Community Search (CS) aims to enable online and personalized discovery of communities. Recently, attention to the CS problem in directed graphs (di-graph) needs to be improved despite the extensive study conducted on undirected graphs. Nevertheless, the existing studies are plagued by several shortcomings, e.g., Achieving high-performance CS while ensuring the retrieved community is cohesive is challenging. This paper uses the D-truss model to address the limitations of investigating the CS problem in large di-graphs. We aim to implement millisecond-level D-truss CS in di-graphs by building a summarized graph index. To capture the interconnectedness of edges within D-truss communities, we propose an innovative equivalence relation known as D-truss-equivalence, which allows us to divide the edges in a di-graph into a sequence of super nodes (s-nodes). These s-nodes form the D-truss-equivalencebased index, DEBI, an index structure that preserves the truss properties and ensures efficient space utilization. Using DEBI, CS can be performed without time-consuming access to the original graph. The experiments indicate that our method can achieve millisecond-level D-truss community query while ensuring high community quality. In addition, dynamic maintenance of indexes can also be achieved at a lower cost. Our code is available athttps://github.com/XieCanhao04/DEBI.

Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph. However, existing heterogeneous graph-based community search methods need help identifying cross-group communities and suffer from efficiency issues, making them unsuitable for large graphs. This paper presents a fast community search model based on the Butterfly-Core Community (BCC) structure for heterogeneous graphs. The Random Walk with Restart (RWR) algorithm and butterfly degree comprehensively evaluate the importance of vertices within communities, allowing leader vertices to be rapidly updated to maintain cross-group cohesion. Moreover, we devised a more efficient method for updating vertex distances, which minimizes vertex visits and enhances operational efficiency. Extensive experiments on several real-world temporal graphs demonstrate the effectiveness and efficiency of this solution.

Combining a series of atomic services into a composite value-added service offers promise in meeting the increasingly diverse needs of users in the cloud market. However, without considering concurrency of composite service requests among multiple users, existing studies seldom focus on the competition relationship among users and thus lack an incentive mechanism to efficiently provide the request strategy of users. In this article, taking into consideration the multi-user competition and interaction with the cloud provider, we design a composite service reservation framework in which users’ requests can be rationally and efficiently multiplexed. Due to selfishness, each user tends to expect to maximize its own utility in terms of revenue, payoff, and performance of composite service. The optimization problem is modeled from the perspective of game theory and defined as a non-cooperative game. The existence of Nash equilibrium solution of the formulated game is proved by an equivalent variational inequality problem. An iterative proximate algorithm (IPA) is proposed to find a utility-balanced request strategy, which theoretically leads to a Nash equilibrium solution. A series of simulation experiments are conducted to verify our theoretical analysis. The experimental results show thatIPAconverges to a Nash equilibrium in an acceptable number of iterations. The stable request strategies can improve the utilities of users and the resource utilization of the cloud provider, as the proposed framework empowers users to transfer requests from peak periods into non-peak ones.

With the popularity of the Internet of Things (IoT) and large-scale deployment of sensors, data have exploded. Big data is utilized to extract useful knowledge and information and served as data services to consumers. While most of the current researches in the field of big data services focus on developing and improving algorithms for data mining and information extraction, and rarely studies “big data” from an economic perspective. This article thus studies the pricing and profit maximization problems in big data markets from an economic perspective. First, a method of quantifying the value of data is proposed to study the utility of raw data. Then, we build an economic model of the data market, which consists of three parties: 1) data vendor; 2) service provider; and 3) service users. The data vendor gathers various raw data and sells them to the service provider. The raw data are further processed into data services by a service provider, who provides service subscriptions to users to obtain profits. The interactions among them are formulated as a Stackelberg game to maximize the profits of all participators. The existence and uniqueness of equilibria pricing strategies are proved. Finally, numerical results show that the participators of the data market can achieve the maximum profit through the proposed pricing mechanism and economic model.

As an increasing number of providers are offering cloud service reservations, an increasingly competitive market has emerged. While most of these providers charge users for metered usage based on fixed prices, they cannot make a timely response to demand changes and may risk losing users. Hence, we highlight the importance of an autonomous provider who self-adjusts the price according to the latest demands of the cloud market. Extensive research has shown that this is not a problem for a single provider. However, when it comes to multiple providers, the main challenge is coordinating the interactions between users and users, providers and providers, as well as users and providers. In this paper, we focus on request balancing among users in the multiple-autonomous-provider environment. Since the request strategy and payment of each user are affected by decisions of other users, we take game theory to address this problem and formulate it into a noncooperative game among multiple users, without sharing specific strategies. Each user tries to maximize its utility, which combines the net profit with time efficiency. The variational inequality theory is employed and proved that there exists a Nash equilibrium solution set for the game. Then, an iterative proximal algorithm is proposed to find the Nash equilibrium solution, and its convergence is proven. Simulation results demonstrate that the proposed scheme makes requests balanced and increases users' utilities, and also shifts demands from peak to off-peak periods.

A hybrid invasive weed optimization (HIWO) algorithm based on the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is proposed for the problems on parameter inversion of the nonlinear models of sun shadow with integer variables in the study. Our presented algorithm can take full advantage of the local search ability of BFGS algorithm and the global search ability of invasive weed optimization (IWO) algorithm. The HIWO algorithm can not only reverse the date of sun shadow model successfully, but also conquer the weaknesses that the classic mathematical methods are hard to address integer nonlinear optimization problems by utilizing integers in some random variables from algorithms. The results of numerical experiments demonstrate that the HIWO algorithm has not only high computing accuracy, but also fast convergence speed. It can effectively improve the accuracy and efficiency of the techniques of sun shadow location, and afford an effective and efficient technique to handle the issues of integer parameter inversion in engineering applications.

Non-Volatile Memories (NVMs) have many advantages over traditional DRAM. It is desirable to apply NVM as main memory in embedded Chip Multi-Processor (CMP) systems. However, NVMs have drawbacks that need to be overcome. That is, a write to the NVMs is expensive. Loops are the most critical and time-consuming part in digital signal processing (DSP) applications. However, loops are difficult to parallelize on multi-processor systems due to the inter-iteration dependencies. This paper targets on embedded CMP systems and proposes techniques to improve loop parallelism while considering reducing the write activities to the NVMs when they are used as main memory. The experimental results show that the proposed algorithm can reduce the number of write activities on NVM by 21.1% on average. In other words, the average lifetime of NVM can be extended to at least 2 times longer than before and the total schedule length is reduced by 19.6% on average.