Skyline Operator Research Articles

A P2P multi-path routing algorithm based on Skyline operator for data aggregation in IoMT environments

The integration of Internet of Things (IoT) technologies, particularly the Internet of Medical Things (IoMT), with wireless sensor networks (WSNs) has revolutionized the healthcare industry. However, despite the undeniable benefits of WSNs, their limited communication capabilities and network congestion have emerged as critical challenges in the context of healthcare applications. This research addresses these challenges through a dynamic and on-demand route-finding protocol called P2P-IoMT, based on LOADng for point-to-point routing in IoMT. To reduce congestion, dynamic composite routing metrics allow nodes to select the optimal parent based on the application requirements during the routing discovery phase. Nodes running the proposed routing protocol use the multi-criteria decision-making Skyline technique for parent selection. Experimental evaluation results show that P2P-IoMT protocol outperforms its best rivals in the literature in terms of residual network energy and packet delivery ratio. The network lifetime is extended by 4% while achieving a comparable packet delivery ratio and communication delay compared to LRRE. These performances are offered on top of the dynamic path selection and configurable route metrics capabilities of P2P-IoMT.

SkyWords: An automatic keyword extraction system based on the skyline operator and semantic similarity

This study presents a hybrid keyword extraction method called SkyWords. It implements a novel supervised step based on the skyline operator and the majority voting principle for high-quality candidate keyword selection and an unsupervised step based on contextual semantics for the candidate keyword ranking. To achieve this, firstly, we build a feature vector database using the features of known keywords and then apply the skyline operator to retrieve the dominating feature vectors. To select the candidate keywords of a document, we compare each word of the document against the dominating feature vectors and assume the words that are as good as the majority of the dominating feature vectors as candidate keywords. To obtain the final set of keywords, we rank the candidate keywords based on their semantic similarity to the document based on their vector representation using the MPNet sentence transformer. We conducted experiments on six benchmark scholarly datasets to evaluate the performance of SkyWords and compared the results against eleven baseline keyword extraction systems. The experimental results show that the proposed novel keyword selection algorithm reduced the number of candidate keywords by several folds. Moreover, SkyWords achieved statistically significant improvements over the baseline methods in precision, recall, and F1 score. Compared to the baseline regarding ranking-based metrics, SkyWords achieved the highest mean average precision score for all datasets and the highest mean reciprocal rank score for all datasets but one. Furthermore, SkyWords extracted more relevant keywords than the baseline methods.

An improved discrete flower pollination algorithm for fuzzy QoS-aware IoT services composition based on skyline operator

An improved discrete flower pollination algorithm for fuzzy QoS-aware IoT services composition based on skyline operator

Skyline recomputation in Big Data

Retrieving relevant information in Big Data is a difficult task. Many approaches are used to select highly relevant information. One of them is the Skyline operator, which is used to select the best records in multidimensional data. The computation of the Skyline in large data or Big Data environments is highly expensive. Furthermore, updating the data requires a recomputation of the Skyline which leads to an exponential increase in the computation time. In this paper, we proposed a system that significantly reduces the computation time of the Skyline after updating the data. Our system uses the Skyline computation history to compute the new Skyline after the data is updated. We used in this system our proposed data representation and algorithms to handle the Skyline queries and the update queries. Based on the execution time, the experimental results show that the proposed system gives better results compared to existing approaches by keeping the Skyline Up to date.

Characterizing cable-logging enterprises in Italy: production, workforce, equipment and potential for growth

ABSTRACT Interviews with 51 entrepreneurs indicated that Italian cable-yarding specialist companies are small-scale businesses with a workforce of 3 to 5 operators and an annual production of about 5000 m3 over bark. Their primary piece of equipment is a tower yarder, which relies on a support fleet consisting of adapted farm tractors and excavators. There is a clear divide between alpine and non-alpine entrepreneurs, the latter being less productive and somewhat more versatile than the former. That difference is likely dependent on the different work environment: alpine operators can rely on larger public owners, bigger softwood trees and more valuable timber products, whereas non-alpine loggers are constrained by fragmented private ownerships, smaller hardwood trees (often coppice) and less valuable firewood products. Non-alpine loggers also yard on shorter distances and use smaller towers. Further expansion of cable-logging south of the Alps could be achieved by developing new equipment capable of overcoming those limitations. In that regard, one may consider unguyed towers designed for live skyline operation.

Simulation-based multi-objective optimization for enhanced safety of fire emergency response in metro stations

This study establishes a multi-objective optimization (MOO) model for fire emergency response in the metro station using a simulation-based analysis, in which the emergency response time and the number of firefighters are both minimized. A Hierarchical Timed Color Petri Nets (HTCPN) model is constructed to simulate different scenarios. The Skyline operator is adopted to solve the MOO problem based on the simulated dataset. Eventually, a case study of fire emergency response at the Simenkou metro station in Wuhan is conducted to verify the feasibility of the proposed approach. The case study implies that: (1) The constructed HTCPN model is correct, reasonable, and effective; (2) Compared to the baselines, simulation-based MOO can reduce the number of assigned firefighters and the emergency response time by 38.7% and 31.4%, respectively; (3) After considering the interaction between the firefighting and rescue teams, the average improvement of all scenarios can be further improved. The novelty of this research lies in that: (a) Through Petri nets combined with the emergency response process of metro stations, an HTCPN model is constructed, which appropriately represents the relationship between the relevant parts; (b) The optimal emergency response strategy is given from an MOO perspective based on the simulated dataset, which considers the response time and allocation of firefighters simultaneously.

An extended artificial bee colony with local search for solving the Skyline-based web services composition under interval QoS properties

In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset.

Smart-Approach Based Internet of Things and Skyline Query for Multicriteria Decisions for Travel Services

The Internet of Things (IoT) and the recent advancements in cloud computing have gained importance with the surge in the amount of data generated globally. Moreover, the rapidly increasing applications of the Internet in many scientific and real-time practical applications have ushered in a new era of complex applications of data flow. Tourism and related services are routinely accessed by millions of customers worldwide. Furthermore, with newer, attractive, rapidly growing services, it has become essential for dealers to promote their services using up-to-date technological tools. The major challenge is to efficiently determine and select the best travel options conforming to the needs and financial requirements of the customers. In this study, the use of a dynamic skyline operator for multicriteria decisions is examined using a time-dependent database to select the best services. Moreover, the impact of implementing the operator on optimizing resource consumption is explored. Results indicate that the implementation of this operator is more efficient than the existing techniques.

An efficient skyline query algorithm in the distributed environment

The skyline operator plays an important role in multiple criteria decision making. The traditional skyline query algorithm has high efficiency in dealing with a small amount of data. However, as the data scale increases, the previous algorithm of skyline query in a centralized environment cannot be applied to new applications. Therefore, in this paper, the distributed skyline query algorithm (DSQ) is proposed to process skyline queries in the distributed environment. DSQ contains two stages. In the first stage, redundant data tuples are pruned based on a spatial index. In the second stage, a scheduling plan based on rotation is used to calculate the global skyline. The rotation scheduling strategy ensures that the load of each data node is balanced. Finally, the effectiveness of DSQ is evaluated through a series of experiments.

When universities rise (Rank) high into the skyline

The quality of the education provided and the research impact produced by universities is continuously evaluated at national and international level. This phenomenon is not new. However, nowadays education is not only considered as a social value and right/privilege, but also as a big economic sector, which addresses to large portions of population worldwide. In this ecosystem, university rankings play a crucial role since they provide filtered information which is reproduced in surveys, newspapers, social media etc. All university rankings are based on a set of ad hoc evaluation criteria. Moreover, the final score is based on a set of arbitrary weights summing up to 1. Thus, at the end, these university rankings differ significantly producing ambiguities and doubts. In this paper, we propose a novel university ranking method based on the Skyline operator, which is used on multi-dimensional objects to extract the non-dominated (i.e., “prevailing”) ones. Our method is characterized by several advantages, such as: it is transparent, reproducible, without any arbitrarily selected parameters, based on the research output of universities only and not on publicly not traceable or questionnaires. Our method does not provide absolute rankings, but rather it ranks universities categorized in equivalence classes. Thus, we develop a generic framework which can be used for ranking universities and departments, and even individual persons. For the proof of concept we apply the framework in our Greek academic space, providing a case study on ranking persons and departments on computer science and engineering using data extracted from Microsoft Academic.

Skyline computation over multiple points and dimensions

Skyline is a technique in database management system for multi-criterion decision making based on dominance analysis. Skyline overcomes the limitation of relational databases by handling the criteria that are inversely proportional to each other. Traditional skyline operation is conceptualized over two dimensions only, and it finds out single interesting point. In this paper we extend the capability of skyline to work with multiple dimensions and to search the multiple interesting points from the given search space. The work furthermore ranks skyline points with respect to the multiple interesting points. However, we restrict the computational complexity within a fixed upper bound. Skyline is commonly applied on tourism industries, and we consider two different case studies from this domain and execute the proposed methodology over the real-life data. Comparative study is given based on different parameters, and statistical analysis is also performed to illustrate the efficacy of the proposed method over the existing methods.

IDSA: An Efficient Algorithm for Skyline Queries Computation on Dynamic and Incomplete Data With Changing States

Skyline queries have been widely used as an effective query tool in many contemporary database applications. The main concept of skyline queries relies on retrieving the non-dominated tuples in the database which are known skylines. In most database applications, the contents of the databases are dynamic due to the continuous changes made towards the database. Typically, the changes in the contents of the database occur through data manipulation operations (INSERT and/or UPDATE). Performing these operations on the database results in invalidating the most recent skylines before changes are made on the database. Furthermore, the presence of incomplete data in databases becomes frequent phenomena in recent database applications. Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic . Reapplying skyline technique on the entire updated incomplete database to determine the new skylines is unwise due to the exhaustive pairwise comparisons. Thus, this paper proposes an approach, named Incomplete Dynamic Skyline Algorithm (IDSA) which attempts to determine the skylines on dynamic and incomplete databases. Two optimization techniques have been incorporated in IDSA, namely: pruning and selecting superior local skylines. The pruning process attempts to exploit the derived skylines before the INSERT/UPDATE operation made on the database to identify the new skylines. Moreover, selecting superior local skylines process assists in further eliminating the remaining non-skylines from further processing. These two optimization techniques lead to a large reduction in the number of domination tests due to avoiding re-computing of skylines over the entire updated database to derive the new skylines. Extensive experiments have been accomplished on both real and synthetic datasets, and the results demonstrate that IDSA outperforms the existing solutions in terms of the number of domination tests and the processing time of the skyline operation.

A nonlinear service composition method based on the Skyline operator

The concept of service composition can provide the complex functionality for users. As the widespread application of cloud computing, the number of services grows exponentially. It becomes more difficult to find out the optimal service composition solution quickly. This paper proposes a nonlinear service composition method based on the Skyline operator. The Skyline operator is to find a collection of data that cannot be dominated by others, which is used to prune the redundant services to reduce the search space. Then the service composition problem is formulated as a nonlinear integer programming model by a mathematical programming language (AMPL), and solved by the existing nonlinear solvers Bonmin. The experiments show that the proposed method can effectively improve the efficiency of service composition, while ensuring the quality of solution.

SkySlide: A Hybrid Method for Landslide Susceptibility Assessment based on Landslide-Occurring Data Only

Abstract Landslide susceptibility assessment is the problem of determining the likelihood of a landslide occurrence in a particular area with respect to the geographical and morphological properties of the area. This paper presents a hybrid method, namely SkySlide, that incorporates clustering, skyline operator, classification and majority voting principle for region-scale landslide susceptibility assessment. Clustering and skyline operator are utilized to model landslides while classification and majority voting principle are utilized to assess landslide susceptibility. The contribution of the study is 2-fold. First, the proposed method requires properties of landslide-occurring data only to model landslides. Second, the proposed method is evaluated on imbalanced data and experimental results include performance metrics of imbalanced data. Experiments conducted on two real-life datasets show that clustering greatly improves performance of SkySlide. Experiments further demonstrate that SkySlide achieves higher class balance accuracy, Matthews correlation coefficient, geometric mean and bookmaker informedness scores compared with the most commonly used methods for landslide susceptibility assessment such as support vector machines, logistic regression and decision trees.

Landslide Susceptibility Assessment using Skyline Operator and Majority Voting

Landslide susceptibility assessment is the problem of determining the likelihood of a landslide to occur in a particular area based on the geological and morphological properties of the area. In this study we propose a method wherein skyline operator is used to model landslides and majority voting is used to assess landslide susceptibility. Experiments conducted on a real life data set show that the proposed method achieves 83.07% classification accuracy and is superior over logistic regression, support vector machine and neural network based approaches and achieves similar results when compared to a decision trees-based model.

Parallel k-dominant skyline queries in high-dimensional datasets

The skyline operator has been used to select preference points in many applications. The previously proposed k-dominant skyline, which relaxes the idea of dominance, reduces the number of skyline points in high-dimensional datasets. However, retrieving both skyline and k-dominant skyline points in high-dimensional datasets are computationally expensive. In this paper, we aim to consider all attributes simultaneously when retrieving skylines and k-dominant skyline points using a new data representation. Moreover, we propose a parallel k-dominant skyline algorithm, which obtains efficiency by exploring data parallelism. In the proposed algorithm, we introduce a data representation that significantly reduces the number of verifications between points and the computation of verifications. We implement the proposed algorithm on GPU frameworks, which are designed to perform data-parallel computation. For skyline queries, the experimental results show that the proposed algorithm outperforms the state-of-the-art GPU-based algorithms. In high dimensional space, the proposed algorithm is up to 10 times faster than the state-of-the-art GPU-based algorithm for skyline queries. We also evaluate and discuss the performance of the proposed algorithm for k-dominant skyline queries. With the proposed data representation, each point is averagely checked with less than 5% of points in k-dominant skyline queries, and 95% of verifications take only two comparisons.

An Efficient User-Centric Web Service Composition Based on Harmony Particle Swarm Optimization

Generally, the composition is the process of combining services to fulfill complex tasks based on their functional and non-functional values such as quality of services (QoS) and context attributes. However, to produce a composition with values that satisfy many requirements is a challenging focus. In this article, the authors proposed a new approach centered evolutionary algorithm called the harmony particle swarm optimization (HPSO) algorithm that leads to an efficient composition with better performance and execution time. The authors' proposed method is a new hybrid version of the harmony search and the particle swarm optimization. The HPSO is designed to generate the best web service composition in a discrete search space. Furthermore, the method includes two filtering processes called Skyline operator and local consistency reinforcement techniques. These methods filter the search space and keep only the most representative candidate services. Results show the effectiveness and the accuracy of the proposed approach.

A Tree Pattern Matching Algorithm for XML Queries with Structural Preferences

In the XML community, exact queries allow users to specify exactly what they want to check and/or retrieve in an XML document. When they are applied to a semi-structured document or to a document with an overly complex model, the lack or the ignorance of the explicit document model (DTD-Document Type Definition, Schema, etc.) increases the risk of ob-taining an empty result set when the query is too specific, or, too large result set when it is too vague (e.g. it contains wildcards such as "*"). The reason is that in both cases, users write queries according to the document model they have in mind; this can be very far from the one that can actually be extracted from the document. Opposed to exact queries, preference queries are more flexible and can be relaxed to expand the search space during their evalua-tions. Indeed, during their evaluation, certain constraints (the preferences they contain) can be relaxed if necessary to avoid precisely empty results; moreover, the returned answers can be filtered to retain only the best ones. This paper presents an algorithm for evaluating such queries inspired by the TreeMatch algorithm proposed by Yao et al. for exact queries. In the pro-posed algorithm, the best answers are obtained by using an adaptation of the Skyline operator (defined in relational databases) in the context of documents (trees) to incrementally filter into the partial solutions set, those which satisfy the maximum of preferential constraints. The only restriction imposed on documents is No-Self-Containment.

MapReduce Algorithm for Location Recommendation by Using Area Skyline Query

Location recommendation is essential for various map-based mobile applications. However, it is not easy to generate location-based recommendations with the changing contexts and locations of mobile users. Skyline operation is one of the most well-established techniques for location-based services. Our previous work proposed a new query method, called “area skyline query”, to select areas in a map. However, it is not efficient for large-scale data. In this paper, we propose a parallel algorithm for processing the area skyline using MapReduce. Intensive experiments on both synthetic and real data confirm that our proposed algorithm is sufficiently efficient for large-scale data.

Adding ReputationRank to member promotion using skyline operator in social networks

BackgroundTo identify potential stars in social networks, the idea of combining member promotion with skyline operator attracts people’s attention. Some algorithms have been proposed to deal with this problem so far, such as skyline boundary algorithms in unequal-weighted social networks.MethodsWe propose an improved member promotion algorithm by presenting ReputationRank based on eigenvectors as well as Influence and Activeness and introduce the concept of skyline distance. Furthermore, we perform skyline operator over non-skyline set and choose the infra-skyline as our candidate set. The added ReputationRank helps a lot to describe the importance of a member while the skyline distance assists us to obtain the necessary condition for not being dominated so that some meaningless plans can be pruned.ResultsExperiments on the DBLP and WikiVote datasets verify the effectiveness and efficiency of our proposed algorithm.ConclusionsTreating the infra-skyline set as candidate set reduces the number of candidates. The pruning strategies based on dominance and promotion cost decrease the searching space.