Big Data and Knowledge Management Systems

Goals

This course is the first course in big data for the ABC courses (AI model/algorithm, Big data, Computing) of the Graduate School of Data Science. The course covers the foundation of data management for data science and related fields. Students will understand the theoretical background of data management and data analytics and eventually be able to design databases and implement database functions.

Student Project Requirements

Student Presentations

1-Legal Case Analysis

김은지 (KIM Eungi) , 나윤진 (NA Yun Jin) , 여광은 (YEO Kwang Eun), 이규성 (LEE Gyu Seong), 최광호 (CHOI Gwang Ho)

Efficient searching system of similar legal cases applying NLP models to cases and referencing laws.

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2-Hidden Community Detector for South Korean Politicians

박정섭 (PARK Jeong Sup), 이경민 (LEE Gyung Min), 이다예 (LEE Daye), 이동영 (LEE Dongyoung), 전용훈 (JEON Yonghoon)

Create a database supporting queries, detection of hidden communities, and visualization on South Korean politicians’ relational map and statements through the SNS.

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3-Grooming DB

권유진 (KWON Yoojin), 김소정 (KIM So Jung 73), 김정수 (KIM Jung Soo), 백은수 (BAEK Eunsu), 임다희 (YIM Dah Hee)

Create a DB system supports the automatic update of animal data, helps sheltered animals find their family by affording advanced search using various features.

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4-Graph-based Search :
Making your travel within Korea enjoyable

채규병 (CHAE Kyubyung), 김소정 (KIM So Jung 28), 김이경 (KIM Yikyung), 민지홍 (MIN Ji Hong), 송종현 (SONG Jonghyun)

Based on the map of connected locations in Korea, the system recommends transportation, sightseeing places, accommodations, and restaurants on the way to your final destination.

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5-Collaborative View on Netflix

곽남주(KWAK Namju), 권세희(Kweon Se Hee), 김성문(KIM Soung Mun), 민경현 (MIN Kyong Hyun), 윤용상(YOON Yongsang)

Improve Netflix's recommendation based on user’s detailed evaluations(or feedbacks) about contents.

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6-Paper Recommendation using DBLP Bibliography Dataset

이정현(LEE Jeunghyun), 권민아(KWON Mina), 박희선(PARK Heesun), 양재영(YANG Jaeyeong), 최윤준(Choi YunJun)

Implement a live pipeline that recommends papers relevant to a paper of interest, based on a hybrid recommendation system.

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7-Material Database

최진우(CHOI Jin Woo), 문정하(MOON Jeong Ha), 신정우(SHIN Jeong Woo), 홍주표(HONG joopyo), 소연경(SO Yeon Kyoung)

Build a single database system that covers both Molecular + Ionic materials and that can predict properties of unknown materials with minimum information in short calculation time.

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8-Abnormality Detection Early Alert System

이효진 (Lee Hyojin), 김진리(Kim Jinri), 장해민(Haemin Jang), 조보금(Cho Bokeum), 김우진 (Kim Woojin)

The early alert system detects tweets with abnormality by identifying certain usage patterns based on three assumptions and informs Twitter investigators to take appropriate action on time to protect our Twitter world from malicious contents.

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9-Building Database System for Personalized Promotion

윤혜선(Yun Hyesun), 황연중(Hwang Yeonjung), 이찬희 (Lee Chanhee), 박성일(Park SeongIl), 강민정(Kang MinJeong)

Provide useful information for H&M to target the right customer for batch coupon marketing. Benefit customers with the right predictions and favorable recommendation items.

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10-Recommendation System based on Movie Preference

김동근(Kim DongGeun), 박성우 (PARK Sungwoo), 유지상(YU Jisang), 이세라(LEE Sera), 임상수(Im Sangsoo)

To build a Movie or a TV show recommendation system based on the preference of each person. Design a recommendation system by using users’ preferences on movies or TV shows.

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11-Influencer detection on Twitter during the COVID-19 pandemic

류양선(Yangsun Ryu) , 표정식 (PYO Jeongsik) , 김유진 (KIM Eugine), 이금하(Lee KeumHa), 김예하 (Kim Yeha)

Build a new system that can provide visual representations of Tweets and results of various analyses, such as influencer analysis, sentiment analysis, and link prediction.

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12-Data Pipeline for Spotify Recommendation

이창욱(Lee Changuk), 조우성(CHO Wooseong), 이승주(Lee Seungju), 김세진(Kim Sejin), 김종환(KIM jonghwan)

Implementation of an OLAP data pipeline for music recommendation services (Spotify) based on user histories & musical features.

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