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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.

Data incompleteness becomes a frequent phenomenon in a large number of contemporary database applications such as web autonomous databases, big data, and crowd-sourced databases. Processing skyline queries over incomplete databases impose a number of challenges that negatively influence processing the skyline queries. Most importantly, the skylines derived from incomplete databases are also incomplete in which some values are missing. Retrieving skylines with missing values is undesirable, particularly, for recommendation and decision-making systems. Furthermore, running skyline queries on a database with incomplete data raises a number of issues influence processing skyline queries such as losing the transitivity property of the skyline technique and cyclic dominance between the tuples. The issue of estimating the missing values of skylines has been discussed and examined in the database literature. Most recently, several studies have suggested exploiting the crowd-sourced databases in order to estimate the missing values by generating plausible values using the crowd. Crowd-sourced databases have proved to be a powerful solution to perform user-given tasks by integrating human intelligence and experience to process the tasks. However, task processing using crowd-sourced incurs additional monetary cost and increases the time latency. Also, it is not always possible to produce a satisfactory result that meets the user’s preferences. This paper proposes an approach for estimating the missing values of the skylines by first exploiting the available data and utilizes the implicit relationships between the attributes in order to impute the missing values of the skylines. This process aims at reducing the number of values to be estimated using the crowd when local estimation is inappropriate. Intensive experiments on both synthetic and real datasets have been accomplished. The experimental results have proven that the proposed approach for estimating the missing values of the skylines over crowd-sourced enabled incomplete databases is scalable and outperforms the other existing approaches.

Skyline queries intend to retrieve the most superior data items in the database that best fit with the user’s given preference. However, processing skyline queries are expensive and uneasy when applying on large distributed databases such as cloud databases. Moreover, it would be further sophisticated to process skyline queries if these distributed databases have missing values in certain dimensions. The effect of data incompleteness on skyline process is extremely severe because missing values result in un-hold the transitivity property of skyline technique and leads to the problem of cyclic dominance. This paper proposes an efficient model for computing skyline data items in cloud incomplete databases. The model focuses on processing skyline queries in cloud incomplete databases aiming at reducing the domination tests between data items, the processing time, and the amount of data transfer among the involved datacenters. Various set of experiments are conducted over two different types of datasets and the result demonstrates that the proposed solution outperforms the previous approaches in terms of domination tests, processing time, and amount of data transferred.

Skyline queries rely on the notion of Pareto dominance, filter the data items by keeping only those data items that are the best, most preferred, also known as skylines, from a database to meet the user’s preferences. 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 a reduction in the processing time of skyline computations. In today’s era, the presence of incomplete data in a database is inevitable. Furthermore, databases are dynamic in nature in which their states change throughout the time to reflect the current and latest information of the applications. The skylines derived before changes are made towards the initial database are no longer valid in the new state of the database. Blindly examining the entire database to identify the new set of skylines is unwise as not all data items are affected by the changes made towards the database. Hence, in this paper we propose a solution, named DyIn-Skyline , which is capable of deriving skylines over a dynamic and incomplete database, by exploiting only those data items that are affected by the changes. Several experiments have been conducted and the results show that our proposed solution outperforms the previous works with regard to the number of pairwise comparisons and processing time.

Given the significance of skyline queries, they are incorporated in various modern applications including personalized recommendation systems as well as decision-making and decision-support systems. Skyline queries are used to identify superior data items in the database. Most of the previously proposed skyline algorithms work on a complete database where the data are always present (non-missing). However, in many contemporary real-world databases, particularly those databases with large cardinality and high dimensionality, such assumption is not necessarily valid. Hence, missing data pose new challenges if the processing skyline queries cannot easily apply those methods that are designed for complete data. This is due to the fact that imperfect data cause the loss of the transitivity property of the skyline method and cyclic dominance . This paper presents a framework called Optimized Incomplete Skyline (OIS) which utilizes a technique that simplifies the skyline process on a database with missing data and helps prune the data items before performing the skyline process. The proposed strategy assures that the number of the domination tests is significantly reduced. A set of experiments has been accomplished using both real and synthetic datasets aimed at validating the performance of the framework. The experiment results confirm that the OIS framework is indeed superior and steadily outperforms the current approaches in terms of the number of domination tests required to retrieve the skylines.

Skyline queries have been extensively incorporated in various contemporary database applications. The list includes but is not limited to multi-criteria decision-making systems, decision support systems, and recommendation systems. Due to its great benefits and wide application range, many skyline algorithms have already been proposed in numerous data settings. Nonetheless, most researchers presume the completion of data meaning that all data item values are available. Since this assumption cannot be sustained in a large number of real-world database applications, the existing algorithms are rather inadequate to be directly applied on a database with incomplete data. In such cases, processing skyline queries on incomplete data incur exhaustive pairwise comparisons between data items, which may lead to loss of the transitivity property of the skyline technique. Losing the transitivity property may in turn give rise to the problem of cyclic dominance. In order to address these issues, we propose a new skyline algorithm called Sorting-based Cluster Skyline Algorithm (SCSA) that combines the sorting and partitioning techniques and simplifies the skyline computation on an incomplete dataset. These two techniques help boost the skyline process and avoid many unnecessary pairwise comparisons between data items to prune the dominated data items. The comprehensive experiments carried out on both synthetic and real-life datasets demonstrate the effectiveness and versatility of our approach as compared to the currently used approaches.

Distributed Denial of Service (DDoS) attack is a major threat impeding service to legitimate requests on any network. Although the first DDoS attack was reported in 1996, the complexity and sophistication of these attacks has been ever increasing. A 2 TBps attack was reported in mid-August 2020 directed towards critical infrastructure, such as finance, amidst the COVID-19 pandemic. It is estimated that these attacks will double, reaching over 15 million, in the next 2 years. A number of mitigation schemes have been designed and developed since its inception but the increasing complexity demands advanced solutions based on emerging technologies. Blockchain has emerged as a promising and viable technology for DDoS mitigation. The inherent and fundamental characteristics of blockchain such as decentralization, internal and external trustless attitude, immutability, integrity, anonymity and verifiability have proven to be strong candidates, in tackling this deadly cyber threat. This survey discusses different approaches for DDoS mitigation using blockchain in varied domains to date. The paper aims at providing a comprehensive review, highlighting all necessary details, strengths, challenges and limitations of different approaches. It is intended to serve as a single platform to understand the mechanics of current approaches to enhance research and development in the DDoS mitigation domain.

A binary grammar is a relational grammar with two nonterminal alphabets, two terminal alphabets, a set of pairs of productions and the pair of the initial nonterminals that generates the binary relation, i.e., the set of pairs of strings over the terminal alphabets. This paper investigates the binary context-free grammars as mutually controlled grammars: two context-free grammars generate strings imposing restrictions on selecting production rules to be applied in derivations. The paper shows that binary context-free grammars can generate matrix languages whereas binary regular and linear grammars have the same power as Chomskyan regular and linear grammars.

In the recent years, skyline queries become one of the predominant and most frequently used queries among preference queries in the database system. Its main theme is to identify and return those data items that are not dominated by any other data item in the database. In the past decade, a tremendous number of research have been conducted emphasized on skyline queries by proposing many variations of skyline techniques for a different type of database. Most of these techniques claimed that a database has complete data and values are always present when process skyline queries. However, this is not necessary to be always the case, particularly for large databases with a high number of dimensions as some values may be missing. Thus, existing techniques cannot be easily tailored to derive skylines in a database with missing values. Two significant issues might be raised, the issue of losing transitivity property which thus leads to the issue of cyclic dominance. Finding skylines in a database with partially complete data has not received enough attention. This paper proposes an efficient model to identify skylines over a database with partial complete data. Experimental results on various types of datasets demonstrate that the proposed approach outperforms the previous approach in terms of the number of pairwise comparisons.

In many contemporary database applications such as multi-criteria decision-making and real-time decision-support applications, data mining, ecommerce and recommendation systems, users need query operators to process the data aiming at finding the results that best fit with their preferences. Skyline queries are one of the most predominant query operators that privileges to find the query results that return only those data items whose dimension vector is not dominated by any other data item in the database. Because of their usefulness and ubiquity, skyline queries have been incorporated into different types of databases such as complete, incomplete and uncertain. This paper attempts to survey and analyze the previous works proposed to process skyline queries in the incomplete database. The discussion focuses on examining these approaches highlighting the strengths and the weaknesses of each work. Besides, we also discuss in detail the current challenges in processing skyline queries in the incomplete database and investigate the impact of incomplete data on skyline operation. A summary of the most well-known works has been reported to identify the limitations of these works. Some recommendations and future work directions have been drawn to help researchers investigate the unsolved problems related to skyline queries in a database system.

International audience; Over the last few years, the research into agriculture has gained momentum, showing signs of rapid growth. The latest to appear on the scene is bringing convenience in how agriculture can be done by employing various computational technologies. There are lots of factors that affect agricultural production, with seed quality topping the list. Seed classification can provide additional knowledge about quality production, seed quality control and impurity identification. The process of categorising seeds has been traditionally done based on characteristics like colour, shape and texture. Generally, this is performed by specialists by visually inspecting each sample, which is a very tedious and time-consuming task. This procedure can be easily automated, providing a significantly more efficient method for seed sorting than having them be inspected using human labour. In related areas, computer vision technology based on machine learning (ML), symmetry and, more particularly, convolutional neural networks (CNNs) have been generously applied, often resulting in increased work efficiency. Considering the success of the computational intelligence methods in other image classification problems, this research proposes a classification system for seeds by employing CNN and transfer learning. The proposed system contains a model that classifies 14 commonly known seeds with the implication of advanced deep learning techniques. The techniques applied in this research include decayed learning rate, model checkpointing and hybrid weight adjustment. This research applies symmetry when sampling the images of the seeds during data formation. The application of symmetry generates homogeneity with regards to resizing and labelling the images to extract their features. This resulted in 99% classification accuracy during the training set. The proposed model produced results with an accuracy of 99% for the test set, which contained 234 images. These results were much higher than the results reported in related research.

Processing skyline queries in incomplete data is challenging, particularly, for a database with dynamic contents in which the database is frequently updated. These update operations not only affect the skyline computation, but also influence the skyline results. Furthermore, the incompleteness of data raises the issue of losing transitivity property of skyline technique, which leads to the problem of cyclic dominance. It is undesirable to process skyline queries on a dynamic and incomplete database by directly applying skyline process over the entire updated database due to the prohibitive cost. Thus, this paper proposes a framework called D-SKY for processing skyline queries in a dynamic and incomplete database. D-SKY aims at avoiding scanning the whole database after an update operation is performed to identify the new skylines. In this paper, we consider the case of an insert operation, in which database is updated by adding new data items. D-SKY framework exploits the existing skylines to identify the newly added dominated data items before applying the skyline process. Therefore, a large amount of dominated data items is pruned, which reduces the number of domination tests to be conducted and helps in avoiding scanning the whole data after an update operation is performed. Experiment result conducted on both real and synthetic datasets demonstrates that our solution outperforms the existing solutions in terms of reducing the number of pairwise comparisons and processing time.

Nowadays, in most of the modern database applications, lots of critical queries and tasks cannot be completely addressed by machine. Crowd-sourcing database has become a new paradigm for harness human cognitive abilities to process these computer hard tasks. In particular, those problems that are difficult for machines but easier for humans can be solved better than ever, such as entity resolution, fuzzy matching for predicates and joins, and image recognition. Additionally, crowd-sourcing database allows performing database operators on incomplete data as human workers can be involved to provide estimated values during run-time. Skyline queries which received formidable attention by database community in the last decade, and exploited in a variety of applications such as multi-criteria decision making and decision support systems. Various works have been accomplished address the issues of skyline query in crowd-sourcing database. This includes a database with full and partial complete data. However, we argue that processing skyline queries with partial incomplete data in crowd-sourcing database has not received an appropriate attention. Therefore, an efficient approach processing skyline queries with partial incomplete data in crowd-sourcing database is needed. This paper attempts to present an efficient model tackling the issue of processing skyline queries in incomplete crowd-sourcing database. The main idea of the proposed model is exploiting the available data in the database to estimate the missing values. Besides, the model tries to explore the crowd-sourced database in order to provide more accurate results, when local database failed to provide precise values. In order to ensure high quality result could be obtained, certain factors should be considered for worker selection to carry out the task such as workers quality and the monetary cost. Other critical factors should be considered such as time latency to generate the results.

Research interest in skyline queries has been significantly increased over the years, as skyline queries can be utilized in many contemporary applications, such as multi-criteria decision-making system, decision support system, recommendation system, data mining, and personalized systems. Skyline queries return data item that is not dominated by any other data items in all dimensions (attributes). Most of the existing skyline approaches assumed that database is complete and values are present during the skyline process. However, such assumption is not always to be true, particularly in a real world database where values of data item might not be available (missing) in one or more dimensions. Thus, the incompleteness of the data impacts negatively on skyline process due to losing the transitivity property which leads into the issue of cyclic dominance. Therefore, applying skyline technique directly on an incomplete database is prohibitive and might result into exhaustive pairwise comparison. This paper presents an approach that efficiently evaluates skyline queries in incomplete database. The approach aims at reducing the number of pairwise comparisons and shortens the searching space in identifying the skylines. Several experiments have been conducted to demonstrate that our approach outperforms the previous approach through producing a lower number of pairwise comparisons. Furthermore, the result also illustrates that our approach is scalable and efficient.