Research on Reverse Skyline Query Algorithm Based on Decision Set

This paper aims at answering the so called why-not questions in reverse skyline queries. A reverse skyline query retrieves all data points whose dynamic skylines contain the query point. We outline the benefit and the semantics of answering why-not questions in reverse skyline queries. In connection with this, we show how to modify the why-not point and the query point to include the why-not point in the reverse skyline of the query point. We then show, how a query point can be positioned safely anywhere within a region (i.e., called safe region) without losing any of the existing reverse skyline points. We also show how to answer why-not questions considering the safe region of the query point. Our approach efficiently combines both query point and data point modification techniques to produce meaningful answers. Experimental results also demonstrate that our approach can produce high quality explanations for why-not questions in reverse skyline queries.

Spatial databases have become a critical part of modern applications. Some important applications of a spatial database include Geographic Information System (GIS), Computer Aided Design(CAD), image processing and robotics. Spatial queries retrieve the required geographic data from spatial databases. In this thesis, we classify spatial queries into two categories based on their objective with regards to the notion of influence. The first category consists of the queries that aim to find the important/influential facilities. Some queries that fall into this category include range queries, k nearest neighbor queries, top-k queries and skyline queries. The second category is to find the influenced users. Queries in this category include reverse k nearest neighbor (RkNN) queries, reverse top-k queries and reverse skyline queries. In this thesis, we present efficient algorithms to solve queries in both categories

The skyline operator and its variants such as dynamic skyline and reverse skyline operators have attracted considerable attention recently due to their broad applications. However, computations of such operators are challenging today since there is an increasing trend of applications to deal with big data. For such data-intensive applications, the MapReduce framework has been widely used recently. In this paper, we propose efficient parallel algorithms for processing the skyline and its variants using MapReduce. We first build histograms to effectively prune out nonskyline (non-reverse skyline) points in advance. We next partition data based on the regions divided by the histograms and compute candidate (reverse) skyline points for each region independently using MapReduce. Finally, we check whether each candidate point is actually a (reverse) skyline point in every region independently. Our performance study confirms the effectiveness and scalability of the proposed algorithms.

With the expanding number of communications from different mobile applications that acquire location information, the demand for continuous skyline queries has increased. In addition, the extremely fast increase in the data volume and mobile applications that deal with such volume of data such as check-ins recommendation, information services and applications of road networks; have both driven the need to adapt new processing environments to deal with huge amounts of data. In this paper, we present a number of efficient algorithms for processing continuous skyline queries on large datasets using MapReduce framework. The main idea of our proposed algorithms is to compute the skyline query only once at the starting position; then update on the result at the movement of the query point rather than computing the skyline at every time from scratch. In addition, experimental results are conducted which demonstrate the accuracy, performance and efficiency of the proposed algorithms.

The application of skyline queries on outsourced databases significantly aids online analysis, yet efficiently handling encrypted queries remains a formidable obstacle. Moreover, query outcomes are vulnerable to potential malicious cloud services. To circumvent these limitations, this work presents the <u>H</u>onest-<u>M</u>ajority and <u>M</u>aliciously <u>S</u>kyline <u>Q</u>uery scheme (HMMSQ), which facilitates efficient skyline queries while safeguarding the privacy of datasets, queries, and skylines, as well as detecting malevolent activities. The core of HMMSQ is an optimized skyline diagram constructed by a novel skyline region-splitting algorithm for accurate skyline queries. Furthermore, it mitigates the frequency of dataset accesses by leveraging a multi-path R-tree for secure skyline retrieval. Notably, the majority of malicious behavior detection is focused on the servers, thereby minimizing user authentication overhead. The complexity and security are thoroughly analyzed, and experimental evaluations on various datasets demonstrate its efficiency and practicality in terms of computational cost and communication overhead. Remarkably, HMMSQ outperforms existing methods in query latency, achieving up to an order of magnitude improvement.

With the rapid development of network data communication technology, the complexity of the network environment makes the data in the data stream have uncertain characteristics, and a large number of data streams are generated in many fields. Therefore, it is necessary to find an efficient and accurate processing method to process a large amount of data in a computer network. This paper takes the skyline probability calculation and the actual efficiency of the unknown object index structure in the skyline query processing technology on the uncertain data stream as the research object. Based on the skyline query technology, we study an efficient skyline query processing method on the unknown network traffic based on the model SUMG. The method includes two algorithms: dynamic modeling algorithm DMG and skyline query algorithm GST. The DMG algorithm samples the data in the sliding window of the uncertain data stream and establishes a model to convert the data stream into the parameter stream in the uncertain object probability density function, the GST algorithm establishes the R-tree index structure, which is in order to reduce the amount of calculation, it uses the parameter flow of the model. In both methods, the set of local skyline results is first obtained at the distributed node, and the skyline query is performed again on the union of the local skyline results to obtain the global skyline result set. The experimental results show that compared with the skyline query method BNL for unknown network traffic without an index structure, the SUMG method can not only effectively model the link-type unknown object to assist the skyline query, but also effectively prune the uncertain data object and improve the skyline. The distributed skyline query method can cope with the skyline query task on the distributed data stream.

Skyline queries are well-known for their intuitive query formalization and easy to understand semantics for selecting the most interesting data objects from large data sets. They naturally fill the gap between set-based queries using strict predicates and only few personalization options and rank-aware database retrieval, offering a high degree of personalization at the cost of very complex query formalization. Thus, skyline queries enjoyed popularity in the database personalization research community. Unfortunately, the simplicity and elegance of the query paradigm come at high costs: skyline queries often suffer from a problem usually known as “curse of dimensionality”. With the increasing number of query attributes, the size of skyline result sets grows exponentially and the results are thus hardly useful or manageable by users. This problem severely hinders the practical application of the skyline paradigm. In this paper, the concept of trade-offs skylines is proposed as a natural extension to the skyline paradigm which is specifically designed as a remedy for the curse of dimensionality.

Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query, but processes various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages.

The skyline query is an efficient data analysis tool for multi-criteria decision making that has received significant attention in the database community. As multi-core architectures have gone mainstream, we present a new parallel skyline query algorithm that can be applied to multi-core and multiprocessor systems, to progressively return skyline points as they are identified efficiently. In this paper, we proposed a parallel skyline algorithm which can eliminate redundant computations and improve parallelism of the skyline query. Experimental results show that our algorithm successfully exploits the features of multiple cores to improve the performance of skyline computation for large high-dimensional datasets.

An efficient skyline query algorithm in the distributed environment

Nowadays, big data is coming to the force in a lot of applications. Processing a skyline query on big data in more than linear time is by far too expensive and often even linear time may be too slow. It is obviously not possible to compute an exact solution to a skyline query in sublinear time, since an exact solution may itself have linear size. Fortunately, in many situations, a fast approximate solution is more useful than a slower exact solution. This paper proposes two sampling-based approximate algorithms for processing skyline queries. The first algorithm obtains a fixed size sample and computes the approximate skyline on it. The error of the algorithm is not only relatively small in most cases, but also is almost unaffected by the input size. The second algorithm returns an [Formula: see text]-approximation for the exact skyline efficiently. The running time of the algorithm has nothing to do with the input size in practical, achieving the goal of sublinearity on big data. Experiments verify the error analysis of the first algorithm, and show that the second is much faster than the existing skyline algorithms.

Wireless sensor network amalgamates sensing, computing and communication technologies. Because of the energy limitation of sensor nodes, how to manage the data collected by the sensor nodes energy-efficiently becomes the focus of the research recently. As the main mean of multi-decision and data mining, skyline query plays a more and more important role in sensing applications. However, as the increase of data dimensionality, skyline query results extend greatly, which not only obstacles the decision, but also costs most of the energy of the nodes. In this article, k-dominant skyline query is researched deeply to deal with the above problem. An energy-efficient $k$-dominant skyline query algorithm (EKS) is proposed to calculate the $k$-dominant skyline of the wireless sensor network. The experimental results show that EKS could reduce the communication cost of the sensor network,while it calculates the k-dominant skyline, therefore, prolong the life-span of it.

Skyline computation for frequent queries in update intensive environment

In many decision-making applications, the skyline query is frequently used to find a set of dominating data points (called skyline points) in a multi-dimensional dataset. In a high-dimensional space skyline points no longer offer any interesting insights as there are too many of them. In this paper, we introduce a novel metric, called skyline frequency that compares and ranks the interestingness of data points based on how often they are returned in the skyline when different number of dimensions (i.e., subspaces) are considered. Intuitively, a point with a high skyline frequency is more interesting as it can be dominated on fewer combinations of the dimensions. Thus, the problem becomes one of finding top-k frequent skyline points. But the algorithms thus far proposed for skyline computation typically do not scale well with dimensionality. Moreover, frequent skyline computation requires that skylines be computed for each of an exponential number of subsets of the dimensions. We present efficient approximate algorithms to address these twin difficulties. Our extensive performance study shows that our approximate algorithm can run fast and compute the correct result on large data sets in high-dimensional spaces.

In this paper, we design and analyze parallel algorithms for skyline queries. The skyline of a multidimensional set consists of the points for which no other point exists that is at least as good along every dimension. As a framework for parallel computation, we use both the MP model proposed in Koutris and Suciu (2011), which requires that the data is perfectly load-balanced, and a variation of the model in Afrati and Ullman (2010), the GMP model, which demands weaker load balancing constraints. In addition to load balancing, we want to minimize the number of blocking steps, where all processors must wait and synchronize. We propose a 2-step algorithm in the MP model for any dimension of the dataset, as well a 1-step algorithm for the case of 2 and 3 dimensions. Finally, we present a 1-step algorithm in the GMP model for any number of dimensions and a 1-step algorithm in the MP model for uniform distributions of data points.