging provenance for replicating results include [18,37,50,126,137,157].”