Dynamic reduction of query result sets for interactive visualizaton

Battle, Leilani; Chang, Remco

doi:10.1109/bigdata.2013.6691708

Cited by 64 publications

(50 citation statements)

References 18 publications

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“…Not surprisingly, the problem of providing efficient support for visualization tools and interactive queries over large data has attracted substantial attention recently, predominantly for relational data [1,6,27,30,31,33,35,56,66]. While methods have also been proposed for speeding up selection queries over spatio-temporal data [17,70], these do not support interactive rates for aggregate queries, that slice and summarize the data in different ways, as required by visual analytics systems [4,20,44,51,58,67].…”

Section: Introductionmentioning

confidence: 99%

GPU rasterization for real-time spatial aggregation over arbitrary polygons

et al. 2017

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Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-inpolygon tests that associate data points to polygonal regions, challenging the responsiveness of visualization tools. This challenge is compounded by the sheer amounts of data, requiring a large number of such tests to be performed. Traditional pre-aggregation approaches are unsuitable in this setting since they fix the query constraints and support only rectangular regions. On the other hand, query constraints are defined interactively in visual analytics systems, and polygons can be of arbitrary shapes. In this paper, we convert a spatial aggregation query into a set of drawing operations on a canvas and leverage the rendering pipeline of the graphics hardware (GPU) to enable interactive response times. Our technique trades-off accuracy for response time by adjusting the canvas resolution, and can even provide accurate results when combined with a polygon index. We evaluate our technique on two large real-world data sets, exhibiting superior performance compared to index-based approaches.

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Section: Introductionmentioning

confidence: 99%

GPU rasterization for real-time spatial aggregation over arbitrary polygons

et al. 2017

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“…Techniques range from Visual optimization (like query result reduction [4]), automatic exploration (like query recommendation [9]), assisted query formulation (like data space segmentation [31]), data prefetching (like result diversification [19]) and query approximation [16]. The core of most of these approaches consists of a function that, given the database instance and users' history with the database (i.e., past and current queries), computes new relevant queries, tuples or visualizations that are meant to support user exploration.…”

Section: Related Workmentioning

confidence: 99%

Benchmarking Exploratory OLAP

Djedaini

Furtado

Labroche

et al. 2017

Performance Evaluation and Benchmarking. Traditional - Big Data - Interest of Things

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Abstract. Supporting interactive database exploration (IDE) is a problem that attracts lots of attention these days. Exploratory OLAP (OnLine Analytical Processing) is an important use case where tools support navigation and analysis of the most interesting data, using the best possible perspectives. While many approaches were proposed (like query recommendation, reuse, steering, personalization or unexpected data recommendation), a recurrent problem is how to assess the effectiveness of an exploratory OLAP approach. In this paper we propose a benchmark framework to do so, that relies on an extensible set of user-centric metrics that relate to the main dimensions of exploratory analysis. Namely, we describe how to model and simulate user activity, how to formalize our metrics and how to build exploratory tasks to properly evaluate an IDE system under test (SUT). To the best of our knowledge, this is the first proposal of such a benchmark. Experiments are two-fold: first we evaluate the benchmark protocol and metrics based on synthetic SUTs whose behavior is well known. Second, we concentrate on two different recent SUTs from IDE literature that are evaluated and compared with our benchmark. Finally, potential extensions to produce an industrystrength benchmark are listed in the conclusion.

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“…The ScalaR system [2] computes aggregates and samples in the DBMS at runtime to avoid overwhelming the client with data. However, aggregation and sampling queries are too slow to complete at interactive speeds.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the goal of this project is to make all user interactions extremely fast (i.e., 500 ms or less), thereby providing a seamless exploration experience for users. However, although modern database management systems (DBMS's) allow users to perform complex scientific analyses over large datasets [20], DBMS's are not designed to respond to queries at interactive speeds, resulting in long interaction delays for browsing tools that must wait for answers from a backend DBMS [2]. Thus, new optimization techniques are needed to address the noninteractive performance of modern DBMS's, within the context of exploratory browsing.…”

Section: Introductionmentioning

confidence: 99%

“…The user cycles through the following steps when browsing data in ForeCache: (1) she analyzes the result of the previous request, (2) performs an action in the interface to update or refine the request (e.g., zooms in), and then (3) waits for the result to be rendered on the screen. ForeCache eliminates step 3 by prefetching neighboring tiles and storing them in main memory while the user is still in step 1, thereby providing the user with a seamless browsing experience.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Prefetching of Data Tiles for Interactive Visualization

Battle

Chang

Stonebraker

2016

Proceedings of the 2016 International Conference on Management of Data

Self Cite

148

123

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In this paper, we present ForeCache, a general-purpose tool for exploratory browsing of large datasets. ForeCache utilizes a clientserver architecture, where the user interacts with a lightweight clientside interface to browse datasets, and the data to be browsed is retrieved from a DBMS running on a back-end server. We assume a detail-on-demand browsing paradigm, and optimize the back-end support for this paradigm by inserting a separate middleware layer in front of the DBMS. To improve response times, the middleware layer fetches data ahead of the user as she explores a dataset.We consider two different mechanisms for prefetching: (a) learning what to fetch from the user's recent movements, and (b) using data characteristics (e.g., histograms) to find data similar to what the user has viewed in the past. We incorporate these mechanisms into a single prediction engine that adjusts its prediction strategies over time, based on changes in the user's behavior. We evaluated our prediction engine with a user study, and found that our dynamic prefetching strategy provides: (1) significant improvements in overall latency when compared with non-prefetching systems (430% improvement); and (2) substantial improvements in both prediction accuracy (25% improvement) and latency (88% improvement) relative to existing prefetching techniques.

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Dynamic reduction of query result sets for interactive visualizaton

Cited by 64 publications

References 18 publications

GPU rasterization for real-time spatial aggregation over arbitrary polygons

GPU rasterization for real-time spatial aggregation over arbitrary polygons

Benchmarking Exploratory OLAP

Dynamic Prefetching of Data Tiles for Interactive Visualization

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