TENTATIVE PROGRAM for BIGDAS2015/ICDIM2015
|
|
|
- Kelley Chandler
- 10 years ago
- Views:
Transcription
1 TENTATIVE PROGRAM for BIGDAS2015/ICDIM2015 Date/ Location Sapphire Hall (Basement 2) Emerald Hall (Basement 2) Rose Hall (Basement-1) Camellia Hall (Basement-1) Diamond Hall (Basement-1) Registration (09:00 12:00) Oct.20 (Tue) Session A-1: Big Data Models and Algorithms Session B-1: Big Data in Transportation Session C-1: Big Data Search and Mining Session D-1: Big Data Applications in Manufacturing Oct. 21 (Wed) Workshop 1: Smart Emergency Management Using Big Data Session A-2: Big Data Applications in Safety and Healthcare Opening Ceremony and Invited Speakers Session (13:00 17:50) ICDIM-1: ICDIM-2: Short Paper Session S-1 Banquet (18:00 21:00) Workshop 2: 3D Data and Virtual Training System Standards Session A-3: Big Data Models and Algorithms ICDIM-3: ICDIM-4: Short Paper Session S-2 (10:00 11:50) Oct. 22 (Thu) Workshop 3: Smart Factory and Transportation (13:30 17:00) Session B-3: Big Data Visualization ICDIM-5: ICDIM-6: Short Paper Session ICDIM-1 Workshop 4: Industrial-Educational Cooperation Workshop on Business and Data Convergence (17:00 19:00) Session C-3: Big Data in Business ICDIM-7: ICDIM-8: Short Paper Session ICDIM-2 Oct. 23 (Fri) Meeting of Conference Organizing Committee *Tentative program of ICDIM2015 will be released soon.
2 Tentative Program Oral Presentation Sessions October 20, 2015 (Tuesday) Session A-1: Big Data Models and Algorithms 13:30 15:30 (Sapphire Hall) Chair: Young-Ho Park (Sookmyung Women s University, South Korea) 1. A GPS Trajectory Map-Matching Mechanism with DTG Big Data on the HBase System Wonhee Cho (Kookmin University, South Korea) and Eunmi Choi (Kookmin University, South Korea) 2. Analyzing Subgraph Isomorphism on Graphs with Diverse Structural Properties Noorul Amin (Kyung Hee University, South Korea), Kifayat Ullah Khan (Kyung Hee University, South Korea) and Young-Koo Lee (Kyung Hee University, South Korea) 3. Generating New Ground Truth Data by Editing Previous Data from Integrated Video Annotation Database HyunSeok Ahn (Inha University, South Korea), DongHyun Kim (Inha University, South Korea) and Yoo-Sung Kim (Inha University, South Korea) 4. MapReduce Accounting System Integrated with High-Performance Computing Infrastructure Chia-Chuan Chuang (National Center for High-performance Computing, Taiwan) 5. Multimodal Data Fusion and Intention Recognition for Horse Riding Simulators Sangseung Kang (Electronics and Telecommunications Research Institute, South Korea), Kyekyung Kim (Electronics and Telecommunications Research Institute, South Korea), Suyoung Chi (Electronics and Telecommunications Research Institute, South Korea) Session B-1: Big Data in Transportation 13:30 15:30 (Emerald Hall) Chair: Sang-Hyun Choi (Chungbuk National University, South Korea) 1. Analysis on the Transportation Point in Cheongju-City Using Pagerank Algorithm Yong-Yeon Kim (Chungbuk National University, South Korea), Hyeon-A Kim (Chungbuk National University, South Korea), Chul-Ho Shin (Chungbuk National University, South Korea), Kyung-Hee Lee (Chungbuk National University, South Korea), Chi-Hwan Choi (Chungbuk National University, South Korea) and Wan-Sup Cho (Chungbuk National University, South Korea) 2. Mobility Pattern Analysis of Bus Passengers with LDA Ah Cho (Chungbuk National University, South Korea), Kyung-Hee Lee (Chungbuk National University, South Korea), Hyeon-Jin Song (Chungbuk National University, South Korea), Uram Jeong (Chungbuk National University, South Korea), Chihwan Choi (Chungbuk National University, South Korea) and
3 Wan-Sup Cho (Chungbuk National University, South Korea) 3. Dynamic Taxi Trip Information Management using G* System Batjargal Dolgorsuren (Kyung Hee University, South Korea), Waqas Nawaz (Kyung Hee University, South Korea) and Young-Koo Lee (Kyung Hee University, South Korea) 4. Smart Car Use Case: Dynamic Reconfigurable IoT Convergence with BigData Rustam Rakhimov Igorevich (Konkuk University, South Korea) and Dugki Min (Konkuk University, South Korea) 5. A Freeway Crash Involvement Analysis Model based on Real-Time and Historical Traffic Big Data Xuhua Rui (Konkuk University, South Korea), Mino Ku (Konkuk University, South Korea), Nayun Cho (Konkuk University, South Korea), Kihong Han (Konkuk University, South Korea), Hwasoo Yeo (KAIST, South Korea) and Dugki Min (Konkuk University, South Korea) Session C-1: Big Data Search and Mining 15:40 17:40 (Sapphire Hall) Chair: Kwan-Hee Yoo (Chungbuk National University, South Korea) 1. A Novel Pattern Search Engine for Time Series Supporting Dynamic Expected Patterns within a Short Period of Time Hai T. Mai (Hanbat National University, South Korea) and Young-chan Kim (Hanbat National University, South Korea) 2. High Recall-Low Cost Model for Patent Search Justin JongSu Song (Inha University, South Korea) and Wookey Lee (Inha University, South Korea) 3. Hybrid Clustering Framework for Multi-dimensional Array Data Hyeon Park (Electronics and Telecommunications Research Institute, South Korea), Dae-Heon Park (Electronics and Telecommunications Research Institute, South Korea), Eun-Ju Lee(Electronics and Telecommunications Research Institute, South Korea) and Se-Han Kim(Electronics and Telecommunications Research Institute, South Korea) 4. Graphical-Information Central of Composite Analysis on Big Sensor-Data of Engineering Inspection Min-Hwan Ok (Informatics Center/KRRI, South Korea) and Hyun-seung Jung (Safety-technics Center/KRRI, South Korea) 5. Disguised Face Identification Using Face Graph and SVM Classifier Kyekyung Kim (ETRI, South Korea), Sangseung Kang (ETRI, South Korea), Suyoung Chi (ETRI, South Korea), Jaehong Kim (ETRI, South Korea), Jinho Kim (ETRI, South Korea) Session D-1: Big Data Applications in Manufacturing 15:40 17:40 (Emerald Hall) Chair: Wan-Sup Cho (Chungbuk National University, South Korea) 1. A scheme and study of establishing FEMS through the virtual manufacturing environment Lee Jongho (Ajou University, South Korea), Gu Jauk (Ajou University, South Korea) and Lee Jooyeoun (Ajou University, South Korea)
4 2. Applications of Machine Learning Algorithms to Predictive Manufacturing: Trends and Application of Tool Wear Compensation Parameter Recommendation Ji-Hyeong Han (Electronics and Telecommunications Research Institute, South Korea), Su-Young Chi (Electronics and Telecommunications Research Institute, South Korea) 3. Design Strategy for Enhancing Adoption of Manufacturing Big Data System (MBDS) in Korean Small and Medium-Sized Manufacturing Firms (SMMFs) Ji-Dae Kim (Chungbuk National University, South Korea), Su-Young Chi (Electronics and Telecommunications Research Institute, South Korea), Young-Wook Song (Chungbuk National University, South Korea), Wan-Sup Cho (Chungbuk National University, South Korea) and Kwan-Hee Yoo (Chungbuk National University, South Korea) 4. Schemes for Modeling Flexible Manufacturing Processes in Big Data Environment Kyeongsik Kim (Chungbuk National University, South Korea), Byung-Muk Lim (Chungbuk National University, South Korea), Ji-Dae Kim (Chungbuk National University, South Korea), Su-Young Chi (Electronics and Telecommunications Research Institute, South Korea), Wan-Sup Cho (Chungbuk National University, South Korea) and Kwan-Hee Yoo (Chungbuk National University, South Korea) 5. The Method to Establish In-Memory Data Grid System for Real-Time Processing of Machine Sensor Data in a Smart Factory Environment Han-Sol Park (Chungbuk National University, South Korea), Jin-Hyuk Kim (Chungbuk National University, South Korea), Chi-Hwan Choi (Chungbuk National University, South Korea), Bo-Ra Jung (Chungbuk National University, South Korea), Kyung-Hee Lee (Chungbuk National University, South Korea) and Wan-Sup Cho (Chungbuk National University, South Korea) October 21, 2015 (Wednesday) Session A-2: Big Data Applications in Safety and Healthcare 10:00 12:00 (Emerald Hall) Chair: Jooseok Park (Kyung Hee University, South Korea) 1. Hybrid App Service for Managing Five Main Chronic Diseases based on Disease-Data Analysis Gyu-Jung Lee (Chungbuk National University, South Korea), Byung-Muk Lim (Chungbuk National University, South Korea), Dong-Hee Lee (Chungbuk National University, South Korea), Hoon Kang (Chungbuk National University, South Korea), Hyun-jun Sohn (Chungbuk National University, South Korea) and Kwan-Hee Yoo (Chungbuk National University, South Korea) 2. Big Data Visual Analytics System for Disease Pattern Analysis Seokyeon Kim (Sejong University, South Korea), Seongmin Jeong (Sejong University, South Korea), Sung Uk An (Sejong University, South Korea), Jae Seok Yoo (Sejong University, South Korea), Sang Min Han (Sejong University, South Korea), Hanbyul Yeon (Sejong University, South Korea), Sangbong Yoo (Sejong University, South Korea) and Yun Jang (Sejong University, South Korea) 3. Fast Fourier Transform and Tucker Decomposition for Feature Extraction in EEG signals Thao Nguyen Thieu (Chonnam National University, South Korea), Hyung-Jeong Yang (Chonnam National University, South Korea), Sun-Hee Kim (Korea University, South Korea) and Aran Oh (Chonnam National University, South Korea)
5 4. Development of disaster damage estimation system based on inductive reasoning Jung-Ho Um (Korea Institute of Science and Technology Information, South Korea), Tran Quang Khai (Korea Institute of Science and Technology Information/University of Science and Technology, South Korea) and Sa-Kwang Song (Korea Institute of Science and Technology Information, South Korea) 5. A Social Network Service-based Disaster-Detection Technique through Content-based Location Extraction Choong-Nyoung Seon (Korea Institute of Science And Technology Information, South Korea), Minhee Cho (Korea Institute of Science And Technology Information, South Korea), Sungho Shin (Korea Institute of Science And Technology Information, South Korea), Jung-Ho Um (Korea Institute of Science And Technology Information, South Korea), Seungkyun Hong (Korea Institute of Science And Technology Information, South Korea), Sa-kwang Song (Korea Institute of Science And Technology Information, South Korea) and Hyung-Jun Yim (Korea Institute of Science And Technology Information, South Korea) 6. Performance Evaluation of Apache Spark According to the Number of Nodes using Principal Component Analysis Sungjin Hong (Chungbuk National University, South Korea), Sangho Kim (Chungbuk National University, South Korea), Jongsun Jang (Chungbuk National University, South Korea), Chi-hwan Choi (Chungbuk National University, South Korea), In-sun Jung (Chungbuk National University, South Korea), Jonghwa Na (Chungbuk National University, South Korea) and Wan-Sup Cho (Chungbuk National University, South Korea) Big Data Special Session 13:30 17:40 (Emerald Hall) Chair: Aziz Nasridinov (Chungbuk National University, South Korea) 1. Technology Foresight through the collaboration with human expert and machine intelligence Sun-Hwa Hahn (Korea Institute of Science and Technology Information, South Korea) 2. The Concept and Its Applications of Big Data Ming Zhou (San Jose State University, USA), Menglin Cao (Wells Fargo Bank, USA), Taeho Park (San Jose State University, USA), Jae-Ho Pyeon (San Jose State University, USA) 3. Cloud Computing and Big Data Analytics: What is new from DB Perspective? Mukesh Mohania (IBM Research, India) 4. Stress Prediction, Social Routing, and Privacy Protection for Pedestrians Masatoshi Yoshikawa (Kyoto University, Japan) 5. Big data mining applications and services Carson Leung (University of Manitoba, Canada) 6. Beyond Self-Reporting Polymeasures: from Behavioural Genetics, Biomarketing and Human- Computer Interaction to Neurophysiology and Neuroscience Luiz Moutinho (University of Glasgow, Scotland) 7. Big Data Stream Mining: Opportunities and Challenge Simon Fong (University of Macau, Macau) Banquet 18:00 20:00
6 October 22, 2015 (Thursday) Session A-3: Big Data Models and Algorithms 10:00 12:00 (Emerald Hall) Chair: Eunmi Choi (Kookmin University, South Korea) 1. A graph based representative keywords extraction model from news articles Kaaen Kwon (Chungbuk National University, South Korea), Chi-Hwan Choi (Chungbuk National University, South Korea), Jihyeon Lee (Chungbuk National University, South Korea), Jisoo Jeong (Chungbuk National University, South Korea) and Wan-Sup Cho (Chungbuk National University, South Korea) 2. Flexible Multi-level Model for Prediction of Abnormal Behavior Yu-Jin Jung (Sookmyung Women s University, South Korea) and Yong-Ik Yoon (Sookmyung Women s University, South Korea) 3. POI Estimation Method with Incremental Update based on Smartphones Seungwoog Jung (Electronics and Telecommunications Research Institute, South Korea), Seung-Ik LEE (Electronics and Telecommunications Research Institute, South Korea), Su-Young Chi (Electronics and Telecommunications Research Institute, South Korea) and Hoon Choi (Chungnam National University, South Korea) 4. A Big Data Driven Prediction Model for Share Rating of Drama Kim Doyeon(Chung Buk National University, South Korea) and Choi Sanghyun(Chung Buk National University, South Korea) 5. Structuring Mobile User Context Based on Spatio-Temporal Information Seung-Ik Lee (Electronics and Telecommunications Research Institute South Korea), Seungwoog Jung (Electronics and Telecommunications Research Institute South Korea) and Su-Young Chi (Electronics and Telecommunications Research Institute South Korea) Session B-3: Big Data Visualization 13:30 15:30 (Emerald Hall) Chair: Yong-Ik Yoon (Sookmyung Women s University, South Korea) 1. User Preference Analysis and Visualization through the Browser History of Smart Devices Yeong Hyeon Gu (Sejong University, South Korea), Seong Joon Yoo (Sejong University, South Korea), Zhegao Piao (Sejong University, South Korea), Yinhelin (Sejong University, South Korea), Jiangzhiyan (Sejong University, South Korea), Jung Hwan Park (Sejong University, South Korea) 2. An Analysis of Deployment Models of HBase-based Hadoop Platform in Virtualized Computing Environment Nayun Cho (Konkuk University, South Korea), Mino Ku (Konkuk University, South Korea), Xuhua Rui (Konkuk University, South Korea) and Dugki Min (Konkuk University, South Korea) 3. Sensor Representation in 3D Virtual Environments Changhyuk Im (The University of Suwon, South Korea) and Myeong Won Lee (The University of Suwon, South Korea)
7 4. An Integrated Visualization System for Spatial Database with Real-Time Text Queries Min-Ho Song (Chungbuk National University, South Korea), Hong-Jik Moon (Chungbuk National University, South Korea), Nakhoon Baek (Kyungpook National, South Korea) and Kwan-Hee Yoo (Chungbuk National University, South Korea) 5. Domain Knowledge-based User-friendly Design for Leading Software Industry Jooyeoun Lee (Ajou University, South Korea), Jiwon Son (Seoul Women s University, South Korea) and Taikyeong Jeong (Seoul Women s University, South Korea) 6. A Prototype Implementation of the Computer Graphics Metafile Decoding Front End Nakhoon Baek (Kyungpook National University, South Korea) Session C-3: Big Data in Business 15:40 17:40 (Emerald Hall) Chair: Wookey Lee (Inha University, South Korea) 1. Big Data and Internet of Things: An Asset for Urban Planning M. Mazhar Rathore (Kyungpook National University, South Korea), Awais Ahmad (Kyungpook National University, South Korea) and Anand Paul (Kyungpook National University, South Korea) 2. Local Festival Marketing and Application Plan for Agricultural Products by Utilizing Big Data from Online Shopping Mall Ji-hye Kim (Chungbuk National University, South Korea), Sang-woo Cho (Chungbuk National University, South Korea), Da-jeong Park (Chungbuk National University, South Korea), Kyung-hee Lee (Chungbuk National University, South Korea), Chi-hwan Choi (Chungbuk National University, South Korea) and Wan-sup Cho (Chungbuk National University, South Korea) 3. Marketing Strategy Support System for Small Businesses Minhee Cho (KISTI, South Korea), Sa-kwang Song (KISTI, South Korea) 4. A Requirement for Traceability of Production Logs in Large-scale Shop Floor Data Jaehui Park (Electronics and Telecommunications Research Institute, South Korea) and Su-young Chi (Electronics and Telecommunications Research Institute, South Korea) 5. Data Acquisition for Control Level Automation for SMEs: Requirements and Architecture Rockwon Kim (ETRI, South Korea), Ji-Hyeong Han (ETRI, South Korea) and Suyoung Chi (ETRI, South Korea) 6. Database Construction for Tunnel Management in Korea Yong-Seok Seo (Chungbuk National University, South Korea), Hyun-Seok Yun (Chungbuk National University, South Korea), Seong-Woo Moon (Chungbuk National University, South Korea), Dong- Gyou Kim (Korea Institute of Construction Technology, South Korea) and Kwang-Yeom Kim (Korea Institute of Construction Technology, South Korea)
8 Short Paper Sessions October 21, 2015 (Wednesday) Session S-1: Big Data Models and Algorithms 10:00 12:00 (Diamond Hall) Chair: Young-Koo Lee (Kyung Hee University, South Korea) 1. Research on Merging Three-way Decisions with Decision-theoretic Rough Sets MENG Chao (Yunnan University of Finance and Economics, China) and YU Jiankun (Yunnan University of Finance and Economics, China) 2. A Derivation and an Interpretation of the Cox Partial Likelihood Function Kai Fun Yu (BNU-HKBU United International College, China) and Youmei Liu (BNU-HKBU United International College, China) 3. Limitations of Skyline Algorithms Saydiolim Ganiev (Dongguk University, South Korea), Aziz Nasridinov (Chungbuk National University, South Korea) and Jeong-Yong Byun (Dongguk University, South Korea) 4. Computer-generated holograms using stereo disparity with block-matching algorithm Yan-Ling Piao (Chungbuk National University, South Korea), Ki-Chul Kwon (Chungbuk National University, South Korea), Nam Kim (Chungbuk National University, South Korea) 5. Flying KIWI: Design of Approximate Query Processing Engine for Interactive Data Analytics at Scale Sung-Soo Kim (Electronics and Telecommunications Research Institute, South Korea), Taewhi Lee (Electronics and Telecommunications Research Institute, South Korea), Moonyoung Chung (Electronics and Telecommunications Research Institute, South Korea) and Jongho Won (Electronics and Telecommunications Research Institute, South Korea) 6. Fast Global Alignment Technique Using Kmer-Distance and Parallelism Leang Bunrong and Tae-Kyung Kim 7. Hologram generation for a real object from depth camera using polygon-based method Yu Zhao (Chungbuk National University, South Korea), Ki-Chul Kwon (Chungbuk National University, South Korea), Yan-Ling Piao (Chungbuk National University, South Korea), Kwan-Hee Yoo (Chungbuk National University, South Korea) and Nam Kim (Chungbuk National University, South Korea) 8. Learning Listener s Preference for Music Recommender System Young Sung Cho (Chungbuk National University, South Korea), Song Chul Moon (Namseoul University, South Korea) and Seon-Phil Jeong (BNU-HKBU United International College, China) 9. An Ontology Navigation System for 3D Spinal Model Min-Ho Song (Chungbuk National University, South Korea), Ji-Sung Jung(Chungbuk National University, South Korea), Mihye Kim(Catholic University, South Korea), Sang-Ho Lee(Institute of Science and Technology Information, South Korea) and Kwan-Hee Yoo(Chungbuk National University, South Korea)
9 October 22, 2015 (Thursday) Session S-2: Big Data Applications 10:00 12:00 (Diamond Hall) Chair: Yoo-Sung Kim (Inha University, South Korea) 1. Institutions attractiveness: early warning system based on social media indicators Vasiliy Kuznetsov (Westminster International University in Tashkent, Uzbekistan) and Olga Yugay (Westminster International University in Tashkent, Uzbekistan) 2. Recommendation Service Model in Copyright Management Portal System for National Research Reports Hyoungkwan Cho (Inha University, South Korea), Kwangho Song (Inha University, South Korea), Yoo-Sung Kim (Inha University, South Korea) 3. Twitter-based Urban Area Characterization by Non-negative Matrix Factorization Shoko Wakamiya (Kyoto Sangyo University, Japan), Ryong Lee (Korea Institute of Science and Technology Information, South Korea), Yukiko Kawai (Kyoto Sangyo University, Japan) and Kazutoshi Sumiya (Kwansei Gakuin University, Japan) 4. A Recommender System in u-commerce based on Segmentation Method YoungSung Cho (Chungbuk National University, South Korea) and Seon-Phil Jeong (BNU-HKBU United International College, China) 5. Sentiment Analysis of Consumer Opinion in Blogs Kim Yoosin (ChungBuk National University, South Korea), Kim Taeyun (ChungBuk National University, South Korea), Park Miri (ChungBuk National University, South Korea), Kang Suna (ChungBuk National University, South Korea), Kang Suna (ChungBuk National University, South Korea) and Choi Sanghyun (ChungBuk National University, South Korea) 6. The Influence of IT Investment and IT Governance on Corporate Performance of Multibusiness Firms Kyung Seok Ryu (Kyung Hee University, South Korea), Joo Seok Park (Kyung Hee University, South Korea) and Jae Hong Park (Kyung Hee University, South Korea) 7. The Research Trends and Comparing about the Big Data between Korea and China Using Text Mining Zilong Zhao (Chungbuk National University, South Korea), Hogeun Yoo (Chungbuk National University, South Korea), Jounggi Jo (Chungbuk National University, South Korea), Seongryong Hong (Chungbuk National University, South Korea), Sunghyun Ryu (Chungbuk National University, South Korea) and Sanghyun Choi (Chungbuk National University, South Korea) 8. A Safe Return Home Service Based on Real-Time Big-Data Analytics Jae-Won Lee (Chungbuk National University, South Korea), Ji-Seong Jeong (Chungbuk National University, South Korea), Mihye Kim (Catholic University, South Korea) and Kwan-Hee Yoo (Chungbuk National University, South Korea) 9. A Study on Tactile map design for blind people in Korea ChungWeon Oh (Namseoul University, South Korea) 10. An Implementation of a Skyline Method over a Crime Dataset for Top-k Queries Sun-Young Ihm, Jae-Hee Hur, Soo-Bin Ou, So-Hyun Park, Yu-Jeong Song, Wu-In Jang and Young-Ho Park (Sookmyung Women s University, South Korea)
Crime Hotspots Analysis in South Korea: A User-Oriented Approach
, pp.81-85 http://dx.doi.org/10.14257/astl.2014.52.14 Crime Hotspots Analysis in South Korea: A User-Oriented Approach Aziz Nasridinov 1 and Young-Ho Park 2 * 1 School of Computer Engineering, Dongguk
UPS battery remote monitoring system in cloud computing
, pp.11-15 http://dx.doi.org/10.14257/astl.2014.53.03 UPS battery remote monitoring system in cloud computing Shiwei Li, Haiying Wang, Qi Fan School of Automation, Harbin University of Science and Technology
COMP9321 Web Application Engineering
COMP9321 Web Application Engineering Semester 2, 2015 Dr. Amin Beheshti Service Oriented Computing Group, CSE, UNSW Australia Week 11 (Part II) http://webapps.cse.unsw.edu.au/webcms2/course/index.php?cid=2411
Cyber Forensic for Hadoop based Cloud System
Cyber Forensic for Hadoop based Cloud System ChaeHo Cho 1, SungHo Chin 2 and * Kwang Sik Chung 3 1 Korea National Open University graduate school Dept. of Computer Science 2 LG Electronics CTO Division
Cloud Computing based Livestock Monitoring and Disease Forecasting System
, pp.313-320 http://dx.doi.org/10.14257/ijsh.2013.7.6.30 Cloud Computing based Livestock Monitoring and Disease Forecasting System Seokkyun Jeong 1, Hoseok Jeong 2, Haengkon Kim 3 and Hyun Yoe 4 1,2,4
Developing Safety Management Systems for Track Workers Using Smart Phone GPS
, pp.137-148 http://dx.doi.org/10.14257/ijca.2013.6.5.13 Developing Safety Management Systems for Track Workers Using Smart Phone GPS Jin-Hee Ku 1 and Duk-Kyu Park 2 1 Dept of Liberal Education and 2 Dept
Internet of Things for Smart Crime Detection
Contemporary Engineering Sciences, Vol. 7, 2014, no. 15, 749-754 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ces.2014.4685 Internet of Things for Smart Crime Detection Jeong-Yong Byun, Aziz
A Study on Integrated Operation of Monitoring Systems using a Water Management Scenario
, pp. 55-64 http://dx.doi.org/10.14257/ijseia.2015.9.9.06 A Study on Integrated Operation of Monitoring Systems using a Water Management Scenario Yong-Hyeon Gwon 1, Seung-Kwon Jung 2, Su-Won Lee 2 and
Big Data and Analytics: Challenges and Opportunities
Big Data and Analytics: Challenges and Opportunities Dr. Amin Beheshti Lecturer and Senior Research Associate University of New South Wales, Australia (Service Oriented Computing Group, CSE) Talk: Sharif
Table of Contents. Modulation and Coding. Cellular Mobile Communications. IMT-2000 System
Table of Contents Modulation and Coding Signal Power and Interference of a Spectrally Overlaid Macro/Micro Cellular CDMA System Supporting Multimedia Traffic... 1 Chang Soon Kang, Hyun Seo Oh, and Sun
Development of Integrated Management System based on Mobile and Cloud Service for Preventing Various Hazards
, pp. 143-150 http://dx.doi.org/10.14257/ijseia.2015.9.7.15 Development of Integrated Management System based on Mobile and Cloud Service for Preventing Various Hazards Ryu HyunKi 1, Yeo ChangSub 1, Jeonghyun
On a Hadoop-based Analytics Service System
Int. J. Advance Soft Compu. Appl, Vol. 7, No. 1, March 2015 ISSN 2074-8523 On a Hadoop-based Analytics Service System Mikyoung Lee, Hanmin Jung, and Minhee Cho Korea Institute of Science and Technology
3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India
3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India Call for Papers Cloud computing has emerged as a de facto computing
Designing and Embodiment of Software that Creates Middle Ware for Resource Management in Embedded System
, pp.97-108 http://dx.doi.org/10.14257/ijseia.2014.8.6.08 Designing and Embodiment of Software that Creates Middle Ware for Resource Management in Embedded System Suk Hwan Moon and Cheol sick Lee Department
How To Test A Robot Platform And Its Components
An Automated Test Method for Robot Platform and Its Components Jae-Hee Lim 1, Suk-Hoon Song 1, Jung-Rye Son 1, Tae-Yong Kuc 2, Hong-Seong Park 3, Hong-Seok Kim 4 1,2 School of Information and Communication,
How To Make Sense Of Data With Altilia
HOW TO MAKE SENSE OF BIG DATA TO BETTER DRIVE BUSINESS PROCESSES, IMPROVE DECISION-MAKING, AND SUCCESSFULLY COMPETE IN TODAY S MARKETS. ALTILIA turns Big Data into Smart Data and enables businesses to
Development of Integrated Management System based on Mobile and Cloud service for preventing various dangerous situations
Development of Integrated Management System based on Mobile and Cloud service for preventing various dangerous situations Ryu HyunKi, Moon ChangSoo, Yeo ChangSub, and Lee HaengSuk Abstract In this paper,
The Design and Implementation of the Integrated Model of the Advertisement and Remote Control System for an Elevator
Vol.8, No.3 (2014), pp.107-118 http://dx.doi.org/10.14257/ijsh.2014.8.3.10 The Design and Implementation of the Integrated Model of the Advertisement and Remote Control System for an Elevator Woon-Yong
Design and Analysis of Mobile Learning Management System based on Web App
, pp. 417-428 http://dx.doi.org/10.14257/ijmue.2015.10.1.38 Design and Analysis of Mobile Learning Management System based on Web App Shinwon Lee Department of Computer System Engineering, Jungwon University,
Development of Bio-Cloud Service for Genomic Analysis Based on Virtual
Development of Bio-Cloud Service for Genomic Analysis Based on Virtual Infrastructure 1 Jung-Ho Um, 2 Sang Bae Park, 3 Hoon Choi, 4 Hanmin Jung 1, First Author Korea Institute of Science and Technology
Big Data Collection Study for Providing Efficient Information
, pp. 41-50 http://dx.doi.org/10.14257/ijseia.2015.9.12.03 Big Data Collection Study for Providing Efficient Information Jun-soo Yun, Jin-tae Park, Hyun-seo Hwang and Il-young Moon Computer Science and
Big Data, Cloud Computing, Spatial Databases Steven Hagan Vice President Server Technologies
Big Data, Cloud Computing, Spatial Databases Steven Hagan Vice President Server Technologies Big Data: Global Digital Data Growth Growing leaps and bounds by 40+% Year over Year! 2009 =.8 Zetabytes =.08
Effective Use of Android Sensors Based on Visualization of Sensor Information
, pp.299-308 http://dx.doi.org/10.14257/ijmue.2015.10.9.31 Effective Use of Android Sensors Based on Visualization of Sensor Information Young Jae Lee Faculty of Smartmedia, Jeonju University, 303 Cheonjam-ro,
IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper
IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper CAST-2015 provides an opportunity for researchers, academicians, scientists and
Feasibility Study of Searchable Image Encryption System of Streaming Service based on Cloud Computing Environment
Feasibility Study of Searchable Image Encryption System of Streaming Service based on Cloud Computing Environment JongGeun Jeong, ByungRae Cha, and Jongwon Kim Abstract In this paper, we sketch the idea
1 st Symposium on Colossal Data and Networking (CDAN-2016) March 18-19, 2016 Medicaps Group of Institutions, Indore, India
1 st Symposium on Colossal Data and Networking (CDAN-2016) March 18-19, 2016 Medicaps Group of Institutions, Indore, India Call for Papers Colossal Data Analysis and Networking has emerged as a de facto
Home Appliance Control and Monitoring System Model Based on Cloud Computing Technology
Home Appliance Control and Monitoring System Model Based on Cloud Computing Technology Yun Cui 1, Myoungjin Kim 1, Seung-woo Kum 3, Jong-jin Jung 3, Tae-Beom Lim 3, Hanku Lee 2, *, and Okkyung Choi 2 1
Tutorial: Big Data Algorithms and Applications Under Hadoop KUNPENG ZHANG SIDDHARTHA BHATTACHARYYA
Tutorial: Big Data Algorithms and Applications Under Hadoop KUNPENG ZHANG SIDDHARTHA BHATTACHARYYA http://kzhang6.people.uic.edu/tutorial/amcis2014.html August 7, 2014 Schedule I. Introduction to big data
Industry Impact of Big Data in the Cloud: An IBM Perspective
Industry Impact of Big Data in the Cloud: An IBM Perspective Inhi Cho Suh IBM Software Group, Information Management Vice President, Product Management and Strategy email: [email protected] twitter: @inhicho
Multimodal Web Content Conversion for Mobile Services in a U-City
Multimodal Web Content Conversion for Mobile Services in a U-City Soosun Cho *1, HeeSook Shin *2 *1 Corresponding author Department of Computer Science, Chungju National University, 123 Iryu Chungju Chungbuk,
Developing Scalable Smart Grid Infrastructure to Enable Secure Transmission System Control
Developing Scalable Smart Grid Infrastructure to Enable Secure Transmission System Control EP/K006487/1 UK PI: Prof Gareth Taylor (BU) China PI: Prof Yong-Hua Song (THU) Consortium UK Members: Brunel University
IMAV: An Intelligent Multi-Agent Model Based on Cloud Computing for Resource Virtualization
2011 International Conference on Information and Electronics Engineering IPCSIT vol.6 (2011) (2011) IACSIT Press, Singapore IMAV: An Intelligent Multi-Agent Model Based on Cloud Computing for Resource
Social Big Data Analysis on Perception Level of Electromagnetic Field
, pp.90-94 http://dx.doi.org/10.14257/astl.2014.78.18 Social Big Data Analysis on Perception Level of Electromagnetic Field Jwageun Kim 1, Jonghwa Na 2, 1 Department of Business Data Convergence, Chungbuk
KAAB - Accredited Professional Degree Programs in Architecture
사단법인 한 국 건 축 학 교 육 인 증 원 Korea Architectural Accrediting Board 137-843 서울시 서초구 방배동 917-9 (효령로 87) 건축센터 202호 917-9, Bangbae-dong, Seocho-gu, 137-843 Seoul Korea phone : + 82-2 - 521-1930, 1940 fax : + 82-2
Internet of Things (IoT): A vision, architectural elements, and future directions
SeoulTech UCS Lab 2014-2 st Internet of Things (IoT): A vision, architectural elements, and future directions 2014. 11. 18 Won Min Kang Email: [email protected] Table of contents Open challenges
FINAL REPORT For Japan-Korea Joint Seminar
FINAL REPORT For Japan-Korea Joint Seminar AREA 1. Humanities and Social Sciences 2. Science/Engineering(Excluding Biology and Medicine) 3. Biology/Medicine 4. Interdisciplinary Study 1. Title of Seminar:
Department of Information and Computer Engineering
Department of Information and Computer Engineering Agenda College of Information Technology Department of Information and Computer Engineering Global IT Track 2 Departments, Students, Faculty Members Departments
Integrating a Big Data Platform into Government:
Integrating a Big Data Platform into Government: Drive Better Decisions for Policy and Program Outcomes John Haddad, Senior Director Product Marketing, Informatica Digital Government Institute s Government
Ms. Hae-Kyong HOLDAWAY Minister Counsellor Australian Treasury. Mr. Seng HUN Nagoya University
First UNCITRAL Regional Workshop in Asia hosted by Law School and Korean 23-24 November 2010 Law School (Seoul, Republic of Korea) LIST OF PARTICIPANTS (in alphabetical order) AUSTRALIA CAMBODIA Ms. Hae-Kyong
Smart Integrated Multiple Tracking System Development for IOT based Target-oriented Logistics Location and Resource Service
, pp. 195-204 http://dx.doi.org/10.14257/ijsh.2015.9.5.19 Smart Integrated Multiple Tracking System Development for IOT based Target-oriented Logistics Location and Resource Service Ju-Su Kim, Hak-Jun
Comparative Study of Health Promoting Lifestyle Profiles and Subjective Happiness in Nursing and Non- Nursing Students
Vol.128 (Healthcare and Nursing 2016), pp.78-82 http://dx.doi.org/10.14257/astl.2016. Comparative Study of Health Promoting Lifestyle Profiles and Subjective Happiness in Nursing and Non- Nursing Students
Energy Monitoring and Management Technology based on IEEE 802.15. 4g Smart Utility Networks and Mobile Devices
Monitoring and Management Technology based on IEEE 802.15. 4g Smart Utility Networks and Mobile Devices Hyunjeong Lee, Wan-Ki Park, Il-Woo Lee IT Research Section IT Convergence Technology Research Laboratory,
Safe Harbor Statement
Defining a Roadmap to Big Data Success Robert Stackowiak, Oracle Vice President, Big Data 17 November 2015 Safe Harbor Statement The following is intended to outline our general product direction. It is
Design of Media measurement and monitoring system based on Internet of Things
Design of Media measurement and monitoring system based on Internet of Things Hyunjoong Kang 1, Marie Kim 1, MyungNam Bae 1, Hyo-Chan Bang 1, 1 Electronics and Telecommunications Research Institute, 138
Monitoring and Mining Sensor Data in Cloud Computing Environments
Monitoring and Mining Sensor Data in Cloud Computing Environments Wen-Chih Peng and Yu-Chee Tseng Dept. of Computer Science National Chiao Tung University {wcpeng, yctseng}@cs.nctu.edu.tw 1 Outline Sensor
HYBRID WORKFLOW POLICY MANAGEMENT FOR HEART DISEASE IDENTIFICATION DONG-HYUN KIM *1, WOO-RAM JUNG 1, CHAN-HYUN YOUN 1
HYBRID WORKFLOW POLICY MANAGEMENT FOR HEART DISEASE IDENTIFICATION DONG-HYUN KIM *1, WOO-RAM JUNG 1, CHAN-HYUN YOUN 1 1 Department of Information and Communications Engineering, Korea Advanced Institute
Deploying Big Data to the Cloud: Roadmap for Success
Deploying Big Data to the Cloud: Roadmap for Success James Kobielus Chair, CSCC Big Data in the Cloud Working Group IBM Big Data Evangelist. IBM Data Magazine, Editor-in- Chief. IBM Senior Program Director,
What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy
What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy Much higher Volumes. Processed with more Velocity. With much more Variety. Is Big Data so big? Big Data Smart Data Project HAVEn: Adaptive Intelligence
Security Infrastructure for Trusted Offloading in Mobile Cloud Computing
Security Infrastructure for Trusted Offloading in Mobile Cloud Computing Professor Kai Hwang University of Southern California Presentation at Huawei Forum, Santa Clara, Nov. 8, 2014 Mobile Cloud Security
Big Data Explained. An introduction to Big Data Science.
Big Data Explained An introduction to Big Data Science. 1 Presentation Agenda What is Big Data Why learn Big Data Who is it for How to start learning Big Data When to learn it Objective and Benefits of
2012 International Symposium on Historical Astronomy
2012 International Symposium on Historical Astronomy Organization Institute Place SohNam Institute For History of Astronomy ( 召 南 天 文 學 史 硏 究 所 ) Korea Astronomy and Space Science Institute ( 韓 國 天 文 硏
Big Data and Analytics: Getting Started with ArcGIS. Mike Park Erik Hoel
Big Data and Analytics: Getting Started with ArcGIS Mike Park Erik Hoel Agenda Overview of big data Distributed computation User experience Data management Big data What is it? Big Data is a loosely defined
PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS.
PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS Project Project Title Area of Abstract No Specialization 1. Software
Information Management course
Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli ([email protected])
Modeling and Design of Intelligent Agent System
International Journal of Control, Automation, and Systems Vol. 1, No. 2, June 2003 257 Modeling and Design of Intelligent Agent System Dae Su Kim, Chang Suk Kim, and Kee Wook Rim Abstract: In this study,
USE OF DATA MINING TO DERIVE CRM STRATEGIES OF AN AUTOMOBILE REPAIR SERVICE CENTER IN KOREA
USE OF DATA MINING TO DERIVE CRM STRATEGIES OF AN AUTOMOBILE REPAIR SERVICE CENTER IN KOREA Youngsam Yoon and Yongmoo Suh, Korea University, {mryys, ymsuh}@korea.ac.kr ABSTRACT Problems of a Korean automobile
5 Keys to Unlocking the Big Data Analytics Puzzle. Anurag Tandon Director, Product Marketing March 26, 2014
5 Keys to Unlocking the Big Data Analytics Puzzle Anurag Tandon Director, Product Marketing March 26, 2014 1 A Little About Us A global footprint. A proven innovator. A leader in enterprise analytics for
The 4 Pillars of Technosoft s Big Data Practice
beyond possible Big Use End-user applications Big Analytics Visualisation tools Big Analytical tools Big management systems The 4 Pillars of Technosoft s Big Practice Overview Businesses have long managed
Text Opinion Mining to Analyze News for Stock Market Prediction
Int. J. Advance. Soft Comput. Appl., Vol. 6, No. 1, March 2014 ISSN 2074-8523; Copyright SCRG Publication, 2014 Text Opinion Mining to Analyze News for Stock Market Prediction Yoosin Kim 1, Seung Ryul
List of Participants
List of Participants CHINA Mr. XU, Qinghua General Department of International Cooperation State Environment Protection Administration of China 115 Xizhimennei Nanxiaojie Beijing China 100035 Mr. GUO,
The UTOPIA Video Surveillance System Based on Cloud Computing
The UTOPIA Video Surveillance System Based on Cloud Computing Jong Won Park, Chang Ho Yun, Hak Geun Lee, Chul Sang Yoon, Hae Sun Jung, Yong Woo Lee School of Electrical & Computer Engineering The Smart
Star rating driver traffic and safety behaviour through OBD and smartphone data collection
International Symposium on Road Safety Behaviour Measurements and Indicators Belgian Road Safety Institute 23 April 2015, Brussels Star rating driver traffic and safety behaviour through OBD and smartphone
Leveraging Big Data Technologies to Support Research in Unstructured Data Analytics
Leveraging Big Data Technologies to Support Research in Unstructured Data Analytics BY FRANÇOYS LABONTÉ GENERAL MANAGER JUNE 16, 2015 Principal partenaire financier WWW.CRIM.CA ABOUT CRIM Applied research
RUBA: Real-time Unstructured Big Data Analysis Framework
RUBA: Real-time Unstructured Big Data Analysis Framework Jaein Kim, Nacwoo Kim, Byungtak Lee IT Management Device Research Section Honam Research Center, ETRI Gwangju, Republic of Korea jaein, nwkim, [email protected]
An Empirical Analysis on the Performance Factors of Software Firm
, pp.121-132 http://dx.doi.org/10.14257/ijseia.2014.8.7,10 An Empirical Analysis on the Performance Factors of Software Firm Moon-Jong Choi, Jae-Won Song, Rock-Hyun Choi and Jae-Sung Choi #3-707, DGIST,
Design and Implementation of Automatic Attendance Check System Using BLE Beacon
, pp.177-186 http://dx.doi.org/10.14257/ijmue.2015.10.10.19 Design and Implementation of Automatic Attendance Check System Using BLE Beacon Mi-Young Bae and Dae-Jea Cho * Dept. Of Multimedia Engineering,
Hadoop Technology for Flow Analysis of the Internet Traffic
Hadoop Technology for Flow Analysis of the Internet Traffic Rakshitha Kiran P PG Scholar, Dept. of C.S, Shree Devi Institute of Technology, Mangalore, Karnataka, India ABSTRACT: Flow analysis of the internet
In-memory Distributed Processing Method for Traffic Big Data to Analyze and Share Traffic Events in Real Time among Social Groups
, pp. 51-58 http://dx.doi.org/10.14257/ijseia.2016.10.1.06 In-memory Distributed Processing Method for Traffic Big Data to Analyze and Share Traffic Events in Real Time among Social Groups Dojin Choi 1,
Smart City Australia
Smart City Australia Slaven Marusic Department of Electrical and Electronic Engineering The University of Melbourne, Australia ARC Research Network on Intelligent Sensors, Sensor Networks and Information
How Companies are! Using Spark
How Companies are! Using Spark And where the Edge in Big Data will be Matei Zaharia History Decreasing storage costs have led to an explosion of big data Commodity cluster software, like Hadoop, has made
Big Data Storage Architecture Design in Cloud Computing
Big Data Storage Architecture Design in Cloud Computing Xuebin Chen 1, Shi Wang 1( ), Yanyan Dong 1, and Xu Wang 2 1 College of Science, North China University of Science and Technology, Tangshan, Hebei,
TECHNOLOGY ANALYSIS FOR INTERNET OF THINGS USING BIG DATA LEARNING
TECHNOLOGY ANALYSIS FOR INTERNET OF THINGS USING BIG DATA LEARNING Sunghae Jun 1 1 Professor, Department of Statistics, Cheongju University, Chungbuk, Korea Abstract The internet of things (IoT) is an
CLOUDDMSS: CLOUD-BASED DISTRIBUTED MULTIMEDIA STREAMING SERVICE SYSTEM FOR HETEROGENEOUS DEVICES
CLOUDDMSS: CLOUD-BASED DISTRIBUTED MULTIMEDIA STREAMING SERVICE SYSTEM FOR HETEROGENEOUS DEVICES 1 MYOUNGJIN KIM, 2 CUI YUN, 3 SEUNGHO HAN, 4 HANKU LEE 1,2,3,4 Department of Internet & Multimedia Engineering,
Development of an Ignition Interlock Device to Prevent Illegal Driving of a Drunk Driver
, pp.161-165 http://dx.doi.org/10.14257/astl.205.98.41 Development of an Ignition Interlock Device to Prevent Illegal Driving of a Drunk Driver Jeong MyeongSu 1, Moon ChangSoo 1, Gwon DaeHyeok 1 and Cho
Wireless Sensor Network apply for the Blind U-bus System
Wireless Sensor Network apply for the Blind U-bus System Trung Pham Quoc 1, Min Chul Kim 1, Hyn Kwan Lee 2, Ki Hwan Eom 1 Electronic Engineering 1Dongguk University: 26, Pil-dong 3-ga jung-gu Seol, Korea
Exploiting the power of Big Data
Exploiting the power of Big Data Timos Sellis School of Computer Science and Information Technology [email protected] ITECHLAW Asia-Pacific Conference, February 26-28, 2014 Melbourne Australia Timeline
Exploiting Data at Rest and Data in Motion with a Big Data Platform
Exploiting Data at Rest and Data in Motion with a Big Data Platform Sarah Brader, [email protected] What is Big Data? Where does it come from? 12+ TBs of tweet data every day 30 billion RFID tags
BIG DATA STRATEGY. Rama Kattunga Chair at American institute of Big Data Professionals. Building Big Data Strategy For Your Organization
BIG DATA STRATEGY Rama Kattunga Chair at American institute of Big Data Professionals Building Big Data Strategy For Your Organization In this session What is Big Data? Prepare your organization Building
Density Map Visualization for Overlapping Bicycle Trajectories
, pp.327-332 http://dx.doi.org/10.14257/ijca.2014.7.3.31 Density Map Visualization for Overlapping Bicycle Trajectories Dongwook Lee 1, Jinsul Kim 2 and Minsoo Hahn 1 1 Digital Media Lab., Korea Advanced
Redundant Data Removal Technique for Efficient Big Data Search Processing
Redundant Data Removal Technique for Efficient Big Data Search Processing Seungwoo Jeon 1, Bonghee Hong 1, Joonho Kwon 2, Yoon-sik Kwak 3 and Seok-il Song 3 1 Dept. of Computer Engineering, Pusan National
Industry 4.0 and Big Data
Industry 4.0 and Big Data Marek Obitko, [email protected] Senior Research Engineer 03/25/2015 PUBLIC PUBLIC - 5058-CO900H 2 Background Joint work with Czech Institute of Informatics, Robotics and
Challenges for Data Driven Systems
Challenges for Data Driven Systems Eiko Yoneki University of Cambridge Computer Laboratory Quick History of Data Management 4000 B C Manual recording From tablets to papyrus to paper A. Payberah 2014 2
Introduction to Data Mining
Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:
Massive Cloud Auditing using Data Mining on Hadoop
Massive Cloud Auditing using Data Mining on Hadoop Prof. Sachin Shetty CyberBAT Team, AFRL/RIGD AFRL VFRP Tennessee State University Outline Massive Cloud Auditing Traffic Characterization Distributed
Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics
Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics Please note the following IBM s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice
The Analytics Value Chain Key to Delivering Value in IoT
Vitria Operational Intelligence The Value Chain Key to Delivering Value in IoT Dr. Dale Skeen CTO and Co-Founder Internet of Things Value Potential $20 Trillion by 2025 40% 2015 Vitria Technology, Inc.
The Development of an Intellectual Tracking App System based on IoT and RTLS
, pp.9-13 http://dx.doi.org/10.14257/astl.2015.85.03 The Development of an Intellectual Tracking App System based on IoT and RTLS Hak-Jun Lee 1, Ju-Su Kim 1, Umarov Jamshid 1, Man-Kyo Han 2, Ryum-Duck
Towards a Thriving Data Economy: Open Data, Big Data, and Data Ecosystems
Towards a Thriving Data Economy: Open Data, Big Data, and Data Ecosystems Volker Markl [email protected] dima.tu-berlin.de dfki.de/web/research/iam/ bbdc.berlin Based on my 2014 Vision Paper On
Customized Efficient Collection of Big Data for Advertising Services
, pp.36-41 http://dx.doi.org/10.14257/astl.2015.94.09 Customized Efficient Collection of Big Data for Advertising Services Jun-Soo Yun 1, Jin-Tae Park 1, Hyun-Seo Hwang 1, Il-Young Moon 1 1 1600 Chungjeol-ro,
Big Data: Overview and Roadmap. 2015 eglobaltech. All rights reserved.
Big Data: Overview and Roadmap 2015 eglobaltech. All rights reserved. What is Big Data? Large volumes of complex and variable data that require advanced techniques and technologies to enable capture, storage,
