Big Data Visualization Tools
⋆
Nikos Bikakis
ATHENA Research Center, Greece
1 Synonyms
Visual exploration; Interactive visualization; Information visualization; Visual analytics; Exploratory data analysis.
2 Definition
Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively
explore and analyze data, enabling them to effectively identify interesting
patterns, infer correlations and causalities, and supports sense-making activities.
3 Overview
Exploring, visualizing and analysing data is a core task for data scientists and
analysts in numerous applications. Data visualization 2 [1] provides intuitive
ways for the users to interactively explore and analyze data, enabling them
to effectively identify interesting patterns, infer correlations and causalities,
and support sense-making activities.
⋆
This article appears in Encyclopedia of Big Data Technologies, Springer, 2018
2 Throughout the article, terms visualization and visual exploration, as well as terms tool
and system are used interchangeably.
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Nikos Bikakis
The Big Data era has realized the availability of a great amount of massive
datasets that are dynamic, noisy and heterogeneous in nature. The level of
difficulty in transforming a data-curious user into someone who can access
and analyze that data is even more burdensome now for a great number of
users with little or no support and expertise on the data processing part. Data
visualization has become a major research challenge involving several issues
related to data storage, querying, indexing, visual presentation, interaction,
personalization [2, 3, 4, 5, 6, 7, 8, 9].
Given the above, modern visualization and exploration systems should
effectively and efficiently handle the following aspects.
− Real-time Interaction. Efficient and scalable techniques should support the
interaction with billion objects datasets, while maintaining the system
response in the range of a few milliseconds.
− On-the-fly Processing. Support of on-the-fly visualizations over large and
dynamic sets of volatile raw (i.e., not preprocessed) data is required.
− Visual Scalability. Provision of effective data abstraction mechanisms is
necessary for addressing problems related to visual information overloading
(a.k.a. overplotting).
− User Assistance and Personalization. Encouraging user comprehension
and offering customization capabilities to different user-defined exploration
scenarios and preferences according to the analysis needs are important
features.
4 Visualization in Big Data Era
This section discusses the basic concepts related to Big Data visualization.
First, the limitations of traditional visualization systems are outlined. Then,
the basic characteristics of data visualization in the context of Big Data era
are presented. Finally, the major prerequisites and challenges that should be
addressed by modern exploration and visualization systems are discussed.
4.1 Traditional Systems
Most traditional exploration and visualization systems cannot handle the size
of many contemporary datasets. They restrict themselves to dealing with
small dataset sizes, which can be easily handled and analysed with conventional data management and visual explorations techniques. Further, they
operate in an offline way, limited to accessing preprocessed sets of static
data.
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4.2 Current Setting
On the other hand, nowadays, the Big Data era has made available large numbers of very big datasets, that are often dynamic and characterized by high
variety and volatility. For example, in several cases (e.g., scientific databases),
new data constantly arrive (e.g., on a daily/hourly basis); in other cases, data
sources offer query or API endpoints for online access and updating. Further,
nowadays, an increasingly large number of diverse users (i.e., users with different preferences or skills) explore and analyze data in a plethora of different
scenarios.
4.3 Modern Systems
Modern systems should be able to efficiently handle big dynamic datasets,
operating on machines with limited computational and memory resources
(e.g., laptops). The dynamic nature of nowadays data (e.g., stream data),
hinders the application of a preprocessing phase, such as traditional database
loading and indexing. Hence, systems should provide on-the-fly processing
over large sets of raw data.
Further, in conjunction with performance issues, modern systems have to
address challenges related to visual presentation. Visualizing a large number of data objects is a challenging task; modern systems have to “squeeze
a billion records into a million pixels” [3]. Even in small datasets, offering
a dataset overview may be extremely difficult; in both cases, information
overloading (a.k.a. overplotting) is a common issue. Consequently, a basic requirement of modern systems is to effectively support data abstraction over
enormous numbers of data objects.
Apart from the aforementioned requirements, modern systems must also
satisfy the diversity of preferences and requirements posed by different users
and tasks. Modern systems should provide the user with the ability to customize the exploration experience based on her preferences and the individual
requirements of each examined task. Additionally, systems should automatically adjust their parameters by taking into account the environment setting
and available resources; e.g., screen resolution/size, available memory.
5 Systems and Techniques
This section presents how state-of-the-art approaches from Data Management and Mining, Information Visualization and Human-Computer Interaction communities attempt to handle the challenges that arise in the Big Data
era.
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Nikos Bikakis
5.1 Data Reduction
In order to handle and visualize large datasets, modern systems have to
deal with information overloading issues. Offering visual scalability are crucial in Big Data visualization. Systems should provide efficient and effective abstraction and summarisation mechanisms. In this direction, a large
number of systems use approximation techniques (a.k.a. data reduction techniques), in which abstract sets of data are computed. Considering the
existing approaches, most of them are based on: (1) sampling and filtering [10, 11, 12, 13, 14] and/or (2) aggregation (e.g., binning, clustering)
[15, 16, 17, 18, 19, 20].
5.2 Hierarchical Exploration
Approximation techniques are often defined in a hierarchical manner [15,
16, 19, 20], allowing users to explore data in different levels of detail (e.g.,
hierarchical aggregation).
Hierarchical approaches 3 allow the visual exploration of very large datasets
in multiple levels, offering both an overview, as well as an intuitive and effective way for finding specific parts within a dataset. Particularly, in hierarchical approaches, the user first obtains an overview of the dataset before
proceeding to data exploration operations (e.g., roll-up, drill-down, zoom, filtering) and finally retrieving details about the data. Therefore, hierarchical
approaches directly support the visual information seeking mantra “overview
first, zoom and filter, then details on demand ” [21]. Hierarchical approaches
can also effectively address the problem of information overloading as they
adopt approximation techniques.
Hierarchical techniques have been extensively used in large graphs visualization, where the graph is recursively decomposed into smaller sub-graphs
that form a hierarchy of abstraction layers. In most cases, the hierarchy is
constructed by exploiting clustering and partitioning methods [22, 23, 24, 25].
In other works, the hierarchy is defined with hub-based [26] and densitybased [27] techniques. [28] supports ad-hoc hierarchies which are manually defined by the users. Differents approaches have been adopted in [29,30], where
sampling techniques have been exploited. Other works adopt edge bundling
techniques which aggregate graph edges to bundles [31, 32, 33, 34, 35, 36].
3
sometimes also referred as multilevel
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5.3 Progressive Results
Data exploration requires real-time system’s response. However, computing
complete results over large (unprocessed) datasets may be extremely costly
and in several cases unnecessary. Modern systems should progressively return
partial and preferably representative results, as soon as possible.
Progressiveness can significantly improve efficiency in exploration scenarios, where it is common that users attempt to find something interesting
without knowing what exactly they are searching for beforehand. In this
case, users perform a sequence of operations (e.g., queries), where the result
of each operation determines the formulation of the next operation. In systems where progressiveness is supported, in each operation, after inspecting
the already produced results, the user is able to interrupt the execution and
define the next operation, without waiting the exact result to be computed.
In this context, several systems adopt progressive techniques. In these techniques the results/visual elements are computed/constructed incrementally
based on user interaction or as time progresses [16, 37, 38]. Further, numerous recent systems integrate incremental and approximate techniques. In
these cases, approximate results are computed incrementally over progressively larger samples of the data [10, 12, 13].
5.4 Incremental and Adaptive Processing
The dynamic setting established nowadays hinders (efficient) data preprocessing in modern systems. Additionally, it is common in exploration scenarios
that only a small fragment of the input data to be accessed by the user.
In situ data exploration [16,39,40,41,42,43] is a recent trend, which aims at
enabling on-the-fly exploration over large and dynamic sets of data, without
(pre)processing (e.g., loading, indexing) the whole dataset. In these systems,
incremental and adaptive processing and indexing techniques are used, in
which small parts of data are processed incrementally “following” users’ interactions.
5.5 Caching and Prefetching
Recall that, in exploration scenarios, a sequence of operations is performed
and, in most cases, each operation is driven by the previous one. In this
setting, caching and/or prefetching the sets of data that are likely to be accessed by the user in the near future can significantly reduce the response
time [16, 38, 44, 45, 46, 47, 48]. Most of these approaches use prediction tech-
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Nikos Bikakis
niques which exploit several factors (e.g., user behavior, user profile, use case)
in order to determine the upcoming user interactions.
5.6 User Assistance
The large amount of available information makes it difficult for users to manually explore and analyze data. Modern systems should provide mechanisms
that assist the user and reduce the effort needed on their part, considering
the diversity of preferences and requirements posed by different users and
tasks.
Recently, several approaches have been developed in the context of visualization recommendation [49]. These approaches recommend the most suitable
visualizations in order to assist users throughout the analysis process. Usually, the recommendations take into account several factors, such as data
characteristics, examined task, user preferences and behavior, etc.
Especially considering data characteristics, there are several systems that
recommend the most suitable visualization technique (and parameters) based
on the type, attributes, distribution, or cardinality of the input data [16, 50,
51,52,53,54]. In a similar context, some systems assist users by recommending
certain visualizations that reveal surprising and/or interesting data [55, 56,
57]. Other approaches provide visualization recommendations based on user
behavior and preferences [58, 59]. Finally, systems provide recommendations
and explanations regarding data trends and anomalies [60, 61].
6 Examples of Applications
Visualization techniques are of great importance in a wide range of application areas in the Big Data era. The volume, velocity, heterogeneity and
complexity of available data make it extremely difficult for humans to explore and analyze data. Data visualization enables users to perform a series
of analysis tasks that are not always possible with common data analysis
techniques [64].
Major application domains for data visualization and analytics are Physics
and Astronomy. Satellites and telescopes collect daily massive and dynamic
streams of data. Using traditional analysis techniques, astronomers are able
to identify noise, patterns and similarities. On the other hand, visual analytics can enable astronomers to identify unexpected phenomena and perform
several complex operations, which are not are feasible by traditional analysis
approaches.
Another application domain is atmospheric sciences like Meteorology and
Climatology. In this domain high volumes of data are collected from sensors
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and satellites on a daily basis. Storing these data over the years results in
massive amounts of data that have to be analyzed. Visual analytics can assist
scientists to perform core tasks, such as climate factors correlation analysis,
event prediction, etc. Further, in this domain, visualization systems are used
in several scenarios in order capture real-time phenomena, such as, hurricanes,
fires, floods, and tsunamis.
In the domain of Bioinformatics, visualization techniques are exploited in
numerous tasks. For example, analyzing the large amounts of biological data
produced by DNA sequencers is extremely challenging. Visual techniques can
help biologist to gain insight and identify interesting “areas of genes on which
to performs their experiments.
In the Big Data era, visualization techniques are extensively used in the
business intelligence domain. Finance markets is one application area, where
visual analytics allow to monitor markets, identify trends and perform predictions. Besides, market research is also an application area. Marketing agencies
and in-house marketing departments analyze a plethora of diverse sources
(e.g., finance data, customer behavior, social media). Visual techniques are
exploited to realize task such as, identifying trends, finding emerging market
opportunities, finding influential users and communities, optimizing operations (e.g., troubleshooting of products and services), business analysis and
development (e.g., churn rate prediction, marketing optimization).
7 Further Reading
The literature on visualization is extensive, covering a large range of fields
and many decades. Data visualization is discussed in a great number of recent
introductory-level textbooks, such as [1, 62, 63, 64, 65].
Also, there are various articles discussing Big Data visualization; see [3, 4,
5, 6, 9]. Surveys of Big Data visualization systems can be found at [2, 7, 8].
In what follows we provide some surveys/studies related to issues discussed
in this article:
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Graph visualization [66, 67, 68]
Hierarchical exploration [15]
Visualization recommendations [49]
Linked and Web data visualization [2, 69, 70, 71]
High-dimensional data visualization [72]
Temporal data visualization [73]
Some of the major workshops and symposiums focusing on Big Data visualization include:
− Workshop on Big Data Visual Exploration and Analytics (BigVis)
− Symposium on Big Data Visual Analytics (BDVA)
− Big Data Analysis and Visualization (LDAV)
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Nikos Bikakis
−
−
−
−
Workshop on Data Mining Meets Visual Analytics at Big Data era (DAVA)
Workshop on Human-In-the-Loop Data Analytics (HILDA)
Workshop on Data Systems for Interactive Analysis (DSIA)
Workshop on Immersive Analytics: Exploring Future Interaction and Visualization Technologies for Data Analytics
Finally, there is a great deal of information regarding visualization tools
available in the Web. We mention dataviz.tools4 and datavizcatalogue5
which are catalogs containing a large number of visualization tools, libraries
and resources.
8 Cross-References
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Visualization
Visualization Techniques
Visualizing Semantic Data
Graph exploration and search
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