Research Interests
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Technical Skills
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Research Systems
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TweetCASP : Tweet collection, analytics, and storage platform(TweetCASP) has been developed to collect, store, and analyze tweets that are collected from Twitter microblogging service with the purpose of performing real-time data collection and analytics. TweetCASP provides a user- friendly web application to start streaming Tweets by making use of Twitter Streaming API by Tweepy, RabbitMQ, and Apache Casandra. Moreover, the TweetCASP is an example of a data- intensive software system that provides an exaple technologies can be combined to develop scalable, reliable, and efficient.
Developer(s): Tugba Beril Doguc (MS Student) Supervisor: Ahmet Arif Aydin Year : 2021-2022 |
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IDCAP : Incremental Data Collection & Analytics Platform (IDCAP) is a new data analytics platform that addresses certain limitations of EPIC Collect and EPIC Analyze that prevented them from providing real-time analytics over Project EPIC’s datasets. The IDCAP thus provides a way to transition Project EPIC from batch-oriented style data processing to real-time data collection and analytics. The IDCAP is a robust, reliable, fault tolerant and 24/7 available data collection and analytics platform. The IDCAP collects tweets via the Twitter Streaming API and stores tweets in Cassandra in an incremental and scalable fashion; it is able to provide greatly improved and significantly faster batch data analysis on previously collected Twitter data sets while also providing real-time analytics on streaming crisis data. Furthermore, the IDCAP is an example of a data- intensive software system that provides insight into the types of techniques and technologies that must be combined to implement such systems and ensure that they are scalable, reliable, and efficient.
Developer(s): Ahmet Arif Aydin (Principal Designer) Supervisor: Ken Anderson Year : 2015-2020 |
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ISM : Project EPIC tweet datasets are collected during mass emergencies from Twitter microblogging service. Dataset collections can be active for months. Sorting active datasets in the incremental fashion is challenging. In order to provide incremental sorting for Project EPIC active datasets, incremental sorting method (ISM) is developed.
ISM sorts newly collected event tweets and incrementally appends them into existing event's dataset with providing entire sorted set and without changing existing tweets status. Also, ISM supports data analytics by providing efficient and fast sorting, searching, filtering for large-scale datasets collected during mass emergency events.
Developer(s): Ahmet Arif Aydin (Principal Designer) Supervisor: Ken Anderson Year : 2014-2016 |
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EPIC Analyze: The Epic Analyze project is one of the sub projects in Project EPIC. Epic Analyze is a data analytics web application to support crisis informatics research. The ultimate goal of the project is to develop a platform that provide browsing, searching, filtering, sampling, and annotating of large-scale social media datasets collected during mass emergency events. Developer(s): Adam Cardenas, Ahmet Arif Aydin, Mario Barrenechea, Mazin Hakem, Sahar Jambi Supervisor: Ken Anderson Year : 2013-2016 |
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Recommendation System: Recommendation System Project was the final project of CSCI 5502 Data Mining class at University of Colorado Boulder . The main objective was to build a book recommendation system. During the course of the project following algorithms are implemented: User-based Collaborative Filtering (CF) Algorithm, User-based CF Improved Algorithm , and Singular Value Decomposition (SVD) User-based CF Algorithm. Developer: Ahmet Arif Aydin Supervisor: Qin Lv Year : 2013 |