Workshops

“Healthcare and well-being” is an important area of research in many countries as one of the emerging application domains for both “big data” and “smart computing”.
 
This workshop aims to create opportunities to share on-going works and enhance collaboration among healthcare researchers related to big data or data science, and to foster new innovations of big data and smart computing in the areas of mobile healthcare, smart homes, digital cities, innovations in healthcare and sensing devices, and other related areas.

 
 
2. The 3rd Exobrain Workshop – Natural language question answering for human-machine knowledge communication (Exobrain 2017)
 
Organizer : Sang-Kyu Park and Ho-Jin Choi
Date : February 13, 2017
Website : http://sigai.or.kr/workshop/bigcomp/2017/exobrain/
 

With the amount of textual data increases rapidly, it is important to find not only appropriate, but also more trustworthy answers to user’s natural language questions. Natural language question answering systems have proven to be helpful to users because they can provide succinct answers that do not require users to wade through a large number of documents. In this workshop, we would like to discuss research outcomes of Exobrain project, consisting of natural language processing, information extraction, ontology reasoning and population, question answering technologies.
 
The theme of this workshop is to discuss recent progress of Natural language question answering technology for providing knowledge services with users.

3. The international Workshop on Managing and Mining Heterogeneous Big Mobile Data from Smart Devices (BigMobile 2017) Canceled
 
Organizer : Vincent W. Zheng, Bin Guo, Defu Lian and Hong Cao
Date : February 13, 2017
Website : https://sites.google.com/site/bigmobileworkshop/
 

With the proliferation of smart devices, we are generating big mobile data every day. According to CISCO’s report, in year 2015 the smart devices have already accounted for 89% of the global mobile data traffic; besides, the mobile data traffic will continue to grow quickly by 53% per year from 2015 to 2020. Such big mobile data is precious, as they naturally carry a lot of useful information about the environments and the mobile users. It is important for us to be able to parse, understand and derive useful insights from the big mobile data.
 
The theme of this workshop is how to manage the heterogeneity of big mobile data. Heterogeneity naturally exists in big mobile data, because there are many different kinds of smart devices (e.g., smartphones, smart watches, fitness wristband, augmented reality and virtual reality head mounted devices, etc.) and different kinds of sensors (e.g., GPS, WiFi, Bluetooth, cell tower, gyroscope, compass, camera and so on) to generate various kinds of information. The resulting data can be either time series of sensor readings, text, images and even multimedia.

 
 
4. The International Workshop on Affective and Sentimental Computing (ASC 2017)
 
Organizer : Haoran Xie, Tak-Lam Wong, Fu Lee Wang, Raymond Wong and Xiaohui Tao
Date : February 13, 2017
Website : http://www.cihe.edu.hk/asc2017/
 

As the rapid growth of user-generated data from social networks, wikis and social tagging systems, it is necessary to understand the high-level semantics and user subjective perceptions from such a large volume of data. Emotions or sentiments are one of the most important aspects as the user-generated data are always with emotional loads of their creators. Along with the development of the computational techniques for sentiment analysis and opinion mining, the increasing psychological and cognitive models/theories are exploited for modeling sentiments and emotions by incorporating with social computing techniques such as social network and personalization, mining user reviews, user profiling in social network and so on. Connecting affective/sentimental models and social computing techniques not only can facilitate the understanding big data in at semantic-levels but also improve the performance of various social computing applications in the big data era. It combines affective/sentimental models with social computing as a promising direction and offers opportunities for developing novel algorithms, methods and tools.
 
The International Workshop on Affective and Sentimental Computing (ASC 2017) in conjunction with the 4th IEEE International Conference on Big data and smart Computing (BigComp 2017) will bring together the academia, researchers, and industrial practitioners from computer science, information systems, psychology, behavior science, and organization science discipline, and provide a forum for recent advances in the field of sentiment analysis, affective computing, emotion detections, and opinion mining from the perspectives of various computing techniques.

Workshop 1. BigData4Healthcare 2017
Keynote Speech: Patient Safety Events Prediction and Prevention using Clinical Big Data from Electronic Health Records

Hyeoun-Ae Park
Professor, College of Nursing, Seoul National University, Korea
President, International Medical Informatics Association

Abstract:
The study aims to develop and test a patient safety event prediction and prevention system integrated with electronic health records (EHR) system. Specific objectives are to develop risk prediction models for patient safety events such as fall, pressure ulcer and adverse drug events of chemotherapy using big data analytics and integrate the models into EHR, and to develop an evidence-based CDSS providing healthcare providers with tailored recommendations to prevent and manage these patient safety events.
We used a database containing EHRs of the patients discharged from Jan 1, 2015 to May 31, 2016 in a tertiary teaching hospital in Korea. The case group consists of patients with a safety event and the control group without any safety event during hospital stay. Features were extracted from 10 different data sources and normalized based on criteria such as completeness, currency, structure and granularity of documentation. Significant features were selected using data visualization techniques and univariate statistical analyses. For fall and pressure ulcer, Cox-hazard model was used to predict the time of patient safety event and logistic regression to predict the risk of patient safety event. For side effect of chemotherapy, neural network architectures were used.
The performance of prediction models for measures of binary and survival outcomes was assessed using a variety of different methods and metrics such as goodness of fit statistics, area under the ROC curve, sensitivity and specificity, and true positives.
 
Bio:
Hyeoun-Ae Park is the President of International Medical Informatics Association (IMIA). She is also a professor of College of Nursing at Seoul National University (SNU) teaching Nursing and Medical Informatics at SNU. Prior to joining SNU in 1992, she was a research fellow at Korean Institute of Health and Social Affairs. Dr. Park received her BS in Nursing from SNU, her MS and PhD in Biostatistics and Health Informatics from the University of Minnesota. She had been a visiting scholar at health informatics department at the University of Minnesota, and SNOMED CT International at the College of American Pathologists. Her areas of research are healthcare vocabulary and terminology, especially around ICNP and SNOMED CT. Her recent research focuses on ontology as a framework for social big data, and data mining and big data analytics in healthcare. She served as a vice president of the IMIA in charge of Working Groups and Special Interest Groups for over 6 years from 2007 to 2013. She also served as the chair of NI Special Interest Group of the IMIA from 2012 to 2015. She received 2004 Distinguished Leadership Award for Internationals and School of Nursing 100 Distinguished Alumni Award in 2008 from University of Minnesota. She is a Fellow of American Academy of Nursing. She received Lael Cranmer Gatewood Distinguished Lectureship Award for her leadership in Health Informatics at the 50th anniversary of Health Informatics Program at University of Minnesota.
 
 

Workshop 2. Exobrain 2017
Keynote Speech: Korean Semantic Resources & Korean Semantic Processing Systems

Cheol-Young Ock
Professor, School of IT Convergence, University of Ulsan, Korea
http://klplab.ulsan.ac.kr

Abstract:
For semantic processing such as sentence understanding and QA, many sematic resources are needed. One of most important semantic resources is a semantic network. Prof. Ock has been constructing a Korean WordNet, namely Ulsan Word Map (UWordMap) since 2002. The Ulsan Word Map has semantic hierarchy(hypernym and hyponym) like WordNet, but it has another important information, subcategorization of argument of predicate. In this speech Prof. Ock will introduce a structure of the UWordMap and how to use for word sense disambiguation of Korean and for understanding a Query in Exo Brain SW.
Word representation, an important area in natural language processing(NLP) used machine learning, is a method that represents a word not by text but by distinguishable symbol. Existing word embedding employed a large number of corpora to ensure that words that were position near by in text. However, corpus-base word embedding need a lot of corpora because the frequency of word occurrence and increase the number of words. Another word embedding(Sense Vector) is done using dictionary definitions and semantic relationship information(hypernyms and antonyms). Then similar sense’s words have similar vector. Furthermore, it was possible to distinguish vectors of antonym words. In this speech Prof. Ock will introduce the Sense Vector and its applications of sematic operations.
 
Bio:
Cheol-Young Ock is a professor of the school of IT convergence, University of Ulsan, Ulsan, Rep. of Korea. He received his BS (1982), MS (1984), and PhD (1993) degrees in computer engineering from the National University of Seoul, Seoul, Rep. of Korea. He has been a visiting professor at the Russia Tomsk Institute (1994) and Glasgow University (1996). He has been the chairman of the sigHCLT (2007~2008) in the KIISE, Rep. of Korea. He has been a visiting researcher at the National Institute of Korean Language (2008). He received an honorary doctorate from the department of Information and Computer Science, National University of Mongolia, Ulaanbaatar, Mongolia (2007). He received a government medal as a meritorious engineer of Korean language development (2016). He has been constructing a Korean WordNet, namely Ulsan Word Map (UWordMap) since 2002. His research interests include natural language processing (WSD), machine learning, and text mining.