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Information service is always a hot topic especially when the Web is accessible anywhere. In university, lecture information is very important for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Many open information extraction methods have been proposed, but due to the high heterogeneity of websites, this task is still a challenge. In this paper, we propose a method based on fusing multiple features to locate lecture news on the university website. These features include the linked relationship between parent webpage and child webpages, the visual similarity, and the semantics of webpages. Additionally, this paper provides an information service based on a main content extraction algorithm for extracting the lecture information. Stable and invariant features enable the proposed method to adapt to various kinds of campus websites. The experiments conducted on 50 websites show the effectiveness and efficiency of the provided service.

Many anomaly detection algorithms have been provided as services for more convenient and efficient utilization in IoT era. Due to concept drift existed in dynamic IoT stream data, it is a changeling task to apply proper anomaly detection services at run time. For effective on-line anomalous data discovery, this paper proposes a service selection framework to dynamically select and configure anomaly detection services. A fast classification model based on XGBoost is trained to identify the pattern of various stream data, so that suitable service can be selected and configured according to the pattern of stream data. Extensive experiments on real and synthetic data sets show that our framework can select suitable service for different scenarios, and the accuracy of the chosen services outperforms state-of-the-art methods.

Stream data from devices and sensors is considered a typical kind of big data. Though being promising, they have a good prospect only when we can reasonably correlate and effectively use them. Herein, services come back to the spotlight. The paper reports some of the authors' efforts in promoting service-based fusion and correlation of such stream data in a real setting – monitoring and optimized coordination of individual devices in a power plant. This paper advocates a decentralized and service-based approach to dynamically correlating the sensor data and proactively generating higher-level events between sensors and applications. A novel service model for transforming and correlating massive stream data is proposed. This service model shows potential in realizing various middle-way programmable nodes to form larger-granularity and software-defined ‘sensors' in an IoT context.

The complex interventions among sensor streams bring new challenges for IoT applications to derive meaningful information from large amounts of sensor streams. This paper aims to provide a data-driven service creation method for effectively capturing events based on our previous service abstraction – proactive data service. For improving the effectiveness of proactive data service, we consider the potential correlations among sensor streams besides user's pre-definitions when creating service. Based on the assumption that events frequently co-occurred in history have high probability to co-occur again, we regard frequent event sets as one kind of correlations among sensor streams, and propose an algorithm called FP-MFIM to efficiently find the maximum frequent event sets co-occurred in multiple sensor streams. For providing more effective information, we create PD-services with frequent co-occurred event types besides user-defined event types. This paper reports the tryout use of the method in China power grid for power quality event detection and location. Through a series of experiments based on real sensor data from power grid, we verified the efficiency of FP-MFIM algorithm and the effectiveness of our PD-services in real-world scenario.

With the increasing abundance of traffic data from sensors and devices, the integration and analysis of such streaming data are gaining importance in many application scenarios. This paper proposes a service-based approach for integrating and analyzing the traffic sensor data to support green commute in China by automatically discovering carpooling companions within a community. Our focuses are on modeling and design of the carpooling discovery services, algorithms for implementing the services, and performance enhancement when the data volume scales up. The proposed approach is verified with experiments using real-world data.

Though the existing data service mashup tools are gaining acceptance, it is still challenging for developers with no or little programming skills to develop data service mashups for dealing with situational and ad-hoc business problems. The paper focuses on interactive recommendation in which assistance is provided in a context-sensitive manner when the mashup plan can't be determined in advance. The paper analyzes the problem with a motivating scenario of mashup building for criminal investigation. Inspired by the observation that there exist dataflow patterns for certain integration functionalities, a dataflow-pattern-based recommendation framework is proposed to solve the problems. The framework can not only recommend data services by discovering similar situations, but also recommend mashup patterns and target data services. We propose a method to analyze the relationships between data services and dataflow patterns through both mining history logs and matching the input/output parameters. Further, to recommend target data services, we propose a method to transform the data mashup plans into mixed graphs and apply the graph-based substructure pattern mining (gSpan) algorithm on them. Experiments show that the dataflow-pattern-based recommendation approach for data service mashup is effective and efficient.

IoT service and service composition provide an effective means to develop IoT applications based on correlating multiple sensor data. The change of specific sensor data can cause others' changes under uncertain situations. It makes difficult for defining service composition plan in advance to build IoT application. This paper proposes a data-driven service composition method based on our previous proactive data service model. We regard service events frequently happen together with given service event as its situation, and the service events happen next as reacted actions under the situation. We analyze two kinds of correlation among service events via an improved FP-tree algorithm, and realize the service composition at runtime based on the real-time service events. Based on the real sensor data set in a coal-fired power plant, a series of experiments demonstrate that our method can effectively detect new service events based on current service events.

With the social development and vehicle increasing, traffic congestion becomes a pressing problem. Carpooling is one solution for traffic congestion. This paper begins with a motivation case and proposes a service-based method to automatically identify vehicles which travel together (i.e., Traveling companion) and provide carpooling candidates for users in heavy urban traffic. This paper focuses on services design and implementation of the carpooling functions in a distributed parallel computing environment. Through a comprehensive evaluation by experiments, our proposal is shown to deliver good efficiency and effectiveness.

Data service mashup provides a development fashion that integrates heterogeneous data from multiple data sources into a single Web application. This paper focuses on the problem of recommending useful suggestions for developing data service mashups based on the association relationship of data services. Firstly the data service association relationship is analyzed from three angles: the data dependence, inheritance and the potential association between data services. Based on the analysis, a measure of the data service association relationship called connectivity is proposed to assess the relationship of any two data services. Then a recommendation method is proposed to suggest the next useful data services based on the connectivity. The experimental evaluation demonstrates the utility of our method.

As an open source software platform, Hadoop has been applied widely. Aim at the need of Hadoop resource monitoring, this paper introduces Hadoop and two distributed monitoring systems, which are Ganglia and Nagios. We design a resource monitoring system of Hadoop, which can monitor Hadoop comprehensively, and provide real-time monitoring, warning messages and reports. It makes use of the advantages of Ganglia and Nagios, and resolves the problem that Ganglia can't store data permanently. Finally, an effective solution is provided for resource monitoring of Hadoop.

A novel dual watermark technology based on digital copyright technology is proposed, which, making full use of the advances of the web, stores the correlation information including the keys and the dual watermarks in an XML document. A new image watermarking technology to spread a digital image with copyright protection is realised successfully on the Internet. The arithmetic has very good robustness against the most common, non-malevolent data manipulations, including digital-to-analogue conversion and digital format conversion. Finally, the experimental results confirm that the two watermarks embedded by the proposed algorithm are invisible and robust against commonly used image-processing manipulations such as JPEG compression, adding noise, cropping, and rescaling and soon. The proposed algorithm is shown to provide very good results in term of image imperceptibility too.