News
- DSAA’2014 proceedings has been included in IEEE Xplore http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7050498.
- DSAA’2014 has been very successful, with over 200 participants. We look forward to seeing you in IEEE DSAA’2015 in Paris, fully sponsored by IEEE and co-sponsored by ACM.
- Congratulations to the Best Research Paper: Masahiro Kimura, Kazumi Saito, Kouzou Ohara and Hiroshi Motoda. Efficient Analysis of Node Influence Based on SIR Model over Huge Complex Networks; and Best Application Paper: Yang Liu and Mingyan Liu. Detecting Hidden Propagation Structure and Its Application to Analyzing Phishing.
- Congratulations to Prof Jian Cao from SJTU for receiving the DSAA’2014 Service Award for his great leadership in making such a successful conference.
- Conference program is online now, see you in DSAA’2014. We have got 11 main conference sessions, 7 special sessions, 5 keynote speeches, 4 trends and controversies invited talks, a panel, plus 2 tutorials for you to enjoy a very busy 3-day data science gathering.
- Registration for papers should be done before 30 September 2014
- The paper decisions have been announced on 13 August. DSAA 2014 has a very competitive acceptance rate to ensure the quality of the conference: 10.4% for Long Presentation papers, 16.1% for Short Presentation papers.
- IEEE DSAA: DSAA will be IEEE fully sponsored since 2015! IEEE DSAA is the first IEEE conference on data science.
- Due to numerous requests, the submission has been extended to 12 July, 2014, 11:59 PM Pacific Time.
- Submit your papers by clicking Submit a Paper.
- Special sessions are added here.
- Paper submissions should be limited to a maximum of seven (7) pages in the IEEE 2-column format (see the IEEE Press Proceedings Author Guidelines: http://www.ieee.org/conferences_events/conferences/publishing/templates.html ). DSAA’2014 will incorporate double blind review, please refer to Paper Submission for more information about submissions.
- Five prestigious keynote speeches will be presented by leading experts in the relevant domains.
- All accepted conference papers (including special session papers) will be published by IEEE and included in the IEEE Xplore Digital Library.
- Selected papers will be considered for extension and inclusion into several SCI-index journal special issues.
- DSAA’2014 is in cooperation with ACM SIGKDD and technically co-sponsored by IEEE.
Introduction
The 2014 International Conference on Data Science and Advanced Analytics (DSAA’2014), jointly technically co-sponsored by IEEE and ACM, aims to provide a premier forum that brings researchers and industry/government practitioners together who are interested in data science, analytics science, big data and advanced analytics, to share new ideas and practices about theoretical challenges, new opportunities, and the best practices for a wide range of applications. The conference solicits experimental and theoretical works on data science and advanced analytics along with their applications to real life situations.
Publications
All accepted papers will be published by IEEE and included in IEEE Xplore Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE. Selected top quality papers accepted and presented in the conference for extension and publication in the special issues of some international journals, including IEEE Intelligent Systems, WWWJ, and Neurocomputing. The accepted workshop papers will be published through Springer CCIS series.
Program
DSAA – S1: Machine Learning
Minimizing Expected Loss for Risk-Avoiding Reinforcement Learning
Jung-Jung Yeh, Tsung-Ting Kuo, William Chen and Shou-De Lin
Large-Scale Factorization of Type-Constrained Multi-Relational Data
Denis Krompass, Maximilian Nickel and Volker Tresp
Pseudo Labels for Imbalanced Multi-Label Learning
Wenrong Zeng, Xuewen Chen and Hong Cheng
Proactive Learning with Multiple Class-Sensitive Labelers
Seungwhan Moon and Jaime Carbonell
Itemset Approximation Using Constrained Binary Matrix Factorization
Seyed Hamid Mirisaee, Eric Gaussier and Alexandre Termier
Community Detection in Social Networks: the Power of Ensemble Methods
Rushed Kanawati
DSAA – S2: Retrieval, Query & Search
Semi-randomized Hashing for Large Scale Data Retrieval
Haichuan Yang, Xiao Bai, Jun Zhou, Peng Ren, Jian Cheng and Lu Bai
Storage Efficient Graph Search by Composite Dynamic-and-Static Indexing of a Single Network
Yan Xie and Philip Yu
Indexing and Retrieval of Human Motion Data Based on a Growing Self-Organizing Map
Da-Cheng Yu and Wei-Guang Teng
An Improved Dynamic Adaptive Multi-tree Search Anti-collision Algorithm Based on RFID
Min Shao, Xiao-Fang Jin and Li-Biao Jin
Exploiting Mobility for Location Promotion in Location-based Social Networks
Wen-Yuan Zhu, Wen-Chih Peng and Ling-Jyh Chen
Investigating Sample Selection Bias in the Relevance Feedback Algorithm of the Vector Space Model for Information Retrieval
Massimo Melucci
DSAA – S3: Analytics Foundations
Learning a Proximity Measure to Complete a Community
Maximilien Danisch, Jean-Loup Guillaume and Benedicte Le Grand
A Confidence-based Entity Resolution Approach with Incomplete Information
Qi Gu, Yan Zhang, Jian Cao and Guandong Xu
Human Behaviour Analysis Using Multimodal Emotion Recognition
Prashant Jha and Rammohana Reddy Guddeti
Matrix Completion Based on Feature Vector and Function Approximation
Shiwei Ye, Yuan Sun and Yi Sun
An Accuracy Enhancement Algorithm for Fingerprinting Method
Yuntian Brian Bai, Mani Williams, Falin Wu, Allison Kealy and Kefei Zhang
DSAA – S4: Recommendation & Services
Probabilistic Category-based Location Recommendation Utilizing Temporal Influence and Geographical Influence
Dequan Zhou and Xin Wang
Recommending Funding Collaborators with Scholar Social Networks
Juan Zhao, Kejun Dong and Jianjun Yu
An Incremental Scheme for Large-scale Social-based Recommender Systems
Chia-Ling Hsiao, Zih-Syuan Wang and Wei-Guang Teng
Diversification in News Recommendation for Privacy Concerned Users
Maunendra Sankar Desarkar and Neha Shinde
Similarity Analysis of Service Descriptions for E?cient Web Service Discovery
Sowmya Kamath S and Ananthanarayana V.S
DSAA – S5: Classification & Clustering
Active Learning for Text Classification Using the LSI Subspace Signature Model
Weizhong Zhu and Robert B. Allen
A Semisupervised Associative Classification Method for POS Tagging
Pratibha Rani, Vikram Pudi and Dipti Sharma
Optimizing Specificity under Perfect Sensitivity for Medical Data Classification
Cho-Yi Hsiao, Hung-Yi Lo, Tu-Chun Yin and Shou-De Lin
Interactive Correlation Clustering
Floris Geerts and Reuben Ndindi
Rough Possibilistic Meta-Clustering of Retail Datasets
Asma Ammar, Zied Elouedi and Pawan Lingras
DSAA – S6: Infrastructure, Management & Privacy
Detecting Hidden Propagation Structure and Its Application to Analyzing Phishing
Yang Liu and Mingyan Liu
Account Level Demand Estimation and Intelligence Framework
Pranjal Mallick, Vikash Kumar Sharma, Parikshit Bhinde and Mutha Reddy Mandapati
A New Pre-Pushing VoD Scheme in Hierarchical Network Environment
Fei Long and Xingjun Wang
Usage Signatures Analysis – An Alternative Method for Preventing Fraud in e-Commerce Applications
Gabriel Mota, Joana Fernandes and Orlando Belo
Exploring New Privacy Approaches in a Scalable Classification Framework
M Saravanan, Mohamed Thoufeeq, S Akshaya and V.L Jayasre Manchari
DSAA – S7: Influence Analysis
Efficient Analysis of Node Influence Based on SIR Model over Huge Complex Networks
Masahiro Kimura, Kazumi Saito, Kouzou Ohara and Hiroshi Motoda
Social Influence-Aware Reverse Nearest Neighbor Search
Hui-Ju Hung, De-Nian Yang and Wang-Chien Lee
Influence Maximization in a Social Network in the Presence of Multiple Influences and Acceptances
Jun-Li Lu, Ling-Yin Wei and Mi-Yen Yeh
Diversified Ranking on Graphs from the Influence Maximization Viewpoint
Li-Yen Kuo and Ming-Syan Chen
Mining Influence in Evolving Entities: A Study on Stock Market
Chang Liao, Yanfei Huang, Xibin Shi and Xin Jin
DSAA – S8: Data Science Applications
Sentiment Detection and Visualization of Chinese Micro-blog
Zhitao Wang, Zhiwen Yu, Liming Chen and Bin Guo
Critical Class Sensitive Active Learning Method for Classification of Remote Sensing Imagery
Lian-Zhi Huo, Zheng Zhang and Liang Tang
Content Specific Coverage Patterns for Banner Advertisement Placement
Venkata Trinath Atmakuri, Gowtham Srinivas Parupalli and Krishna Reddy Polepalli
Appliance and State Recognition using Hidden Markov Models
Antonio Ridi, Christophe Gisler and Jean Hennebert
Exploring Technological Trends for Patent Evaluation
Shuting Wang, Wang-Chien Lee, Zhen Lei, Xianliang Zhang and Yu-Hsuan Kuo
Crowdsourced Data Analytics: A Case Study of Predictive Modeling Competition
Yukino Baba, Nozomi Nori, Shigeru Saito and Hisashi Kashima
DSAA – S9: Complex Data Analysis
A Model-Selection Framework for Concept-Drifting Data Streams
Bo-Heng Chen and Kun-Ta Chuang
A Probabilistic Condensed Representation of Data for Stream Mining
Michael Geilke, Andreas Karwath and Stefan Kramer
Incrementally Mining Temporal Patterns in Interval-based Databases
Yi-Cheng Chen, Julia Zhu-Ya Weng, Jun-Zhe Wang, Chien-Li Chou, Jiun-Long Huang and Suh-Yin Lee.
The Purpose of Motion: Learning Activities from Individual Mobility Networks
Salvatore Rinzivillo, Lorenzo Gabrielli, Mirco Nanni, Luca Pappalardo, Fosca Giannotti and Dino Pedreschi
On Selecting Feature-Value Pairs on Smart Phones for Activity Inferences
Gunarto Sindoro Njoo, Yu-Hsiang Peng, Wen-Chih Peng and Kuo-Wei Hsu
DSAA – S10: Community and Network Analysis
Learning Sparse and Scale-free Networks
Melih Aslan, Xuewen Chen and Hong Cheng
Overlapping Community Detection in Social Network Based on Microblog User Model
Yajun Gu, Bofeng Zhang, Guobing Zou, Mingqing Huang and Keyuan Jiang
Information Diffusion among Users on Facebook Fan Pages over Time: Its Impact on Movie Box Office
Wan-Hsin Tang, Mi-Yen Yeh and Anthony J.T. Lee
Inferring Potential Users in Mobile Social Networks
Tsung-Hao Hsu, Chien-Cheng Chen, Meng-Fen Chiang, Kuo-Wei Hsu and Wen-Chih Peng
DSAA – S11: Cloud Computing & Parallel Computing
A Token Authentication Solution for Hadoop Based on Kerberos Pre-Authentication
Kai Zheng and Weihua Jiang
Hadoop based Deep Packet Inspection System for Traffic Analysis of E-Business Websites
Jiangtao Luo, Yan Liang, Wei Gao and Junchao Yang
A Data Reusing Strategy Based On Hive
Heng Xie, Mei Wang and Jiajin Le
Dehazing Algorithm’s High Performance and Parallel Computing for GF-1 Satellite Images
Changmiao Hu, Xiaojun Shan and Zheng Zhang
General In-Situ Matrix Transposition Algorithm for Massively Parallel Environments
Marcin Gorawski and Michal Lorek
DSAA – SS1: Statistical and Mathematical Tools for Data Mining – SMTDM (1)
Efficient Learning of General Bayesian Network Classifier by Local and Adaptive Search
Sein Minn, Shunkai Fu and Michel C. Desmarais
A Naive Bayesian Classifier in Categorical Uncertain Data Streams
Jiaqi Ge, Yuni Xia and Jian Wang
A New Set of Random Forests with Varying Dynamic Data Reduction and Voting Techniques
Hussein Mohsen, Hasan Kurban, Mark Jenne and Mehmet Dalkilic
Centroid Training to Achieve Effective Text Classification
Libiao Zhang, Yuefeng Li, Yue Xu, Dian Tjondronegoro and Chao Sun
Representing Sentence with Unfolding Recursive Autoencoders and Dynamic Average Pooling
Yin Hang, Zhang Chunhong, Zhu Yunkai and Ji Yang
DSAA – SS2: Statistical and Mathematical Tools for Data Mining – SMTDM (2)
A Novel Context-based Implicit Feature Extracting Method
Li Sun, Sheng Li, Jiyun Li and Jutao Lv
Local Feature Based Dynamic Time Warping
Zheng Zhang, Liang Tang and Ping Tang
Mobile User Stability Prediction with Random Forest Model
Danqin Wang and Xiaolong Zhang
Detecting Stock Market Manipulation using Supervised Learning Algorithms
Koosha Golmohammadi, Osmar Zaiane and David Diaz
Recommending Missing Citations for Newly Granted Patents
Sooyoung Oh, Zhen Lei, Wang-Chien Lee and John Yen
DSAA – SS3: Warehousing and Intelligent Analysis of Complex Network Big Data – WIACNBD (1)
FCA for Common Interest Communities discovering
Soumaya Guesmi, Chiraz Trabelsi and Chiraz Latiri
Diversification Recommendation of Popular Articles in Micro-blog Scenario
Jianxing Zheng, Bofeng Zhang, Guobing Zou and Xiaodong Yue
Analysis of Circadian Rhythms from Online Communities of Individuals with Affective Disorders
Bo Dao, Thin Nguyen, Dinh Phung and Svetha Venkatesh
Link Prediction and Threads in Email Networks
Qinna Wang
Mining Approximate Multi-Relational Patterns
Eirini Spyropoulou and Tijl De Bie
A Detecting Community Method in Complex Networks with Fuzzy Clustering
Xiao Feng Wang, Gong Shen Liu and Jian Hua Li
DSAA – SS4: Warehousing and Intelligent Analysis of Complex Network Big Data – WIACNBD (2)
Neural Network-Based Approaches for Predicting Query Response Times
Elif Ezgi Yusufoglu, Murat Ayyildiz and Ensar Gul
User Preference Space Partition and Product Filters for Reverse Top-k Queries
Zong-Hua Yang and Hung-Yu Kao
Finding Top-k Semantically Related Terms in Relational Keyword Search
Xiangfu Meng and Jingyu Shao
Design Process for Big Data Warehouses
Francesco Di Tria, Ezio Lefons and Filippo Tangorra
23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and Management
Nima Bari, Roman Vichr, Kamran Kowsari and Simon Berkovich
DSAA – SS5: Environmental and Geo-spatial Data Analytics – EGSDA
Location Semantics Prediction for Living Analytics by Mining Smartphone Data
Chi-Min Huang, Jia-Ching Ying, Vincent S. Tseng and Zhi-Hua Zhou
Management of Complex Data objects in Ship Designing Process
Ruihan Bao and Hongming Cai
SAR Target Recognition Based on Deep Learning
Sizhe Chen and Haipeng Wang
Mining Frequent Time Interval-based Event with Duration Patterns from Temporal Database
Kuan-Ying Chen, Bijay Jaysawal, Jen-Wei Huang and Yong-Bin Wu
DSAA – SS6: Bioinformatics, Biomedical, Health & Medical Analytics – BHMA
Sharing Sensitive Medical Data Sets for Research Purposes – A Case Study
Kalpana Singh, Jia Rong and Lynn Batten
Mobile-based Food Classification For Type-2 Diabetes Using Nutrient and Textual Features
Yan Luo, Charles Ling and Shuang Ao
Solving Longest Overlap Region Problem For Noncoding DNA Sequence With GPU
Yukun Zhong, Jianbiao Lin, Chen Tao, Baoqiu Wang and Che Nian
Investigation of SEE on a 32-bit Microprocessor Based on SPARC V8 Architecture by Laser Test
Chunqing Yu, Long Fan, Suge Yue, Maoxin Chen and Shougang Du
Individualized Arrhythmia Detection with ECG Signals from Wearable Devices
Thanh Binh Nguyen, Wei Luo, Terry Caelli, Svetha Venkatesh and Dinh Phung
DSAA – SS7: Exploratory Computing – EC
Exploratory Computing: A Draft Manifesto
Nicoletta Di Blas, Mirjana Mazuran, Paolo Paolini, Elisa Quintarelli and Letizia Tanca
Exploratory Portals. The Need for a New Generation
Paolo Paolini and Nicoletta Di Blas
Exploring Emotions over Time within the Blogosphere
Patrick Hennig, Philipp Berger, Lukas Pirl, Lukas Schulze and Prof. Dr. Christoph Meinel
StatsReduce in the Cloud for Approximate Analytics
Michel De Rougemont
An Implementation of the Efficient Huge Amount of Pseudo-random Unique Numbers Generator and the Acceleration Analysis of Parallelization
Yun-Te Lin, Yung-Hsiang Huang, Yu-Jung Cheng, Yi-Hao Hsiao, Fang-Pang Lin, Jih-Sheng Chang and Shengwen Wang
Toward Robust Classification using the Open Directory Project
Jongwoo Ha, Jung-Hyun Lee, Won-Jun Jang, Yong-Ku Lee and Sangkeun Lee
Tutorial 1: Network Mining and Analysis for Social Applications
Lecturer: Feida Zhu
Date: November 1, 2014, 13:20 – 15:20
Room: East Asia Hall, B/F
Abstract:
The recent blossom of social network and communication services in both public and corporate settings have generated a staggering amount of network data of all kinds. Unlike the bio-networks and the chemical compound graph data often used in traditional network mining and analysis, the new network data grown out of these social applications are characterized by their rich attributes, high heterogeneity, enormous sizes and complex patterns of various semantic meanings, all of which have posed significant research challenges to the graph/network mining community. In this tutorial, we aim to examine some recent advances in network mining and analysis for social applications, covering a di- verse collection of methodologies and applications from the perspectives of network patterns, relationship mining and identity linkage. We would present the problem setting, the research challenges, the recent research advances and some future directions for each perspective.
Bio:
Feida Zhu is an assistant professor at the School of Information Systems of SingaporeManagement University (SMU). He has founded and managed as Academic Director the Pinnacle Lab a joint lab with China Ping An Insurance Group to focus on social media mining and analysis for finance innovation. He obtained his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. His current research interests include large-scale data mining, graph/network mining and social network analysis. His research on large-scale frequent pattern mining has won the Best Student Paper Awards at 2007 IEEE International Conference on Data Engineering (ICDE) and 2007 Pacific- Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
Tutorial 2: Harvesting, Integrating, Maintaining and Leveraging Knowledge Graphs
Lecturer: Seung-won Hwang
Date: November 1, 2014, 15:40 – 17:40
Room: East Asia Hall, B/F
Abstract:
Knowledge graphs have served as an integral component of modern search engines and software in general. However, it is non-trivial to harvest, cleanse, and integrate web-scale knowledge graphs, which has been actively studied in diverse fields of computer science. This two-hour tutorial will cover recent work on broad issues of harvesting, integrating, maintain, and leveraging knowledge graphs.
Bio:
Seung-won Hwang is an associate professor of computer science and engineering in POSTECH, Korea. Her recent research projects have been integrating multiple knowledge graphs for data-driven understanding of entities. Her recent findings have been published at database and NLP venues, including ACL, TKDE, TOIS, TODS, SIGMOD, VLDB, and ICDE.
Advisory/Steering Committees
Usama Fayyad , Barclays, United Kingdom
Masaru Kitsuregawa, University of Tokyo, Japan
Vipin Kumar, University of Minnesota, USA
Bengchin Ooi, National University of Singapore
Xin Yao , University of Birmingham, UK
Hiroshi Motoda,Osaka University and AFOSR/AOARD, Japan
Geoff Webb, Monash University, Australia
Osmar Zaiane, University of Alberta, Canada
Longbing Cao, University of Technology, Sydney, Australia
Vincent Tseng, National Cheng kung University, Taiwan
Limsoon Wong, National University of Singapore
Herve Martin, Laboratoire d’Informatique de Grenoble, France
Jian Pei, Simon Fraser University, Canada
Diane J. Cook, Washington State University
Bart Goethals, University of Antwerp, Belgium
Ming-Syan Chen, National Taiwan University, Taiwan
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Dr Usama Fayyad Barclays Bank, UK |
| Title: BigData, AllData, Old Data: Predictive Analytics in a Changing Data Landscape | |
| Abstract:The landscape of the platform, access methodologies, shapes, and storage representations has changed dramatically. Much of the assumptions of a structured data world dominated by relational databases have been rendered obsolete. Today’s data analyst faces big challenges and a bewildering environment of technologies and challenges involving semi-structured and unstructured data with access methodologies that have almost no relation to the past. This talk will cover issues and challenges in how to make the benefits of advanced analytics fit within the application environment. The requirement for Real-time data streaming and in situ data mining is stronger than ever. We demonstrate how many of the critical problems remain open with much opportunity for innovative solutions to play a huge enabling role. This opportunity makes Data Science and several related fields critical to almost all future analytical tasks. | |
| Bio for Dr Usama Fayyad: USAMA M. FAYYAD usama@fayyad.com (www.fayyad.com)Usama M. Fayyad, Ph.D. is Chief Data Officer and Group Managing Director at Barclays in London where his responsibilities include data governance, information risk management and the data infrastructure for BI, data warehousing, BigData and analytics technologies across the Barclays Group globally. He is also Chairman of Oasis500 in Jordan following his appointment in 2010 by King Abdullah II of Jordan to be the founding Executive Chairman. Oasis500 a tech startup investment fund that runs an accelerator, entrepreneurship training program, and angel investment network that aims to fund 500 Internet and Technology startups in the MENA Region. Up until September 2008, Fayyad was based in Sunnyvale, CA as Yahoo!’s chief data officer & Executive VP responsible for Yahoo!’s global data strategy, architecting Yahoo!’s data policies and systems, prioritizing data investments, and managing the Company’s data analytics and data processing infrastructure which processed over 25 Terabytes of data per day. He was the industry’s first Chief Data Officer. Under his EVP role, Fayyad also founded and managed the Yahoo! Research Labs organization with offices around the world to develop the new sciences of the Internet, on-line marketing, Microeconomics, and algorithmic Advertising. At Yahoo! he applied Big Data techniques to content and advertising targeting and built the world’s largest group of data scientist – helping Yahoo! grow its revenues from user targeting by 20 times in 4 years. After Yahoo! and prior to Barclays he founded Open Insights, LLC a data strategy, technology and consulting firm based in Bellevue, WA to help enterprises understand data strategy and deploy data-driven solutions that effectively and dramatically grow revenue and competitive advantages. In 2003 Fayyad co-founded and led the DMX Group, a data mining and data strategy consulting and technology company that was specializing in BigData Analytics major projects with some of the Fortune 500 clients. DMX Group was acquired by Yahoo! in 2004. In early 2000, he co-founded and served as CEO of Audience Science (digiMine, Inc.), a venture backed company addressing hosted business analytics and leading the market in targeted advertising. From 1995 to 2000, Fayyad was at Microsoft in Redmond, WA where he led the data mining and exploration group at Microsoft Research and headed the data mining products group for Microsoft’s server division. From 1989 to 1996 Fayyad held a leadership role at NASA’s Jet Propulsion Laboratory (JPL), where his work in the analysis and exploration of Big Data in scientific applications gathered from observatories, remote-sensing platforms and spacecraft garnered him the top research excellence award that Caltech awards to JPL scientists – The Lew Allen Award for Excellence in Research, as well as a U.S. Government medal from NASA. Fayyad earned his Ph.D. in engineering from the University of Michigan, Ann Arbor (1991), and also holds BSE’s in both electrical and computer engineering (1984); MSE in computer science and engineering (1986); and M.Sc. in mathematics (1989). He has published over 100 technical articles in the fields of data mining, Artificial Intelligence, machine learning, and databases. He holds over 30 patents, is a Fellow of the AAAI (Association for Advancement of Artificial Intelligence) and a Fellow of the ACM (Association of Computing Machinery), has edited two influential books on the data mining and launched and served as editor-in-chief of both the primary scientific journal in the field of data mining (Data Mining and Knowledge Discovery) and the primary newsletter in the technical community published by the ACM: SIGKDD Explorations. He continues to be active in the academic community serving at Chairman of ACM’s SIGKDD Executive Committee which runs the world’s premiere data science, big data, and data mining conferences: the KDD international annual conferences. He is a recipient of the ACM SIGKDD Innovation Award (2007) and Service Award (2003) – the only person to receive both awards. |
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Prof Ofer Azar Ben-Gurion University of the Negev, Israel |
| Title: Relative thinking | |
| Abstract:The talk will present evidence showing that when people consider different sellers of the same good, or differentiated goods that differ in price and quality, they exhibit a decision-making bias of “relative thinking”: relative price differences affect them even when economic theory suggests that only absolute price differences should matter. This result is obtained in several experiments with different populations and different consumption categories. Sometimes subjects are affected only by relative price differences (“full relative thinking”) and sometimes also by absolute price differences (“partial relative thinking”). This behavior has implications for various research areas, including economic psychology, behavioral economics, judgment and decision making, marketing, consumer behavior, advertising, and industrial organization. | |
| Bio for Prof Ofer Azar: Prof. Ofer Azar received his Ph.D. in economics from Northwestern University and is now an Associate Professor at Ben-Gurion University of the Negev. His main research areas include behavioral economics, experimental economics, business strategy, industrial organization, and certain aspects of academic publishing. Prof. Azar is the Editor of the Journal of Behavioral and Experimental Economics (formerly the Journal of Socio-Economics), an Associate Editor in the Journal of Economic Behavior & Organization and the Journal of Economic Psychology, and an Advisory Board Member in the SSRN Journals in Behavioral & Experimental Economics. Prof. Azar serves as the President of the Society for the Advancement of Behavioral Economics (SABE) and is the Chairperson of the Executive Committee of The International Confederation for the Advancement of Behavioral Economics and Economic Psychology (ICABEEP). He is also the Head of the Multidisciplinary Specialty in the Department of Business Administration and the Teaching Committee Chairperson of the Guilford Glazer Faculty of Business and Management at Ben-Gurion University of the Negev. In a recent ranking of economists whose first publication appeared in the last 10 years, Prof. Azar was ranked 69th. In 2008 one of his articles was chosen by The New York Times as one of the important ideas of the year, and in 2009 he won the Toronto Prize for Research Excellence of Young Researchers. |
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Prof Dirk P. Kroese School of Mathematics and Physics The University of Queensland, Australia |
| Title: Monte Carlo Methods for Big Data and Big Models | |
| Abstract:Many quantitative problems in science, finance, and engineering are nowadays solved via Monte Carlo methods. Examples range from job scheduling and robot design in industrial engineering, chemical kinetics and charge transport in science, to option pricing and portfolio selection in economics and finance. Monte Carlo methods have dramatically changed the way in which statistics is used in today’s analysis of data, in particular in the presence of increasing complex data. In this talk I will explore how Monte Carlo methods can be used to effectively solve complicated sampling, estimation, and optimization problems. I will give examples of Monte Carlo methods for spatial data generation, charge transport simulation, and the construction of optimal tessellations. | |
| Bio for Prof Dirk P. Kroese: Dirk P. Kroese is a professor of Mathematics and Statistics at The University of Queensland, Brisbane, Australia. He is the co-author of several influential monographs on simulation and Monte Carlo methods, including Handbook of Monte Carlo Methods and Simulation and the Monte Carlo Method, (2nd Edition). Dirk is a pioneer of the well-known Cross-Entropy method – an adaptive Monte Carlo technique which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance. He is a Chief Investigator in the recently announced Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers for Big Data, Big Models and New Insights (ACEMS). His web address is http://www.maths.uq.edu.au/~kroese. |
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Prof Feiyue Wang Chinese Academy of Sciences, China |
| Title: Social Computing and Computational Societies: An ACP based Approch for Smart and Parallel Economic Systems | |
| Abstract: | |
| Bio for Prof Feiyue Wang: Fei-Yue Wang received his Ph.D. in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. Dr. Wang has been a researcher, educator, and practitioner of intelligent and complex systems for over 30 years. He joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Lab (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he found the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Oversea Chinese Talents Program from the State Planning Council and “100 Talent Program” from CAS, and in 2002, was appointed as the Director of the Key Lab of Complex Systems and Intelligence Science, CAS. From 2006 to 2010, he was Vice President for research, education, and academic exchanges at the Institute of Automation, CAS. Since 2005, he is the Dean of the School of Software Engineering, Xian Jiaotong University. In 2011, he became the Director of the State Key Laboratory of Management and Control for Complex Systems.
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Dr Ravi Kumar Google, USA |
| Title: Some Patterns in Online Behavior | |
| Abstract:In this talk we study patterns in online user behavior. We focus ontwo interesting cases: repeat consumption in online settings anddirections queries issued on maps. For repeat consumption, we analyzethe patterns by which a user consumes the same item repeatedly overtime, in wide variety domains ranging from check-ins at the samebusiness location to re-watches of the same video. We develop atemporal re-consumption model that depends on historical behavior andstudy its various properties. For direction queries, we focus on therestaurant-visiting process, where a user chooses one among severalalternatives. Here, we develop a spatial choice model that capturesthe interplay of the restaurant’s distance, rank, and the intrinsicrestaurant quality. | |
Online Sources
http://datamining.it.uts.edu.au/conferences/dsaa14/
http://datamining.it.uts.edu.au/conferences/dsaa14/?page_id=28
http://datamining.it.uts.edu.au/conferences/dsaa14/?page_id=12
http://datamining.it.uts.edu.au/conferences/dsaa14/?page_id=18