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225
Progressive Skyline Computation in Database Systems
- ACM TRANS. DATABASE SYST
, 2005
"... The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without r ..."
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Cited by 205 (13 self)
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The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing algorithms, however, have some serious shortcomings which limit their applicability in practice. In this article we develop branch-- skyline (BBS), an algorithm based on nearest-neighbor search, which is I/O optimal, that is, it performs a single access only to those nodes that may contain skyline points. BBS is simple to implement and supports all types of progressive processing (e.g., user preferences, arbitrary dimensionality, etc). Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing.
Efficient distributed skylining for web information systems
- IN EDBT
, 2004
"... Though skyline queries already have claimed their place in retrieval over central databases, their application in Web information systems up to now was impossible due to the distributed aspect of retrieval over Web sources. But due to the amount, variety and volatile nature of information accessible ..."
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Cited by 123 (14 self)
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Though skyline queries already have claimed their place in retrieval over central databases, their application in Web information systems up to now was impossible due to the distributed aspect of retrieval over Web sources. But due to the amount, variety and volatile nature of information accessible over the Internet extended query capabilities are crucial. We show how to efficiently perform distributed skyline queries and thus essentially extend the expressiveness of querying today’s Web information systems. Together with our innovative retrieval algorithm we also present useful heuristics to further speed up the retrieval in most practical cases paving the road towards meeting even the realtime challenges of on-line information services. We discuss performance evaluations and point to open problems in the concept and application of skylining in modern information systems. For the curse of dimensionality, an intrinsic problem in skyline queries, we propose a novel sampling scheme that allows to get an early impression of the skyline for subsequent query refinement.
Selecting Stars: The k Most Representative Skyline Operator
- In Proc. of the Int. IEEE Conf. on Data Engineering (ICDE
, 2007
"... Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of selecting k skyline points so that the number of points, which are dominated by at least one of these k skyline points, is maximized. We first present an efficient dynamic progr ..."
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Cited by 93 (3 self)
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Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of selecting k skyline points so that the number of points, which are dominated by at least one of these k skyline points, is maximized. We first present an efficient dynamic programming based exact algorithm in a 2d-space. Then, we show that the problem is NP-hard when the dimensionality is 3 or more and it can be approximately solved by a polynomial time algorithm with the guaranteed approximation ratio 1 − 1 e. To speed-up the computation, an efficient, scalable, index-based randomized algorithm is developed by applying the FM probabilistic counting technique. A comprehensive performance evaluation demonstrates that our randomized technique is very efficient, highly accurate, and scalable. 1.
Maximal Vector Computation in Large Data Sets
- IN VLDB
, 2005
"... Finding the maximals in a collection of vectors is relevant to many applications. The maximal set is related to the convex hull -- and hence, linear optimization -- and nearest neighbors. The maximal vector problem has resurfaced with the advent of skyline queries for relational databases and skyl ..."
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Cited by 89 (1 self)
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Finding the maximals in a collection of vectors is relevant to many applications. The maximal set is related to the convex hull -- and hence, linear optimization -- and nearest neighbors. The maximal vector problem has resurfaced with the advent of skyline queries for relational databases and skyline algorithms that are external and relationally well behaved. The initial
Catching the best views of skyline: A semantic approach based on decisive subspaces
- In VLDB
, 2005
"... The skyline operator is important for multicriteria decision making applications. Although many recent studies developed efficient methods to compute skyline objects in a specific space, the fundamental problem on the semantics of skylines remains open: Why and in which subspaces is (or is not) an o ..."
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Cited by 87 (12 self)
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The skyline operator is important for multicriteria decision making applications. Although many recent studies developed efficient methods to compute skyline objects in a specific space, the fundamental problem on the semantics of skylines remains open: Why and in which subspaces is (or is not) an object in the skyline? Practically, users may also be interested in the skylines in any subspaces. Then, what is the relationship between the skylines in the subspaces and those in the super-spaces? How can we effectively analyze the subspace skylines? Can we efficiently compute skylines in various subspaces? In this paper, we investigate the semantics of skylines, propose the subspace skyline analysis, and extend the full-space skyline computation to subspace skyline computation. We introduce a novel notion of skyline group which essentially is a group of objects that are coincidentally in the skylines of some subspaces. We identify the decisive subspaces that qualify skyline groups in the subspace skylines. The new notions concisely capture the semantics and the structures of skylines in various subspaces. Multidimensional roll-up and drilldown analysis is introduced. We also develop
Finding k-dominant skylines in high dimensional space
- SIGMOD
"... Given a d-dimensional data set, a point p dominates another point q if it is better than or equal to q in all dimensions and better than q in at least one dimension. A point is a skyline point if there does not exists any point that can dominate it. Skyline queries, which return skyline points, are ..."
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Cited by 76 (9 self)
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Given a d-dimensional data set, a point p dominates another point q if it is better than or equal to q in all dimensions and better than q in at least one dimension. A point is a skyline point if there does not exists any point that can dominate it. Skyline queries, which return skyline points, are useful in many decision making applications. Unfortunately, as the number of dimensions increases, the chance of one point dominating another point is very low. As such, the number of skyline points become too numerous to offer any interesting insights. To find more important and meaningful skyline points in high dimensional space, we propose a new concept, called k-dominant skyline which relaxes the idea of dominance to k-dominance. A point p is said to k-dominate another point q if there are k ( ≤ d) dimensions in which p is better than or equal to q and is better in at least one of these k dimensions. A point that is not k-dominated by any other points is in the k-dominant skyline. We prove various properties of k-dominant skyline. In particular, because k-dominant skyline points are not tran-sitive, existing skyline algorithms cannot be adapted for k-dominant skyline. We then present several new algorithms for finding k-dominant skyline and its variants. Extensive experiments show that our methods can answer different queries on both synthetic and real data sets efficiently.
Efficient Computation of the Skyline Cube
- IN VLDB
, 2005
"... Skyline has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and user-preference queries. In this paper, we consider the problem of efficiently computing a Skycube, which consists of skylines of all possible non-empty subsets of a given ..."
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Cited by 71 (5 self)
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Skyline has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and user-preference queries. In this paper, we consider the problem of efficiently computing a Skycube, which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computation algorithms can be immediately extended to computing each skyline query independently, such "shared-nothing" algorithms are inefficient. We develop several computation sharing strategies based on e#ectively identifying the computation dependencies among multiple related skyline queries. Based on these sharing strategies, two novel algorithms, Bottom-Up and Top-Down algorithms, are proposed to compute Skycube efficiently. Finally, our extensive performance evaluations confirm the effectiveness of the sharing strategies. It is
Stratified computation of skylines with partially-ordered domains
- PROC. OF THE ACM SIGMOD INT'L CONF. ON MANAGEMENT OF DATA
, 2005
"... In this paper, we study the evaluation of skyline queries with partially-ordered attributes. Because such attributes lack a total ordering, traditional index-based evaluation algorithms (e.g., NN and BBS) that are designed for totally-ordered attributes can no longer prune the space as effectively. ..."
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Cited by 70 (2 self)
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In this paper, we study the evaluation of skyline queries with partially-ordered attributes. Because such attributes lack a total ordering, traditional index-based evaluation algorithms (e.g., NN and BBS) that are designed for totally-ordered attributes can no longer prune the space as effectively. Our solution is to transform each partially-ordered attribute into a two-integer domain that allows us to exploit index-based algorithms to compute skyline queries on the transformed space. Based on this framework, we propose three novel algorithms: BBS + is a straightforward adaptation of BBS using the framework, and SDC (Stratification by Dominance Classification) and SDC + are optimized to handle false positives and support progressive evaluation. Both SDC and SDC + exploit a dominance relationship to organize the data into strata. While SDC generates its strata at runtime, SDC + partitions the data into strata offline. We also design two dominance classification strategies (MinPC and MaxPC) to further optimize the performance of SDC and SDC +. We implemented the proposed schemes and evaluated their efficiency. Our results show that our proposed techniques outperform existing approaches by a wide margin, with SDC +-MinPC giving the best performance in terms of both response time as well as progressiveness. To the best of our knowledge, this is the first paper to address the problem of skyline query evaluation involving partially-ordered attribute domains.
Efficient Computation of Reverse Skyline Queries
, 2007
"... In this paper, for the first time, we introduce the concept of Reverse Skyline Queries. At first, we consider for a multidimensional data set P the problem of dynamic skyline queries according to a query point q. This kind of dynamic skyline corresponds to the skyline of a transformed data space whe ..."
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Cited by 63 (0 self)
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In this paper, for the first time, we introduce the concept of Reverse Skyline Queries. At first, we consider for a multidimensional data set P the problem of dynamic skyline queries according to a query point q. This kind of dynamic skyline corresponds to the skyline of a transformed data space where point q becomes the origin and all points of P are represented by their distance vector to q. The reverse skyline query returns the objects whose dynamic skyline contains the query object q. In order to compute the reverse skyline of an arbitrary query point, we first propose a Branch and Bound algorithm (called BBRS), which is an improved customization of the original BBS algorithm. Furthermore, we identify a super set of the reverse skyline that is used to bound the search space while computing the reverse skyline. To further reduce the computational cost of determining if a point belongs to the reverse skyline, we propose an enhanced algorithm (called RSSA) that is based on accurate pre-computed approximations of the skylines. These approximations are used to identify whether a point belongs to the reverse skyline or not. Through extensive experiments with both real-world and synthetic datasets, we show that our algorithms can efficiently support reverse skyline queries. Our enhanced approach improves reversed skyline processing by up to an order of magnitude compared to the algorithm without the usage of pre-computed approximations.
Multi-objective query processing for database systems
- In International Conference on Very Large Data Bases (VLDB
, 2004
"... Query processing in database systems has developed beyond mere exact matching of attribute values. Scoring database objects and retrieving only the top k matches or Pareto-optimal result sets (skyline queries) are already common for a variety of applications. Specialized algorithms using either para ..."
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Cited by 58 (10 self)
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Query processing in database systems has developed beyond mere exact matching of attribute values. Scoring database objects and retrieving only the top k matches or Pareto-optimal result sets (skyline queries) are already common for a variety of applications. Specialized algorithms using either paradigm can avoid naïve linear database scans and thus improve scalability. However, these paradigms are only two extreme cases of exploring viable compromises for each user‘s objectives. To find the correct result set for arbitrary cases of multi-objective query processing in databases we will present a novel algorithm for computing sets of objects that are non-dominated with respect to a set of monotonic objective functions. Naturally containing top k and skyline retrieval paradigms as special cases, this algorithm maintains scalability also for all cases in between. Moreover, we will show the algorithm’s correctness and instance-optimality in terms of necessary object accesses and how the response behavior can be improved by progressively producing result objects as quickly as possible, while the algorithm is still running. 1.
