We are calling for papers, both research and applications, and from both academia and industry, for presentation at the workshop. All papers will go through peer-review by a program committee of international experts, and accepted papers will be published on the workshop Website, with selected papers to be invited for extension and inclusion into a possible special issue of a relevant journal.

PopInfo'15 invites contributions addressing current research in Population Informatics, as well as experiences, novel applications and future challenges. Topics of interest include, but are not restricted to:

• Algorithms and techniques for managing, processing, analyzing, and mining large population databases
• Requirements analysis for Population Informatics
• Models and algorithms for Population Informatics
• Scalable algorithms for dealing with dynamic and temporal population databases
• Scalable algorithms for dealing with uncertain and probabilistic population databases
• Scalable algorithms for dealing with privacy aspects in Population Informatics
• Parallel and distributed algorithms for large-scale Population Informatics
• Visualization, visual analytics, and user-interfaces for Population Informatics
• Architectures and frameworks for Population Informatics
• Large-scale Population Informatics in the cloud
• Research case studies of Population Informatics in health, demographics, ecology, economics, the social sciences, and other research domains
• Applications of Population Informatics in governments and businesses
• Policy issues around population databases and Population Informatics, and using Population Informatics to manage public resources
• Ethical, social, privacy, and confidentiality aspects when dealing with population databases

We invite two types of submissions for PopInfo’15:

• Research submissions:
  Normal academic submissions reporting on research progress, novel algorithms and techniques relevant to Population Informatics.
  
  
• Application / case study submissions:
  Papers of this type report on example implementations, experiences, and case studies of Population Informatics from a diverse range of application areas. We encourage submissions from researchers outside of the main KDD/computer science community, as well as from practitioners working in government and industry.

Paper submissions are required to follow the standard double-column ACM Proceedings Template (http://www.acm.org/sigs/publications/proceedings-templates). We accept papers of length between 4 and 10 pages, including references, diagrams, and appendices. LaTeX styles and Word templates may be found on the above site. LaTeX is the recommended typesetting package.

We encourage shorter papers that describe ongoing work relevant to Population Informatics, or initial results of larger projects. As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed.

We are pleased to announce that Assoc Prof Hye-Chung Kum will be giving the keynote at PopInfo’15.

Titie : Social Genome: Putting Big Data to Work for Population Informatics

Abstract : Population informatics is the burgeoning field at the intersection of social sciences, health sciences, computer science, and statistics that applies quantitative methods and computational tools to answer questions about human populations. It relies on using distributed, federated, person-level datasets, our social genome, in near real time to transform social, behavioral, economic, and health sciences but issues around privacy, confidentiality, access, and data integration have slowed progress in this area. The social genome represents a core set of data that information scientists can use to explore connections, build theories, and propel breakthroughs in managing a society. When technology is properly used to manage both privacy concerns and uncertainty, big data technology will help move the growing field of population informatics forward. This will enable big data to be used for the benefit of society in areas like population health, just as it has been used for intelligence and marketing. We will touch on topics of knowledge base platform required for the social genome data infrastructure, secure data access, privacy preserving data integration, and privacy preserving data analysis.

Biography : Dr. Hye-Chung Kum is an associate professor at the School of Public Health at Texas A&M. She holds a joint appointment in the Department of Computer Science at the University of North Carolina at Chapel Hill (UNC-CH). She received her Ph.D. (2004) in Computer Science and MSW (1998) in Policy and Management from UNC-CH. She is the founder and co-lead of the Population Informatics Research Group which applies informatics, data science, and computational methods to the increasingly large digital traces available about people to advance public health, social science, and population research by bringing together domain experts and computer science students. Her vision paper on population informatics and social genome was published in the IEEE Computer Special Outlook Issue in January 2014.

To provide an application perspective for Population Informatics, we are pleased to announce that Dr James Farrow will be speaking on his work designing and implementing next generation health data linkage applications.

Titie : Are relational databases the right tool for data linkage?

Abstract : Betteridge’s Law of Headlines would tell us, ‘No!’ Record linkage and linked data management is all about relationships between records, yet the dominant paradigm is to store and manipulate data using tools which are great for storing record data but suboptimal for querying the relationships between records.

Graph databases improve on this situation. Graph databases, in addition to storing record level data, allow the relationships between data to be explicitly and efficiently described and managed as first order objects. Emergent patterns of the nodes (records) and edges (relationships) and their properties can be therefore explored. SA-NT DataLink has built a system using graph databases and algorithms to store, manipulate and query linked record data. This next generation link management system will be described along with technical descriptions and benefits of the approach.

Biography : Dr James Farrow is a computer scientist and software engineer working with SA-NT DataLink to develop techniques based on graph theory and using graph databases for the better management and exploration of linked data to enhance research outcomes. He has worked in the areas of machine learning and text classification for NSW Health and ASIC, mapping and visualisation of historical and near real-time geocoded information for NSW Health, and biomedical record linkage for SA-NT DataLink. He helped designed and prototype SURE, a secure research environment for linked data for the Population Health Research Network (PHRN). He has recently developed a new technique for the anonymisation of geospatial data which removes location information but preserves the ability to make distance comparisons

Note:
- Each full paper is allocated with 25 minutes for the presentation plus 5 minutes for Q&A
- Each short paper is allocated with 15 minutes for the presentation plus 5 minutes for Q&A