Papyrus

Bailey, Stuart; Grossman, Robert L.; Sivakumar, H.; Turinsky, Andrei L.

doi:10.1145/331532.331595

Cited by 43 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only the induced models are communicated between agents and fused into a final, resulting model. Papyrus, however, can move data from one location to another, and considers various trade‐offs between local computation and transfer of the data to multiple computers to optimize computational load.…”

Section: Architectures For Concurrent Data Analysismentioning

confidence: 99%

“…This goal has never been achieved. Instead, agents in the present systems are simple and specialized, and often dependent on the user's interaction through graphical user interfaces (Figure ) or query languages . Some systems are also able to graphically present resulting data models, and, e.g., provide visualizations of decision trees or interactive graphs .…”

Section: Architectures For Concurrent Data Analysismentioning

confidence: 99%

“…Agent based architectures have been used in wide range of data analysis applications that include text mining, medical data analysis, and credit card fraud detection . Agents were found to be of particular benefit when the datasets were spread across multiple sites and could not be moved or combined due to the regulatory restrictions.…”

Section: Architectures For Concurrent Data Analysismentioning

confidence: 99%

See 2 more Smart Citations

Concurrent software architectures for exploratory data analysis

Starič

Demšar

Zupan

2015

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Decades ago, increased volume of data made manual analysis obsolete and prompted the use of computational tools with interactive user interfaces and rich palette of data visualizations. Yet their classic, desktop-based architectures can no longer cope with the ever-growing size and complexity of data. Next-generation systems for explorative data analysis will be developed on client-server architectures, which already run concurrent software for data analytics but are not tailored to for an engaged, interactive analysis of data and models. In explorative data analysis, the key is the responsiveness of the system and prompt construction of interactive visualizations that can guide the users to uncover interesting data patterns. In this study, we review the current software architectures for distributed data analysis and propose a list of features to be included in the next generation frameworks for exploratory data analysis. The new generation of tools for explorative data analysis will need to address integrated data storage and processing, fast prototyping of data analysis pipelines supported by machine-proposed analysis workflows, pre-emptive analysis of data, interactivity, and user interfaces for intelligent data visualizations. The systems will rely on a mixture of concurrent software architectures to meet the challenge of seamless integration of explorative data interfaces at client site with management of concurrent data mining procedures on the servers.

show abstract

Section: Architectures For Concurrent Data Analysismentioning

confidence: 99%

Section: Architectures For Concurrent Data Analysismentioning

confidence: 99%

Section: Architectures For Concurrent Data Analysismentioning

confidence: 99%

See 1 more Smart Citation