The skyline query can help identify the “best” objects in a multi-attribute dataset. During the past decade, this query has received considerable attention in the database research community. Most research focused on computing the “skyline” of a dataset, or the set of “skyline objects” that are not dominated by any other object. Such algorithms are not appropriate in an online system, which should respond in real time to skyline query requests with arbitrary subsets of the attributes (also called subspaces). To guarantee real-time response, an online system should precompute the skylines for all subspaces, and look up a skyline upon query. Unfortunately, because the number of subspaces is exponential to the number of attributes, such pre computation has very expensive storage cost and update cost. We propose the Compressed SkyCube (CSC) that is much more compact, yet can still return the skyline of any subspace without consulting the base table. The CSC therefore combines the advantage of precomputation in that it can respond to queries in real time, and the advantage of no-precomputation in that it has efficient space cost and update cost. This article presents the CSC data structures, the CSC query algorithm, the CSC update algorithm, and the CSC initial computation scheme. A solution to extend to high-dimensional data is also proposed.

This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general in that it is applicable for both continuous monochromatic and bichromatic reverse nearest neighbor queries. This problem is faced in a number of applications such as enhanced 911 services and in army strategic planning. A main challenge in these problems is to maintain the most up-to-date query answers as the data set frequently changes over time. Previous algorithms for monochromatic continuous reverse nearest neighbor queries rely mainly on monitoring at the worst case of six pie regions, whereas IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-the-art algorithms in monochromatic queries. We also propose a new optimization for the monochromatic IGERN to reduce the number of nearest neighbor searches. Furthermore, a filter and refine approach for IGERN (FR-IGERN) is proposed for the continuous evaluation of bichromatic reverse nearest neighbor queries which is an optimized version of our previous approach. The computational complexity of IGERN and FR-IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic and bichromatic cases. In addition, the correctness of IGERN and FR-IGERN in both the monochromatic and bichromatic cases, respectively, are proved. Extensive experimental analysis using synthetic and real data sets shows that IGERN and FR-IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries.

Given a set of d dimensional objects, a skyline query finds the objects ("skyline") that are not dominated by others. However, skylines do not always provide useful query results to users, and existing methods of various skyline queries have at least one of the following drawbacks: (1) the size of skyline objects can not be controlled, or can be only increased or only decreased but not both; (2) skyline objects do not have built-in ranks; (3) skylines do not reflect users' weights (preferences) at different dimensions. In this paper, we propose a unified approach, the ε-skyline, to effectively solve all three drawbacks. We explore the properties of ε-skylines and propose two different algorithms to compute ε-skylines.

This paper presents the first theoretical study, on using local-recoding generalization (LRG) to compute a k-anonymous table with quality guarantee. First, we prove that it is NP-hard both to find the table with the maximum quality, and to discover a solution with an approximation ratio at most 5/4. Then, we develop an algorithm with good balance between the approximation ratio and time complexity. The quality of our solution is verified by experiments.

This paper presents a novel algorithm for Incremental and General Evaluation of continuous Reverse Nearest neighbor queries (IGERN, for short). The IGERN algorithm is general as it is applicable for both the monochromatic and bichromatic reverse nearest neighbor queries. The incremental aspect of IGERN is achieved through determining only a small set of objects to be monitored. While previous algorithms for monochromatic queries rely mainly on monitoring six pie regions, IGERN takes a radical approach by monitoring only a single region around the query object. The IGERN algorithm clearly outperforms the state-of-the-art algorithms in monochromatic queries. In addition, the IGERN algorithm presents the first attempt for continuous evaluation of bichromatic reverse nearest neighbor queries. The computational complexity of IGERN is presented in comparison to the state-of-the-art algorithms in the monochromatic case and to the use of Voronoi diagrams for the bichromatic case. In addition, the correctness of IGERN in both the monochromatic and bichromatic cases are proved. Extensive experimental analysis shows that IGERN is efficient, is scalable, and outperforms previous techniques for continuous reverse nearest neighbor queries.

This paper proposes and solves the Time-Interval All Fastest Path (allFP) query. Given a user-defined leaving or arrival time interval I, a source node s and an end node e, allFP asks for a set of all fastest paths from s to e, one for each sub-interval of I. Note that the query algorithm should find a partitioning of I into sub-intervals. Existing methods can only be used to solve a very special case of the problem, when the leaving time is a single time instant. A straightforward solution to the allFP query is to run existing methods many times, once for every time instant in I. This paper proposes a solution based on novel extensions to the A* algorithm. Instead of expanding the network many times, we expand once. The travel time on a path is kept as a function of leaving time. Methods to combine travel-time functions are provided to expand a path. A novel lower-bound estimator for travel time is proposed. Performance results reveal that our method is more efficient and more accurate than the discrete-time approach.

Continuous spatio-temporal queries have recently received increasing attention due to the abundance of location-aware applications. This paper addresses the Continuous Reverse Nearest Neighbor (CRNN) Query. Given a set of objects O and a query set Q, the CRNN query monitors the exact reverse nearest neighbors of each query point, under the model that both the objects and the query points may move unpredictably. Existing methods for the reverse nearest neighbor (RNN) query either are static or assume a priori knowledge of the trajectory information, and thus do not apply. Related recent work on continuous range query and continuous nearest neighbor query relies on the fact that a simple monitoring region exists. Due to the unique features of the RNN problem, it is non-trivial to even define a monitoring region for the CRNN query. This paper defines the monitoring region for the CRNN query, discusses how to perform initial computation, and then focuses on incremental CRNN monitoring upon updates. The monitoring region according to one query point consists of two types of regions. We argue that the two types should be handled separately. In continuous monitoring, two optimization techniques are proposed. Experimental results prove that our proposed approach is both efficient and scalable.

The skyline query is important in many applications such as multi-criteria decision making, data mining, and user-preference queries. Given a set of d-dimensional objects, the skyline query finds the objects that are not dominated by others. In practice, different users may be interested in different dimensions of the data, and issue queries on any subset of d dimensions. This paper focuses on supporting concurrent and unpredictable subspace skyline queries in frequently updated databases. Simply to compute and store the skyline objects of every subspace in a skycube will incur expensive update cost. In this paper, we investigate the important issue of updating the skycube in a dynamic environment. To balance the query cost and update cost, we propose a new structure, the compressed skycube, which concisely represents the complete skycube. We thoroughly explore the properties of the compressed skycube and provide an efficient object-aware update scheme. Experimental results show that the compressed skycube is both query and update efficient.