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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.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.

The paper aims to propose an effective method to process keyword-based queries over graph-structured databases which are widely used in various applications such as XML, semantic web, and social network services. To satisfy users' information need, it proposes an extended answer structure for keyword queries, inverted list indexes on keywords and nodes, and query processing algorithms exploiting the inverted lists. The study aims to provide more effective and relevant answers to a given query than the previous approaches in an efficient way.

A new relevance measure for nodes to a given keyword query is defined in the paper and according to the relevance metric, a new answer tree structure is proposed which has no constraint on the number of keyword nodes chosen for each query keyword. For efficient query processing, an inverted list-style index is suggested which pre-computes connectivity and relevance information on the nodes in the graph. Then, a query processing algorithm based on the pre-constructed inverted lists is designed, which aggregates list entries for each graph node relevant to given keywords and identifies top-k root nodes of answer trees most relevant to the given query. The basic search method is also enhanced by using extend inverted lists which store additional relevance information of the related entries in the lists in order to estimate the relevance score of a node more closely and to find top-k answers more efficiently.

Experiments with real datasets and various test queries were conducted for evaluating effectiveness and performance of the proposed methods in comparison with one of the previous approaches. The experimental results show that the proposed methods with an extended answer structure produce more effective top-k results than the compared previous method for most of the queries, especially for those with OR semantics. An extended inverted list and enhanced search algorithm are shown to achieve much improvement on the execution performance compared to the basic search method.

This paper proposes a new extended answer structure and query processing scheme for keyword queries on graph databases which can satisfy the users' information need represented by a keyword set having various semantics.

Automobile development needs more and more collaborative work involving geographical dispersed designers, that brings difficulty for model verification, conception review and assembly process evaluation, so a collaborative virtual environment for automobile based on network is required. In this kind of environment, designers can do interactive assembly operations collaboratively, such as grasp, move, release, collision detection (CD), assembly evaluation report generation, etc. Furthermore, automobile structure becomes more complicated, how to process this large real‐time data effectively in real‐time interactive virtual environment is a great challenge. The purpose of this paper is focus on this.

A distributed parallel virtual assembly environment (DPVAE) is developed. In this environment, the mechanism of event synchronization based on high‐level architecture/run‐time infrastructure) is applied to realize multi‐user collaboratively interactive operation. To meet the large data set real‐time processing demand, a creative parallel processing approach supported by a single supercomputer or a parallel processing environment composed of common personal computer in a high‐speed local area network is developed. The technologies such as real‐time CD, multiple interactive operation modals are applied in DPVAE and several auxiliary tools are provide to help achieving whole scheme review, component model verification and assembly evaluation.

This paper finds that DPVAE system is an available and efficient tool to support automobile collaborative assembly design.

Designers can discuss and verify the assembly scheme to realize the previous design scenario in DPVAE, so it is useful for reducing costs, improving quality and shortening the time to market, especially for new type automobile development.

A combination of distributed technology and parallel computing technology is applied in product virtual assembly, solving the problems including collaborative work of multi‐user and large data real‐time processing successfully, that provides a useful tool for automobile development.

This modeling facilitates the determination of control responses (or possibly reconfiguration) upon such events and the identification of which segments of the pipeline can continue to function uninterrupted. Based on this modeling, an algorithm is presented to implement the control responses and to establish this determination. In this work, the authors propose using Message Queuing Telemetry Transport (MQTT), which is an integrated method to perform the system-wide control based on message exchanging among local node controllers (agents) and the global controller (broker).

Complex manufacturing lines in industrial plants are designed to accomplish an overall task in an incremental mode. This typically consists of a sequence of smaller tasks organized as cascaded processing nodes with local controls, which must be coordinated and aided by a system-wide (global) controller. This work presents a logic modeling technique for such pipelines and a method for using its logic to determine the consequent effects of events where a node halts/fails on the overall operation.

The method uses a protocol for establishing communication of node events and the algorithm to determine the consequences of node events in order to produce global control directives, which are communicated back to node controllers over MQTT. The algorithm is simulated using a complex manufacturing line with arbitrary events to illustrate the sequence of events and the agents–broker message exchanging.

This approach (MQTT) is a relatively new concept in Cyber-Physical Systems. The proposed example of feed-forward is not new; however, for illustration purposes, it was suggested that a feed-forward be used. Future works will consider practical examples that are at the core of the manufacturing processes.

To reengineer the applications' processes of the land department of Al‐Ain Municipality and to develop an electronic version of the reengineered processes.

The research approach used in this work is similar to the five‐point incremental approach and the work of Gunasekaran et al. These steps are: establishment of the business vision and objectives; identification and focus on the core business processes that support them; modeling and analysis of the business environment; streamlining; and continuous control and improvement of previous steps.

The best possible solution for shorter and more efficient processing path was achieved. Great savings in cost, time and human resources were achieved. Many factors have contributed to the success of the work. These include the efficient reengineered process, efficient database design, search facilities, electronic document archive and easy‐to‐use user interface.

The authors were not absolutely free to select the model they think is better. There were some critical factors that had to be considered. Perhaps, one of the most serious obstacles was the reluctance of some key decision makers to cease or reduce some of their authorities. Those officers believe that a direct supervision or control on the different stages of the processes is essential. The justification given is that the issue of land allocation is critical and may be problematic. Special considerations may be required under certain circumstances and these are only dealt with by key decision makers. Other factors such as reluctance of the users for change, job redundancy resulting from the change and financial resources were important but less significant. Therefore, many models were suggested before the final one was agreed on.

The paper is a case study that describes the reengineering processes in the public sector in UAE. This sector suffers from a great deficiency and this work represents a leading step towards improving its working practices. The value of the paper lies in the comprehensive study and analysis of this department and the efficient customized solutions it offered.

In the past recent years, wireless sensor network (WSN) has progressively grown as an emerging technology. Various research efforts have been made in the literature to address the problem associated with WSN security. Based on the review analysis, it is found that the existing methods are mostly associated with complex security operations that are not suitable for resource constraint sensor nodes. The proposed paper has presented cost-effective modeling of the security framework that addresses the problem of security and energy in WSN.

The proposed security framework implements two different protocols to attain maximum security services and optimizes the security operation of the proposed security models to achieve higher energy efficiency and privacy preservation against a majority of the lethal attacks. The first security model introduces a novel cost-efficient pairwise key-based authentication mechanism to identify the availability of optimal routes under the presence of adversary in the network. The second security model introduces an integrated part of the first security model that optimizes security operation to perform secure communication using a lightweight encryption mechanism.

Based on the experimental outcome and analysis, the proposed system attains a 60% performance improvement in terms of security and computational efficiency compared to the existing Sec-LEACH. The second security model has achieved a 50% improvement in terms of overall aspects like reduction in transmission delay, packet delivery ratio, remaining energy and communication performance.

The proposed study has presented a computationally efficient model that provides lightweight security operations based on secure hash function. It also focuses on the security associations between WSN nodes and the selection of reliable routes for secure data transmission. The design of the proposed security model is best suited for homogeneous and heterogeneous sensor networks, which will be robust to any attacking scenario.

Applies a parallel backward‐chaining technique to a rule‐based expert system on a shared‐memory multiprocessor system. The condition for a processor to split up its search tree (task‐node) and generate new OR nodes is based on the level in the goal tree at which the task‐node is found. The results indicate satisfactory speed‐up performance for a small number of processors (< 10) and a reasonably large number of rules.

In a small‐lot manufacturing facility, process planning is the task of specifying a machine series that will produce a certain part from a given raw material. Traditionally, the same machine path or routing is followed each time the part is released for manufacture. A prototype system is developed which adapts routings according to job specification (process quantity and due date), as well as shop conditions (the relative cost and availability of alternative resources). The performance of this system is then compared against the traditional fixed method, as well as two single‐focus, adaptive strategies (least‐cost and least‐load), drawn from research.

Security is one of the major challenges in the design and implementation of protocols for mobile ad hoc networks (MANETs). In such systems, the cooperation between nodes is one of the important principles being followed in the current research works to formulate various security protocols. Many existing works assume that mobile nodes will follow prescribed protocols without deviation. However, this is not always the case, because these networks are subjected to a variety of malicious attacks. Since there are various models of attack, trust routing scheme can guarantee security and trust of the network. The purpose of this paper is to propose a novel trusted routing model for mitigating attacks in MANETs.

The proposed model incorporates the concept of trust into the MANETs and applies grey relational analysis theory combined with fuzzy sets to calculate a node’s trust level based on observations from neighbour nodes’ trust level, these trust levels are then used in the routing decision-making process.

In order to prove the applicability of the proposed solution, extensive experiments were conducted to evaluate the efficiency of the proposed model, aiming at improving the network interaction quality, malicious node mitigation and enhancements of the system’s security.

The proposed solution in this paper is a new approach combining the fundamental basics of fuzzy sets with the grey theory, where establishment of trust relationships among participating nodes is critical in order to enable collaborative optimisation of system metrics. Experimental results indicate that the proposed method is useful for reducing the effects of malicious nodes and for the enhancements of system’s security.