=Paper=
{{Paper
|id=Vol-1510/paper13
|storemode=property
|title=A Framework for Uncertainty-Aware Visual Analytics in Big Data
|pdfUrl=https://ceur-ws.org/Vol-1510/paper13.pdf
|volume=Vol-1510
|dblpUrl=https://dblp.org/rec/conf/aic/Karami15
}}
==A Framework for Uncertainty-Aware Visual Analytics in Big Data==
https://ceur-ws.org/Vol-1510/paper13.pdf
A Framework for Uncertainty-Aware Visual
Analytics in Big Data
Amin Karami1,2
1
Computer Architecture Department (DAC), Universitat Politècnica de Catalunya
(UPC), Campus Nord, C. Jordi Girona 1-3, 08034 Barcelona, Spain
2
amin@ac.upc.edu
Abstract. Visual analytics has become an important tool for gaining
insight on big data. Numerous statistical tools have been integrated with
visualization to help analysts understand big data better and faster. How-
ever, data is inherently uncertain, due to sampling error, noise, latency,
approximate measurement or unreliable sources. It is very important
and vital to quantify and visualize uncertainties for analysts to improve
the results of decision making process and gain valuable insights during
analytic process on big data. In this paper, we propose a new frame-
work to support uncertainty in the visual analytics process through a
fuzzy self-organizing map algorithm running in MapReduce framework
for parallel computations on massive amounts of data. This framework
uses an interactive data mining module, uncertainty modeling and knowl-
edge representation that supports insertion of the user’s experience and
knowledge for uncertainty modeling and visualization in the big data.
1 Introduction
The rapid development of data collection technologies in the last decades has
led to accumulate the massive amounts of data referred to as Big Data. Today,
big data has become an important and hot research topic and a very realis-
tic problem in industry [15]. One of the important and vital aspects of the big
data is its veracity, which accounts for the degree of uncertainty (e.g. vagueness,
ambiguity, imprecision, and noise) in the content of user- or system-generated
data. There are various factors that lead to data uncertainty including approx-
imate measurement, data sampling fault, transmission error or latency, data
integration with noise and so on [8][9]. These factors produce a lot of vague
and imprecise data which implicitly contains valuable information. The repre-
sentation of uncertainty is an ongoing unresolved problem and emerging as a
problem of great importance in the field of visualization [16]. Hence, various
companies and many researchers have been recently attempting to enable and
identify new opportunities for markets and design innovative products through
the uncertainty visualization in the big data era [1]. The value of uncertainty
visualization in the big data is to accurately convey uncertainty to help users
and decision makers understand potential risks and hidden knowledge, and to
minimize misleading results and interpretations [7]. A challenging and key ques-
tion is how users can effectively and efficiently understand the uncertain data
in the big data sets and interact with them through the user interface. Inter-
action and user interface challenges are critical aspects of extreme-scale visual
analysis to understand and cope with uncertainties. Adapting and applying vi-
sual analytics to the big data problems presents new challenges and opens new
research questions [18]. Visual analytics is a relatively new field of study that
aims at bridging this gap by integrating visualization and analytics in order to
turn the information overhead into an opportunity [12]. Contributions in this
area integrate information visualization, interaction and computational analysis
by data mining techniques in order to transform massive data into knowledge.
There have been several researches about visual analytics in the big data such as
[18][3][13]. The disadvantages of the existing works are their inability to quantify
and visualize uncertainty accurately.
The main contribution of this paper is a novel prototype system embracing un-
certainty in the big data through the visual analytics. This system can provide
valuable guidance through a close interaction between human operators, pre-
processing data, refining model’s parameters, building model, visualizing and
understanding uncertainty in the data through the visual interface where op-
erators are able to interact and provide desired inputs and configurations. For
uncertainty modeling in the big data, we extend our previous work in [8] -a
mechanism for mining and visualizing uncertainty in a centralized-batch data
processing- through the MapReduce framework. MapReduce [5] is a program-
ming model for executing distributed computations on massive amounts of data
in order to model a decentralized-batch data processing. This system leads to an
appropriate uncertainty-aware visualization in a massive amounts of data to help
both experienced and novice users understand hidden knowledge through mini-
mizing misleading interpretations. In section 2 we present background material
related to uncertainty modeling, visual analytics and MapReduce framework.
Section 3 presents our designed prototype for uncertainty visualization in the
big data. Section 4 discusses proposed interface design suitability from a visual
analytics perspective. Finally, section 5 concludes this paper and outlines future
work.
2 Background
2.1 Uncertainty modeling
Uncertainty is widely spread in real-world data. A data can be considered un-
certain, vague or imprecise where some things are not either entirely true nor
entirely false. To model uncertainty, numerous techniques have been proposed,
including probabilistic measures, Bayesian networks, belief functions, interval
sets and fuzzy sets theory [4]. There has been a lot of research in the applica-
tion of fuzzy sets theory to model uncertainty [8]. The Fuzzy set (FS) theory
introduced by Zadeh [17] is a more flexible approach than classical set theory,
where objects belong to sets (clusters) with certain degree of membership rang-
ing [0..1]. In this paper, we use fuzzy sets theory as a mean to measure and
quantify uncertainty.
2.2 Visual analytics process model
Visual analytics is defined as analytical reasoning supported by highly interac-
tive visual interfaces that involves information gathering, data pre-processing,
knowledge representation, interaction and decision making. A process model of
visual analytics by Keim et al. [11] is illustrated in Fig. 1. According to Fig. 1,
the first step is pre-processing such as data cleaning and data transformation
over input data to be able to use it in the desired format for further inves-
tigations. After the pre-processing step, visualization methods and automated
analysis methods are applied to the data. Afterward, automated analysis meth-
ods using data mining methods are applied to generate models. These models
can be evaluated and refined by the user through a modification of initial pa-
rameters or selecting other type of analysis algorithms. User interaction with the
visualization is needed to reveal information by applying different visualization
techniques on the data such as descriptive analysis, graphical representations etc.
Based on this interaction, the user can conduct the model building and refine-
ment in the automatic analysis. Furthermore, knowledge can be gained during
mentioned different types of user interaction. Finally, the feedback loop stores
this knowledge of insightful analyses in the system and enables the analyst to
draw faster and better conclusions in the future.
Fig. 1: The visual analytics process model (adapted from [11])
2.3 MapReduce framework for big data processing
MapReduce is a programming model popularized by Google for processing and
generating large data sets with a parallel and distributed algorithm using many
low-end computing nodes [14]. It is a scalable, fault-tolerant, and ubiquitous data
processing tool gaining significant attention from both industry and academia.
The main idea of the MapReduce is to hide details of parallel execution and allow
users to focus only on data processing strategies [6]. The MapReduce model is
composed of two procedures: Map and Reduce, written by the user. The Map
function computes a set of intermediate key/value pairs (i.e. a list of (key, value))
from the input. The intermediate key/value pairs are then grouped together on
the key-equality basis as (key, list(value)). The Reducer function performs a
summary operation on the list of all values based on each unique key. This
allows us to handle lists of values that are too large to fit in memory. The reduce
function finishes the computation started by the map function, and outputs the
final answer.
3 Proposed method: A Framework for Uncertainty-Aware
Visual Analytics in Big Data
Fig. 2: The proposed model for visual analytics
Our proposed model (see Fig. 2) is derived from the model of visual analytics
presented by Keim et al. in Fig. 1. Input data is collected, transformed and pre-
processed, both automatically, through the visualization and the user interaction
to be ready in the desired format for the analysis. After pre-processing, one of
the main challenges is the selection of an appropriate technique for uncertainty
modeling. The applied technique is based on our previous work in [8], a fuzzy
self-organizing map for uncertainty visualization in uncertain data sets. We have
extended our previous work integrating by MapReduce framework to be able to
use the big data for uncertainty modeling and visualization (see section 3.1).
We add an interactive module in our prototype design that allows refinement
of the applied techniques by the user. This prototype also consists of a graph-
ical representation to support uncertainty visualization as well as a descriptive
analysis for knowledge representation to draw conclusion.
3.1 Uncertainty modeling
Our proposed uncertainty modeling is derived from our previous work in [8],
called Fuzzy Self-Organizing Map (FSOM). In [8], we proposed a fuzzy self-
organizing map algorithm using fuzzy c-mean (FCM) to model uncertainties
based on a centralized-batch processing framework. FSOM works in three phases.
In the first phase (we called it fuzzy competition), FCM technique has been
employed to assign a membership degree in clusters’ centers in terms of the input
data. Then in the second phase (we called it fuzzy cooperation), all the clusters’
centers cooperate by a Gaussian function with their neighbors in terms of the
membership degree. Finally at the third phase (we called it fuzzy adaption), all
the centers’ positions are updated. These three phases are repeated, until the
maximum number of iterations is reached or the changes become smaller than a
predefined threshold.
First, in this section we present the main design for parallel FSOM based on
MapReduce framework for a decentralized-batch processing which is depicted in
Fig. 3. Then we explain how the necessary computations can be formalized as
map and reduce operations in detail.
Fig. 3: The schematic of the MapReduce framework. C1, C2, C3 refer to cluster centers,
X1, X2, X3, X4 refer to corresponding uncertain data points in each mapper, and the
color of data points refers to target class (red = class 1 and black = class 2).
According to Fig. 3, The map phase applies FSOM algorithm from [8] per-
forming the procedure of defining the membership degree of cluster centers from
corresponding uncertain data points while the reducer phase performs the pro-
cedure of updating the new centers.
Map Function: The input data set is stored in Hadoop Distributed File System
(HDFS) [2]. Data in HDFS is broken down into smaller pieces (called chunks)
and distributed throughout the cluster. In this way, the map and reduce func-
tions can be executed on smaller subsets of larger data sets, and this provides
the scalability that is needed for the big data processing. MapReduce reads a
single chunk of data on the input datastore, then call the map function to work
on the chunk. The map function then works on the individual chunk of data
and adds one or more key-value pairs to the intermediate KeyValueStore ob-
ject. MapReduce repeats this process for each of the chunks of data, so that the
total number of calls to the map function is equal to the number of chunks of
data. Each mapper runs FSOM algorithm from [8]. The result of this phase is a
KeyValueStore object that contains all of the key-value pairs added by the map
function. The key is the cluster centers and the corresponding values are the
position of centers in each mapper, the membership degree of each center, and
the membership degree of each center for different target classes. After the map
phase, MapReduce prepares for the reduce phase by grouping all the values in
the KeyValueStore object by unique key in the intermediate phase.
Reduce Function: The reduce function scrolls through the values from the
KeyValueStore to perform a summary calculation. We calculate the average of
aggregated values to sum up the results (see Fig. 3).
The MapReduce framework is repeated until the clusters’ centers do not change
any more in the predefined number of iteration (we set 500 iterations) or a max-
imum purity has been reached. It is highly probable that the formed clusters
containing normal data (correct classification) will have a number of abnormal
data (incorrect classification) and vice versa. Therefore, we assigned a good-
ness value in range of [0..1] for each cluster by purity metric. The purity metric
determines the frequency of the most common category/class into each cluster:
k
1X
P urity = max nj (1)
n q=1 1≤j≤l q
Where, n is the total number of samples; l is the number of categories, njq is the
number of samples in cluster q that belongs to the original class j(1 ≤ j ≤ l). A
large purity (close to 1) is desired for a good clustering. If the all data samples
in a cluster have the same class, the purity value set to 1 as a pure cluster.
3.2 Case study
To test our framework, we use a case study based on KDD-CUP’99 anomaly de-
tection data set contains a standard set of data, which includes a wide variety of
intrusions simulated in a military network environment. Each record in this data
set was labeled as either normal or as exactly one specific kind of attack. Attack
labels are classified as DOS (denial-of-service, e.g. syn flood), R2L (unauthorized
access from a remote machine, e.g. guessing password), U2R (unauthorized ac-
cess to local superuser (root) privileges, e.g., various buffer overflow attacks),
and probing (surveillance and other probing, e.g., port scanning). These differ-
ent attacks are considered as a single attack by same labeling in our study. This
data set consists of 41 features and 494021 records. In the experiments, 75%
of data set is used as training and the rest is considered as testing in order to
validate the functionality of the proposed method. To add uncertainty in the
considered data set, we add a Gaussian white noise with a zero mean and the
standard deviation with the normal distribution [0, 2 ∗ f ], where, f is an integer
parameter from the set of {1, 2, 3} to define different uncertain levels for some
features randomly.
This example helps security data analysts to monitor computer network traffic
for security purposes. The challenge for an analyst is the discrimination between
real attacks and normal traffic, where the nature of the traffic data is uncertain.
The proposed framework for uncertainty-aware visual analytics enables insight-
ful analyses in the system and allows the analyst to understand uncertainty for
drawing faster and more accurate conclusions.
Fig. 4: Prototype design: uncertainty visualization in the big data including configura-
tion section (top left); numerical results section for evaluating model by training and
testing data (bottom left); uncertainty visualization plot (top right); the history of
recent training (bottom right).
3.3 Performance measurement
To evaluate the results by the proposed algorithm, we apply several criteria
including detection rate (DR), false positive rate (FPR), F-measure, accuracy
and specificity (true negative rate) which are frequently used measures in the
classification problems [10].
4 Prototype system design for visualizing uncertain
clusters
The prototype design is depicted in Fig. 4 to provide an useful and effective
uncertainty visualization of KDD-CUP’99 traffic data. This prototype was im-
plemented by the MATLAB R2014b. The graphical user interface is designed
to allow users for visual analytics through the embedded modules. The graph-
ical interface has been divided into three main modules: data preparation (top
left: input data, model properties and pre-processing), numerical results (bottom
left: performance metrics for knowledge representation), and graphical represen-
tation (right: uncertainty visualization in the top and history of the training
in the bottom). The operators can consistently train and test the data, then
save the results for further usage or open preexisting results. To visualize the
uncertainty, we map the magnitude of the propagated uncertainty to the size (to
visualize the volume of the clusters) and the color (to encode the purity of the
clusters) of nodes in a 2D plot defined as the projection of the 41 variables from
the uncertain input big data. This projection is shown in Fig. 5.
Fig. 5: Uncertainty visualization in the big data
The blue nodes denote normal traffic while the red nodes denote attack traf-
fic. We multiply the third value of the KeyValueStore (see Fig. 3) to the corre-
sponding red and blue colors in order to define the impurity of the normal and
attack clusters. The more uncertain a cluster is, the more impure is its visual
representation. For instance, the purple color denotes a 100% uncertainty in a
formed cluster (purity = 0.5), neither completely normal nor attack traffic. This
is useful for discovering the sources of uncertainty. This visualizes the effect of
uncertainty and steers the user’s attention towards the most reliable clusters
over uncertain data points so that only the most reliable clusters are highlighted
to the user. On the other hand, a large size of a node denotes the more uncertain
data involved while a small size of a node denotes the less uncertain data in-
volved which can be interpreted as outliers. As a consequence, these small nodes
steer the user’s attention visually towards the most unreliable nodes as outliers.
This prototype design displays a high-level view of entire uncertain big data
together with the numerical results. Preliminary results show that the designed
prototype produces satisfactory outcomes. Users can steer and control uncer-
tainty based on their own practices or analytic needs in the data preparing step,
find outliers visually as well as distinguish visually reliable and unreliable clus-
ters. User evaluations by zooming into sub-regions of clusters and reveal more
details (i.e., details on demand) will be carried out in the future.
5 Conclusion
In this paper, we propose a framework for uncertainty-aware visual analytics in
the big data. We integrated a fuzzy self-organizing map algorithm with MapRe-
duce framework in order to execute a parallel computing on big data.
The prototype system includes a set of interactive visual representations that
supports the analysis of the uncertain data and user interaction. We believe that
this prototype system is useful when the analyst wants to extract a model that
explains the behavior of uncertain data, find outliers visually and makes insight-
ful decisions. The future work is needed by more user evaluations: zooming into
sub-regions of uncertain clusters and reveal more details.
6 Acknowledgment
This work was partially supported by projects TIN2013-47272-C2-2 and SGR-
2014-881.
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