Assignment on Web and Mobile Applications

Executive Summary
We often tend to think that each most of the web and mobile applications we
interact wit hon our daily basis is a unit block sitting in a cloud server
somewhere. However, this is not always the case. Most applications today are
part of what is commonly known as service integration. These apps aggregate
data and services from various sources and format it to present a useful app to
the user as a final product. In this paper, we are going to step by step in the
process of moving from desperate CSV files to a Mashup web application. The
application uses a REST API to query fire stations and then Google maps to help
users locate the exact physical location of the stations.

Table of Contents
1.0 Introduction...................................................................................................1
2.0 Key System Concepts....................................................................................1
2.1 Data merging and cleaning........................................................................1
2.2 RESTful Web Services................................................................................2
2.3 Mashups.....................................................................................................3
3.0 Demo Running Instructions...........................................................................3
4.0 Conclusion & Recommendations...................................................................5
References...........................................................................................................6
Appendices..........................................................................................................7
Appendix 1 – Data_integration.py....................................................................7
Appendix 2 – Data_Services.py........................................................................8
Appendix 3 – Stations_map.html...................................................................10
Appendix 3 – Stations_map.css......................................................................11

Table of Figures
Figure 1 : REST demo response..............................................................................2
Figure 2 : Final Mashup...........................................................................................3
Figure 3 : A pop-up message concerning the Google Maps API Key.......................4

1.0 Introduction
System integration is the process of bringing together and aggregating the
necessary components or subsystems into one system(Pietzcker et al., 2017).
The components should coordinate and function together to achieve a defined
functionality. In the field of information technology and computing, a
combination of technical and business processes is used to extract, validate,
map, transform and consolidate data from various sources into meaningful and
useful information(Huang, Chaudhary and Garmire, 2017). This is known as
system and data integration. System integration aims to integrate existing
systems which are often not independently useful, into a useful system. This
will increase the value to the consumers in any given setup.
Data and system integration involves techniques such as data mining, data
cleaning, computer networking, artificial intelligence, and manual
programming. With plenty of both structured and unstructured data available
today, proper integration of such data into systems we interact with on our
daily activities is vital. In most cases, system and data integration involve using
data and services from more than one source. Public and custom APIs are the
main sources of these data and services. This report illustrates such a scenario
whereby we take data stored in CSV files and integrate it into a basic web
application. The final product is a system that can help people quickly locate
and contact appropriate fire stations in their respective regions.
2.0 Key System Concepts
2.1 Data merging and cleaning
Data merging is the process of taking two or more groups of data and
combining them into a single unified set (Krishnan et al., 2016). The merging
could be either temporary for display purposes (such as in SQL JOIN operations)
or permanent for compact storage (such as merging excel sheets).
Data cleaning is the process of removing or modifying invalid data points from
the data set (Krishnan et al., 2016). It involves detecting and removing errors
and inconsistencies from data to improve the quality of data; cleansing the
data. From the above definition, there is an option of either removing or
modifying the invalid data points. The choice of either action depends on
several factors. Among them is the type of errors in the data, user preference
or the best available solution. For instance, non-ASCII characters are best
removed while missing numerical values can be replaced with the mean of the
available values.
In this exercise, data cleaning was a relatively easy task: removing non-
printable Unicode characters. However, I faced some challenges in the data
merging process. The main challenge was merging CSV files with a different
number of records, with an extra constraint of conditional merge decision. My
approach was to read the required files with python, use nested loops to iterate
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