The TF-DSAA will take the following major roles and goals into consideration:
- Forming the community infrastructure and communication platforms for data sciences and big data analytics
- Producing a roadmap for research, education and development of data sciences and big data analytics
- Fostering capabilities for creating technical standards and services in data science and big data analytics
- Promoting research, education and development of data science and big data analytics, and
- Establishing broad-based connections with relevant communities including ACM, IEEE, and ASA.
In particular, the TF-DSAA will aim to support the creation of unique research excellence and next-generation data scientists in the following areas:
Data-driven scientific research on significant and complex real-life problems:
While there have been studies on performing machine learning and knowledge discovery from data, the TF-DSAA encourages original and high-impact research and development on significant real-life problems from scientific, nature-inspired, socio-economic and philosophical perspectives. This will support the communications and collaborations to produce new theories and solutions in complex mathematical and statistical, scientific, and natural problem-solving systems and methodologies that have not been addressed in the existing sciences, and lead to significant impact on economy, society, decision and future;
Complex big data understanding and analytics:
While infrastructures such as map-reduce have been used for handling large scale data via parallelizable algorithms, there are still significant gaps in meeting the requirements of building computational intelligence from terabytes or even exabytes of data across media, domains and modals in aspects including representing, learning, analyzing, managing, processing, and presenting complex data with heterogeneity, coupling relationships, invisibility, high frequency and dimensionality. The TF-DSAA encourages significant research foci on such matters toward real-time, personalized, transformative and actionable problem-solving;
Reproducible data science applications and practices:
Cutting edge data analytics algorithms have been continuously proposed and extensively explored in the academia, but many of them have not been developed in alignment with real-life challenges, few of them successfully applied in the real world to benefit our business, living, society and future. The TF-DSAA encourages the transformation of research and practice culture of inventing and applying the state-of-the-art data analytics methods into the business, government, economy, life, health and many other domains, to advance policy-making, decision-making, productivity, industry and economy transformation by extracting real values from data.