Abstract
Data visualization is by far the most commonly used mechanism to explore and extract insights from datasets, especially by novice data scientists. And yet, current visual analytics tools are rather limited in their ability to operate on collections of visualizations-by composing, filtering, comparing, and sorting them-to find those that depict desired trends or patterns. The process of visual data exploration remains a tedious process of trial-and-error. We propose zenvisage, a visual analytics platform for effortlessly finding desired visual patterns from large datasets. We introduce zenvisage's general purpose visual exploration language, ZQL ("zee-quel") for specifying the desired visual patterns, drawing from use-cases in a variety of domains, including biology, mechanical engineering, climate science, and commerce. We formalize the expressiveness of ZQL via a visual exploration algebra-an algebra on collections of visualizations-and demonstrate that ZQL is as expressive as that algebra. Zenvisage exposes an interactive front-end that supports the issuing of ZQL queries, and also supports interactions that are "short-cuts" to certain commonly used ZQL queries. To execute these queries, zenvisage uses a novel ZQL graph-based query optimizer that leverages a suite of optimizations tailored to the goal of processing collections of visualizations in certain pre-defined ways. Lastly, a user survey and study demonstrates that data scientists are able to effectively use zenvisage to eliminate error-prone and tedious exploration and directly identify desired visualizations.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 457-468 |
| Number of pages | 12 |
| Journal | Proceedings of the VLDB Endowment |
| Volume | 10 |
| Issue number | 4 |
| DOIs | |
| State | Published - Jan 1 2016 |
| Event | 43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany Duration: Aug 28 2017 → Sep 1 2017 |
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ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science(all)
Cite this
Effortless data exploration with zenvisage : An expressive and interactive visual analytics system. / Siddiqui, Tarique; Kim, Albert; Lee, John; Karahalios, Karrie; Parameswaran, Aditya.
In: Proceedings of the VLDB Endowment, Vol. 10, No. 4, 01.01.2016, p. 457-468.Research output: Contribution to journal › Conference article
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TY - JOUR
T1 - Effortless data exploration with zenvisage
T2 - An expressive and interactive visual analytics system
AU - Siddiqui, Tarique
AU - Kim, Albert
AU - Lee, John
AU - Karahalios, Karrie
AU - Parameswaran, Aditya
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Data visualization is by far the most commonly used mechanism to explore and extract insights from datasets, especially by novice data scientists. And yet, current visual analytics tools are rather limited in their ability to operate on collections of visualizations-by composing, filtering, comparing, and sorting them-to find those that depict desired trends or patterns. The process of visual data exploration remains a tedious process of trial-and-error. We propose zenvisage, a visual analytics platform for effortlessly finding desired visual patterns from large datasets. We introduce zenvisage's general purpose visual exploration language, ZQL ("zee-quel") for specifying the desired visual patterns, drawing from use-cases in a variety of domains, including biology, mechanical engineering, climate science, and commerce. We formalize the expressiveness of ZQL via a visual exploration algebra-an algebra on collections of visualizations-and demonstrate that ZQL is as expressive as that algebra. Zenvisage exposes an interactive front-end that supports the issuing of ZQL queries, and also supports interactions that are "short-cuts" to certain commonly used ZQL queries. To execute these queries, zenvisage uses a novel ZQL graph-based query optimizer that leverages a suite of optimizations tailored to the goal of processing collections of visualizations in certain pre-defined ways. Lastly, a user survey and study demonstrates that data scientists are able to effectively use zenvisage to eliminate error-prone and tedious exploration and directly identify desired visualizations.
AB - Data visualization is by far the most commonly used mechanism to explore and extract insights from datasets, especially by novice data scientists. And yet, current visual analytics tools are rather limited in their ability to operate on collections of visualizations-by composing, filtering, comparing, and sorting them-to find those that depict desired trends or patterns. The process of visual data exploration remains a tedious process of trial-and-error. We propose zenvisage, a visual analytics platform for effortlessly finding desired visual patterns from large datasets. We introduce zenvisage's general purpose visual exploration language, ZQL ("zee-quel") for specifying the desired visual patterns, drawing from use-cases in a variety of domains, including biology, mechanical engineering, climate science, and commerce. We formalize the expressiveness of ZQL via a visual exploration algebra-an algebra on collections of visualizations-and demonstrate that ZQL is as expressive as that algebra. Zenvisage exposes an interactive front-end that supports the issuing of ZQL queries, and also supports interactions that are "short-cuts" to certain commonly used ZQL queries. To execute these queries, zenvisage uses a novel ZQL graph-based query optimizer that leverages a suite of optimizations tailored to the goal of processing collections of visualizations in certain pre-defined ways. Lastly, a user survey and study demonstrates that data scientists are able to effectively use zenvisage to eliminate error-prone and tedious exploration and directly identify desired visualizations.
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UR - http://www.scopus.com/inward/citedby.url?scp=85019872052&partnerID=8YFLogxK
U2 - 10.14778/3025111.3025126
DO - 10.14778/3025111.3025126
M3 - Conference article
AN - SCOPUS:85019872052
VL - 10
SP - 457
EP - 468
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
SN - 2150-8097
IS - 4
ER -