Call for papers
Workshop descriptionThis is the joint workshop from the following three workshops:
- Data Economy: Workshop on Data Economy in the Big Data Era
- Real-Time Mining and Analytics:Workshop on Real-Time Mining and Analytics for Big Data
- Mining Techniques for Online and Customer Service:Workshop on Big Data Mining Techniques for Online Sales and Customer Service
In the famous article "What is Web 2.0", Tim O'Reilly said that "data is the next Intel Inside". However, creating value from Big Data cannot be accomplished by merely aggregating large amounts of data or performing analysis. Rather, the real value of Big Data resides in the composition of data products (or, more conventionally, applications). Leveraging the value of Big Data through data products and building a new data economy by creating a market where data products can be traded, exchanged and composed is key in the Big data era.
Big Data economy is about developing a value-centric view of the whole data lifecycle including collection, fusion, search, analysis, application development and consumption. This means, for example, that pricing the data should be interrelated to their potential for supporting the creation of valuable products, which is affected by factors such as their quality and history of usage. Furthermore, this creates the need for research on proper data supply chain and product composition models.
This one-day workshop aims to identify these key research topics and create a community that will contribute to establishing a data economy to assess and generate value from Big Data throughout its whole lifecycle.
Real-Time Mining and Analytics
With the rapid development of the new generation information technology, including internet, mobile internet, wearable computing, and sensor networks, the demand for real-time data processing, mining and analysis has an explosive growth in recent years. Typical examples include online and mobile advertising, real-time search for social media, recommendation for location-based service, real-time coupon apps for online-to-offline service, augmented reality for mobile phones and wearable computers such as Google glass etc. Such applications generate data volumes reaching hundreds of terabytes and even petabytes but require retrieval of computing results in sub-seconds or even milliseconds.
This workshop will serve as a general medium and platform for discussion, communication and brain-storming for all scientists, researchers, engineers, and application developers who deal with large volume of diverse data and require real-time data mining and analytics in their Cloud computing research, simulations, and applications. We will specifically target communities from the data mining, Cloud computing, and big data disciplines.
Mining Techniques for Online and Customer Service
The last few years have seen explosive growth of e-commerce, fuelled by cloud-based technologies and smart devices. As a consequence, businesses have experienced massive increases in volume of online customer engagements and have evolved their sales and service programs to leverage platforms like social media, online chat and mobile apps.
Large amounts of data are generated today from various channels like web, IVR and mobile. This increase in volume and diversity of data presents severe challenges in understanding and modeling of customer intents and behaviour, needs and expectations because of factors like data size, data noise, anonymity of customers, among others. However, sales and service organizations fully realize the value of the competitive advantage that effective mining of this data can offer them.
Effective mining of Big Data continues to be a challenge and has emerged as one of the hottest areas of research among academicians and data scientists. This has led to an aggressive search for suitable methods that can intelligently process such data at scale to understand customer behavior in general, and to achieve specific goals like increasing conversion rate, reducing shopping cart abandonment rate, providing personalized multichannel and multimodal interactive support, etc.
This workshop aims to bring together researchers from both industry and academia to participate and present their work related to various aspects of Big Data mining. The focus of the workshop is on the methods, frameworks, tools, and platforms related to big-data mining in the area of online sales and customer service.
Research topics included in the workshopData Economy:
- Business models on Big Data applications
- Big Data products
- Pricing models for data and data products
- Supply chain of big data and data products
- Data and data product ecosystem
- Quality of data and data products
- Data economy and social impact
- Revenue management of data products
- Ethics issues in data economy
- Novel data product design
Real-Time Mining and Analytics:
- Real-time distributed computing infrastructure
- Real-time machine learning algorithms
- Real-time data mining in mobile internet
- Real-time computing in online and mobile advertising
- Real-time pattern recognition in mobile voice processing and computer vision
- Performance evaluation of real-time systems
- Real-time search and analytics in social media
- Large-scale network data analysis
- Real-time computing for wearable computers
- Streaming computing systems
- Large-scale real-time recommender systems and user reputation systems
- Real-time computing for sensor network and internet-of-things
- Open source real-time computing system for data mining
Mining Techniques for Online and Customer Service:
- Big data Infrastructure for mining customer interaction data (e.g. cloud-based computation, map-reduce)
- Large scale data storage and retrieval for ad-hoc querying or otherwise
- Automatic discovery of new trends in online customer interests and intents
- Machine learning algorithms to provide customers predictive multichannel and multimodal support
- Dynamic predictive models for providing multichannel/multimodal customer support
- Algorithms for developing user profiles
- IVR analytics
- Mobile analytics
- Case studies of big data mining applications for providing online customer support
- 30 August 2013: Due date for full workshop papers submission
- 5 September 2013: Notification of paper acceptance to authors
- 10 September 2013: Camera-ready of accepted papers
- 6-9 October 2013: Workshops
Please submit a paper (up to 10 pages in the IEEE 2-column format) through the online submission system: http://wi-lab.com/cyberchair/2013/bigdata13/cbc_index.html.
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines:
Program Committee Members
- Aija Leiponen, Cornell University
- Schahram Dustdar, Vienna Technical University
- Robert Grossman, University of Chicago
- Weisong Shi, Wayne State University
- Yong Shi, University of Nebraska Omaha, and Chinese Academy of Science
- Dr. Yong Zhao， University of Electronic Science and Technology of China, China
- Dr. Dou Shen Baidu Inc., China
- Ian Foster, University of Chicago and Argonne National Laboratory, U.S.A.
- Roger Barga, Microsoft Research, U.S.A.
- Weimin Zheng, Tsinghua University, China
- Xiaofeng Meng, Remin University, China
- Yi Pan, Georgia State University,U.S.A.
- L.J. Zhang, Kingdee Enterprise, China
- Hui Lin, Tsinghua Suzhou Research Institute, China
- Tingjie Lu, Beijing University of Posts and Telecommunications, China
- Jerome Friedman, Stanford University, U.S.A.
- Shawn Bowers, Gonzaga University, U.S.A.
- Ioan Raicu, Illinois Institute of Technology, U.S.A.
- Douglas Thain, University of Notre Dame, U.S.A.
- Ian Gorton, Pacific Northwest National Laboratory, U.S.A.
- Wei Tan, IBM T. J. Watson Research Center, U.S.A.
- Ping Yang, Binghamton University, U.S.A.
- Gregor von Laszewski, Indiana University, U.S.A.
- Yogesh Simmhan, University of Southern California, U.S.A.
- Yong Chen, Texas Tech University, U.S.A.
- Ji Zhu, Professor, Department of Statistics and EECS, University of Michigan
- Qiaozhu Mei, Assistant Professor, School of Information, University of Michigan
- Jerome Friedman, Professor, Department of Statistics and EECS, Stanford University
- Bee-Chung Chen, Senior Applied Researcher, LinkedIn Inc
- Deepak Agarwal, Director of Engineering, LinkedIn Inc
- Rong Yan, Senior Research Scientist, Facebook Inc
- Ding Zhou, Chief Scientist, Klout Inc
- Adwait Ratnaparkhi, Senior Principal Manager, Nuance Communications Inc
- Lihong Li, Researcher, Microsoft Research
- Changfeng Wang, CEO, Adelphic Mobile Inc
- Lei Tang, Senior Scientist, Walmart Lab
- Jie Yang, Senior Software Engineer, Google Inc
- Yufan Zhu, Staff Engineer, Google Inc
- Cong Yu, Research Scientist, Google Inc
- Song Zhang, Chinese Academy of Sciences, China
- Huihua Yang, Guilin Dianzi University, China
- Ruini Xue, University of Electronic Science and Technology of China, China
- Xinzheng Niu, University of Electronic Science and Technology of China, China
- Xiaoliang Fan, Lanzou University, China
- Wenjun Wu, Beihang University,China
- Jia Chen, Researcher of Noah's Ark Lab at Huawei, U.S.A.
- Ping Zhu, LinkedIn Inc, U.S.A.
- Prof. Jaideep Srivastava (University of Minnesota, USA)
- Dr. Ravi Vijayaraghavan (7 Innovation Labs, Bangalore)
- Prof Ram Akella (University of California, USA)
- Prof. Galit Shmueli (Indian School of Business, India)
- Dr Rajesh Parekh (Director, GroupOn, USA)
- Prof. Joydeep Ghosh (University of Texas. Austin, USA)
- Dr. B. Ravindran (Indian Institute of Technology, Madras, India)
- Dr. Ashish Tendulkar (Researcher, Machine Learning, Reliance Industries Ltd., Mumbai, India)
- Dr S R Kulkarni (7 Innovation Labs, Bangalore, India)
- Dr. Ravi Vijayaraghavan, Chief Scientist and Global Head - Analytics and Data Sciences
- Dr. Subhash Kulkarni, Distinguished Data Scientist
- Dr. Kranthi Adusumilli, Principal Scientist
- Abhishek Ghose, Senior Data Scientist
- The workshop schedule could be downloaded here.