Stream reasoning refers to inference approaches and deduction mechanisms which are concerned with providing continuous inference capabilities over dynamic data. The paradigm shift from current batch-like approaches toward timely and scalable stream reasoning leverages the natural temporal order in data streams and applies windows-based processing to complex deduction tasks that go beyond continuous query processing such as those involving preferential reasoning, constraint optimization, planning, uncertainty, non-monotonicity, non-determinism, and solution enumeration.
We are witnessing an unprecedented shift in the available quantity and quality of data drawn from all aspects of our lives, opening tremendous new opportunities but also significant challenges for scalable decision analytics due to its dynamicity. This makes it harder to go from data to insightand support effective decision-making. Such...
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