Provenance for Interactive Visualizations

Psallidas, Fotis; Wu, Eugene

doi:10.1145/3209900.3209904

Cited by 15 publications

(8 citation statements)

References 57 publications

(42 reference statements)

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“…However, this threshold may be unrealistic for datasets larger than one million points, so we instead set a more modest worst-case threshold of 10 frames per second (or 100 ms), consistent with the type1 latency constraint discussed in §2.2. Note that this threshold is five times lower than most thresholds reported in previous visualization work [8,16,53,63]. As mentioned in §4, live updates are a critical feature for crossfilter use cases, and 500 ms is simply too slow to maintain even our 10 frames per second worst-case threshold.…”

Section: Responsivenessmentioning

confidence: 74%

“…such that the interface can quickly provide a visual response [39,42,56]. To meet this growing demand for interactive and real-time performance, the database and visualization communities have developed a variety of techniques, including approximate query processing [2,14], online aggregation/progressive visualization [3,19,26], data cubes [8,36,38], spatial indexing [63], speculative query execution [8,30], and lineage tracking [53].…”

Section: Introductionmentioning

confidence: 99%

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

Battle

Eichmann

Angelini

et al. 2020

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

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In this paper, we present a new benchmark to validate the suitability of database systems for interactive visualization workloads. While there exist proposals for evaluating database systems on interactive data exploration workloads, none rely on real user traces for database benchmarking. To this end, our long term goal is to collect user traces that represent workloads with different exploration characteristics. In this paper, we present an initial benchmark that focuses on "crossfilter"-style applications, which are a popular interaction type for data exploration and a particularly demanding scenario for testing database system performance. We make our benchmark materials, including input datasets, interaction sequences, corresponding SQL queries, and analysis code, freely available as a community resource, to foster further research in this area: https://osf.io/9xerb/?view_only= 81de1a3f99d04529b6b173a3bd5b4d23. CCS CONCEPTS • Information systems → Data management systems; Data analytics; • Human-centered computing → Visualization systems and tools.

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Section: Responsivenessmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

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

Battle

Eichmann

Angelini

et al. 2020

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

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“…Visualization is a broad topic studied in many communities, and here we focus on efficiency-related works. A survey [11] summarized studies on interactive data analytics and visualization, and there are several recent studies on this topic [18,20,21,42].…”

Section: Related Workmentioning

confidence: 99%

Maliva: Using Machine Learning to Rewrite Visualization Queries Under Time Constraints

Bai¹,

Alsudais²,

Li³

et al. 2021

Preprint

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We consider data-visualization systems where data is stored in a database, and a middleware layer translates a frontend request to a SQL query to the database to compute visual results. We focus on the problem of handling visualization requests with predetermined time constraints. We study how to rewrite the original query by adding hints and/or conducting approximations so that the total time is within the time constraint. We develop a novel middleware solution called Maliva, which adopts machine learning (ML) techniques to solve the problem. It applies the Markov Decision Process (MDP) model to decide how to rewrite queries, and uses training instances to learn an agent that can make a sequence of decisions judiciously for an online request. Our experiments on both real and synthetic datasets show that compared to the baseline approach that relies on the original SQL query, Maliva performs significantly better in terms of both the chance of serving requests interactively, and query execution time.

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“…These include industry systems such as Hyper [1,46,47] which is an efficient main-memory and hybrid OLTP and OLAP DBMS that is now customized for Tableau's data engine. In academia, systems such as the Data Visualization Management System [51-53, 75, 80] explore how relational query languages can be used to express interactive visualizations [80], and how relational DBMS designs can be adapted and extended -for instance with fast lineage support [51][52][53] -to speed up interactive visualization.…”

Section: Efficient Data Visualizationmentioning

confidence: 99%

Towards Democratizing Relational Data Visualization

Tang

2019

Proceedings of the 2019 International Conference on Management of Data

Self Cite

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The problem of data visualization is to transform data into a visual context such that people can easily understand the significance of data. Nowadays, data visualization becomes especially important, because it is the de facto standard for modern business intelligence and successful data science. This tutorial will cover three specific topics: visualization languages define how the users can interact with various visualization systems; efficient data visualization processes the data and produces visualizations based on well-specified user queries; smart data visualization recommends data visualizations based on underspecified user queries. In this tutorial, we will go logically through these prior art, paying particular attentions on problems that may attract the interest from the database community.

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Provenance for Interactive Visualizations

Cited by 15 publications

References 57 publications

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

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

Maliva: Using Machine Learning to Rewrite Visualization Queries Under Time Constraints

Towards Democratizing Relational Data Visualization

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