Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data

Battle, Leilani; Eichmann, Philipp; Angelini, Marco; Catarci, Tiziana; Santucci, Giuseppe; Zheng, Yukun; Binnig, Carsten; Fekete, Jean-Daniel; Moritz, Dominik

doi:10.1145/3318464.3389732

Cited by 26 publications

(16 citation statements)

References 58 publications

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“…We designed four visual analytics tasks (see Table 2) for each dataset based on prior studies of data analysis [2,3,36,37]. These four tasks cover all three analysis task classes discussed by Battle et al [2]: quantitative, qualitative, and exploratory. T1 and T2 are focused tasks; T1 involves two data attributes, while T2 involves three data attributes.…”

Section: Methodsmentioning

confidence: 99%

An Evaluation-Focused Framework for Visualization Recommendation Algorithms

Zeng¹,

Moh²,

Du³

et al. 2021

Preprint

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Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios.

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Section: Methodsmentioning

confidence: 99%

An Evaluation-Focused Framework for Visualization Recommendation Algorithms

Zeng¹,

Moh²,

Du³

et al. 2021

Preprint

Self Cite

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“…Each trace is 3 minutes long, with 20ms average think time. For Falcon, we used the 70 traces from [7]. The interface used to collect these traces differs from the interface in the Falcon paper [53] by one chart (a bar chart instead of the heat map in [53]).…”

Section: Methodsmentioning

confidence: 99%

Continuous Prefetch for Interactive Data Applications

Mohammed

2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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Interactive data visualization and exploration (DVE) applications are often network-bottlenecked due to bursty request patterns, large response sizes, and heterogeneous deployments over a range of networks and devices. This makes it difficult to ensure consistently low response times (< 100ms). Khameleon is a framework for DVE applications that uses a novel combination of prefetching and response tuning to dynamically trade-off response quality for low latency. Khameleon exploits DVE's approximation tolerance: immediate lower-quality responses are preferable to waiting for complete results. To this end, Khameleon progressively encodes responses, and runs a server-side scheduler that proactively streams portions of responses using available bandwidth to maximize user-perceived interactivity. The scheduler involves a complex optimization based on available resources, predicted user interactions, and response quality levels; yet, decisions must also be made in real-time. To overcome this, Khameleon uses a fast greedy heuristic that closely approximates the optimal approach. Using image exploration and visualization applications with real user interaction traces, we show that across a wide range of network and client resource conditions, Khameleon outperforms existing prefetching approaches that benefit from perfect prediction models: Khameleon always lowers response latencies (typically by 2-3 orders of magnitude) while keeping response quality within 50-80%.

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“…(4) Prefetching and re-partitioning: Interactions impose an even stricter latency requirement for visualizations [1]. Based on the idea of partial execution in subsection 2.1, partially processed data can be brought back to the client earlier so that a downstream interaction parameterized by such data will trigger a fast partial execution.…”

Section: Middleware Optimization Dynamicmentioning

confidence: 99%

“…As a result, the visualizations remain lightweight, stand-alone and agnostic of the optimization work behind. Second, Vega is also expressive enough to capture the computational complexity of most visualization interfaces, including those tested in recent database management system (DBMS) benchmarks designed for visual exploration scenarios [1,4]. Third, Vega is the backbone of a popular ecosystem of visualization tools, including Vega-Lite [9], Voyager [12], and Falcon [6], so making improvements to Vega is of interest to thousands of data enthusiasts, researchers, and companies worldwide.…”

Section: Introductionmentioning

confidence: 99%

Demonstration of VegaPlus: Optimizing Declarative Visualization Languages

Yang,

Joo,

Yerramreddy

et al. 2022

Preprint

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While many visualization specification languages are user-friendly, they tend to have one critical drawback: they are designed for small data on the client-side and, as a result, perform poorly at scale. We propose a system that takes declarative visualization specifications as input and automatically optimizes the resulting visualization execution plans by offloading computational-intensive operations to a separate database management system (DBMS). Our demo will enable users to write their own Vega specifications (or import existing ones), view the optimized plans from our system, and even modify these plans and compare their performance via a dedicated performance dashboard. CCS CONCEPTS• Human-centered computing → Visualization systems and tools.

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Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data

Cited by 26 publications

References 58 publications

An Evaluation-Focused Framework for Visualization Recommendation Algorithms

An Evaluation-Focused Framework for Visualization Recommendation Algorithms

Continuous Prefetch for Interactive Data Applications

Demonstration of VegaPlus: Optimizing Declarative Visualization Languages

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