Well-organized Estimation of Range Aggregates against Uncertain Location-Based Queries?

We consider the problem of efficient estimation of distance between uncertain objects. In many real-life applications, data such as sensor readings and weather forecasts are usually uncertain when they are collected or produced. An uncertain object has a probability distribution function (PDF) to represent the probability that it is actually located in a particular location. A fast and accurate distance computation between uncertain objects is important to many uncertain query evaluation (e.g., range queries and nearest-neighbor queries) and uncertain data mining tasks (e.g., classifications, clustering and outlier detection). However, existing approaches involve distance computations between samples of two objects, which is very computationally intensive. On one hand, it is expensive to calculate and store the actual distribution of the possible distance values between two uncertain objects. On the other hand, the expected distance (the weighted average of the pair wise distances among samples of two uncertain objects) provides very limited information and also restricts the definitions and usefulness of queries and mining tasks.