Gokhan Akdugan

At build time, the asset packaging project optimizes our project's static resources. My main tools in this project were Google's Closure Compiler and Yahoo's YUI compressor. By concatenating and compressing CSS and JavaScript, busting resource caches, and automating lint check with JSLint, this project reduces both the number of HTTP requests and the size of their payload. I was able to reduce Time to Interact by 50% at the end of the project. There were also fewer JavaScript requests from 32… Show more

At build time, the asset packaging project optimizes our project's static resources. My main tools in this project were Google's Closure Compiler and Yahoo's YUI compressor. By concatenating and compressing CSS and JavaScript, busting resource caches, and automating lint check with JSLint, this project reduces both the number of HTTP requests and the size of their payload. I was able to reduce Time to Interact by 50% at the end of the project. There were also fewer JavaScript requests from 32 to 5, fewer CSS files from 23 to 9, and a smaller overall download size of 1.5MB to 688KB. Show less

In the context of caching, speed is one of the first things that come to mind. As far as the goal is concerned, it is to set something and retrieve it as quickly as possible. Our primary database is MongoDB, which is a distributed database which runs on top of Redis, which will serve as a cache layer.

We set out with the objective of creating a scorecard for our domains so that we would be able to determine the importance level of our customers based on the scores received. To determine the importance level of a customer, a predefined set of criteria was used, such as Google rank, activity levels, number of reviews, and more. For this project, I have created a scoring algorithm that will take all the parameters, and turn them into an actual score that can be compared to other parameters. The… Show more

We set out with the objective of creating a scorecard for our domains so that we would be able to determine the importance level of our customers based on the scores received. To determine the importance level of a customer, a predefined set of criteria was used, such as Google rank, activity levels, number of reviews, and more. For this project, I have created a scoring algorithm that will take all the parameters, and turn them into an actual score that can be compared to other parameters. The technologies I used during the project included Spring Data-JPA, Hibernate, MySQL, Spring Boot, as well as Restful Web Services. Show less

The goal of the project was to use the intelligence of humans in order to perform tasks that computers are not capable of performing at the moment. For the purpose of integrating Amazon's Mturk API, I have used the SDK for their Mturk API. I have also created our tasks` pattern on HTML, with multiple questions that can be answered. The framework I used during this project was the Spring Framework, the Mturk's SDK, Faster/XML, MySQL, and HTML/CSS to build the application. Through the… Show more

The goal of the project was to use the intelligence of humans in order to perform tasks that computers are not capable of performing at the moment. For the purpose of integrating Amazon's Mturk API, I have used the SDK for their Mturk API. I have also created our tasks` pattern on HTML, with multiple questions that can be answered. The framework I used during this project was the Spring Framework, the Mturk's SDK, Faster/XML, MySQL, and HTML/CSS to build the application. Through the implementation of this project, we have been able to outsource our manual tasks and have been able to successfully collect all the responses. Show less

As a result of the outcome of this project, users were able to generate reports based on the alumni data and gather statistical results according to their academic success and business career based on the results of the project. Developed LinkedIn integration system that automates data updates. I used Spring, Hibernate, MySQL, Restful Web-Services in the project.