ICT705 Data And System Integration

Introduction

The integration of data and system is known as the process which is used to technical and business development. And it uses the many resources and get the data from that and also produce the outputs kind of data with integrity and it works as to combine the data for the vision. And it uses the ware house for data storing and also used in both as technical and business and it had two operations like mapping and validation (Huang and Zhu, 2013).  And using the warehouse the system is integrated and the vision to the customers is performed by the data. And it also used for recognize the map operation and data integration and in the warehouse each data has the identification. In the system integration it uses the requirements such as functional and nonfunctional requirements and these requirements are also analyzed and it also has a part in business development.

Concept of key system

The system is formed by collections of elements. The key system concept is used for finding the relationship between two elements in same system. In the key system concept two main methods are used for obtain the solution such as interaction and holism. It provides recognition that in complex (Wang, Shen and Sun, 2013). The system boundary is available for every system elements. The interaction process is done not only the system elements they also used in other system elements within an environment.

Data cleaning and merging

The concept of working data is done by two important factors they are data cleaning, and data merging. The data is collected from multiple files, so we are using data merging technique. R function is the type of function is used for collect the data from different sources. VLOOKUP is a command using this command the above operations are performed.

Functions

Now we have two different data sets one is sales and another one is customers. The first data set is sales it contains types of variables like data, id, product id, and sales. The R function is used for load the variables. But they are specified in a common name like my data. Next one is customer datasets; this datasets are also having some variables like id, age, and country. The R function is used to load operation, but they are specified in another common name like my data.

RESTful web services

REST means representational state transfer. REST is not a protocol it just a architectural style (Olmsted, 2017).  RESTful web services provide many aspects, and it will achieve lot of advantages.

• FAST- of course all the RESTful web services are executed in rapidly. In this architectural style is not following any strict specification. SOAP is a type of strict specification, but it’s not following that specification. It requires only the minimum number of requirements. And also it contains only less no of bandwidth.
• Platform independence and languages- any type of programming language is supported by RESTful web services. And the most important thing is it’s a platform independence, so it allows any kind of operating system. 

Mashups

Mashup is a kind of method and it is used to the user to create or develop the application. In the business and customers development the data and the system integration mainly contribute to get the data. And for the better development of business the knowledge needed about the language used to program. And for the business development it uses the interfaces such as application programming interface. By the usage of this data integration we can analyze the mashup performance. In the mashup it had a logic for the components and operators related to mashup in the way off analyzing and construction (LANs, 2012). And mashup tool is used to deliver the operation and in the integration the components usage is based on the logic. And the system integration performance recognized through the mashup operation. In that programming language some script is used to the mapping and also it is used for execution as dynamic. And the mashup methods include the supporting languages and gadgets.

Demo Running Instruction:

Combining two files:

Explanation about code:

The python program that is “dataMerger.py” is used to merge given content and the files are imported by using the keyword “import” and every attributes in the codes are used to form the tree and the web service side the python program that is “clinics_locator.py” used to search and locate the address in the nearest tab.

Figure 1 Combine two files

Restful web services:

Explanation

Which was carried out to the execution of python files and save the location on .csv files. For show the results of the operation “import csv” was used. For opening the information “Clinicopen()”  was used. To read the file we need to use “ClinicFileReader()” was used. For length checking purpose we need to use the “If (Len! =row)”. To increase the no of rows we need to use “ClinicList[]=ClinicList[]+row”. For exiting from the file we need to use “ClinicFile.close”.

Figure 2 Find out the restful web services

To search the Location:

Figure 3  To search the location

Displayed the Location (Google Map):

Figure 4 Display the location using Map

Code Explain:

The clinics_html file used to view the exact geolocation and direction of the position of the clinic wants to know. Here we can able to see the MAP which contains the direction for the clinic. It very useful show the location of the clinics services location easily.

Conclusion

The techniques are used to compute the responsibilities of the system. The system integration of various data the final required data was recovered. Scalability of the system was ensured by the virtualizing techniques. And finally the integrating the information, demonstrations also performed. The position of the stores in the MAP was identified. The IT structure are mainly used to access the data centers and based on the Functionality of the dependent on the type of the Infrastructure.

Reference

Huang, X. and Zhu, W. (2013). An Enterprise Data Integration ERP System Conversion System Design and Implementation. Applied Mechanics and Materials, 433-435, pp.1765-1769.

Lens, R. (2012). Data virtualization for business intelligence architectures. Amsterdam: Elsevier/MK.

Olmsted, A. (2017). Heterogeneous system integration data integration guarantees. Journal of Computational Methods in Sciences and Engineering, 17, pp.S85-S94.

Wang, X., Shen, J. and Sun, C. (2013). Data Warehouse Oriented Data Integration System Design and Implementation. Applied Mechanics and Materials, 321-324, pp.2532-2538.