Real Time Data Analytics Literature Synthesis Report for ITECH5500

This report is a literature synthesis on real-time data analytics, examining various aspects of big data processing and analysis. It begins with a proposal outlining the importance of real-time data analytics for uncovering hidden patterns and correlations in large datasets. The report then delves into key concepts such as data acquisition, storage, visualization, and optimization. A synthesis matrix compares and contrasts different research ideas and systems, including Facebook's real-time processing systems, the PlanetSense platform for geospatial intelligence, and the IncApprox system for incremental approximate computing. The report also covers real-time data analysis in ClowdFlows, real-time data analysis for water distribution networks using Storm, and the use of social media platforms like LinkedIn for accruing real-time data. References from various research papers are provided to support the analysis. The report aims to provide a comprehensive overview of the current state of real-time data analytics and its applications in different domains.

Running head: REAL TIME DATA ANALYTICS
Real Time Data Analytics
Name of the Student
Name of the University
Author Note

1REAL TIME DATA ANALYTICS
Table of Contents
Proposal........................................................................................................................2
Synthesis Matrix...........................................................................................................3
References...................................................................................................................8

2REAL TIME DATA ANALYTICS
Proposal
Real time data analytics peruses the large quantity of data for unveil the
correlations, hidden patterns and the other insights. There are some key aspects of
data analytics.
The operating and roadmap model keeps the record of worker’s processes
and action. It validates the key performance indicators (KPIs) quality. To accomplish
the mission and vision of the organization it formulates the strategies.
Data Acquisition (DAQ) is defining the process, which measures the electrical
and physical changes.
It is very essential to monitor and detect the harmful activities in the network.
Otherwise, it will cause threats for global security. Real time data analytics is a new
generation tools for security, which detects malicious content.
To meet the regulatory requirement of an organization data standard and
governance is required. The data standard and governance enhance the data
management quality and builds acquired data structure for the portfolios of
international real estate.
Data storage, data visualization and data optimizations are strategies of the
bid data analysis. Data storage is essential to regain the data using the computers.
Data visualization is a visual representation on the gained data analysis. Data
optimization helps in reducing the response time of database system.

3REAL TIME DATA ANALYTICS
Synthesis Matrix
Idea A Idea B Idea C
Survey of Real-
time Processing
Systems for Big
Data
In Facebook, single
page is separated
into many portlets.
It can retrieve data
or information from
the database
system (Liu,
Iftikhar, & Xie,
2014).
Scribe is executed
as thrift service in
Facebook, which
executes multiple
machines and
reliable delivery of
uncountable
messages (Liu,
Iftikhar, & Xie,
2014).
For logging, the
aggregation of data
is used as purpose
of Flume. Flume
transport huge
quantity of
Facebook
generated data
(Liu, Iftikhar, & Xie,
2014).
PlanetSense: A
Real-time
Streaming and
Spatio-temporal
Analytics Platform
for Gathering Geo-
spatial Intelligence
from Open Source
Data “(Vision
Paper)”
Facebook data are
dynamic source to
provide the
temporal resolution
to generate
accuracy and use
of check-ins
(Thakur et al., 2015
).
AGI data is
contributed by the
Facebook usages.
The users share
their information,
concerns and
responses (Thakur
et al., 2015 ).
This research
implements the
fresh form of
information or data
in the highest
resolution of the
model of
population
distribution in
Facebook (Thakur
et al., 2015 ).

4REAL TIME DATA ANALYTICS
Big data and
management.
Postings on
Facebook affect
the products and
the strategies.
Facebook is used
to light the data on
new product’s
creation (George,
Haas & Pentland,
2014).
.
Companies are
using digital track
system for
Facebook on the
real time. It creates
longitudinal
structure of data by
posts and reviews
(George, Haas &
Pentland, 2014).
Facebook study
can be Big Data for
observing
progressive formal
or informal
network. They
create impacts on
individual network
(George, Haas &
Pentland, 2014).
IncApprox: A Data
Analytics System
for Incremental
Approximate
Computing
Analyzing social
network like Twitter
is the research
area. It evaluates
real time data
stream for
computing
emerging topic
(Krishnan et al.,
2016).
Loss of accuracy in
Twitter has various
sampling rates and
higher curves in
network monitoring
(Krishnan et al.,
2016).
The API rate of
Twitter sets limit for
returned tweets.
This analytical tool
creates a control
over the tweet
stream (Krishnan et
al., 2016).

5REAL TIME DATA ANALYTICS
Real-Time Data
Analysis in
ClowdFlows
Implementation of
new workflow
components works
for the tweets and
Twitter API and
these are reusable
in stream mining
(Kranjc, Podpečan
& Lavrač, 2013).
On the real time
data the mining
process of Twitter
adapts the
Clowdflows and the
sentiment is on
arbitrary query in
real time (Kranjc,
Podpečan &
Lavrač, 2013).
For consuming
incoming streams a
widget is
implemented,
which creates
connection to
Twitter via Twitter
API (Kranjc,
Podpečan &
Lavrač, 2013).
Real Time Data
Analysis for Water
Distribution
Network using
Storm
The process of
Twitter tweets in
real time with the
Storm for analytic
product. Storm
analyzes the
regular impression
of the information
in real time (Kumar,
2014).
The Twitter Storm
detects the
malicious content
but it fails to
calculate
statistically in real
time (Kumar,
2014).
The values are
stored in the
database of the
real time by using
the Twitter Storm
(Kumar, 2014).
Big data research Using social media
like LinkedIn, the
real time data is
Offline or online
environments are
providing the
The analytic
infrastructure of
LinkedIn presents

6REAL TIME DATA ANALYTICS
accrued from
uncountable end
users collecting
their information
(Pääkkönen &
Pakkala, 2015).
analysis of real
time data for the
end users
(Pääkkönen &
Pakkala, 2015).
the data from two
sources. One is
snapshots and
other one is end
user’s activity data
(Pääkkönen &
Pakkala, 2015).
Survey of Real-
time Processing
Systems for Big
Data
Users of LinkedIn
need to process
real time data and
analyze the data. It
supports various
subscribing
systems, which
delivers messages
(Liu, Iftikhar & Xie,
2014).
LinkedIn has
developed a real
time message
publish system
known as Kafka.
Kafka maintains
message topic and
message sequence
(Liu, Iftikhar & Xie,
2014).
Emerging system
of LinkedIn has
advantages for
dealing with
smooth data
streams and
provides data
analytics of real
time (Liu, Iftikhar &
Xie, 2014).
The “Big Data”
Ecosystem at
LinkedIn
The system of
LinkedIn has
effortless egress
and ingress in case
of building
In the key value
store of LinkedIn,
the system creates
index file and data
using the elastic
Frontend
framework of
LinkedIn throws the
event activity as a
part of member of

7REAL TIME DATA ANALYTICS
application of data
mining (Sumbaly,
Kreps & Shah,
2013).
resources
(Sumbaly, Kreps &
Shah, 2013).
LinkedIn (Sumbaly,
Kreps & Shah,
2013).
Moving beyond
Operations:
Leveraging Big
Data for Urban
Planning Decisions
Waze mapping
service polls the
location of the
phones using
information and
generate the real
time map of traffic
(French, Barchers
& Zhang, 2015).
Waze is crowd-
sources mapping
application that
obtain the condition
of traffic by time
and location
(French, Barchers
& Zhang, 2015).
Waze has data
representation
problem.
Development
processes are used
to incorporate the
big data in
transportation of
long term (French,
Barchers & Zhang,
2015).

8REAL TIME DATA ANALYTICS
References
French, S., Barchers, C., & Zhang, W. (2015, July). Moving beyond operations:
Leveraging big data for urban planning decisions. In 56th Annual Conference
of Association of College Schools of Planning (ACSP), Portland (pp. 194-1).
George, G., Haas, M. R., & Pentland, A. (2014). Big data and management.
Kranjc, J., Podpečan, V., & Lavrač, N. (2013, October). Real-time data analysis in
ClowdFlows. In 2013 IEEE International Conference on Big Data (pp. 15-22).
IEEE.
Krishnan, D. R., Quoc, D. L., Bhatotia, P., Fetzer, C., & Rodrigues, R. (2016, April).
Incapprox: A data analytics system for incremental approximate computing.
In Proceedings of the 25th International Conference on World Wide Web (pp.
1133-1144). International World Wide Web Conferences Steering Committee.
Kumar, S. (2014). Real time data analysis for water distribution network using
storm (Doctoral dissertation, Master’s Thesis, University of Fribourg, Fribourg,
Switzerland).
Liu, X., Iftikhar, N., & Xie, X. (2014, July). Survey of real-time processing systems for
big data. In Proceedings of the 18th International Database Engineering &
Applications Symposium (pp. 356-361). ACM.
Liu, X., Iftikhar, N., & Xie, X. (2014, July). Survey of real-time processing systems for
big data. In Proceedings of the 18th International Database Engineering &
Applications Symposium (pp. 356-361). ACM.

9REAL TIME DATA ANALYTICS
Pääkkönen, P., & Pakkala, D. (2015). Reference architecture and classification of
technologies, products and services for big data systems. Big data
research, 2(4), 166-186.
Sumbaly, R., Kreps, J., & Shah, S. (2013, June). The big data ecosystem at linkedin.
In Proceedings of the 2013 ACM SIGMOD International Conference on
Management of Data(pp. 1125-1134). ACM.
Thakur, G. S., Bhaduri, B. L., Piburn, J. O., Sims, K. M., Stewart, R. N., & Urban, M.
L. (2015, November). PlanetSense: a real-time streaming and spatio-temporal
analytics platform for gathering geo-spatial intelligence from open source
data. In Proceedings of the 23rd SIGSPATIAL International Conference on
Advances in Geographic Information Systems (p. 11). ACM.