Abstract:
Making sense of Big Data within various and often highly disparate data domains has become an important research, industrial, and commercial topic during the last couple of years. The sense-making process used to turn raw and often unstructured high-volume data into actionable information and insights spans a broad collection of (un)supervised, (semi-)automated approaches, techniques, and algorithms that can be used in a complementary fashion. SynerScope demonstrates how interactive visual analysis solutions can be used to perform Active Discovery by means of a "Human-in-the-Loop" approach and in conjunction with (automated) data mining, machine learning, and analytics approaches to quickly discover unexpected (or confirm expected) trends, patterns, outliers, and interesting data aspects.