Results 1  10
of
23
Probabilistic skylines on uncertain data
 In Proceedings of the 33rd International Conference on Very Large Data Bases (VLDB’07), Viena
, 2007
"... Uncertain data are inherent in some important applications. Although a considerable amount of research has been dedicated to modeling uncertain data and answering some types of queries on uncertain data, how to conduct advanced analysis on uncertain data remains an open problem at large. In this pap ..."
Abstract

Cited by 103 (19 self)
 Add to MetaCart
Uncertain data are inherent in some important applications. Although a considerable amount of research has been dedicated to modeling uncertain data and answering some types of queries on uncertain data, how to conduct advanced analysis on uncertain data remains an open problem at large. In this paper, we tackle the problem of skyline analysis on uncertain data. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline, and a pskyline contains all the objects whose skyline probabilities are at least p. Computing probabilistic skylines on large uncertain data sets is challenging. We develop two efficient algorithms. The bottomup algorithm computes the skyline probabilities of some selected instances of uncertain objects, and uses those instances to prune other instances and uncertain objects effectively. The topdown algorithm recursively partitions the instances of uncertain objects into subsets, and prunes subsets and objects aggressively. Our experimental results on both the real NBA player data set and the benchmark synthetic data sets show that probabilistic skylines are interesting and useful, and our two algorithms are efficient on large data sets, and complementary to each other in performance. 1.
Stochastic Skyline Operator
"... Abstract — In many applications involving the multiple criteria optimal decision making, users may often want to make a personal tradeoff among all optimal solutions. As a key feature, the skyline in a multidimensional space provides the minimum set of candidates for such purposes by removing all ..."
Abstract

Cited by 11 (3 self)
 Add to MetaCart
(Show Context)
Abstract — In many applications involving the multiple criteria optimal decision making, users may often want to make a personal tradeoff among all optimal solutions. As a key feature, the skyline in a multidimensional space provides the minimum set of candidates for such purposes by removing all points not preferred by any (monotonic) utility/scoring functions; that is, the skyline removes all objects not preferred by any user no mater how their preferences vary. Driven by many applications with uncertain data, the probabilistic skyline model is proposed to retrieve uncertain objects based on skyline probabilities. Nevertheless, skyline probabilities cannot capture the preferences of monotonic utility functions. Motivated by this, in this paper we propose a novel skyline operator, namely stochastic skyline. In the light of the expected utility principle, stochastic skyline guarantees to provide the minimum set of candidates for the optimal solutions over all possible monotonic multiplicative utility functions. In contrast to the conventional skyline or the probabilistic skyline computation, we show that the problem of stochastic skyline is NPcomplete with respect to the dimensionality. Novel and efficient algorithms are developed to efficiently compute stochastic skyline over multidimensional uncertain data, which run in polynomial time if the dimensionality is fixed. We also show, by theoretical analysis and experiments, that the size of stochastic skyline is quite similar to that of conventional skyline over certain data. Comprehensive experiments demonstrate that our techniques are efficient and scalable regarding both CPU and IO costs. I.
(Approximate) uncertain skylines
 IN ICDT
, 2011
"... Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point’s uncertainty is described as a probability distribution over a discrete ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point’s uncertainty is described as a probability distribution over a discrete set of locations, we improve the best known exact solution. We also suggest why we believe our solution might be optimal. Next, we describe simple, nearlinear time approximation algorithms for computing the probability of each point lying on the skyline. In addition, some of our methods can be adapted to construct data structures that can efficiently determine the probability of a query point lying on the skyline.
Prefjoin: An efficient preferenceaware join operator
 In ICDE
, 2011
"... AbstractPreference queries are essential to a wide spectrum of applications including multicriteria decisionmaking tools and personalized databases. Unfortunately, most of the evaluation techniques for preference queries assume that the set of preferred attributes are stored in only one relation ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
AbstractPreference queries are essential to a wide spectrum of applications including multicriteria decisionmaking tools and personalized databases. Unfortunately, most of the evaluation techniques for preference queries assume that the set of preferred attributes are stored in only one relation, waiving on a wide set of queries that include preference computations over multiple relations. This paper presents PrefJoin, an efficient preferenceaware join query operator, designed specifically to deal with preference queries over multiple relations. PrefJoin consists of four main phases: Local Pruning, Data Preparation, Joining, and Refining that discard irrelevant tuple from the input relations, prepare tuples for next phases, joins nonpruned objects, and refine the join result respectively. PrefJoin supports a variety of preference function including skyline, multiobjective and kdominance preference queries. An interesting characteristic of PrefJoin is that it is tightly integrating with join hence we can early prune join only those tuples that are guaranteed not to be an answer, and hence it saves significant unnecessary computations cost. We show the correctness of PrefJoin. Experimental evaluation based on a real system implementation inside PostgreSQL shows that PrefJoin consistently achieves from one to three orders of magnitude performance gain over its competitors in various scenarios. I. INTRODUCTION Most of the research efforts for the preference query evaluation are designed to compute the preference set over a single relation (e.g., see
M.: Getting the Best from Uncertain Data
 In: SEBD
, 2011
"... Abstract. The skyline of a relation is the set of tuples that are not dominated by any other tuple in the same relation, where tuple u dominates tuple v if u is no worse than v on all the attributes of interest and strictly better on at least one attribute. Previous attempts to extend skyline queri ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Abstract. The skyline of a relation is the set of tuples that are not dominated by any other tuple in the same relation, where tuple u dominates tuple v if u is no worse than v on all the attributes of interest and strictly better on at least one attribute. Previous attempts to extend skyline queries to probabilistic databases have proposed either a weaker form of domination, which is unsuitable to univocally define the skyline, or a definition that implies algorithms with exponential complexity. In this paper we demonstrate how, given a semantics for linearly ranking probabilistic tuples, the skyline of a probabilistic relation can be univocally dened. Our approach preserves the three fundamental properties of skyline: 1) it equals the union of all top1 results of monotone scoring functions, 2) it requires no additional parameter to be specified, and 3) it is insensitive to actual attribute scales. We also detail efficient sequential and indexbased algorithms. 1
Computing Exact Skyline Probabilities for Uncertain Databases
"... Abstract—With the rapid increase in the amount of uncertain data available, probabilistic skyline computation on uncertain databases has become an important research topic. Previous work on probabilistic skyline computation, however, only identifies those objects whose skyline probabilities are high ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract—With the rapid increase in the amount of uncertain data available, probabilistic skyline computation on uncertain databases has become an important research topic. Previous work on probabilistic skyline computation, however, only identifies those objects whose skyline probabilities are higher than a given threshold, or is useful only for 2D data sets. In this paper, we develop a probabilistic skyline algorithm called PSkyline which computes exact skyline probabilities of all objects in a given uncertain data set. PSkyline aims to identify blocks of instances with skyline probability zero, and more importantly, to find incomparable groups of instances and dispense with unnecessary dominance tests altogether. To increase the chance of finding such blocks and groups of instances, PSkyline uses a new inmemory tree structure called Ztree. We also develop an online probabilistic skyline algorithm called OPSkyline for uncertain data streams and a topk probabilistic skyline algorithm called KPSkyline to find topk objects with the highest skyline probabilities. Experimental results show that all the proposed algorithms scale well to large and highdimensional uncertain databases. Index Terms—Skyline computation, skyline probability, uncertain database, data stream. Ç 1
lipid profile
 Eur Heart J 1992; 13 Suppl G: 61
"... The APC/C subunit Cdc16/Cut9 is a contiguous tetratricopeptide repeat superhelix with a homodimer interface similar to Cdc27 ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
The APC/C subunit Cdc16/Cut9 is a contiguous tetratricopeptide repeat superhelix with a homodimer interface similar to Cdc27
<10.1016/j.ins.2014.02.135>. <lirmm01076096>
, 2015
"... HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte p ..."
Abstract
 Add to MetaCart
(Show Context)
HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et a ̀ la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Identifying Interesting Instances for Probabilistic Skylines
, 2009
"... Uncertain data arises from various applications such as sensor networks, scientific data management, data integration, and location based applications. While significant research efforts have been dedicated to modeling, managing and querying uncertain data, advanced analysis of uncertain data is s ..."
Abstract
 Add to MetaCart
Uncertain data arises from various applications such as sensor networks, scientific data management, data integration, and location based applications. While significant research efforts have been dedicated to modeling, managing and querying uncertain data, advanced analysis of uncertain data is still in its early stages. In this paper, we focus on skyline analysis of uncertain data, modeled as uncertain objects with probability distributions over a set of possible values called instances. Computing the exact skyline probabilities of instances is expensive, and unnecessary when the user is only interested in instances with skyline probabilities over a certain threshold. We propose two filtering schemes for this case: a preliminary scheme that bounds an instance’s skyline probability for filtering, and an elaborate scheme that
Finding Probabilistic kSkyline Sets on Uncertain Data
"... ABSTRACT Skyline is a set of points that are not dominated by any other point. Given uncertain objects, probabilistic skyline has been studied which computes objects with high probability of being skyline. While useful for selecting individual objects, it is not sufficient for scenarios where we wi ..."
Abstract
 Add to MetaCart
(Show Context)
ABSTRACT Skyline is a set of points that are not dominated by any other point. Given uncertain objects, probabilistic skyline has been studied which computes objects with high probability of being skyline. While useful for selecting individual objects, it is not sufficient for scenarios where we wish to compute a subset of skyline objects, i.e., a skyline set. In this paper, we generalize the notion of probabilistic skyline to probabilistic kskyline sets (PkSkylineSets) which computes kobject sets with high probability of being skyline set. We present an efficient algorithm for computing probabilistic kskyline sets. It uses two heuristic pruning strategies and a novel data structure based on the classic layered range tree to compute the skyline set probability for each instance set with a worstcase time bound. The experimental results on the real NBA dataset and the synthetic datasets show that PkSkylineSets is interesting and useful, and our algorithms are efficient and scalable.