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"... Recommendations for twoway selections using skyline view queries ..."
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Recommendations for twoway selections using skyline view queries
A Relaxed but not Necessarily Constrained Way from the Top to the Sky ⋆
"... Abstract. As P2P systems are a very popular approach that may connect a large number of peers, efficient query processing plays an important role. Appropriate strategies, however, have to take the characteristics of these systems into account. Due to the possibly large number of peers, extensive flo ..."
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Abstract. As P2P systems are a very popular approach that may connect a large number of peers, efficient query processing plays an important role. Appropriate strategies, however, have to take the characteristics of these systems into account. Due to the possibly large number of peers, extensive flooding is not possible. The application of routing indexes is a commonly used technique to avoid this. Promising techniques to further reduce execution costs and interesting aspects are query operators like topN and skyline, constraints, and the relaxation of exactness and/or completeness requirements. In this paper, we propose strategies that take all these aspects into account. The choice is left to the user if and to what extent he is willing to relax exactness or apply constraints. In any case, the proposed strategies output a guarantee that quantifies the deviation between the exact and the relaxed answer. We provide a thorough evaluation that uses two types of distributed data summaries as examples for routing indexes. 1
Finding the Influence Set through Skylines
, 2009
"... Given a set P of products, a set O of customers, and a product p ∈ P, a bichromatic reverse skyline query retrieves all the customers in O that do not find any other product in P to be absolutely better than p. More specifically, a customer o ∈ O is in the reverse skyline of p ∈ P if and only no oth ..."
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Given a set P of products, a set O of customers, and a product p ∈ P, a bichromatic reverse skyline query retrieves all the customers in O that do not find any other product in P to be absolutely better than p. More specifically, a customer o ∈ O is in the reverse skyline of p ∈ P if and only no other product in P better matches the preference of o on all dimensions. The only existing bichromatic reverse skyline algorithm, which we refer to as basic, is designed for uncertain data. This paper focuses on traditional datasets, where each object is a precise point. Since a precise point can be regarded as a special uncertain object, basic can still be applied. However, as precise data are inherently easier to handle than uncertain data, one should expect that basic can be further improved by taking advantage of the reduced problem complexity. Indeed, we observe several nontrivial heuristics that can optimize the access order to achieve stronger pruning power. Motivated by this, we propose a new algorithm called BRS, and prove that BRS never entails more I/Os than basic. Besides our theoretical analysis, we also perform extensive experiments to show that in practice BRS usually outperforms basic by a large factor. For example, when both P and O follow the anticorrelated distribution, BRS is faster than basic by an order of magnitude. Finally, we address a new variation of bichromatic reverse skyline search where the conventional definition of dynamic skylines no longer makes sense.
Computing Closed Skycubes ∗
"... In this paper, we tackle the problem of efficient skycube computation. We introduce a novel approach significantly reducing domination tests for a given subspace and the number of subspaces searched. Technically, we identify two types of skyline points that can be directly derived without using any ..."
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In this paper, we tackle the problem of efficient skycube computation. We introduce a novel approach significantly reducing domination tests for a given subspace and the number of subspaces searched. Technically, we identify two types of skyline points that can be directly derived without using any domination tests. Moreover, based on formal concept analysis, we introduce two closure operators that enable a concise representation of skyline cubes. We show that this concise representation is easy to compute and develop an efficient algorithm, which only needs to search a small portion of the huge search space. We show with empirical results the merits of our approach. 1.
QSkycube: Efficient Skycube Computation Using PointBased Space Partitioning
"... Skyline queries have gained considerable attention for multicriteria analysis of largescale datasets. However, the skyline queries are known to return too many results for highdimensional data. To address this problem, a skycube is introduced to efficiently provide users with multiple skylines with ..."
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Skyline queries have gained considerable attention for multicriteria analysis of largescale datasets. However, the skyline queries are known to return too many results for highdimensional data. To address this problem, a skycube is introduced to efficiently provide users with multiple skylines with different strengths. For efficient skycube construction, stateoftheart algorithms amortized redundant computation among subspace skylines, or cuboids, either (1) in a bottomup fashion with the principle of sharing result or (2) in atopdownfashion with theprinciple ofsharing structure. However, we observed further room for optimization in both principles. This paper thus aims to design a more efficient skycube algorithm that shares multiple cuboids using more effective structures. Specifically, we first develop each principle by leveraging multiple parents and a skytree, representing recursive pointbased space partitioning. We then design an efficient algorithm exploiting these principles. Experimental results demonstrate that our proposed algorithm is significantly faster than stateoftheart skycube algorithms in extensive datasets. 1.
A Framework for Ranking and KNN Queries in a Probabilistic Skyline Model
"... Skyline computation has gained a lot of attention in recent years. According to the definition of skyline, objects that belong to skyline cannot be ranked among themselves because they are incomparable. This constraint limits the application of skyline. Fortunately, due to the recently proposed prob ..."
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Skyline computation has gained a lot of attention in recent years. According to the definition of skyline, objects that belong to skyline cannot be ranked among themselves because they are incomparable. This constraint limits the application of skyline. Fortunately, due to the recently proposed probabilistic skyline model, skyline objects which contain multiple elements, can now be compared with each others. Different from the traditional skyline model where each object can either be a skyline object or not, in the probabilistic skyline model, each object is assigned a skyline probability to denote its likelihood of being a skyline object. Under this model, two simple but important questions will naturally be asked: (1) Given an object, which of the objects are the K nearest neighbors to it based on their skyline probabilities? (2) Given an object, what is the ranking of the objects which have skyline probabilities greater than the given object? To the best of our knowledge, no existing work can effectively answer these two questions. Yet, answering them is not trivial. For a mediumsize dataset (e.g. 10,000 objects), it may take more than an hour to compute the skyline probabilities of all objects. In this paper, we propose a novel framework to answering the above two questions on the fly efficiently. Our proposed work is based on the idea of boundingpruningrefining strategy. We first compute the skyline probabilities of the target object and
Effective Space Usage Estimation for SlidingWindow Skybands
"... Skyline query computes all the "best" elements which are not dominated by any other elements and thus is very important for decisionmaking applications. Recently, it is generalized to skyband query and a kskyband query returns those elements dominated by no more than k, of other element ..."
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Skyline query computes all the "best" elements which are not dominated by any other elements and thus is very important for decisionmaking applications. Recently, it is generalized to skyband query and a kskyband query returns those elements dominated by no more than k, of other elements. To incorporate the skyband operator into the stream engine for monitoring skybands over sliding windows, space usage estimation for skyband operator becomes a critical issue in the query optimizer. In this paper, we firstly introduce the skyband sketch as the cost model. Based on the cost model, we propose an approach for estimating the space usage of skyband operator over sliding windows of data streams under the assumptions of statistical independence across dimensions, no duplicate values over each dimension, and dimension domains totally ordered. Experiments verify that our approaches can estimate the space usage effectively over arbitrarily distributed data. To the best of our knowledge, this is the first work that attempts to address the issue and proposes effective approaches to solve it.
Goal Directed Relative Skyline Queries in Time Dependent Road Networks
"... ABSTRACT The Wireless GIS technology is progressing rapidly in the area of mobile communications. Locationbased spatial queries are becoming an integral part of many new mobile applications. The Skyline queries are latest apps under Locationbased services. In this paper we introduce Goal Directed ..."
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ABSTRACT The Wireless GIS technology is progressing rapidly in the area of mobile communications. Locationbased spatial queries are becoming an integral part of many new mobile applications. The Skyline queries are latest apps under Locationbased services. In this paper we introduce Goal Directed Relative Skyline queries on Time dependent (GDRST) road networks. The algorithm uses travel time as a metric in finding the data object by considering multiple query points (multisource skyline) relative to user location and in the user direction of travelling. We design an efficient algorithm based on Filter phase, Heap phase and Refine Skyline phases. At the end, we propose a dynamic skyline caching (DSC) mechanism which helps to reduce the computation cost for future skyline queries. The experimental evaluation reflects the performance of GDRST algorithm over the traditional branch and bound algorithm for skyline queries in real road networks.
Dominant and K Nearest Probabilistic Skylines
"... Abstract. By definition, objects that are skyline points cannot be compared with each other. Yet, thanks to the probabilistic skyline model, skyline points with repeated observations can now be compared. In this model, each object will be assigned a value to denote for its probability of being a sk ..."
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Abstract. By definition, objects that are skyline points cannot be compared with each other. Yet, thanks to the probabilistic skyline model, skyline points with repeated observations can now be compared. In this model, each object will be assigned a value to denote for its probability of being a skyline point. When we are using this model, some questions will naturally be asked: (1) Which of the objects have skyline probabilities larger than a given object? (2) Which of the objects are the K nearest neighbors to a given object according to their skyline probabilities? (3) What is the ranking of these objects based on their skyline probabilities? Up to our knowledge, no existing work answers any of these questions. Yet, answering them is not trivial. For just a mediumsize dataset, it may take more than an hour to obtain the skyline probabilities of all the objects in there. In this paper, we propose a tree called SPTree that answers all these queries efficiently. SPTree is based on the idea of space partition. We partition the dataspace into several subspaces so that we do not need to compute the skyline probabilities of all objects. Extensive experiments are conducted. The encouraging results show that our work is highly feasible.
Computational Journalism: from Answering Questions to Questioning Answers and Raising Good Questions
, 2015
"... Our media is saturated with claims of “facts ” made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim “cherrypicking”? This thesis proposes a Query Response ..."
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Our media is saturated with claims of “facts ” made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim “cherrypicking”? This thesis proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A claim is mapped to a point on the QRS. A key insight is that we can learn much about a claim by analyzing its neighborhood on QRS. This framework lets us formulate and tackle practical factchecking tasks — reverseengineering vague claims, and countering questionable claims — as computational problems. Within the QRSbased framework, we take one step further, and propose a model as well as an efficient algorithm for finding highquality claims of a given form from data, i.e., raising good questions, in the first place. This is achieved by using a limited number of highvalued claims to represent highvalued regions of the QRS. Besides the general purpose highquality claim finding problem, leadfinding can