Abstract Cloud storage service has been in full swing in the industry. Delta synchronization technology as a key technology of cloud storage services has not made a key breakthrough. Almost all the existing researches are based on the synchronization process proposed by the Rsync algorithm and mix some optimization appropriately, but the particularity of cloud storage service is not fully considered. This paper proposes a new incremental synchronization method SimpleSync, which makes use of the characteristic that the server does not actively modify the backup files in the cloud storage service, removes the redundant steps in Rsync, and enables the synchronization between the client and the server only through a single communication. Besides, according to the server‐side synchronization request processing logic, this paper puts forward the design idea of parallel processing with the Flink framework, to the best of our knowledge, for the first time. After the server receives the synchronization request, SimpleSync first puts it into Kafka for buffering and then uses Flink to process the synchronization request in parallel. In the experimental part, a large number of experiments are designed to compare SimpleSync with other delta synchronization algorithms. Experimental results show that SimpleSync has obvious advantages in synchronization performance. Meanwhile, experiments show that SimpleSync has correctness.

In mobile crowdsensing, it is a challenge to assign tasks to appropriate smartphones. Existing task allocation mechanisms mainly aim at optimizing the global system performance, while ignoring the personal preferences of individual crowdsensing tasks and smartphone users. Nevertheless, in an open crowdsensing system, a task assignment is prone to be unstable if smartphone users or tasks have incentives to deviate from the global assignment, and seek for alternative choices to improve their own utilities. Besides that, during task competition, the rational smartphone users might choose to adjust their payments after the first few failures, which however, brings new challenges in achieving the stability. To address these issues, this paper constructs a distributed many-to-many matching model to capture the interaction between crowdsensing tasks and smartphone users, taking into account the budget constraints of tasks. Then, we design a stable matching algorithm to allocate the tasks to the users, and determine their payments. We prove that the proposed algorithm achieves several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed scheme achieves at least half of the optimal system efficiency when each smartphone provides homogeneous service quality. Finally, simulation results confirm the effectiveness of the proposed scheme.

Summary Data structure description, conceptual modeling, and logic reasoning for knowledge discovery are three critical factors for the integration of information with heterogeneity. In particular, technologies of NoSQL databases and Internet of Things raise an urgent requirement for a uniform expression of heterogeneous data, and little attention has been paid to researches on the integration of NoSQL databases with traditional data models, as well as the semantic description of big data. To tackle these problems, in this paper, a concept‐and‐relation‐oriented grid data model called GODM model is first proposed based on the definitions of Monad, Compounder, Relation, etc. Then, the GODM model is utilized to uniformly describe traditional data models and NoSQL data models, which eliminates structure differences of heterogeneous data. Next, based on the GODM relation mechanism, an extendable semantic system is built up by choosing SHOIQ(D) description logic as the example to establish the correspondence with GODM grammar subset, providing a fundamental support for semantic integration and knowledge discovery of heterogeneous data. After that, comprehensive comparisons with GODM and other models are made, especially the distinctions between GODM and OWL on the aspects of relation mechanism, hybrid schema, description logic, grammatical constructors, etc. Besides, experimental evaluations and analyses on time and space efficiencies of some primary common data models are conducted after the proposal of a general evaluation model, with the results showing that the GODM model has great advantage on properties of expressiveness, flexibility, etc, particularly time and space efficiency. In summary, the GODM model describes heterogeneous data from both aspects of data structure and semantic relationship and realizes a hybrid schema reconciling the schemaful and schemaless data models, making it especially suitable for dynamic data integration and knowledge discovery from big data models.

Fog computing (FC) is an emerging paradigm that extends computation, communication, and storage facilities towards the edge of a network. In this heterogeneous and distributed environment, resource allocation is very important. Hence, scheduling will be a challenge to increase productivity and allocate resources appropriately to the tasks. We schedule tasks in fog computing devices based on classification data mining technique. A key contribution is that a novel classification mining algorithm I‐Apriori is proposed based on the Apriori algorithm. Another contribution is that we propose a novel task scheduling model and a TSFC (Task Scheduling in Fog Computing) algorithm based on the I‐Apriori algorithm. Association rules generated by the I‐Apriori algorithm are combined with the minimum completion time of every task in the task set. Furthermore, the task with the minimum completion time is selected to be executed at the fog node with the minimum completion time. We finally evaluate the performance of I‐Apriori and TSFC algorithm through experimental simulations. The experimental results show that TSFC algorithm has better performance on reducing the total execution time of tasks and average waiting time.

Summary This paper presents a novel list‐based scheduling algorithm called Improved Predict Earliest Finish Time for static task scheduling in a heterogeneous computing environment. The algorithm calculates the task priority with a pessimistic cost table, implements the feature prediction with a critical node cost table, and assigns the best processor for the node that has at least 1 immediate successor as the critical node, thereby effectively reducing the schedule makespan without increasing the algorithm time complexity. Experiments regarding aspects of randomly generated graphs and real‐world application graphs are performed, and comparisons are made based on the scheduling length ratio, robustness, and frequency of the best result. The results demonstrate that the Improved Predict Earliest Finish Time algorithm outperforms the Predict Earliest Finish Time and Heterogeneous Earliest Finish Time algorithms in terms of the schedule length ratio, frequency of the best result, and robustness while maintaining the same time complexity.

Summary The topology of interconnection networks plays an important role in the performance of parallel and distributed computing systems. In this paper, we propose a new interconnection network called twisted crossed cube (TCQn) and investigate its basic network properties in terms of the regularity, connectivity, fault tolerance, recursiveness, hamiltonicity and ability to simulate other architectures, and so on. Then, we develop an effective routing algorithm Route (u, v) for TCQn that takes no more than d(u, v) + 1 steps for any two nodes (u, v) to communicate with each other, and the routing process shows that the diameter, wide diameter, and fault‐tolerant diameter of TCQn are about half of the corresponding diameters of the equivalent hypercube with the same dimension. In the end, by combining TCQn with crossed cube (CQn), we propose a preferable dynamic network structure, that is, the dynamic crossed cube, which has the same network diameter as TCQn/CQn and better properties in other respects, for example, its connection complexity is half of that of TCQn/CQn when the network scale is large enough, and the number of its average routing steps is also much smaller than that in TCQn/CQn. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Since finding the minimum connected dominating set (MCDS) in an arbitrary graph is a NP‐hard problem, many algorithms have been proposed to construct an approximation of the MCDS for efficient routing in wireless sensor networks (WSNs). To address the weaknesses of these existing algorithms, we propose a novel distributed MCDS approximation algorithm, CDS‐HG, to construct the connected dominating set (CDS) for WSNs. This algorithm models a WSN as a hierarchical graph and uses a competition‐based strategy to select nodes at a certain hierarchical level to route messages for nodes in the next level of the hierarchical graph. Formal analysis and simulation studies show that our CDS‐HG algorithm generates smaller CDS sizes while requiring less communication overhead compared with the existing distributed MCDS approximation algorithms. Copyright © 2007 John Wiley & Sons, Ltd.

Nowadays, enterprise information semantic integration (EISI) constitutes a real and growing need for most large enterprises. The main problem of EISI is the heterogeneity problem, especially the semantic integration. Current solutions mainly focus on structural and syntactical integration, and the solutions for semantic integration are still immature. In this paper, we introduce a novel FuseGrid-based EISI architecture named FEISI which can achieve dynamic, adaptive and semantic integration of enterprise information resources by organizing, sharing and fusing the information of enterprise. The key issues of FEISI are ontology building and query rewriting, we analyses the organization of ontology and describes a multi-level tree-based ontology structure for the former, for the latter, we show the key algorithm of FEISI. The aim of our approach is to correctly capture, structure and also to manage the semantics within large, dynamic and multidisciplinary enterprises especially enterprise groups

This paper studies the system parameters that affect the total cost of managing the multicast group on a router. A Petri net model is first proposed to describe the states and transitions of the multicast group management. Based on this model, a delayed vacation queue, extended from a simple M/M/1 queue, is used to analyze the total cost of the multicast group management under various system conditions. The formal analysis reveals that the total cost of the multicast group management is minimized when a router delays a certain time to send its pruning messages to upper stream routers. Furthermore, a formula is derived to calculate the optimal delay time for sending the pruning messages under various system parameters to minimize the multicast group management cost. Finally, cost analysis examples demonstrate how other system parameters, such as multicast member arrival rate and message sending costs, affect the total cost of the multicast group management under various delay times for sending the pruning messages.

In this paper, a SVM-based online learning algorithm is proposed and applied to the problem of Web prediction. A method to construct an online LS-SVM multi-class learning model has been presented. This method is able to capture the inherent sequentiality of Web visits and successfully predict the future accesses. The experimental results show the effective performance of our method

LOODS, put forward by the author, is a new object-oriented terminal applications development environment, which consists of two parts: system modelling methodology (LOODS abstract models) and LOODS visual language system. The former is an object-oriented concurrent/parallel information system modelling methodology on the basis of requirements analysis results. The later is an automatic generator of LOODS abstract models, supporting concept programming and learning-by-example building of application systems. Also a new method of object-oriented learning is introduced for the LOODS visual knowledge system. LOODS has been realised in C++ on a Novell network environment.