Data Integration and Visualization Project: Fall 2024

Desklib provides solved assignments and past papers to help students succeed.

Table of Contents
1..0 Introduction....................................................................................................................................2
2.0 Key concepts....................................................................................................................................2
2.1......................................................................................................................................................2
2.2 Restful web services....................................................................................................................2
2.3 Mashup........................................................................................................................................3
3.0 Demo Running Instructions.............................................................................................................3
Conclusion.............................................................................................................................................6
References.............................................................................................................................................6

1..0 Introduction
This assignment is based on data and system integration. The CSV file is being provided which have
data with locations of different offices, phone number and state code will be provided in the office.
Data_Integration file will be generated which will have information regarding phone numbers, state
codes, and information regarding offices. Office locator file will clean the data according to the brief
and transform the data so that, it can be converted into CSV file which contains services and office
locations. After that, the data is being plotted over the map which will show the different locations
of the office according to its postal code.
2.0 Key concepts
2.1
Merging two datasets is the way toward uniting two datasets into one, and adjusting the lines
from each dependent on regular properties or sections. The words "union" and "join" are
utilized moderately in Pandas. (McKinney et al., 2012)
A Data frame is a 2D information structure, i.e., information is adjusted in an unthinkable
design in lines and sections. We can join, combine, and concat data frame utilizing various
strategies such as df.join(), df.merge(), and df.concat().
2.2 Restful web services
Full form for REST is Representational State Transfer. REST is utilized to assemble Web benefits
that are lightweight, viable, and versatile in nature. An administration which is based on the REST
engineering is known as a RESTful administration. The hidden convention for REST is HTTP, which is
the fundamental web convention.
Restful Methods
 POST: This would be utilized to make another worker utilizing the RESTful web
administration
 GET: This would be utilized to get a rundown of all representatives utilizing the
RESTful web administration
 PUT: This would be utilized to refresh all representatives utilizing the RESTful
web administration.
 DELETE: This would be utilized to erase all representatives utilizing the
RESTful web administration
State and usefulness are isolated into dispersed assets, each asset should be available through
the typical HTTP directions of getting, POST, PUT, or DELETE. So on the off chance that
somebody needed to get a document from a server, they should have the option to issue the
GET demand and get the record. On the off chance that they need to put a record on the
server, they ought to have the option to either issue the POST or PUT demand. Lastly, in the
event that they needed to erase a record from the server, they can issue the DELETE demand.
It empowers web applications that are based on different programming to speak with one
another
With the assistance of Restful administrations, these web applications can dwell on various
situations, some could be on Windows, and others could be on Linux. (Krishnan et al.,
2016)

2.3 Mashup
 A mashup in web creation, is a web application or site page that utilizations
content from more than one source to make a solitary new administration showed in a
solitary graphical interface
 The fundamental attributes of a mashup combination, aggregation, and
visualization. It is essential to make existing information progressively helpful, for individual
and expert use. To have the option to forever, mashups provide the needful.
 There are numerous sorts of a mashup, for example, customer mashups, business
mashups, and information mashups. The most widely recognized kind of mashup is
the buyer mashup, went for the overall population.
 Mashups can likewise be arranged by the fundamental API type they utilize and
any of these can be joined with one another or installed into different applications.
 a mashup empowering influence is an instrument for changing inconsistent IT
assets into a structure that enables them to be effectively combined, so as to make a
mashup.
 Information quality is a test when naturally coordinating information from self-
sufficient sources. In the open domain, the information aggregator has practically no
impact on the information distributer. Information is frequently wrong, and joining
information regularly aggravates the issue. ( Babik et al., 2006)

3.0 Demo Running Instructions
Figure 1 Starting the localhost server
Figure 2 Bottle Framework
Figure 3 Using Service ID
Figure 4 Map Integrity

Figure 5 Location finder
Figure 6 Location Finder with data

Figure 7 Location on Map

Conclusion
In this file data of different locations are being stored in a CSV file. The data of various office
locations is being cleaned according to the phone number and entered with their state number and
after that, it is merging with the services CSV file and mapped into an HTML file named as
Office_map.html. This program is run on localhost which is being created by the bottle.

References
McKinney, W., 2012. Python for data analysis: Data Wrangling with Pandas, NumPy,
and IPython. " O'Reilly Media, Inc.".
Babik, M. and Hluchy, L., 2006. Deep integration of python with a web ontology
language. In In Proceedings of the 2nd Workshop on Scripting for the Semantic Web.
Krishnan, S., Franklin, M.J., Goldberg, K., Wang, J. and Wu, E., 2016, June. Active
clean: An interactive data cleaning framework for modern machine learning. In
Proceedings of the 2016 International Conference on Management of Data (pp. 2117-
2120). ACM.