Call for Papers

The 1st Workshop on Context in Analytics (CiA) seeks contributions from a broad spectrum of communities to explore the role of context in big data and analytics.

Studying, representing, incorporating, exploiting and explaining context in analytics has the potential to provide significant value and address major challenges. For example, context is critical to establishing trustworthiness of a computational result and corresponding decisions as it provides evidence as to why one should take a recommended action. Such context might include the quality and trustworthiness of the underlying data, what features were selected in machine learning algorithms, who computed the result and for what purpose, who is making the decision and for what purpose, and who may be affected by the decision. Provenance of decisions, with all the accompanying context all the way back to the sources of data used, would facilitate not only trustworthiness, but also enable more robust processes and systems that can be revisited and revised when the context itself changes. Context is also critical to establishing the relevance of a computational result. For example, the context of how the underlying data has been used before should inform whether it is appropriate for the current use. What was appropriate in one context may be completely inappropriate in another context. Last but not least, context can also play a counterbalancing role to the cognitive, organizational, or cultural biases that are now making their way into machine learning and analytics in general.

Trustworthiness, relevance and bias are just three examples of the challenges introduced as computers begin to play a larger role in decision--making. We believe there is an opportunity to capture and inject context into all aspects of computation, from the design and development of applications, to data analysis, visual analytics of data, data governance, and even to the study of how data scientists work and make decisions. In this workshop, we hope to encourage participants from a broad spectrum of communities to explore the role of context in big data and analytics.

We are seeking contributions in two forms:

• Regular papers that describe original research contributions
• Position papers that describe vision, problems, and use cases

Topics

Original unpublished contributions are sought in areas including but not limited to:

• Contextual user experience design
• Context in machine learning
• Contextual Personalized Search
• Collaborative contextual analytics
• Privacy and context
• Context in visual analytics
• Context models and querying
• Context services and architectures
• Context in provenance
• Recommender systems and context
• Data governance with Context