An analytic data engine for visualization in tableau

Wesley, Richard; Eldridge, Matthew; Terlecki, Paweł

doi:10.1145/1989323.1989449

Cited by 48 publications

(28 citation statements)

References 8 publications

(7 reference statements)

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“…We first described the TDE in [10]. It is a read-only column store that has been optimized for use with the Tableau visualization environment.…”

Section: Data Enginementioning

confidence: 99%

“…In the first TDE paper [10] we listed creating new encodings as a topic for future work. After a review of the literature, we settled on several simple encodings that are compatible with the existing, non-segmented storage model used by the TDE.…”

Section: Encodingsmentioning

confidence: 99%

“…We do not measure the performance of the dictionary expansion as that was evaluated in the original TDE paper [10].…”

Section: Experimental Designmentioning

confidence: 99%

“…Our original TPC-H SF-1 database from [10] was about 660MB. Applying the new encodings to the columns reduces the size of the database by about 140MB.…”

Section: Storagementioning

confidence: 99%

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Leveraging compression in the tableau data engine

Wesley

Terlecki

2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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Data sets are growing rapidly and there is an attendant need for tools that facilitate human analysis of them in a timely manner. To help meet this need, column-oriented databases (or "column stores") have come into wide use because of their low latency on analytic workloads. Column stores use a number of techniques to produce these dramatic performance techniques, including the ability to perform operations directly on compressed data.In this paper, we describe how the Tableau Data Engine (an internally developed column store) leverages a number of compression techniques to improve query performance. The approach is simpler than existing systems for operating on compressed data and more unified, removing the necessity for custom data access mechanisms. The approach also uses some novel metadata extraction techniques to improve the choices made by the system's run-time optimizer.

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“…We first described the TDE in [10]. It is a read-only column store that has been optimized for use with the Tableau visualization environment.…”

Section: Data Enginementioning

confidence: 99%

Section: Encodingsmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging compression in the tableau data engine

Wesley

Terlecki

2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

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“…When data become bigger, they cannot be loaded into memory or the computation speed is not sufficient for real-time performance. In order to feed more data into memory, Tableau [3] introduces Tableau Data Engine (TDE) [4]. The TDE highlights a specialized column-oriented data format, which supports a high data compression ratio (considering repeating values in the columns) and provides high level data aggregation information.…”

Section: Introductionmentioning

confidence: 99%

AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets

Sun

Masiane

et al. 2016

Informatics

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This paper presents the Animated VISualization Tool (AVIST), an exploration-oriented data visualization tool that enables rapidly exploring and filtering large time series multidimensional datasets. AVIST highlights interactive data exploration by revealing fine data details. This is achieved through the use of animation and cross-filtering interactions. To support interactive exploration of big data, AVIST features a GPU (Graphics Processing Unit)-centric design. Two key aspects are emphasized on the GPU-centric design: (1) both data management and computation are implemented on the GPU to leverage its parallel computing capability and fast memory bandwidth; (2) a GPU-based directed acyclic graph is proposed to characterize data transformations triggered by users' demands. Moreover, we implement AVIST based on the Model-View-Controller (MVC) architecture. In the implementation, we consider two aspects: (1) user interaction is highlighted to slice big data into small data; and (2) data transformation is based on parallel computing. Two case studies demonstrate how AVIST can help analysts identify abnormal behaviors and infer new hypotheses by exploring big datasets. Finally, we summarize lessons learned about GPU-based solutions in interactive information visualization with big data.

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