ImmVis: Bridging Data Analytics and Immersive Visualisation

Felipe Augusto Pedroso

and Paula Dornhofer Paro Costa

Dept. of Computer Engineering and Automation (DCA), School of Electrical and Computer Engineering, University of

Campinas (UNICAMP), Brazil

Keywords:

Immersive Analytics, Data Analysis, Virtual Reality, Visual Analytics.

Abstract:

One of the signiﬁcant issues from the visualisation ﬁeld is choosing the appropriate tool to conduct a research

project or experiment. The Immersive Analytics (IA) ﬁeld is no different but found support on game en-

gines and web technologies to create their solutions, frameworks and toolkits. While these technologies solve

problems like rendering and interaction, they do not offer functionalities to enable data analysis inside immer-

sive environments. This paper presents ImmVis, a novel open-source framework that enables IA applications

to beneﬁt from the data analysis capabilities from Python programming language well-established libraries.

The framework is enabled to work with different platforms and programming languages and can be used to

complement the capabilities of existing IA tools, empowering them to offer more sophisticated data analysis

functionalities.

1 INTRODUCTION

Visual Analytics (VA) is the research ﬁeld that com-

bines automated analysis techniques with interac-

tive visualisations, enabling a practical understand-

ing, reasoning and decision making on enormous and

complex datasets (Keim et al., 2010).

Typical VA systems are based on a standard desk-

top computer, limiting their user experience by the

affordances of its speciﬁc interface devices: “2D dis-

plays”, keyboard and mouse (Chen et al., 2017; Lin

et al., 2018; Kwon et al., 2019; Wang et al., 2019;

Zhao et al., 2019). While the usage of these types

of interfaces presented good results historically, the

advent of innovative interfaces and devices favoured

the appearance of a “derivative ﬁeld”, the Immersive

Analytics (IA) (Chandler et al., 2015; Dwyer et al.,

2018).

The IA ﬁeld proposes the exploration of data anal-

ysis and decision making systems that beneﬁt from

the new types of interactions allowed by technolo-

gies like Virtual Reality (VR) headsets, large touch

surfaces and Cave Automatic Virtual Environment

(CAVE). Despite the term “immersive” suggesting

only the usage of these technologies, the ﬁeld is not

limited to them, employing different types of tech-

nologies to remove barriers between people and data

https://orcid.org/0000-0002-2463-0459

https://orcid.org/0000-0002-1534-5744

analysis tools (Chandler et al., 2015; Dwyer et al.,

2018).

A signiﬁcant challenge found by IA practitioners

is the fact that while there exist platforms and tools

available for the development of immersive visualisa-

tions, none of them supports the typical tasks of data

analysis or visualisation. More recently, it is possi-

ble to observe the emergence of several tools that,

through the leverage of game engines and web tech-

nologies, present viable alternatives to simplify the

process of authoring and implementation of immer-

sive visualisations (Cordeil et al., 2017; Cordeil et al.,

2019; Sicat et al., 2018; Butcher et al., 2020). While

the results presented by these tools are quite impres-

sive, none of them enables their users to perform more

sophisticated data analysis operations. Also, each one

of them has a different approach to handle and repre-

sent data, making the interoperability between them

more difﬁcult.

This work presents ImmVis, a novel open-source

framework that aids the integration of well established

Python data analysis tools with IA systems developed

on different platforms and programming languages.

During the development of this work, we evaluated

several approaches to enable multiple platforms to in-

teract with Python data analysis tools and created the

initial implementation of the framework, which con-

tains a Python server application and a client library

that integrates the framework with the game engine

Pedroso, F. and Costa, P.

ImmVis: Bridging Data Analytics and Immersive Visualisation.

DOI: 10.5220/0010256001810187

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP, pages

181-187

ISBN: 978-989-758-488-6

181

Unity

. We also evaluated the network performance

while transmitting different volumes of data, having

a satisfactory result as the framework can take ad-

vantage of data analysis capabilities to “simplify” the

data.

The paper is organised as follows: Section 2 dis-

cusses the challenges to choose immersive visuali-

sation tools, which tools are currently available and

where our solution stands among them. Section 3 de-

tails the technical aspects of the ImmVis Framework

and Section 4 shows a performance evaluation. Sec-

tion 5 brings some insights about the development

process and what are going to be the next steps of

our project. Finally, Section 6 presents some ﬁnal re-

marks of the current work.

2 RELATED WORKS

Choosing the necessary software infrastructure dur-

ing the research and development phases is an essen-

tial issue of the visualisation ﬁeld. Using existing

tools and frameworks can speed up the implementa-

tion process but also can be limiting, as the constant

evolution of data science ﬁeld might introduce sce-

narios that were not covered by these tools. Alterna-

tively, researchers often create software infrastructure

for their research groups or projects, tailoring them

for their needs or new scenarios. Choosing one ap-

proach over another is a decision driven by the project

goals, as both present beneﬁts and drawbacks. Ideally,

visualisation researchers should collaborate to lever-

age the advantages of their systems to others and, if

possible, make them interoperable to ease the transi-

tion from one to another (Reina et al., 2020).

In this context, researchers interested in develop-

ing immersive visualisations found support on well-

established game engines and web technologies. De-

spite not having a focus on implementing visual-

isations, the advances of those technologies eased

the process of implementing immersive applications

(Reina et al., 2020).

A pioneering example is the tool iViz, proposed

one year before the term “Immersive Analytics” was

coined. This multi-platform tool was developed us-

ing Unity game engine and provides the possibility of

rendering signiﬁcant amounts of data in a collabora-

tive environment, with multiple users visualising and

interacting with the data at the same time (Donalek

et al., 2014; Chandler et al., 2015). Also, iViz was the

precursor of Virtualitics Immersive Platform

(VIP),

a commercial data analysis solution that enables its

https://unity.com/

users to interact and explore data on desktop and VR

environments. VIP also proposes the usage of ma-

chine learning to extract insights and recommend vi-

sualisations according to the data available (Virtuali-

tics, 2015).

Filonik and colleagues proposed Glance, a con-

ceptual framework and architecture to enable data

exploring using VR and Augmented Reality (AR),

through the implementation of GPU-accelerated visu-

alisations (Filonik et al., 2016). The reported results

were promising, and while the project source code

is available

there is no documentation or indications

that the project became a concrete implementation.

LookVR is an experimental tool that creates im-

mersive visualisations using the commercial data

analysis platform provided by the company Looker

This tool allows the user to interact with different

types of visualisations inside a VR environment, in-

cluding an exciting metaphor of “climbing the big

data” (Gieseler, 2017). LookVR is available for free

on Steam

, allowing its users to explore embedded

samples or to connect into datasets created utilising

Looker’s platform. It was not possible to ﬁnd any

documentation or APIs to extend or create new ap-

plications using the tool.

The open-source toolkits “Immersive Analytics

Toolkit” (IATK), and “Data visualisation applica-

tions for eXtended Reality” (DXR) presented interest-

ing approaches to enable a smoother authoring pro-

cess on the Unity game engine environment. Both

tools enable the creation of immersive visualisations

through a Graphical User Interface (GUI) or an Ap-

plication Programming Interface (API), helping users

with different levels of programming knowledge to

author their visualisations. The IATK framework has

the differential of being optimised to render massive

amounts of data, something the DXR authors pointed

as an improvement opportunity of their work (Cordeil

et al., 2019; Sicat et al., 2018). While both toolkits al-

low the creation of custom solutions using their APIs,

their focus is on the authoring aspect and not offering

data analytics functionalities out of the box.

The Visualisation Toolkit (VTK) also offers built-

in support for the development of VR applications

through the leverage of the OpenVR standard, sup-

porting devices like HTC Vive, Oculus Rift and Win-

dows Mixed Reality headsets (Kitware, 2016). Al-

though the usage of VTK in this context presented

varying results, several advances made by its com-

munity may turn this well-established visualisation

tool into a viable option for immersive visualisations

https://github.com/ﬁlonik/glance

https://looker.com/

https://store.steampowered.com/app/595490/LookVR/

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Figure 1: An early prototype of ImmVis framework show-

casing the usage of K-Means clustering inside the 3D envi-

ronment.

(O’Leary et al., 2017).

VRIA uses open standards from web technolo-

gies to provide a declarative grammar for creating

multi-platforms visualisations that can be shared or

integrated into other applications. VRIA also offers

the visualisation creation tool “VRIA Builder” and an

API to enable developers to interact with the frame-

work (Butcher et al., 2020).

It is important to note that these frameworks and

tools represent a signiﬁcant advance on the author-

ing, interaction with the immersive environment and

performing basic exploration tasks of the data. How-

ever, most of them does not offer tools or APIs out

of the box to perform more sophisticated data anal-

ysis tasks like data wrangling, clustering algorithms,

principal component analysis or calculating correla-

tion between the dimensions. The exceptions are the

commercial tools Virtualitics and LookVR.

The present work aims to ﬁll this gap by proposing

“ImmVis”, an open-source framework that connects

Python data analysis tools with a variety of technolo-

gies used to develop immersive applications. This ap-

proach enables the creation of applications that, to-

gether with their visualisation and interactive capa-

bilities, also offer data analysis features to aid the

knowledge discovery from datasets. The framework

runs on multiple platforms and offers integration with

a variety of programming languages, allowing differ-

ent types of applications beneﬁting from it, including

the frameworks cited in this section.

3 IMMVIS FRAMEWORK

The “ImmVis” framework was initially developed to

support experiments regarding exploratory data anal-

ysis in virtual reality. After evaluating the available

options described in the previous section, we im-

plemented our solution to integrate the data analysis

available in Python with virtual reality visualisations

designed in Unity. Our choice was mainly motivated

by the fact that both platforms are well-established in

their respective domains and have great development

communities behind them.

The main challenge of this approach was pro-

viding interoperability between the two program-

ming environments, as Unity programming language,

which is C#, does not offer a straightforward inte-

gration with Python. The ﬁrst attempt to implement

it was using existing tools like “‘IronPython”

and

“pythonnet”

to embed the Python interpreter inside

the Unity application, enabling C# to interact with

Python seamlessly. However, both technologies made

the Unity environment crash frequently, making their

usage impracticable. Also, we perceived that while

embedding the interpreter was not a big problem on

the traditional computers required to use VR, the ap-

proach could be prohibitive on standalone headsets

and smartphones with VR capabilities, as they can

present processing and memory limitations.

A second attempt was made to implement a net-

work communication system to allow Unity to com-

municate with Python through a local network, which

seemed to remove the computing requirements barrier

and could be implemented using libraries available on

both environments. Initially, we decided to try tech-

nologies like WebSockets and REST, that showed ex-

cellent results on transmitting large amounts of data.

However, we noticed during the implementation of

new features that using them would require signiﬁ-

cant efforts to make both programming languages un-

derstand a shared message format. An initial proto-

type for the WebSockets approach is available at our

Github page

This problem of exchanging messages between

the platforms was solved adopting GRPC, a “Remote

Procedure Call” framework that combined with the

message format deﬁnition also allows client applica-

tions to call methods on the server as if they were a

local resource (Chalin, 2020). The usage of GRPC

for a similar purpose was also observed on Unity Ma-

chine Learning Agents Toolkit, a tool that enables the

usage of Unity games and simulation environments to

train intelligent agents (Juliani et al., 2018). Another

advantage of GRPC was the capability of generating

code for multiple programming languages and differ-

ent platforms, potentially extending the reach of our

framework to other contexts like mobile applications

and the other frameworks and tools discussed in Sec-

tion 2.

With the technology deﬁned, we started to iter-

ate over the solution and noticed that it was possi-

ble to generalise some of the components developed

and make the code open-source, enabling other mem-

bers of the community to beneﬁt from the framework

https://ironpython.net/

http://pythonnet.github.io/

https://github.com/imdavi/immvis-server-websocket

ImmVis: Bridging Data Analytics and Immersive Visualisation

183

or contribute with their expertise. Figure 1 depicts

an early prototype of ImmVis, in which we imple-

mented a VR application that allowed users to use the

k-means clustering method on 3D scatter plots. All

the source code of ImmVis, including the develop-

ment history, is available on our Github page

under

the MIT license.

3.1 Architecture Overview

The ImmVis framework is composed of a Python

server application and client libraries written in differ-

ent programming languages to integrate the available

services into applications (Figure 2). The server was

developed using a modular approach with the follow-

ing goals in mind: enable components re-usability;

improve code readability; and ease the process of

adding new features.

Our ﬁrst version of the framework contains essen-

tial services to access remote and local datasets, infer

descriptive statistics and normalise the data to facili-

tate the plotting process.

3.2 Server

The server is the core of the ImmVis framework,

where all the data analysis functionalities are imple-

mented and made available to client libraries through

data services. The server was written using Python,

enabling the server to run on Windows, OS X and

Linux.

The communication between the server and the

clients is implemented using GRPC, which through

the usage Protocol Buffers (Protobuf), can deserialise

the incoming clients’ messages, handle network re-

quests and serialise the response into a format recog-

nised by the clients (Chalin, 2020).

As it is shown in Figure 2, the server is composed

of 3 components: the Data Manager; the Data Ser-

vices; and the Discovery Service.

3.2.1 Data Manager

The “Data Manager” is responsible by the dataset

management, allowing the users to load and manip-

ulate data through the usage of Python libraries like

pandas, scikit-learn and numpy. The component

can load one dataset at a time from local or remote

sources, supporting the following ﬁle formats: CSV,

JSON and XLS.

One of the main aspects of the data manager is

agnostic regarding the approach used to communicate

toclients, allowing its reuse with the different commu-

https://github.com/imdavi/immvis-server-grpc

nication techniques used during the experimentation

process. This component isolation is achieved by lim-

iting its responsibility only to handling and returning

structures used by the Python data analysis libraries.

The current functionalities implemented on the

data manager are: scan available datasets on a pre-

set directory; load datasets; infer descriptive statistics;

and normalise dataset values. During the initial iter-

ations of the framework, we also experimented with

more advanced analysis tasks like K-Means cluster-

ing method (Figure 1) and outliers detection.

3.2.2 Data Services

The “Data Services” layer is responsible for receiv-

ing the requests from clients to perform a given action

and answer accordingly. The current implementation

of ImmVis contains a single service that exposes the

functions available from “Data Manager”, calling its

methods and mapping its responses to the data struc-

tures used by GRPC.

Since the GRPC framework allows the server to

contain multiple services, this is the proposed way of

adding new functionalities like data ﬁltering, outliers

detection, clusterisation or any speciﬁc analysis that

could be performed on the Python environment. An-

other possibility is the creation of services specialised

on domains like image analysis, machine learning,

and signal processing.

3.2.3 Discovery Service

The “Discovery Service” helps clients to discover the

server without a previous environment conﬁguration

or manual input from the application users, which can

be tricky in some types of immersive environments.

Every time the server starts, the connection parame-

ters are broadcast in the local network to help listeners

to connect without having previous knowledge of the

server.

3.3 Client Libraries

While GRPC already generates all the necessary code

to connect and interact with the server, each platform

and programming language can present some partic-

ularities when it comes to establishing and manag-

ing the network connection. The goal of implement-

ing clients libraries is to abstract these characteris-

tics, facilitating the process of integrating the frame-

work with their applications. It is possible to create

client libraries for any platform and programming lan-

guage supported by GRPC. While its implementation

is not mandatory to integrate with the framework, it

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Figure 2: A high-level overview of ImmVis architecture.

Figure 3: A 3D scatterplot with 500,000 points being ren-

dered in Unity using ImmVis as a data source.

can smooth the process of integration for less experi-

enced developers.

The initial version of ImmVis provides a Unity

client library that simpliﬁes the integration by pro-

viding a prefab, a reusable component that develop-

ers can drag and drop into their projects, conﬁgure

the connection parameters and subscribe to an event

when the client is ready to interact with the server.

Figure 3 shows one of the samples included with the

library, where the ImmVis is used as a data source of

a 3D scatter plot.

4 PERFORMANCE EVALUATION

One of the concerns of adopting a “client-server” so-

lution is the communication latency between both

parties. To evaluate that, we generated a set of

datasets using the machine learning Python library

scikit-learn, and we measured the performance of the

scatter plot application shown in Figure 3.

The tests were performed using the same com-

puter as server and client, using the loopback network

interface. The device used has the following speciﬁ-

Figure 4: The performance evaluation of ImmVis using

“scikit-learn” generated datasets.

cations: Windows 10; Intel(R) Core(TM) i7-8850H

CPU @ 2.60GHz; 32 GB RAM and the Quadro

P1000 graphics card.

All the generated datasets contained twenty-one

dimensions: the ﬁrst one representing the “row in-

dex” and the others containing continuous values rep-

resented by ﬂoating-point numbers. To evaluate the

framework, we generated 13 datasets with different

sizes, varying the number of rows between 100 and

500,000. The code used to generate the dataset is also

available on our Github page

The tests measured the time needed to: load the

dataset; infer descriptive statistics; normalise avail-

able value; and send the data to be plotted by the

client. The tests were executed manually ten times

for each dataset available.

Figure 4 shows the results of the measurements

where the amount of time needed to plot the nor-

malised dataset increases linearly according to the

amount of data. As an example, a dataset containing

50,000 rows takes an average of 21s to be loaded and

displayed for the user. Preliminary results of the same

test performed with the same server and an Oculus

Quest device as client showed that the same dataset

takes up to 14.5 seconds.

https://github.com/imdavi/immvis-datasets-generation

ImmVis: Bridging Data Analytics and Immersive Visualisation

185

5 DISCUSSION AND FUTURE

WORK

In this section, we elaborate a discussion on the next

steps for the framework and points that we think need

improvement to advance the proposed solution.

• Expanding to Other Domains: the initial imple-

mentation of the framework aimed to handle and

analyse tabular datasets. As described in Section

3.2.2, the framework could provide multiple ser-

vices to enable the interaction with other dataset

types. For example, it would be possible to pro-

vide a service that interacts with imaging libraries

like OpenCV

• Loading Multiple Datasets: as mentioned on the

Section 3.2.1, the “Data Manager” component is

only able to load one dataset at a time, limiting

some types of analysis that combine the informa-

tion between two or more existing datasets.

• Improve the Data Transmission: As the Sec-

tion 4 showed, the time needed to transmit the

data grows linearly. We need to invest more

time on investigating possible network optimisa-

tions. Preliminary tests showed that changing

some GRPC parameters and adopting streaming

techniques can improve data transmission times.

Also, there is the possibility of adopting Apache

Arrow Flight, another framework that is on early

stages and also leverages GRPC to transport large

datasets over network (McKinney, 2019).

• Integration with Jupyter Notebooks: currently

it is possible to start and interact with an ImmVis

server from Jupyter Notebooks, but we are exper-

imenting with a version that would allow note-

books users to control and interact with the visu-

alisations in the immersive environment.

• Client Libraries for Other Programming Lan-

guages: since GRPC can generate code to mul-

tiple programming languages, it would be inter-

esting to provide other client libraries to explore

how other types of applications can beneﬁt from

the framework.

6 CONCLUSIONS

The present work introduces ImmVis, an open-source

framework that integrates Python data analysis tools

and IA applications, allowing developers of these ap-

plications to leverage analysis capabilities inside the

https://opencv.org/

immersive space. Preliminary tests showed that the

framework has the potential of being used with large

amounts of data, and there is an opportunity to opti-

mise data transmission. We believe that through the

usage of ImmVis, data science researchers could ex-

plore data analysis tasks inside the immersive space

and expand the types of metaphors and interactions

allowed by the current systems.

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