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The purpose of this paper is to propose Extensible Markup Language (XML) data partitioning schemes that can cope with static and dynamic allocation for parallel holistic twig joins: grid metadata model for XML (GMX) and streams‐based partitioning method for XML (SPX).

GMX exploits the relationships between XML documents and query patterns to perform workload‐aware partitioning of XML data. Specifically, the paper constructs a two‐dimensional model with a document dimension and a query dimension in which each object in a dimension is composed from XML metadata related to the dimension. GMX provides a set of XML data partitioning methods that include document clustering, query clustering, document‐based refinement, query‐based refinement, and query‐path refinement, thereby enabling XML data partitioning based on the static information of XML metadata. In contrast, SPX explores the structural relationships of query elements and a range‐containment property of XML streams to generate partitions and allocate them to cluster nodes on‐the‐fly.

GMX provides several salient features: a set of partition granularities that balance workloads of query processing costs among cluster nodes statically; inter‐query parallelism as well as intra‐query parallelism at multiple extents; and better parallel query performance when all estimated queries are executed simultaneously to meet their probability of query occurrences in the system. SPX also offers the following features: minimal computation time to generate partitions; balancing skewed workloads dynamically on the system; producing higher intra‐query parallelism; and gaining better parallel query performance.

The current status of the proposed XML data partitioning schemes does not take into account XML data updates, e.g. new XML documents and query pattern changes submitted by users on the system.

Note that effectiveness of the XML data partitioning schemes mainly relies on the accuracy of the cost model to estimate query processing costs. The cost model must be adjusted to reflect characteristics of a system platform used in the implementation.

This paper proposes novel schemes of conducting XML data partitioning to achieve both static and dynamic workload balance.

As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients.

On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator.

On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty.

The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

The plain High Dimensional Model Representation (HDMR) method needs Dirac delta type weights to partition the given multivariate data set for modelling an interpolation problem. Dirac delta type weight imposes a different importance level to each node of this set during the partitioning procedure which directly effects the performance of HDMR. The purpose of this paper is to develop a new method by using fluctuation free integration and HDMR methods to obtain optimized weight factors needed for identifying these importance levels for the multivariate data partitioning and modelling procedure.

A common problem in multivariate interpolation problems where the sought function values are given at the nodes of a rectangular prismatic grid is to determine an analytical structure for the function under consideration. As the multivariance of an interpolation problem increases, incompletenesses appear in standard numerical methods and memory limitations in computer‐based applications. To overcome the multivariance problems, it is better to deal with less‐variate structures. HDMR methods which are based on divide‐and‐conquer philosophy can be used for this purpose. This corresponds to multivariate data partitioning in which at most univariate components of the Plain HDMR are taken into consideration. To obtain these components there exist a number of integrals to be evaluated and the Fluctuation Free Integration method is used to obtain the results of these integrals. This new form of HDMR integrated with Fluctuation Free Integration also allows the Dirac delta type weight usage in multivariate data partitioning to be discarded and to optimize the weight factors corresponding to the importance level of each node of the given set.

The method developed in this study is applied to the six numerical examples in which there exist different structures and very encouraging results were obtained. In addition, the new method is compared with the other methods which include Dirac delta type weight function and the obtained results are given in the numerical implementations section.

The authors' new method allows an optimized weight structure in modelling to be determined in the given problem, instead of imposing the use of a certain weight function such as Dirac delta type weight. This allows the HDMR philosophy to have the chance of a flexible weight utilization in multivariate data modelling problems.

An approach to extract machining features for casting parts is presented. It is capable of recognizing interacting machining features. There are five phases in the recognition process: Boolean difference of the final part model and the raw part; identification of machining faces (M‐faces) from the final part model and the raw part model; decomposition of the removed simple volumes into delta simple volumes based on M‐faces; gluing the delta simple volumes into sets of feasible simple volumes based on M‐faces; testing. This strategy is process‐oriented and feature‐independent. It recognizes all features that can be produced by common machining operations in a uniform way and produces alternative sets of machining features.

The purpose of this paper is to present a two‐phase approach for designing an efficient tailored but flexible storage solution for resource description framework (RDF) data based on its query workload characteristics.

The approach consists of two phases. The vertical partitioning phase which aims of reducing the number of join operations in the query evaluation process, while the adjustment phase aims to maintain the efficiency of the performance of the query processing by adapting the underlying schema to cope with the dynamic nature of the query workloads.

The authors perform comprehensive experiments on two real‐world RDF datasets to demonstrate that the approach is superior to the state‐of‐the‐art techniques in this domain.

The main motivation behind the authors' approach is that several benchmarking studies have recently shown that each RDF dataset requires a tailored table schema in order to achieve efficient performance during query processing. None of the previous approaches have considered this limitation.

This paper describes voltage collapse in power system networks and how it could lead to a collapse of the whole system. Discusses the effect of machine learning and artificial intelligence, leading to new methods. Spotlight, the fuzzy decision tree (FDT) method and its application to voltage collapse assessments. Concludes that FDT can identify and group data sets, giving a new understanding of its application in voltage collapse analysis.

Learning from demonstration (LfD) provides an intuitive way for non-expert persons to teach robots new skills. However, the learned motion is typically fixed for a given scenario, which brings serious adaptiveness problem for robots operating in the unstructured environment, such as avoiding an obstacle which is not presented during original demonstrations. Therefore, the robot should be able to learn and execute new behaviors to accommodate the changing environment. To achieve this goal, this paper aims to propose an improved LfD method which is enhanced by an adaptive motion planning technique.

The LfD is based on GMM/GMR method, which can transform original off-line demonstrations into a compressed probabilistic model and recover robot motion based on the distributions. The central idea of this paper is to reshape the probabilistic model according to on-line observation, which is realized by the process of re-sampling, data partition, data reorganization and motion re-planning. The re-planned motions are not unique. A criterion is proposed to evaluate the fitness of each motion and optimize among the candidates.

The proposed method is implemented in a robotic rope disentangling task. The results show that the robot is able to complete its task while avoiding randomly distributed obstacles and thereby verify the effectiveness of the proposed method. The main contributions of the proposed method are avoiding unforeseen obstacles in the unstructured environment and maintaining crucial aspects of the motion which guarantee to accomplish a skill/task successfully.

Traditional methods are intrinsically based on motion planning technique and treat the off-line training data as a priori probability. The paper proposes a novel data-driven solution to achieve motion planning for LfD. When the environment changes, the off-line training data are revised according to external constraints and reorganized to generate new motion. Compared to traditional methods, the novel data-driven solution is concise and efficient.

The defective coin problem involves the identification of a defective coin, if any, and ascertaining the nature of the defect (heavier/lighter) from a set of coins containing at the most one defective coin, using an equal‐arm‐pan‐balance. An algorithmic analysis of the problem is considered. The solution strategy to minimise the number of weighings required to detect the defective coin is based on a problem reduction approach involving successive decomposition of the problem into subproblems until it is trivially solved. The algorithm is capable of generating all possible optimal solutions.

The large volume of big data makes it impractical for traditional clustering algorithms which are usually designed for entire data set. The purpose of this paper is to focus on incremental clustering which divides data into series of data chunks and only a small amount of data need to be clustered at each time. Few researches on incremental clustering algorithm address the problem of optimizing cluster center initialization for each data chunk and selecting multiple passing points for each cluster.

Through optimizing initial cluster centers, quality of clustering results is improved for each data chunk and then quality of final clustering results is enhanced. Moreover, through selecting multiple passing points, more accurate information is passed down to improve the final clustering results. The method has been proposed to solve those two problems and is applied in the proposed algorithm based on streaming kernel fuzzy c-means (stKFCM) algorithm.

Experimental results show that the proposed algorithm demonstrates more accuracy and better performance than streaming kernel stKFCM algorithm.

This paper addresses the problem of improving the performance of increment clustering through optimizing cluster center initialization and selecting multiple passing points. The paper analyzed the performance of the proposed scheme and proved its effectiveness.

The purpose of this paper is twofold: first to carry out a comprehensive literature review for state of the art regarding airline schedule planning and second to identify some new research directions that might help academic researchers and practitioners.

The authors mainly focus on the research work appeared in the last three decades. The search process was conducted in database searches using four keywords: “Flight scheduling,” “Fleet assignment,” “Aircraft maintenance routing” (AMR), and “Crew scheduling”. Moreover, the combination of the keywords was used to find the integrated models. Any duplications due to database variety and the articles that were written in non-English language were discarded.

The authors studied 106 research papers and categorized them into five categories. In addition, according to the model features, subcategories were further identified. Moreover, after discussing up-to-date research work, the authors suggested some future directions in order to contribute to the existing literature.

The presented categories and subcategories were based on the model characteristics rather than the model formulation and solution methodology that are commonly used in the literature. One advantage of this classification is that it might help scholars to deeply understand the main variation between the models. On the other hand, identifying future research opportunities should help academic researchers and practitioners to develop new models and improve the performance of the existing models.

This study proposed some considerations in order to enhance the efficiency of the schedule planning process practically, for example, using the dynamic Stackelberg game strategy for market competition in flight scheduling, considering re-fleeting mechanism under heterogeneous fleet for fleet assignment, and considering the stochastic departure and arrival times for AMR.

In the literature, all the review papers focused only on one category of the five categories. Then, this category was classified according to the model formulation and solution methodology. However, in this work, the authors attempted to propose a comprehensive review for all categories for the first time and develop new classifications for each category. The proposed classifications are hence novel and significant.