This paper introduces a scalable approach for probabilistic top-k similarity ranking on uncertain vector data. Each uncertain object is represented by a set of vector instances that are assumed to be mutually-exclusive. The objective is to rank the uncertain data according to their distance to a reference object. We propose a framework that incrementally computes for each object instance and ranking position, the probability of the object falling at that ranking position. The resulting rank probability distribution can serve as input for several state-of-the-art probabilistic ranking models. Existing approaches compute this probability distribution by applying a dynamic programming approach of quadratic complexity. In this paper we theoretically as well as experimentally show that our framework reduces this to a linear-time complexity while having the same memory requirements, facilitated by incremental accessing of the uncertain vector instances in increasing order of their distance to the reference object. Furthermore, we show how the output of our method can be used to apply probabilistic top-k ranking for the objects, according to different state-of-the-art definitions. We conduct an experimental evaluation on synthetic and real data, which demonstrates the efficiency of our approach.
@cite_15: In this paper, we consider databases representing information about moving objects (e.g., vehicles), particularly their location. We address the problems of updating and querying such databases. Specifically, the update problem is to determine when the location of a moving object in the database (namely its database location) should be updated. We answer this question by proposing an information cost model that captures uncertainty, deviation, and communication. Then we analyze dead-reckoning policies, namely policies that update the database location whenever the distance between the actual location and the database location exceeds a given threshold, x. Dead-reckoning is the prevalent approach in military applications, and our cost model enables us to determine the threshold x. We propose several dead-reckoning policies and we compare their performance by simulation. Then we consider the problem of processing range queries in the database. An example of a range query is ‘retrieve the objects that are currently inside a given polygon P′. We propose a probabilistic approach to solve the problem. Namely, the DBMS will answer such a query with a set of objects, each of which is associated with a probability that the object is inside P.
@cite_16: In many systems, sensors are used to acquire information from external environments such as temperature, pressure and locations. Due to continuous changes in these values, and limited resources (e.g., network bandwidth and battery power), it is often infeasible for the database to store the exact values at all times. Queries that uses these old values can produce invalid results. In order to manage the uncertainty between the actual sensor value and the database value, we propose a system called U-DBMS. U-DBMS extends the database system with uncertainty management functionalities. In particular, each data value is represented as an interval and a probability distribution function, and it can be processed with probabilistic query operators to produce imprecise (but correct) answers. This demonstration presents a PostgreSQL-based system that handles uncertainty and probabilistic queries for constantly-evolving data.
The potential of uncertain data processing has achieved increasing interest in diverse application fields, e.g., sensor monitoring @cite_16 , traffic analysis and location-based services @cite_15 , etc.