Javascript Technology Seminar List of Selected Projects

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(http://got.show Summer Term 2016 - A Song of Ice and Data)
 
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[edit] Summer Term 2016 - A Song of Ice and Data

[edit] Project Description

During the summer semester of 2016 students of the JavaScript Technology seminar set out to answer a question that was asked by many: Which character is likely to be eliminated during the fifth season of the HBO hit show "Games of Thrones". The students developed applications that scour the web for data about the show and put together a website that reports which characters are most likely to die in the sixth season of the TV series. The project received a world-wide media attention with an estimated reach of 1.2 billion people. In the summer semester of 2019 a follow up project was run by the JavaScript seminar and received similar attention.

[edit] Team

2016 Team

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Mentors:, Guy Yachdav, Christian Dallago, Tatyana Goldberg, Dmitrii Nechaev, Students: Kordian Bruck, Michael Legenc, Sohel Mahmud, Togi Dashnyam, Theodor Cheslerean Boghiu, Boris Idesman\, Georg Gar, Subburam Rajaram, Anna Sesselmann, Nicola De Socio, Thuy Tran, Konstantinos Angelopoulos, Julien Schmidt, Jonas Kaltenbach, Marcus Novotny, Camille Mainz, Santanu Mohanta, Dat Nguyen, Georgi Anastasov, Max Muth, Yasar Kücükkaya, Mina Zaki, Alexander Beischl, Maximilian Bandle, Tobias Piffrader, Florian Gareis, Oleksii Moroz, Cavid Salahov, Jonas Ebel, Emiliyana Kalinova, Ange Laure, Temzeung Kouemo

2019 Team

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Mentors:, Guy Yachdav, Christian Dallago, Students: Ashmin Bhattarai, Fabian Emilius, David Schemm, Gerald Mahlknecht, Daniel Homola,Julian Nalenz, Valentin Dimov, Robert Dillitz, Lukas Franke, Robin Brase, Rainier Klopper, Taylor Lei, Jan Schweizer, Boning Li, Florian Donhauser

[edit] Winter Term 2016/17 - Predict'em All!

[edit] Project Description

On July 6th 2016, Niantic released the augmented reality game titled Pokemon Go. The game took the world by storm and is now installed on 5% of smartphones in the US. In the game, players are using their smartphones to locate Pokemons - cute and cuddly virtual creatures that after being captured and nurtured turn into fearless fighters in the name of players' ambition to level up. Locating and capturing Pokemons have quickly become a phenomenon - hoards of people have been sighted in New York as they chase imaginary creatures and try to capture them in Central Park. Pokemons appear on certain places in the real world and wait at those coordinates for a period of time. During the winter semester students came up with an app that predicts a Pokemon’s TLN (Time, Location and Name - that is where Pokemons will appear, at what date and time, and which Pokemon will it be). The app was featured online and managed to predict the appearance of some of the most sought out Pokmeons.

[edit] Team

Mentors:, Guy Yachdav, Christian Dallago, Students: Samit Vaidya, Oleksandr Fedotov, Gani Qinami, Paul Gualotuna, Timo Ludwig, Wolfgang Hobmaier, Elma Gazetic, Faris Cakaric, Karen Reyna, Mustafa Kaptan, Georgi Aylov, Benjamin Strobel, Jochen Hartl, Swathi S Sunder, Vivek Sethia, Jonas Heintzenberg, Gilles Tanson, Fabian Buske, Marcel Wagenländer, Annette Köhler, Siamion Karcheuski, Hannes Dorfmann, Alexander Lill, Aurel Roci, Matthias Baur, Timur Khodzhaev, Philippe Buschmann, Josef Brandl
Pokemon team.png

[edit] Summer Term 2017 - The Music Connection Machine

[edit] Project Description

Information about classical music is scattered all over the internet in the form of scholarly articles, news stories, blogs, wikis, forums and many other venues. Our goal (in collaboration with Peachnote) is to bring this knowledge into one place and make it easily accessible. We summarize the information about composers, musicians and music works as a set of connections - what were the musicians saying about each other and the music works, and what anybody else has written about them online. Moreover, whenever we find temporal or location-based information, we can present this information in geographic and historic context. The result is a tapestry of information that sums up the knowledge available on the internet about the enchanting world of classical music presented in a fun and interactive way.

[edit] Team

Mentors:, Guy Yachdav, Vlaidmir Viro, Christian Dallago, Kordian Bruck, Phillip Fent Students: Lukas Navickas, Angelinrashmi Antonyrajan, Tim Henkelmann, Shilpa Gore, Nikita Basargin, Anshul Sharma, Lukas Streit, Felix Schorer, Krishen Kandwal, Hendrik Leppelsack, René Birkeland Birkeland, Anshul Jindal, Daniel Schubert, Sandro Bauer, Lin Ji, Simon Zachau, Martin Mihaylov, Lyubomir Stoykov, Chaoran Chen, Jörn von Henning, Emir Demirdag, Panagiota Revithi, Markus Sosnowski, Yanko Sabev

[edit] Winter Term 2017/18 - MOVE-II

[edit] Project Description

MOVE-II command and control system (collaboration with the Workgroup for Rocketry and Space Flight at the TUM) - a system that collects telemetry data from a nano satellite, visualizes the data and makes recommendations for the satellite operators.

[edit] Team

Mentors:, Guy Yachdav, Alexander Lill Students: Maximilian Mumme, Manuel Römer, Xin Yan Yu, Alexander Zillner, Karl Kraus, Dominik Winter, Yixuan Liu, Tobias Klesel, Moritz Schöpf, Rodeina Mohamed, Jonatan Juhas, Florian Mauracher, Marco Grasso, Chen-Hao Chiang, Thomas Zwickl, Riccardo Padovani, Debora Jacoby, Bahareh-Sadat Hosseini
Moveii-team.png

[edit] Summer Term 2018 - NLPlot

[edit] Project Description

During the summer term 2018 JST students were building a system (developed in collaboration with Allianz SE) that can translate natural language directives to graphing commands in an agile way. Our prototype receives a set of commands in English and automatically generates visualizations to a given dataset. The system uses then additional commands (again written in English) to manipulate and modify the graphical objects. The system is a first step in designing and building a graphing tool used by professionals who have no expertise in data visualization for their every day business needs. For instance, our system knows how to ingest a dataset from an HR system and show the distribution of employees’ demographics just by taking an input directive such as “plot a histogram of employees age”..

[edit] Team

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Mentors:, Guy Yachdav, Roman Priscepov, Hans Kirchner Students: Shayan Siddiqui, Ahmet Tanakol, Sebastian Stein, Peeranut Chindanonda, Anton Widera, Tobias Priesching, Jyotirmay Senapati, Faisal Hafeez, Shabnam Sadegharmaki, Irakli Tchedia, Carsten Sehlke, Sukanya Raju, Ayishetu Haruna, Jakob Huber,


[edit] Winter term 2018/19 - Software Development Life Cycle Health Predictor

[edit] 'Project Description

Software development project management success predictor (developed in collaboration with Motius Gmb) - an algorithm that gathers data from various project management systems and predicts whether the project is on track.

[edit] Team

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Mentors:, Guy Yachdav, Alexander Prams Students: Justin Lübbers, Beatris Burdeva, Martin Rau, Galina Shalygina, Florian Schmid, Maximilian Biber, Simon Kazemi, Sebastian Holler, Julian Ulrich, Julia Dahmen, Jonathan Mengedoht, Frida Gunnarsson, Sebastian Winkler, Bruno Macedo Miguel, David Gogrichiani, Kailiang Dong, Paul Pillau
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