Xiaoye Miao

Processing k-skyband, constrained skyline, and group-by skyline queries on incomplete data

Abstract The skyline operator has been extensively explored in the literature, and most of the existing approaches assume that all dimensions are available for all data items. However, many practical applications such as sensor networks, decision making, and location-based services, may involve incomplete data items, i.e., some dimensional values are missing , due to the device failure or the privacy preservation. This paper is the first, to our knowledge, study of k-skyband ( k SB) query processing on incomplete data , where multi-dimensional data items are missing some values of their dimensions. We formalize the problem, and then present two efficient algorithms for processing it. Our methods introduce some novel concepts including expired skyline , shadow skyline , and thickness warehouse , in order to boost the search performance. As a second step, we extend our techniques to tackle constrained skyline (CS) and group-by skyline (GBS) queries over incomplete data. Extensive experiments with both real and synthetic data sets demonstrate the effectiveness and efficiency of our proposed algorithms under various experimental settings.

On Efficient k-Skyband Query Processing over Incomplete Data

The Skyline query and its variants have been extensively explored in the literature. Existing approaches, except one, assume that all dimensions are available for all data items. However, many practical applications such as sensor networks, decision making, and location-based services, may involve incomplete data items, i.e., some dimensional values are missing, due to the device failure or the privacy preservation. In this paper, for the first time, we study the problem of efficient k-Skyband (kSB) query processing on incomplete data, where multi-dimensional data items are missing some values of their dimensions. We formalize the problem, and then present several efficient algorithms for tackling it. Our methods employ some novel concepts/structures (e.g., expired skyline, shadow skyline, thickness warehouse, etc.) to improve the search performance. Extensive experiments with both real and synthetic data sets demonstrate the effectiveness and efficiency of our proposed algorithms.

k-dominant skyline queries on incomplete data

The skyline query has been extensively explored as one of popular techniques to filter uninteresting data objects, which plays an important role in many real-life applications such as multi-criteria decision making and personalized services. This query has also been incorporated into commercial database systems for supporting preference queries. However, a skyline query may retrieve too many objects to analyze intensively especially for high-dimensional datasets. As a result, k-dominant skyline query has been introduced to control the number of the objects retrieved. Existing algorithms for k-dominant skyline queries only aim at complete data, which is not well-suited for incomplete data, even though incomplete data is pervasive in scientific research and real life, due to delivery failure, no power of battery, accidental loss, etc. In this paper, we systematically study the problem of k-dominant skyline queries on incomplete data (IkDS), where the data objects might miss their attribute values. We formalize the IkDS query and then present three efficient algorithms for finding k-dominant skyline objects over incomplete data. Several novel concepts/techniques are utilized including local skyline, dominance ability, and bitmap index on incomplete data to shrink the search space. In addition, we extend our techniques to tackle two interesting variants, i.e., weighted dominant skyline query and top-ź dominant skyline query, over incomplete data. Extensive experiments using both real and synthetic data sets demonstrate the performance of our proposed algorithms.

Reverse k-nearest neighbor search in the presence of obstacles

In this paper, we study a new form of reverse nearest neighbor (RNN) queries, i.e., obstructed reverse nearest neighbor (ORNN) search. It considers the impact of obstacles on the distance between objects, which is ignored by the existing work on RNN retrieval. Given a data set P, an obstacle set O, and a query point q in a two-dimensional space, an ORNN query finds from P, all the points/objects that have q as their nearest neighbor, according to the obstructed distance metric, i.e., the length of the shortest path between two points without crossing any obstacle. We formalize ORNN search, develop effective pruning heuristics (via introducing a novel concept of boundary region), and propose efficient algorithms for ORNN query processing assuming that both P and O are indexed by traditional data-partitioning indexes (e.g., R-trees). In addition, several interesting variations of ORNN queries, namely, obstructed reverse k-nearest neighbor (ORkNN) search, ORkNN search with maximum obstructed distance ? (?-ORkNN), and constrained ORkNN (CORkNN) search, have been introduced, and they can be tackled by extending the ORNN query techniques, which demonstrates the flexibility of the proposed ORNN query algorithm. Extensive experimental evaluation using both real and synthetic data sets verifies the effectiveness of pruning heuristics and the performance of algorithms, respectively.

Top- k Dominating Queries on Incomplete Data

The top-k dominating (TKD) query returns the

Processing Incomplete k Nearest Neighbor Search

k

Incomplete data management: a survey

objects that dominate the maximum number of objects in a given dataset. It combines the advantages of skyline and top-

S i 2 p : a restaurant recommendation system using preference queries over incomplete information

k

On efficiently finding reverse k-nearest neighbors over uncertain graphs

queries, and plays an important role in many decision support applications. Incomplete data exists in a wide spectrum of real datasets, due to device failure, privacy preservation, data loss, and so on. In this paper, for the first time, we carry out a systematic study of TKD queries on incomplete data , which involves the data having some missing dimensional value(s). We formalize this problem, and propose a suite of efficient algorithms for answering TKD queries over incomplete data. Our methods employ some novel techniques, such as upper bound score pruning , bitmap pruning , and partial score pruning , to boost query efficiency. Extensive experimental evaluation using both real and synthetic datasets demonstrates the effectiveness of our developed pruning heuristics and the performance of our presented algorithms.

Top-k dominating queries on incomplete data

Given a setS of multidimensional objects and a query object q, a k nearest neighbor (kNN) query finds from S the k closest objects to q. This query is a fundamental problem in database, data mining, and information retrieval research. It plays an important role in a wide spectrum of real applications such as image recognition and location-based services. However, due to the failure of data transmission devices, improper storage, and accidental loss, incomplete data exist widely in those applications, where some dimensional values of data items are missing. In this paper, we systematically study incomplete k nearest neighbor (IkNN) search, which aims at the kNN query for incomplete data. We formalize this problem and propose an efficient lattice partition algorithm using our newly developed LαB index to support exact IkNN retrieval, with the help of two pruning heuristics, i.e., α value pruning and partial distance pruning. Furthermore, we propose an approximate algorithm, namely histogram approximate, to support approximate IkNN search with improved search efficiency and guaranteed error bound. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness of newly designed indexes and pruning heuristics, as well as the performance of our presented algorithms under a variety of experimental settings.