Data Visualization and Communication by Big Data

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1551
Data Visualization and Communication by Big Data
T. Gnana Prakash1
1Assistant Professor, CSE Department, VNR VJIET, Hyderabad, TS, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Data visualization is viewed by many disciplines
as a modern equivalent of visual communication. It is not
owned by any one field, but rather finds interpretation across
many (e.g. it is viewed as a modern branch of descriptive
statistics by some, but also as a grounded theory development
tool by others). It involves the creation and study of the visual
representation of data, meaning “information that has been
abstracted in some schematic form, including attributes or
variables for the units of information”. Data visualization is
both an art and a science. The rate at which data is generated
has increased, driven by an increasingly information-based
economy. Data created by internet activity and an expanding
number of sensors in the environment, such as satellites and
traffic cameras, are referred to as “Big Data”. Processing,
analyzing and communicating this data present a variety of
ethical and analytical challenges for data visualization. The
field of data science and its practitioners called data scientists
have emerged to help address this challenge. In this project
real time data is considered for the analysis.
Key Words: Data visualization, Big Data, Python, Real
time Data and visual data
1. INTRODUCTION
Due to the technical progress of the last decades,
which enables the production of small sized micro
processors and sensors at a low cost, a new research area
has developed, dealingwith wirelesssensornetworks.These
networks mostly consist of a large amount of tiny sensor
nodes, which combine sensing, data processing and
communicatingcomponents. Toensurethesethreefunctions
the nodes feature a number of sensors, a micro computer
and a wireless communication device.
The general purposeofsuch a sensornetwork liesin
its deployment in or very close to a phenomenon a user
wants to observe. After a network is deployed the mere act
of sensing includes the following three working steps. Step
one is the measurement of a physical property by one of the
sensors. The second step involvesthemicrocomputer which
computes the data delivered from the sensor depending on
the desired result. In a third step the computed data has to
be transmitted from the sensor node to its destination,
where it is often stored in a database. Fig 1 shows how data
from sensor node A could get to the user.
Fig. 1: Sensor nodes Scattered in Sensor Sink
After several hops inside the sensor field the sent
information reaches a so-called sink, which communicates
with a task manager node via internetorsatellite.Thesensor
network itself is thereby a self-organizing network with a
certain protocol stack used by the nodes and the sink. After
these three steps, which have been enquired rather widely
follows a fourth step, which is rather poorly explored,
namely the visualization of the sensor data. Just seeing the
raw data of a sensor network stored in a database mostly
does not fulfill the needs of the users. So the data need to be
analyzed and shown to the user in a way, where information
can easily be gained from them. Which kind of information
can be gathered from a sensor network, depends on the
application area the network is used in.
2. VISUALIZING SENSOR DATA
A sensor can be defined as “a device that receives a
stimulus and responds with an electric signal whereby
stimulus is the quantity, property or condition, that is
sensed”. Sensor networks consist of a large amount of tiny
sensor nodes. The following section therefore will deal with
sensors by looking at severalof theiraspects.Itwillbeshown
different classifications of sensors, ways to gather data from
sensors and the relevance of the position of a sensor and the
time of its measurement. At the end of the section an existing
sensor network will be introduced.

All the different kinds of sensors share the same
purpose. They ought to respond to a physical property by
converting it into an electricsignal, whichcanbeoperatedon
to produce an output. How this physical conversion is
managed inside the sensor node is of no importance for our
goal of visualization. The importance lies in the delivered
data such as for example the momentary temperature at the
sensors position.
Depending on the application there are different ways to
gather data from the sensors. By having a look at the
applications themselves, they can roughly be separated into
two parts: Analysis and event detection. In an analytical
application the user wants to access data at a certain time, to
watch the status of a phenomenon. Beneath the data
measured by sensors two other values have to be considered
as important. On the one hand a user of a sensor network
wants to know, where the sensor, that delivers the data, is to
be found.
After a closer look at sensors and some principles of
information visualization both topics have to be combinedto
achieve a visualization of sensor data. This section therefore
shows how to extract useful data out of the sensor data and
how to simplify the choice of a suitable visualization. At the
end the visualization of the existing sensor network will be
shown. When it comes to the visualization of sensor data the
first question to be asked is: Which data shall be shown? In a
sensor network that delivers multiple continuous data, it is
nearly impossible to show allthe data. At this pointtheneeds
of the user have to be considered. For example when
monitoring a factory process a user is interestedinabnormal
data like a pressure value that is too high.
For the extraction of useful knowledge out of raw sensor
data there can be used data mining techniques. But before
this technique can be applied,the dataneedtobepreparedto
increase efficiency. After choosingtherelevantdatatherehas
to be found a visualization to present these data efficiently.
Due to the enormous variety of application areas it is hard
to assign a special visualization to a certain kind of sensed
data. Instead the data are classified to narrow the range of
possible visualizations and thereby simplify the choice. The
sensed data always have more than one dimension. As
mentioned in position and time of a measurement always
play a certain role when analyzing the data.Sothecolumnsof
the tablerepresentthespatial-temporaldimensionsofsensor
data. The temporalaspectistherebydividedintomomentary,
which means an instantaneous on demand value, and
continuous, which means values of a certain time period,
while the spatial aspect is separated into relative and
absolute values. The columns are therefore split into four
parts, as they are divided twice. The rows of the table stand
for the dimensions of a sensor, which are based on the
number of different sensing components a sensor can own.
They can differ from 1- to multidimensional. The entries of
the table consist of the data types of Shneidermans data type
taxonomy. This means, that a certain n-dimensional sensor
with regard to the temporaland spatial aspectbelongstoone
of these data types. Examples for visualizations of the
different data typescan be foundinShneidermanspaper.The
temporal data type is not included, as the temporal aspect is
encoded as a further dimension of the data.
Ascan be seen, the dimension of sensor datagrowsbyone,
when regarding the temporal progression of the value, and
grows by two, when regarding the absolute position of a
value, as the absolute position is thought to be given by a x-
and a y-value not regarding the z-axis of space. Dependingon
the final dimension of the sensor data, there has to be
designed a suitable visualization that fits the needs of the
user? For example a temperature sensor in a factory, that
monitors the temperature of a machine to ensure its
functionality, would be a 1-dimensional sensor (=
temperature) with a relative position (= machine x) and a
continuous measurement. Its data are therefore, 2-
dimensional and could be shown in a line-graph.
3. PYTHON
Python is a multi-paradigm programming language:
object-oriented programming and structured programming
are fully supported, and there are a number of language
features which support functional programming and aspect-
oriented programming (including by met programming and
by magic methods). Many other paradigms are supported
using extensions, including design by contract and logic
programming. Python uses dynamic typing and a
combination of reference counting and a cycle-detecting
garbage collector for memory management. An important
feature of Python is dynamic name resolution (late binding),
which binds method and variable names during program
execution.
The design of Python offers only limited support for
functional programming in the Lisp tradition. The language
has map (), reduce () and filter () functions; comprehensions
for lists, dictionaries, and sets; as well as generator
expressions. The standardlibraryhastwomodules(itertools
and fun tools) that implement functional tools borrowed
from Haskell and Standard ML.

Python's developers strive to avoid premature
optimization, and moreover, reject patches to non-critical
parts of CPython which would offer a marginal increase in
speed at the cost of clarity. When speed is important, Python
programmers use PyPy, a just-in-time compiler, or move
time-critical functions to extension modules written in
languagessuch as C. Cython is alsoavailablewhichtranslates
a Python script into C and makes direct C level API calls into
the Python interpreter.
4. PLOTLY
Plotly is an online analytics and data visualization
tool, headquartered in Montreal, Quebec. Plotly provides
online graphing, analytics, and stats tools for individuals and
collaboration, as well as scientific graphing libraries for
Python, R, MATLAB, Perl, Julia, Arduino,andREST.Plotlywas
built using Python and the Django framework, with a front
end using JavaScriptandthevisualizationlibraryD3.js,HTML
and CSS. Files are hosted on Amazon S3.
Fig 1: Sensor data projection
5. TEST CASES AND DISCUSSIONS
Table -1: Test Cases for Data
Test Cases Expected
Output
Actual
Output
Priorit
y
sensor data
received
consistently
clean flow of
data without
any
discrepancies
data flow
with
missing
values
high
cleaning of
the data
data modified
into a
continuous
steam
datacleaned
successfully
low
storage of
the data
storing data in
an excel sheet
datacopying
errors
high
linking the
data stream
to plotly
successful
integration in
plotly
integrations
issues and
no proper
data
streaming
high
posting of
the data on
to the
website
successful
graph on the
website
continuous
streaming
-----
providing
data
dynamically
to the
program
the data is
accepted and
the
corresponding
graph is
generated
the graph is
generated
successfully
-------
other plots
using the
same data
different plots
like bar graph
or scatter plot
various
kinds of
graphs are
generated
-----
plotting
with
extreme
values
an error or an
out of the
bounds graph
the graph is
generated
but not
visible
-----
Adding
valuestothe
generated
plot
An error or
modified
graph
An error
thatsaysthe
generated
plot cannot
be further
modified
----
6. CONCLUSIONS
Through this interactive and enjoyable project on
data visualization, we could learn the real time issues in data
cleaning as well as visualizing. This project provided deep
insights into the real world of data visualization and how
effective a piece of junk data also can be made something
very interesting for analysis and understanding.
ACKNOWLEDGEMENT
We are very much thankful to our Principal and
Management forproviding all facilities like researchlabsand
software’s to carry out this work in college.
REFERENCES
Usama M. Fayyad, Andreas Wierse, Georges G. Grinstein
“Information Visualization in Data Mining And Knowledge
Discovery” Morgan Kauffman publishing, 2002
Jason W. Osborne “Best Practices In Data Cleaning” John
Wiley & Sons, 2012
Nathan Yau “Visualize This” John Wiley & Sons, 2011.

Martin C. Brown “Python: The Complete Reference”
International Journal on Osborne, 2001, pp: 67.
Buja, Andreas “Interactive Data Visualization” Journal on
Volume On Visualization, Vol. 15, 2014, pp: 153-163.
Chi, Ed H. “A taxonomy of Visualization techniques”
National Journal on Information Visualization, Vol: 3,
2010, pp: 69-75.
Ganti, V. Kaushik, R., Chaudhuri,S.” A primitive operator
For Data Cleaning And Visualization” Journal on Data
Engineering, Vol: 22, 2009, pp: 5.
https://plot.ly/python/streaming-tutorial/
https://docs.python.org/2/tutorial/
http://www.learnpython.org/
BIOGRAPHY
Gnana Prakash.T received his Bachelors of Technology
degree in 2006 and Masters in 2010 from Jawaharlal Nehru
Technological University, Hyderabad. He has more than 8
years of teaching experience Presently working as Assistant
Professor in the department of CSE, in VNR VJIET,
Hyderabad and currently pursuing Ph. D. from JNTU,
Kakinada. His Research interest areas are Mobile Ad Hoc
& Sensor Networks, Image Processing, Computer Vision,
etc.

Data Visualization and Communication by Big Data

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