Showing papers on "Skyline published in 2021"

Marrying Top-k with Skyline Queries: Relaxing the Preference Input while Producing Output of Controllable Size

Abstract: The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records' attributes (typically, using a top-k query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they work, and evaluate their performance against adaptations of previous work that mimic their output.

Answering Skyline Queries Over Incomplete Data With Crowdsourcing

Abstract: Due to the pervasiveness of incomplete data, incomplete data queries are vital in a large number of real-life scenarios. Current models and approaches for incomplete data queries mainly rely on the machine power. In this paper, we study the problem of skyline queries over incomplete data with crowdsourcing . We propose a novel query framework, termed as ${\sf BayesCrowd}$ BayesCrowd , which takes into account the data correlation using the Bayesian network. We leverage the typical c-table model on incomplete data to represent objects. Considering budget and latency constraints, we present a suite of effective task selection strategies. Moreover, we introduce a marginal utility function to measure the benefit of crowdsourcing one task. In particular, the probability computation of each object being an answer object is at least as hard as #SAT problem. To this end, we propose an adaptive DPLL (i.e., Davis-Putnam-Logemann- Loveland) algorithm to speed up the computation. Extensive experiments using both real and synthetic data sets confirm the superiority of ${\sf BayesCrowd}$ BayesCrowd to the state-of-the-art method, in terms of execution time, monetary cost, and latency minimization.

Efficient and Privacy-Preserving Multi-Party Skyline Queries Over Encrypted Data

Abstract: One existing challenge associated with large scale skyline queries on cloud services, particularly when dealing with private information such as biomedical data, is supporting multi-party queries with curious-but-honest parties on encrypted data. In addition, existing solutions designed for performing secure skyline queries incur significant communication and computation costs due to ciphertext calculation. Thus, in this paper, we demonstrate the potential of supporting privacy-preserving multi-party skyline queries on encrypted data using additive homomorphic and proxy re-encryption cryptosystems. However, the secure computation based on these cryptosystems will further slow down query efficiency. To improve the efficiency of comparison on encrypted data, we redesign two lightweight secure comparison protocols. Meanwhile, we present an efficient method named “blind-reading” to securely obtain the skyline point. We also propose a novel method, Privacy Matrix, designed to reduce the scale of the dataset so that the computational cost is significantly decreased without privacy leakage. Then, we construct our secure skyline query protocol by integrating lightweight secure comparison protocols, “blind-reading” and Privacy Matrix techniques. Finally, we evaluate the security of our protocol, where we show it is secure without leaking information. The performance evaluation also shows that our proposed approach significantly improves the efficiency (at least $\times 4.5$ faster) compared to the-state-of-art and has the scalability of query processing under large datasets.

Efficient Constrained Shortest Path Query Answering with Forest Hop Labeling

Abstract: The Constrained Shortest Path (CSP) problem aims to find the shortest path between two nodes in a road network subject to a given constraint on another attribute. It is typically processed as a skyline path problem on the two attributes, resulting in very high computational cost which can be prohibitive for large road networks. The main bottleneck is to deal with a large amount of partial skyline paths, which further makes the existing index-based methods incapable to obtain the complete exact skyline paths. In this paper, we propose a novel skyline path concatenation approach to avoid the expensive skyline path search, which is then used to efficiently construct a 2-hop labeling index for the CSP queries. Specifically, a rectangle-based technique is designed to prune the concatenation space from multiple hops, and a constraint pruning method is used to further speed up the CSP query processing. To further scale up to larger networks, we propose a novel forest hop labeling that constructs labels from different partitions in parallel. Our approach is the first method that can achieve both accuracy and efficiency for CSP query answering. Extensive experiments on real-life road networks demonstrate that our method outperforms the state-of-the-art CSP solutions by several orders of magnitude.

Distributed messaging and light streaming system for combating pandemics: A case study on spatial analysis of COVID-19 Geo-tagged Twitter dataset.

Abstract: Real-time data processing and distributed messaging are problems that have been worked on for a long time. As the amount of spatial data being produced has increased, coupled with increasingly complex software solutions being developed, there is a need for platforms that address these needs. In this paper, we present a distributed and light streaming system for combating pandemics and give a case study on spatial analysis of the COVID-19 geo-tagged Twitter dataset. In this system, three of the major components are the translation of tweets matching with user-defined bounding boxes, name entity recognition in tweets, and skyline queries. Apache Pulsar addresses all these components in this paper. With the proposed system, end-users have the capability of getting COVID-19 related information within foreign regions, filtering/searching location, organization, person, and miscellaneous based tweets, and performing skyline based queries. The evaluation of the proposed system is done based on certain characteristics and performance metrics. The study differs greatly from other studies in terms of using distributed computing and big data technologies on spatial data to combat COVID-19. It is concluded that Pulsar is designed to handle large amounts of long-term on disk persistence.

Secure Dynamic Skyline Queries Using Result Materialization

Abstract: Skyline computation is an increasingly popular query, with broad applicability to many domains. Given the trend to outsource databases, and due to the sensitive nature of the data (e.g., in healthcare), it is essential to evaluate skylines on encrypted datasets. Research efforts acknowledged the importance of secure skyline computation, but existing solutions suffer from several shortcomings: (i) they only provide ad-hoc security; (ii) they are prohibitively expensive; or (iii) they rely on assumptions such as the presence of multiple non-colluding parties in the protocol. Inspired by solutions for secure nearest-neighbors, we conjecture that a secure and efficient way to compute skylines is through result materialization. However, materialization is much more challenging for skylines queries due to large space requirements. We show that pre-computing skyline results while minimizing storage overhead is NP-hard, and we provide heuristics that solve the problem more efficiently, while maintaining storage at reasonable levels. Our algorithms are novel and also applicable to regular skyline computation, but we focus on the encrypted setting where materialization reduces the response time of skyline queries from hours to seconds. Extensive experiments show that we clearly outperform existing work in terms of performance, and our security analysis proves that we obtain a small (and quantifiable) data leakage.

Efficient Skyline Computation on Uncertain Dimensions

Abstract: The database community has observed in the past two decades, the growth of research interest in preference queries, each of which has its unique techniques, benefits, and drawbacks. One of them is skyline queries. Skyline queries aim to report to users interesting objects based on their preferences. Yet, they are not without their limitations. Hence, this paper focuses on efficiently extending skyline query processing to support the uncertainty in dimensions, which in this paper is defined as uncertain dimension . To process skyline queries on data with uncertain dimensions, we propose SkyQUD algorithm, where it provides a mechanism that will partition the dataset according to the characteristics of each object before skyline dominance tests are performed. In the pruning process, we utilise a probability threshold value $ \tau $ to accommodate the large skyline size reported by SkyQUD due to the computed probabilities. The algorithm has been validated through extensive experiments. Its results exhibit that skyline queries can be performed effectively on uncertain dimensions , and the proposed algorithm is efficient in query answering and capable of handling large datasets.

The Ups and Downs of Gravity Energy Storage: Startups are pioneering a radical new alternative to batteries for grid storage

Abstract: Cranes are a familiar fixture of practically any city skyline, but one in the Swiss City of Ticino, near the Italian border, would stand out anywhere: It has six arms. This 110-meter-high starfish of the skyline isn't intended for construction. It's meant to prove that renewable energy can be stored by hefting heavy loads and dispatched by releasing them.

Skyline Query Optimization for Preferable Product Selection and Recommendation System

Abstract: Many social marketing systems use decision-making strategies for implementing product dominance analysis The objective of the proposed work is to classify on-time issues in observing highly preferable market products, which is newer in online market places Existing researches are interested to be useful for customers to identify the best possible products groups from the vast product details To deal with this main objective, different types of product instances are reviewed In this case, the price of popular products and the product groups are evaluated This proposed system analyses the need for online market growth using novel skyline query analysis The proposed system monitor user-based ratings affect the sales of various products After finding the desirable products, the market prices are predicted Once products are predicted, the new packages are assigned with optimal prices and added to the package database Moreover, the proposed Skyline Query Optimization and Security Management System (SQOSMS) approach is focused on authorized user ratings and ensures they are more secured The review system validates each and every user identities with the user activities involved with in review system This is considered as major objective of this proposed system The implementation section shows that the proposed system provides 10–15% of reduced movie lists than other systems This illustrates the proposed SQOSMS’s controlled performance over the selection of preferable products

Parallel computation of probabilistic skyline queries using MapReduce

Abstract: In recent years, numerous applications have been continuously generating large amounts of uncertain data. The advanced analysis queries such as skyline operators are essential topics to extract interesting objects from the vast uncertain dataset. Recently, the MapReduce system has been widely used in the area of big data analysis. Although the probabilistic skyline query is not decomposable, it does not make sense to implement the probabilistic skyline query in the MapReduce framework. This paper proposes an effective parallel method called parallel computation of probabilistic skyline query (PCPS) that can measure the probabilistic skyline set in one MapReduce computation pass. The proposed method takes into account the critical sections and detects data with a high probability of existence through a proposed smart sampling algorithm. PCPS implements a new approach to the fair allocation of input data. The experimental results indicate that our proposed approach can not only reduce the processing time of the probabilistic skyline queries, but also achieve fair precision with varying dimensionality degrees.

Mining Skyline Frequent-Utility Itemsets with Utility Filtering.

Abstract: Skyline frequent-utility itemsets (SFUIs) can provide more actionable information for decision-making with both frequency and utility considered. In this paper, the problem of mining SFUIs by filtering utilities from different perspectives is studied. First, filtering by frequency is considered. The max utility array (MUA) structure is designed, which is proved to have a size no larger than the size of arrays in state-of-the-art algorithms. Using the MUA, the utility-list is verified to prune unpromising itemsets and their extensions. Second, filtering using transaction-weighted utilization is applied. The minimum utility of SFUIs is proposed and the proof that this concept can be used as a pruning strategy in the early stage of search space traversal is provided. Finally, filtering using utility itself is also considered. The minimum utility of extension is presented, and its use as a pruning strategy during the extension stage of search space traversal is validated. Based on these filtering methods, a novel algorithm called SFUIs mining based on utility filtering (SFUI-UF) is proposed. Extensive experimental results show that the SFUI-UF algorithm can discover all correct SFUIs with high efficiency and low memory usage.

Skyline computation over multiple points and dimensions

Abstract: 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.

A Skyline Based Technique for Web Service Selection

Abstract: Service-Oriented Architecture (SOA) proves to be the primary reason behind the advancements of web platforms. Therefore, an enormous quantity of web services with similar functionalities has evolved over the web. However, wide-ranging web services which possess Quality of Service (QoS) with varying dimensionality may be inefficient in terms of performance due to web service selection strategy with traditional service selection methods. Skyline technique yields to improve the efficiency of web service selection. Skyline is based on a domination theory in which only those services can survive which are better than the other services available in the set. The concept of Web Service Selection using Skyline mainly tends to satisfy the user experience and user requirements. The participation of QoS is considered to be a base criterion for the selection of the most favorable services. The presented paper represents the work in the direction of Web Service Selection for composite web services using the Skyline technique.

A reliable method for visibility analysis of tall buildings and skyline: a case study of tall buildings cluster in Jakarta

Abstract: Visibility is an important factor for decision-making regarding the visual quality of the skyline of modern cities which dominated by tall buildings. The basic method of visibility is referred to t...

SkyFlow: A visual analysis of high-dimensional skylines in time-series

Abstract: Decision makers often find themselves in situations where they need to consider time-varying values for multi-criteria decision-making Skyline queries are one of the most widely used methods of approaching multi-criteria decision-making problems because they reduce the size of search space by excluding inferior data However, skylines in time-series data fluctuate with changes in attributes Moreover, the number of skyline points increases as the number of dimensions increases, and the skyline query itself does not provide any ranking method Thus, users are required to direct a considerable amount of effort into analyzing and finding the best selection To address these issues, we propose SkyFlow, a visual analytical system for comparing time-varying data to facilitate the decision-making process We apply two datasets in our system and describe scenarios to demonstrate the effectiveness of SkyFlow In addition, we conduct a qualitative study to highlight the efficiency of our system in assisting users to compare candidates and make decisions involving time-series data

Modifikasi skyline query untuk mengukur daerah prioritas penerima bantuan alat pelindung diri bagi tenaga kesehatan COVID-19

Abstract: The distribution of personal protective equipment (PPE) plays a vital role in meeting the needs of PPE in an area. This study aims to measure the priority of PPE recipient regions in West Java Province using a skyline query algorithm, namely Sort Filter Skyline (SFS). In this study, the SFS algorithm is modified to optimize the dominance measurement section. Regions that do not have hospitals will not be prioritized for PPE recipients. The preferences used in this study are maximum and minimum. The maximum preference rule is used for the number of ODP, PDP, positive and dead cases, while the minimum preference rule is used for the cured and distance attributes. The application of SFS for calculating priority regions has been successfully carried out by developing two models, namely MS1 using unmodified SFS and MS2 using modified SFS by adding a selection process for regions with no hospitals. The MS1 produces 21 skyline objects (55.55 %), while MS2 15 (66.66 %) skyline objects. The MS2 is faster than that of MS1 because fewer objects are being tested. The MS1 takes 0.0222 seconds, while MS2 only 0.0193 seconds.

Group-Based Skyline for Pareto Optimal Groups

Abstract: Skyline computation, aiming at identifying a set of skyline points that are not dominated by any other point, is particularly useful for multi-criteria data analysis and decision making. Traditional skyline computation, however, is inadequate to answer queries that need to analyze not only individual points but also groups of points. To address this gap, we generalize the original skyline definition to the novel group-based skyline (G-Skyline), which represents Pareto optimal groups that are not dominated by other groups. In order to compute G-Skyline groups consisting of $s$ s points efficiently, we present a novel structure that represents the points in a directed skyline graph and captures the dominance relationships among the points based on the first $s$ s skyline layers. We propose efficient algorithms to compute the first $s$ s skyline layers. We then present two heuristic algorithms to efficiently compute the G-Skyline groups: the point-wise algorithm and the unit group-wise algorithm, using various pruning strategies. We observe that the number of G-Skyline groups of a dataset can be significantly large, we further propose the top- $k$ k representative G-Skyline groups based on the number of dominated points and the number of dominated groups and present efficient algorithms for computing them. The experimental results on the real NBA dataset and the synthetic datasets show that G-Skyline is interesting and useful, and our algorithms are efficient and scalable.

A Power Grid Fault Diagnosis Method in the Case of Alarm Information Loss Based on the Top-k Skyline Query Algorithm

Abstract: After the failure of the power system, a large amount of alarm information will flood into the dispatching terminal instantly. At the same time, there are inevitable problems, such as the abnormal operation of the protection and the circuit breaker, the lack of alarm information, and so on. This kind of uncertainty problem brings great trouble to the fault diagnosis algorithm. As a data processing algorithm for an uncertain information set, Top-k Skyline query algorithm can eliminate the data points that do not meet the requirements in the information set, and then output the final K results in order. Based on this background, this paper proposes a power grid fault diagnosis method based on the Top-k Skyline query algorithm considering alarm information loss. Firstly, the fault area is determined by using the information of the electrical quantity and switching value. Then, backward reasoning Petri nets are established for the nodes in the fault area to form the data set of fault hypotheses. Then, the Top-k Skyline query algorithm is used to sort the hypotheses and choose the hypothesis with higher reliability. Finally, an IEEE 39-bus system example is given to verify the reliability of the proposed method.

k -dominant Skyline query algorithm for dynamic datasets

Abstract: At present, most k-dominant Skyline query algorithms are oriented to static datasets, this paper proposes a k-dominant Skyline query algorithm for dynamic datasets. The algorithm is recursive circularly. First, we compute the dominant ability of each object and sort objects in descending order by dominant ability. Then, we maintain an inverted index of the dominant index by k-dominant Skyline point calculation algorithm. When the data changes, it is judged whether the update point will affect the k-dominant Skyline point set. So the k-dominant Skyline point of the new data set is obtained by inserting and deleting algorithm. The proposed algorithm resolves maintenance issue of a frequently updated database by dynamically updating the data sets. The experimental results show that the query algorithm can effectively improve query efficiency.

New Smart Encryption Approach based on Multidimensional Analysis Tools

Abstract: In the last decade, with the new situation forced by the Covide-19 pandemic, the information systems are often forced to work remotely, they must communicate and share confidential data with several interlocutors. In such a context, ensuring the confidentiality of communications becomes a complex and difficult task. Hence, the need to have a flexible system that can adapt with different parameters involved in every exchange of information. We recently presented in [1] a new smart approach to data encryption that serves the same purpose. This approach uses the concept of artificial intelligence and apply BNL skyline algorithm to decide about the most suitable algorithm to ensure the best data privacy. However, with the evolution of dimensions and criteria to be considered for this smart encryption, we find that the complexity of the BNL algorithm increase, then, the response time of the application increase and the skyline encryption quality decreases. In this work, we propose a new idea to resolve this problematic. Indeed, this contribution consists in adding another Intelligence brick to dynamically define the Skyline algorithm depending on the type and number of dimensions. Through this paper, we provide an analysis and a comparison of some skyline algorithms for the multidimensional search. The results obtained by this study show the performance of this new approach whether in terms of execution time or in the quality of the dominant encryption solution. © 2021. All Rights Reserved.

The Efficient Mining of Skyline Patterns from a Volunteer Computing Network

Abstract: In the ever-growing world, the concepts of High-utility Itemset Mining (HUIM) as well as Frequent Itemset Mining (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these algorithms only used one factor to estimate an itemset. In the past, skyline pattern mining by considering both aspects of frequency and utility has been extensively discussed. In most cases, however, people tend to focus on purchase quantities of itemsets rather than frequencies. In this article, we propose a new knowledge called skyline quantity-utility pattern (SQUP) to provide better estimations in the decision-making process by considering quantity and utility together. Two algorithms, respectively, called SQU-Miner and SKYQUP are presented to efficiently mine the set of SQUPs. Moreover, the usage of volunteer computing is proposed to show the potential in real supermarket applications. Two new efficient utility-max structures are also mentioned for the reduction of the candidate itemsets, respectively, utilized in SQU-Miner and SKYQUP. These two new utility-max structures are used to store the upper-bound of utility for itemsets under the quantity constraint instead of frequency constraint, and the second proposed utility-max structure moreover applies a recursive updated process to further obtain strict upper-bound of utility. Our in-depth experimental results prove that SKYQUP has stronger performance when a comparison is made to SQU-Miner in terms of memory usage, runtime, and the number of candidates.

The Skyline of Counterfactual Explanations for Machine Learning Decision Models

Abstract: Counterfactual explanations are minimum changes of a given input to alter the original prediction by a machine learning model, usually from an undesirable prediction to a desirable one. Previous works frame this problem as a constrained cost minimization, where the cost is defined as L1/L2 distance (or variants) over multiple features to measure the change. In real-life applications, features of different types are hardly comparable and it is difficult to measure the changes of heterogeneous features by a single cost function. Moreover, existing approaches do not support interactive exploration of counterfactual explanations. To address above issues, we propose the skyline counterfactual explanations that define the skyline of counterfactual explanations as all non-dominated changes. We solve this problem as multi-objective optimization over actionable features. This approach does not require any cost function over heterogeneous features. With the skyline, the user can interactively and incrementally refine their goals on the features and magnitudes to be changed, especially when lacking prior knowledge to express their needs precisely. Intensive experiment results on three real-life datasets demonstrate that the skyline method provides a friendly way for finding interesting counterfactual explanations, and achieves superior results compared to the state-of-the-art methods.

QoS-aware service composition based on context-free grammar and skyline in service function chaining using genetic algorithm.

Abstract: Service function chaining (SFC) is a mechanism that allows service providers to combine various service functions and exploit the available virtual infrastructure. The best selection of virtual services in the network is essential for meeting user requirements and constraints. This paper proposes a novel approach to generate the optimal composition of the service functions. To this end, a genetic algorithm based on context-free grammar (CFG) that adheres to the Internet Engineering Task Force (IETF) standard and Skyline was developed to use in SFC. The IETF uses cases of the data center, security, and mobile network filtered out the invalid service chains, which resulted in reduced search space. The proposed genetic algorithm found the Skyline service chain instance with the highest quality. The genetic operations were defined to ensure that the service function chains generated in the algorithm process were standard. The experimental results showed that the proposed service composition method outperformed the other methods regarding the quality of service (QoS), running time, and time complexity metrics. Ultimately, the proposed CFG could be generalized to other SFC use cases.

Efficient Skyline Computation Over an Incomplete Database With Changing States and Structures

Abstract: Skyline query has been studied extensively and a significant number of skyline algorithms have been proposed, mostly attempt to resolve the optimisation problem that is mainly associated with reduction in the processing time of skyline computations. While databases change their states and/or structures throughout their lifetime to reflect the current and latest information of the applications, the skyline set derived before changes are made towards the initial state of a database is no longer valid in the new state/structure of the database. The domination relationships between objects identified in the initial state might no longer hold in the new state. Nonetheless, computing the skylines over the entire new state/structure of the database is inefficient, as not all pairwise comparisons between the objects are necessary to be performed. In tackling the above issue, this paper proposes a solution, named $\Delta $ Skyline , which aims at avoiding unnecessary skyline computations when a database changes its state and structure due to a data definition operation(s) (add or remove a dimension(s)). This is achieved by identifying and retaining the prominent dominance relationships when pairwise comparisons are performed; which are then utilised in the process of computing a new skyline set. $\Delta $ Skyline consists of two optimisation components, namely: $\Delta ^{+}$ Skyline which derives a new skyline set when a new dimension(s) is added to a database and $\Delta ^{-}$ Skyline which derives a new skyline set when an existing dimension(s) is removed from a database. To make our solution more useful, it is applied on a database with incomplete data. Extensive experiments have been conducted to evaluate the performance and prove the efficiency of our proposed solution.

SecSky: A Secure Dynamic Skyline Query Scheme With Data Privacy

Abstract: Skyline computation is a typical multi-objective optimization problem and has been a hot spot for current research. Most of them have concentrated on improving the efficiency of skyline computing, while the security issues associated with executing skyline computation in the cloud server are rarely taken into account. The cloud data stored in plaintext form is attacked by hackers or malicious administrators, resulting in potential leakage of users’ private information. One of the most effective approaches to solve this problem is to encrypt the private data before storing it in the cloud server. However, how to compute the skyline on the encrypted data stored on a cloud server efficiently is still challenging. In this paper, we present a novel framework called SecSky. Firstly, the improved B+ tree structure and symmetric encryption were combined to construct a secure storage structure. Then, the pruning idea was applied to reduce unnecessary calculation and to achieve efficient skyline query and dynamic update. The whole process was directly completed on the ciphertext data. Finally, both the security and the efficiency of SecSky were analyzed through theoretical proof and simulation experiments. The results showed that our scheme is of sound efficiency on the premise of ensuring private data security.

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

Abstract: 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.

Privacy-Aware Fuzzy Skyline Parking Recommendation Using Edge Traffic Facilities

Abstract: Drivers have always been confronted with real-time parking difficulties when driving on urban roads, especially in crowded downtown or beauty spots. On the other hand, privacy leakage risks on users’ private parking preferences and the sensitive data of parking lots have triggered increasing worries. Some literatures endeavor to improve parking service qualities through multi-consideration parking decision optimization on edge sides or cloud computing based on outsourced data storage. And some other literatures propose a number of privacy-preserving methods, such as cryptography and authentication, but these privacy strategies are at the expense of other qualities of parking services, especially the real-time performance. In this paper, we propose a fuzzy skyline parking recommendation scheme for real-time parking recommendation based on roadside traffic facilities. Linguistic parking information instead of raw parking-related data is used in fuzzy skyline fusion. We evaluated our solution with real-world data sets collected from parking facilities in Wulin downtown, Hangzhou city, China. The evaluation results show that our approaches achieve an average accuracy of parking recommendation over 91%, low communication cost, and quick response time with privacy protection.