Application for Big Data Visualization using Google BigQuery and Google Charts

Poster also presented at the Northeast Section American Society for Engineering Education 2015 at Northeastern University in Boston, MA on 2015-04-30.Big data visualization conveys information much faster than tables containing numbers and text. Therefore, this poster presents an efficient method for big data visualization on top of Google BigQuery. Google BigQuery is a cloud-based interactive query service for massive datasets built on Dremel. In this project, we used 'natality' dataset that has United States birth data from 1969 to 2008 saved in 137 million rows. For the implementation of dashboard application, we employ Google App Engine using Python and Google Charts with JavaScript

Implementing an Affordable High Performance Computing for Teaching-oriented Computer Science Curriculum

The main objective of this poster is to present an affordable and easy-to-use high performance cluster system that can be used for the classroom in teaching-oriented computer science curriculum. In order to address this, we design and implement an affordable high performance cluster system that is based on PlayStation 3(r). PS3 is a well-known for game console manufactured by Sony. Since each PS3 console has IBM Cell BE processor that consists of 8 Synergistic Processing Elements (SPEs) and 1 Power Processing Element (PPE), it can be used as a processing node with multiple-core processor in the cluster system. In addition, the implemented cluster system has been used for new and existing computer science courses, such as CPSC 592: Parallel and Distributed Database, CPSC 590: Parallel and Distributed Processing, and CPSC 591: Parallel Programming

Data Mining Techniques on Traffic Violations

This paper describes use of Data mining techniques used to model traffic accidents detection. It is done by determing the blackspots by using Association Rule Mining and Clusterization algorithm. It helps to ascertain the traffic violation patterns and blackspot of traffic violations. We looked into K-means clustering with some enhancements to aid in the process of identification of patterns and blackspots. We applied these techniques to real traffic data extracted from the Montgomery County of Maryland and validated our results. We also developed a prioritized scheme for attributes here to deal with the limitations of various out of the box clustering methods and ways. This easy to implement data mining framework works with the geo-spatial plot of blackspots and helps to improve the road accidents zones

Social User Mining

In recent years, the pervasive use of social media has generated huge amounts of data that starts to gain a lot of attentions. Each social media source utilizes different data types such as textual and visual. For example, Twitter is for a short text message, Flickr is for images and videos, and Facebook allows all of these data types. With the use of data mining techniques, the social media data opens a lot of opportunities for researchers. To address these challenges and to discover unknown information about users, we first introducing data assemble module to handle both textual and visual information from different media sources. After that, we Introducing data integration module to integrate textual and visual data. In addition, we proposed two different applications for social user mining

Computer Aided Diagnosis System for Wireless Capsule Endoscopy Video

Along with the advancing of technology in wireless and miniature camera, Wireless Capsule Endoscopy (WCE), the combination of both, enables a physician to diagnose patient's digestive system without actually perform a surgical procedure. Although WCE is a technical breakthrough that allows physicians to visualize the entire small bowel noninvasively, the video viewing time takes 1 - 2 hours. Not only it sets a limit on the wide application of this technology but also it incurs considerable amount of cost. Therefore, it is important to automate such process so that the medical clinicians only focus on interested events. As an extension from our previous work that characterizes the motility of digestive tract in WCE videos, we propose a new assessment system for energy based events detection (EG-EBD) to classify the events in WCE videos. For the system, we first extract general features of a WCE video that can characterize the intestinal contractions in digestive organs. Then, the event boundaries are identified by using High Frequency Content (HFC) function. The segments are classified into WCE event by special features. In this system, we focus on entering duodenum, entering cecum, and active bleeding. This assessment system can be easily extended to discover more WCE events, such as detailed organ segmentation and more diseases, by using new special features. In addition, the system provides a score for every WCE image for each event. Using the event scores, the system helps a specialist to speedup the diagnosis process

Analyzing Taekwondo Poomsae Video Based on Background Modeling Approach

One of the most popular martial art programs in world-wide is Taekwondo. It is an official Olympic game. Over 177 countries, more than five million people world-wide practice Taekwondo as their martial art style. Specifically, the Poomsae is a series of basic movements in Taekwondo for offensive and defensive techniques. Despite the high popularity and long history of Taekwondo, there has been less effort to systemize Taekwondo Poomsae competition, which may cause judging issues and be a hurdle of its proceeding to a new game in Olympics. In this poster we will mention how to use background modeling approach in Taekwondo Poomsae videos that can help in eliminating the noises around the player which could be caused by audience. At the end, it will be very helpful in analyzing Taekwondo Poomsae captured videos

Predicting Educational Relevance For an Efficient Classification of Talent

This research work utilizes machine learning approach to build a predictive model for the prediction of the students and the job seekers’ to quantify their fitness's for the courses and jobs they plan to pursue, respectively. Some of the existing research utilizes GPA for academic prediction and use personality prediction and computing in social domains for various industrial goals. On the other hand, this research work advances the state of the art to correlate and blend the personality features with the academic attributes to identify and classify the relevant talent of the individuals for the academic and real world success with improved predictive modeling. This work incorporates three algorithms to quantify a talent in the relevance, and then predict good fit students and good fit candidates, based on supervised learning, stochastic probability distribution and classification rules, etc. This work opens many opportunities for future research towards Genomics data mining to mine individuals for various areas

Social Profiling of Flickr: Integrating Multiple Types of Features for Gender Classification

With the pervasive use of social media sites, an extraordinary amount of data has been generated in different data types such as text and image. Combining image features and text information annotated by users reveals interesting properties of social user mining, and serves as a powerful way of discovering unknown information about the users. However, there has been few research work reported about combination of image and text data for social user mining. In this study, we propose a novel idea to classify the gender of user by integrating multiple types of features. We utilize not only text information, i.e., tag or description, but also images posted by a user with semantic based data fusion technique

Weather Data Analysis using Hadoop to Mitigate Event Planning Disasters

This poster presents the design and implementation of weather data analysis using Hadoop distributed system, which can be used for planning outdoor events. The proposed event planning system decides how many appropriate days for outdoor events and activities per month for a different attractive cities based on the analysis of historical weather data. All collected data are stored at HDFS, i.e., Hadoop Distributed File System, and then they are processed and analyzed by using MapReduce programming. As results, we can discover useful information about event planning, such as locations (city), time and statistical data

We Are What We Generate - Understanding Ourselves Through Our Data

AbstractWe have tendency to exhibit ourselves through the data we share about ourselves including, liking, friendship, follows, disliking, pictures, audio, videos, causes, blogs and sites. Such data about us have already been used by big data companies to create customized ads and marketing tactics. However, while such data being in unstructured and noisy format, utilization and research is at its early stages. In this paper, we elaborate on the idea of understanding individuals through lens of data they produce in context of our main research work for Predicting Educational Relevance For an Efficient Classification of Talent (PERFECT) algorithm engine. We illustrate some of research problems in relevance of such data and identify research problem as ground for this paper. We present sub set of our framework including algorithm and math constructs, for the problem we identify. We conclude that such analytics and cognitive research can help to improve education, healthcare, Job economy, crime control, etc. Thus we coin the phrase “we are what we generate”, with our work in this paper. We suggest future work and opportunities in relevant directions