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The availability of network big data, such as those from online users' surfing records, communication records, and e-commerce records, makes it possible for us to probe into and quantify the regular patterns of users' long-range and complex interactions between websites. If we see the Web as a virtual living organism, according to the metabolic theory, the websites must absorb 'energy' to grow, reproduce, and develop. We are interested in the following two questions: 1) where does the 'energy' come from? 2) will the websites generate macro influence on the whole Web based on the 'energy'? Our data consist of more than 30 000 online users' surfing log data from China Internet Network Information Center. We would consider the influence as metabolism and users' attention flow as the energy for the websites. We study how collective attention distributes and flows among different websites by the empirical attention flow network. Different from traditional studies which focused on information flow, we study users' attention flow, which is not only a 'reversed' way to study Web structure and transmission mode, but also the first step to understand the underlying dynamics of the World Wide Web. We find that the macro influence of websites scales sub-linearly against the collective attention flow dwelling time, which is not consistent with the heuristics that the more users' dwelling time is, the greater influence a website will have. Further analysis finds a supper-linear scaling relationship between the influence of websites and the attention flow intensity. This is a websites version of Kleiber's law. We further notice that the development cycle of the websites can be split into three phases: the uncertain growth phase, the partially accelerating growth phase, and the fully accelerating growth phase. We also find that compared with the widespread hyperlinks analysis models, the attention flow network is an effective theoretical tool to estimate and rank websites. [ABSTRACT FROM AUTHOR]

With increasing popularity of cloud-based data management, improving the performance of queries in the cloud is an urgent issue to solve. Summary of data distribution and statistical information has been commonly used in traditional databases to support query optimization, and histograms are of particular interest. Naturally, histograms could be used to support query optimization and efficient utilization of computing resources in the cloud. Histograms could provide helpful reference information for generating optimal query plans, and generate basic statistics useful for guaranteeing the load balance of query processing in the cloud. Since it is too expensive to construct an exact histogram on massive data, building an approximate histogram is a more feasible solution. This problem, however, is challenging to solve in the cloud environment because of the special data organization and processing mode in the cloud. In this paper, we present HEDC++, an extended histogram estimator for data in the cloud, which provides efficient approximation approaches for both equi-width and equi-depth histograms. We design the histogram estimate workflow based on an extended MapReduce framework, and propose novel sampling mechanisms to leverage the sampling efficiency and estimate accuracy. We experimentally validate our techniques on Hadoop and the results demonstrate that HEDC++ can provide promising histogram estimate for massive data in the cloud. [ABSTRACT FROM AUTHOR]

We investigate the limitations of existing XML search methods and propose a new semantics, related relationship, to effectively capture meaningful relationships of data elements from XML data in the absence of structural constraints. Then we make an extension to XPath by introducing a new axis, related axis, to specify the related relationship between query nodes so as to enhance the flexibility of XPath. We propose to reduce the cost of computing the related relationship by a new schema summary that summarizes the related relationship from the original schema without any loss. Based on this schema summary, we introduce two indices to improve the performance of query processing. Our algorithm shows that the evaluation of most queries can be equivalently transformed into just a few selection and value join operations, thus avoids the costly structural join operations. The experimental results show that our method is effective and efficient in terms of comparing the effectiveness of the related relationship with existing keyword search semantics and comparing the efficiency of our evaluation methods with existing query engines. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various papers within the issue on topics related to the benefits of flexibility of cloud computing systems, requirements to support online aggregation in cloud computing, and challenges of large-scale database services on cloud computing infrastructure.