text: Given a set @math of @math points in the plane, we study the computation of the probability distribution function of both the area and perimeter of the convex hull of a random subset @math of @math . The random subset @math is formed by drawing each point @math of @math independently with a given rational probability @math . For both measures of the convex hull, we show that it is #P-hard to compute the probability that the measure is at least a given bound @math . For @math , we provide an algorithm that runs in @math time and returns a value that is between the probability that the area is at least @math , and the probability that the area is at least @math . For the perimeter, we show a similar algorithm running in @math time. Finally, given @math and for any measure, we show an @math -time Monte Carlo algorithm that returns a value that, with probability of success at least @math , differs at most @math from the probability that the measure is at least @math .
  @cite_10: We study computing geometric problems on uncertain points. An uncertain point is a point that does not have a fixed location, but rather is described by a probability distribution. When these probability distributions are restricted to a finite number of locations, the points are called indecisive points. In particular, we focus on geometric shape-fitting problems and on building compact distributions to describe how the solutions to these problems vary with respect to the uncertainty in the points. Our main results are: (1) a simple and efficient randomized approximation algorithm for calculating the distribution of any statistic on uncertain data sets; (2) a polynomial, deterministic and exact algorithm for computing the distribution of answers for any LP-type problem on an indecisive point set; and (3) the development of shape inclusion probability (SIP) functions which captures the ambient distribution of shapes fit to uncertain or indecisive point sets and are admissible to the two algorithmic constructions.
  @cite_1: 
  @cite_2: In the Trio project at Stanford, we are building a new kind of database management system: one in which data, uncertainty of the data, and data lineage are all first-class citizens. Trio is based on an extended relational model called ULDBs, and it supports a SQL-based query language called TriQL. Trio was motivated by a number of applications including scientific data management, data cleaning and integration, information extraction systems, and others. We have completed an initial working prototype of the Trio system. We will demonstrate our prototype by illustrating through two applications how uncertainty and lineage are represented in ULDBs, how TriQL operates over ULDBs both from the user and the system perspective, and in general how data, uncertainty, and lineage can work together to support interesting new functionality.
  @cite_5: 
  @cite_16: When dealing with massive quantities of data, top-k queries are a powerful technique for returning only the k most relevant tuples for inspection, based on a scoring function. The problem of efficiently answering such ranking queries has been studied and analyzed extensively within traditional database settings. The importance of the top-k is perhaps even greater in probabilistic databases, where a relation can encode exponentially many possible worlds. There have been several recent attempts to propose definitions and algorithms for ranking queries over probabilistic data. However, these all lack many of the intuitive properties of a top-k over deterministic data. Specifically, we define a number of fundamental properties, including exact-k, containment, unique-rank, value-invariance, and stability, which are all satisfied by ranking queries on certain data. We argue that all these conditions should also be fulfilled by any reasonable definition for ranking uncertain data. Unfortunately, none of the existing definitions is able to achieve this. To remedy this shortcoming, this work proposes an intuitive new approach of expected rank. This uses the well-founded notion of the expected rank of each tuple across all possible worlds as the basis of the ranking. We are able to prove that, in contrast to all existing approaches, the expected rank satisfies all the required properties for a ranking query. We provide efficient solutions to compute this ranking across the major models of uncertain data, such as attribute-level and tuple-level uncertainty. For an uncertain relation of N tuples, the processing cost is O(N logN)—no worse than simply sorting the relation. In settings where there is a high cost for generating each tuple in turn, we provide pruning techniques based on probabilistic tail bounds that can terminate the search early and guarantee that the top-k has been found. Finally, a comprehensive experimental study confirms the effectiveness of our approach.
  @cite_13: In this paper we present several results on the expected complexity of a convex hull of @math points chosen uniformly and independently from a convex shape. (i) We show that the expected number of vertices of the convex hull of @math points, chosen uniformly and independently from a disk is @math , and @math for the case a convex polygon with @math sides. Those results are well known (see rs-udkhv-63,r-slcdn-70,ps-cgi-85 ), but we believe that the elementary proof given here are simpler and more intuitive. (ii) Let @math be a set of directions in the plane, we define a generalized notion of convexity induced by @math , which extends both rectilinear convexity and standard convexity. We prove that the expected complexity of the @math -convex hull of a set of @math points, chosen uniformly and independently from a disk, is @math , where @math is the largest angle between two consecutive vectors in @math . This result extends the known bounds for the cases of rectilinear and standard convexity. (iii) Let @math be an axis parallel hypercube in @math . We prove that the expected number of points on the boundary of the quadrant hull of a set @math of @math points, chosen uniformly and independently from @math is @math . Quadrant hull of a set of points is an extension of rectilinear convexity to higher dimensions. In particular, this number is larger than the number of maxima in @math , and is also larger than the number of points of @math that are vertices of the convex hull of @math . Those bounds are known bkst-anmsv-78 , but we believe the new proof is simpler.
summary: Stochastic finite point sets in the plane, as the one considered in this paper, appear in a natural manner in many database scenarios in which the gathered data has many false positives @cite_2 @cite_16 @cite_10 . This model of random points differs from the model in which @math points are chosen independently at random in some Euclidean region, and questions related to the final positions of the points are considered @cite_13 @cite_5 @cite_1 .