| Proposal for the Theme on Big Data |
| Analytics |
|
|
| Qiang Yang, HKUST |
|
|
| Jiannong Cao, PolyU |
|
|
| Qi-man Shao, CUHK |
|
|
| May 2015 |
|
|
| Motivation |
|
|
| • |
|
|
| The world's technological per-capita capacity to |
|
|
| store information doubled every 40 months |
|
|
| – |
|
|
| As of 2012, 2.5 exabytes (2.5×1018) of data/day |
| Relational database management systems and |
| • |
| desktop statistics and visualization packages often |
| have difficulty handling big data. |
|
|
| – |
|
|
| Big Data: new driver for digital economy&society |
|
|
| |
|
|
| |
|
|
| |
|
|
| Gartner: hundreds of billions of GDP by 2020. |
| Intangible factor after labor and capital |
| Data Science: The fourth paradigm |
|
|
| 2 |
|
|
| The Power of Big Data |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| Big Data can bring “big values” to our life in |
| almost every aspects. |
| Technologically, Big Data is bringing about changes in our lives because it |
| allows diverse and heterogeneous data to be fully integrated and |
| analyzed to help us make decisions. |
| Today, with the Big Data technology, thousands of data from seemingly |
| unrelated areas can help support important decisions. This is the |
| power of Big Data. |
| Areas of Applications |
|
|
| – |
|
|
| – |
|
|
| – |
|
|
| – |
|
|
| – |
|
|
| Health and Well being |
|
|
| Policy making and public opinions |
|
|
| Smart cities and more efficient society |
|
|
| New online educational models: MOOC and Student-Teacher modeling |
|
|
| Robotics and human-robot interaction |
|
|
| • |
|
|
| Much of this power hinges on Research on Analytics |
|
|
| 3 |
|
|
| Hong Kong needs Big Data |
| Research |
|
|
| 1. |
|
|
| 2. |
|
|
| to develop state-of-the-art Big |
| Data platform in research, |
| education and industrial |
| applications, and open it to the |
| Hong Kong society and the world |
| at large, and |
|
|
| to make a difference in Smart |
| Cities, Health and Well-being |
| (including supporting aging |
| populations), and modernizing |
| Finance, Education and Logistics |
| in Hong Kong. |
|
|
| 4 |
|
|
| Big Data Analytics Objectives |
|
|
| 5 |
|
|
| Relation to Smart Cities and IoT |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| World economic forum |
| ranking HK’s |
| infrastructure: #1 |
| – |
|
|
| Maintain the lead in IT |
|
|
| Infrastructure |
|
|
| East Kowloon Project: |
| Energizing Hong Kong |
| via Smart Cities |
| Big Data: |
| – |
|
|
| IoT provides the |
|
|
| infrastructure for |
|
|
| collecting the data |
|
|
| Smart Cities as |
|
|
| important application |
|
|
| goal |
|
|
| – |
|
|
| 6 |
|
|
| Research Objectives |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| Big Data Analytics: data mining and machine learning |
| – |
|
|
| Large-scale machine learning, data mining and data visualization |
|
|
| Big Data Computing: data center support for Analytics |
| – |
|
|
| Big data collection and transformation, integration and distributed |
| data management and computing |
|
|
| Big data sampling and statistical theory, Big data security and privacy |
|
|
| Big Data Theory, Privacy&Security issues on Analytics |
| – |
| Big Data Science: 4th Paradigm – Analytics for Science and |
| Engineering |
| – |
|
|
| Big Data and Multi-disciplines (Bio, Chemistry, Engineering, Social) |
|
|
| 7 |
|
|
| Why Hong Kong is Ready for the Theme |
|
|
| • |
|
|
| We have the best researchers in machine learning, data mining, data |
| management, sensor networks, statistics, and multidisciplinary |
| research such as bioinformatics |
|
|
| – |
|
|
| – |
|
|
| – |
|
|
| China National 973 Projects on Big Data |
|
|
| IEEE Transactions on Big Data: EiC |
|
|
| ACM KDD Conferences: PC and Conference Chairs |
|
|
| Winner of Big Data related international competitions |
|
|
| – |
| New industries based on lots of data |
|
|
| – |
|
|
| Financial industry, logistics industry, education sector, government |
| services, etc. |
|
|
| We have many potential collaborators and partners |
| – |
|
|
| Huawei, Tencent, Baidu, Alibaba, Google, Microsoft, etc. |
|
|
| • |
|
|
| • |
|
|
| 8 |
|
|
| Big Data Analytics Workflow |
|
|
| 9 |
|
|
| Data |
| Extraction |
|
|
| Data |
| Integration |
|
|
| • HKUST |
| CUHK |
| • |
| Baptist |
| • |
| HSBC |
| • |
| Astri |
| • |
|
|
| • HKUST |
| HKU |
| • |
| City U |
| • |
| Alibaba |
| • |
| Astri |
| • |
|
|
| Applications |
|
|
| • Biology and Genetics |
| • |
| • |
| • |
| • |
| • |
| • |
| • |
| • |
|
|
| Chemistry |
| Physics |
| Government Policies |
| Social Sciences |
| General Health |
| Logistics |
| Finance |
| Business |
|
|
| Data Mining & |
| Visualization |
|
|
| • HKUST |
| CUHK |
| • |
| City U |
| • |
| Huawei |
| • |
| Tencent |
| • |
|
|
| Big Data |
| Computing &Data |
| Management |
|
|
| • CUHK |
| • |
| • |
| • |
| • |
| • |
|
|
| HKUST |
| HKU |
| PolyU |
| City U |
| Baptist U |
|
|
| Multi-disciplinary Big-data Analytics |
|
|
| • |
|
|
| Objectives: |
| • |
|
|
| Interdisciplinary, |
| research |
| technological big data analytics |
|
|
| heterogeneous |
|
|
| multi-university, |
|
|
| on |
|
|
| multi-team |
| and |
|
|
| scientific |
|
|
| 10 |
|
|
| Why Big Data needs Team Work? |
|
|
| • |
|
|
| Big data analytics is necessarily a joint effort by |
| researchers from academic institutions, |
| government and society and industry. |
| – |
|
|
| The government and industry are sources of Big |
| Data, and providers of problems and challenges, |
| The academic researchers are solution |
|
|
| providers. |
|
|
| When it comes to package the solutions from |
| university labs to transfer to the real world, |
| universities and industry must work together to |
| build scalable and robust solutions. |
|
|
| – |
|
|
| – |
|
|
| 11 |
|
|
| Expected Outcomes |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| New methodologies and solutions for Big Data |
| research |
| New applications that impact the society and |
| industry in Hong Kong and beyond, and |
| New digital economies created based on big |
| data |
| New educational programs for students; |
| cultivating leaders for the Big Data society and |
| industry. |
|
|
| 12 |
|
|
| Breakthroughs Expected |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| New algorithms, methodologies, systems and |
| applications in Big Data |
| New knowledge from Big data applications in |
| Science, Engineering, and Societal Problems |
| New insights into Big data practices in real |
| world |
| New ways to protect security and privacy of big |
|
|
| data relevant to individuals and organizations |
|
|
| 13 |
|
|
| Call for Proposals in Big Data Analytics |
|
|
| • |
|
|
| • |
|
|
| Foundations in Big Data Analytics Research: |
| – |
|
|
| developing and studying fundamental theories, |
| algorithms, techniques, methodologies, |
| technologies to address the effectiveness and |
| efficiency issues to enable the applicability of Big |
| Data problems; |
|
|
| Innovative Applications in Big Data Analytics: |
| developing techniques, methodologies and |
| – |
| technologies of key importance to a Big Data |
| problem that requires the seamless cooperation |
| of domain scientists with big data researchers. |
|
|
| 14 |
|
|
| Benefit for the Hong Kong Society |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| • |
|
|
| Can Hong Kong transform into a more data- |
| driven society and maintain |
| leadership |
| globally? |
|
|
| its |
|
|
| How can companies in Hong Kong become |
| more competitive with Big Data technology? |
|
|
| Can Data Science in Hong Kong’s research |
| fields benefit from strong foundations on big data? |
|
|
| Can the government become more efficient with |
| big data driven methodologies in decision |
| making? |
|
|
| Can education, finance, logistics and health |
| benefit from the ever increasing data? |
|
|
| 15 |
|
|
|
|
