Vaite: A Visualization-Assisted Interactive Big Urban Trajectory Data Exploration System

Yang, Chuang; Zhang, Yilan; Tang, Bo; Zhu, Min

doi:10.1109/icde.2019.00233

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Wang et al [31] proposed an interactive visualization platform for analyzing traffic jam data. Further, interaction-based systems like Vaite [35] and Vaud [13] were proposed specifically for the analysis of urban spatial data.…”

Section: Background and Related Work 21 Interactive Spatial Systemsmentioning

confidence: 99%

“…To allow data scientists and data engineers to easily and efficiently explore spatial data sets at a large scale, numerous interactionbased systems [8,16,26,35] have been introduced in the past. Unlike traditional instruction-based systems (e.g., those with operational commands in SQL and Python), which require the user to spend considerable amounts of time expressing their trajectory queries as precise instructions with geographical coordinates, modern interaction-based systems make use of user-friendly GUIs for issuing queries.…”

Section: Introductionmentioning

confidence: 99%

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Poet

Paul

Ang

et al. 2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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Interaction-based systems have been widely used in many enterprises like Grab to enable quick and easy analysis of large-scale spatial data. Unlike traditional instruction-based query processing systems, modern interaction-based systems allow users to issue complex queries through simple interactions with a Graphical User Interface (GUI). While such systems have significantly transformed the process of spatial query processing, they still rely on a processafter-query approach for executing the queries. Even though the user is continuously interacting with the GUI, the actual processing is only initiated after the user completes their interactions, thus wasting the opportunities to reduce the response time of query processing. Inside Grab, we develop Poet, a progressive execution framework to continuously analyze user interactions and to perform progressive execution as soon as the system gains reasonable confidence regarding the user intentions. By integrating Poet, the interactionbased system can begin processing before the query is expressed in its whole by the user. The user interactions are captured and modelled in Markov chains, which guide the probability of progressive execution. For handling large-scale trajectory data in Grab, the progressive execution engine of Poet has been designed on top of Apache Flink. Our experiments show that Poet is able to reduce the latency in generating the output, providing a more interactive experience. Our experiments find that Poet helps reduce the query execution latency by up to 25x. CCS CONCEPTS • Information systems → Spatial-temporal systems; • Humancentered computing → Visual analytics.

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Section: Background and Related Work 21 Interactive Spatial Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Poet

Paul

Ang

et al. 2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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Innovation in Data Visualisation for Public Policy Making

Raineri¹,

Molinari

2021

The Data Shake

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In this contribution, we propose a reflection on the potential of data visualisation technologies for (informed) public policy making in a growingly complex and fast changing landscape—epitomized by the situation created after the outbreak of the Covid-19 pandemic. Based on the results of an online survey of more than 50 data scientists from all over the world, we highlight five application areas seeing the biggest needs for innovation according to the domain specialists. Our main argument is that we are facing a transformation of the business cases supporting the adoption and implementation of data visualisation methods and tools in government, which the conventional view of the value of Business Intelligence does not capture in full. Such evolution can drive a new wave of innovations that preserve (or restore) the human brain’s centrality in a decision making environment that is increasingly dominated—for good and bad—by artificial intelligence. Citizen science, design thinking, and accountability are mentioned as triggers of civic engagement and participation that can bring a community of “knowledge intermediaries” into the daily discussion on data supported policy making.

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CheetahVIS

et al. 2020

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Recently, the spatial-temporal data of urban moving objects, e.g., cars and buses, are collected and widely used in urban trajectory exploratory analysis. Urban bus service is one of the most common public transportation services. Urban bus data analysis plays an important role in smart city applications. For example, data analysts in bus companies use the urban bus data to optimize their bus scheduling plan. Map services providers, e.g., Google map, Ten-cent map, take urban bus data into account to improve their service quality (e.g., broadcast road update instantly). Unlike urban moving cars or pedestrians, urban buses travel on known bus routes. The operating buses form the "bus flows" in a city. Efficient analyzing urban bus flows has many challenges, e.g., how to analyze the dynamics of given bus routes? How to help users to identify traffic flow of interests easily? In this work, we present CheetahVIS, a visual analytical system for efficient massive urban bus data analysis. CheetahVIS builds upon Spark and provides a visual analytical platform for the stakeholders (e.g., city planner, data analysts in bus company) to conduct effective and efficient analytical tasks. In the demonstration, demo visitors will be invited to experience our proposed CheetahVIS system with different urban bus data analytical functions, e.g., bus route analysis, public bus flow overview, multiple region analysis, in a real-world dataset. We also will present a case study, which compares different regions in a city, to demonstrate the effectiveness of CheetahVIS.

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Vaite: A Visualization-Assisted Interactive Big Urban Trajectory Data Exploration System

Cited by 3 publications

References 6 publications

Poet

Poet

Innovation in Data Visualisation for Public Policy Making

CheetahVIS

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