When the dimensionality of dataset increases slightly, the number of skyline points increases dramatically as it is usually unlikely for a point to perform equally good in all dimensions. When the dimensionality is very high, almost all points are skyline points. Extract interesting skyline points in high dimensional space automatically is therefore necessary. From our experiences, in order to ...

When issuing user-specific queries, users often have a vaguely defined information need. Skyline queries identify the most “interesting” objects for users’ incomplete preferences, which provides users with intuitive query formulation mechanism. However, the applicability of this intuitive query paradigm suffers from a severe drawback. Incomplete preferences on domain values can often lead to im...

The skyline operator returns records in a dataset that provide optimal trade-offs of multiple dimensions. State-of-theart skyline computation involves complex tree traversals, data-ordering, and conditional branching to minimize the number of point-to-point comparisons. Meanwhile, GPGPU computing offers the potential for parallelizing skyline computation across thousands of cores. However, atte...

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 univo...

Given a set of n points in a d-dimensional space, we seek to compute the skyline, i.e., those points that are not strictly dominated by any other point, using few comparisons between elements. We study the crowdsourcing-inspired setting ([FRPU94]) where comparisons fail with constant probability. In this model, Groz & Milo [GM15] show three bounds on the query complexity for the skyline problem...

We introduce a variant of the classical PAC multi-armed bandit problem. There is an ordered set of n arms A[1], . . . , A[n], each with some stochastic reward drawn from some unknown bounded distribution. The goal is to identify the skyline of the set A, consisting of all arms A[i] such that A[i] has larger expected reward than all lower-numbered arms A[1], . . . , A[i− 1]. We define a natural ...

In the concept of Sensor-Cloud, many sensor networks are unified and provide Sensing as a Service to users. The users can utilize provisioning virtual sensors to perform desired tasks. The expense (cost) is based on the amount and types of data requests. In this paper, unlike other existing works, we propose a cost-minimizing framework for dynamic skyline monitoring. Our approach avoids paying ...

The Internet of Things (IoT) and the recent advancements in cloud computing have gained importance with surge amount data generated globally. Moreover, rapidly increasing applications many scientific real-time practical ushered a new era complex flow. Tourism related services are routinely accessed by millions customers worldwide. Furthermore, newer, attractive, growing services, it has become ...

Along with the application of the sensor network, there will be large amount of dynamic data coming from sensors. How to dig the useful information from such data is significant. Skyline query is aiming to identify the interesting points from a large dataset. The group-based skyline query is to find the outstanding Pareto Optimal groups which cannot be g-dominated by any other groups with the g...

Dynamic skyline query is one of the most popular and significant variants of skyline query in the field of multi-criteria decision-making. However, designing a distributed dynamic skyline query possesses greater challenge, especially for the distributed data centric storage within wireless sensor networks (WSNs). In this paper, a novel Enhanced Distributed Dynamic Skyline (EDDS) approach is pro...