Recruiting or recommending appropriate potential Wikipedia editors to edit a specific Wikipedia entry (or article) can play an important role in improving the quality and credibility of Wikipedia.... Show More

Recruiting or recommending appropriate potential Wikipedia editors to edit a specific Wikipedia entry (or article) can play an important role in improving the quality and credibility of Wikipedia. According to empirical observations based on a small-scale dataset collected from Wikipedia, this paper proposes an Interest Prediction Factor Graph (IPFG) model, which is characterized by editor's social properties, hyperlinks between Wikipedia entries, the categories of an entry and other important features, to predict an editor's editing interest in types of Wikipedia entries. Furthermore, the paper suggests a parameter learning algorithm based on the gradient descent algorithm and the Loopy Sum-Product algorithm for factor graphs. An experiment on a Wikipedia dataset (with different frequencies of data collection) shows that the average prediction accuracy (F1 score) of the IPFG model for data collected quarterly could be up to 0.875, which is approximately 0.49 higher than that of a collaborative filtering approach. In addition, the paper analyzes how incomplete social properties and editing bursts affect the prediction accuracy of the IPFG model. The authors' results can provide insight into effective Wikipedia article tossing and can improve the quality of special entries that belong to specific categories by means of collective collaboration.

Most of the time series data mining tasks attempt to discover data patterns that appear frequently. Abnormal data is often ignored as noise. There are some data mining techniques based on time... Show More

Most of the time series data mining tasks attempt to discover data patterns that appear frequently. Abnormal data is often ignored as noise. There are some data mining techniques based on time series to extract anomaly. However, most of these techniques cannot suit big unstable data existing in various fields. Their key problems are high fitting error after dimension reduction and low accuracy of mining results. This paper studies an approach of mining time series abnormal patterns in the hydrological field. The authors propose a new idea to solve the problem of hydrological anomaly mining based on time series. They propose Feature Points Symbolic Aggregate Approximation (FP_SAX) to improve the selection of feature points, and then measures the distance of strings by Symbol Distance based Dynamic Time Warping (SD_DTW). Finally, the distances generated are sorted. A set of dedicated experiments are performed to validate the authors' approach. The results show that their approach has lower fitting error and higher accuracy compared to other approaches.

Optimizing the Map process is important for the improvement of the MapReduce performance. Many efforts have been devoted into the problem to design more efficient scheduling strategies. However... Show More

Optimizing the Map process is important for the improvement of the MapReduce performance. Many efforts have been devoted into the problem to design more efficient scheduling strategies. However, there exists a kind of MapReduce applications, named imprecise applications, where the imprecise results based on part of map tasks can satisfy the requirements of imprecise applications and thus the job processes can be completed when enough map tasks are processed. According to the feature of imprecise applications, the authors propose an improved MapReduce model, named MapCheckReduce, which can terminate the map process when the requirements of an imprecise application is satisfied. Compared to MapReduce, a Check mechanism and a set of extended programming interfaces are added to MapCheckReduce. The Check mechanism receives and analyzes messages submitted by completed map tasks and then determines whether to terminate the map phase according to the analysis results. The programming interfaces are used by the programmers to define the termination conditions of the map process. A data-prefetching mechanism is designed and implemented in MapCheckReduce which can improve the performance of MapCheckReduce effectively. The MapCheckReduce prototype has been implemented and experiment results verify the feasibility and effectiveness of MapCheckReduce.

Social business moves beyond linear, process-driven organizations to create new, dynamic, networked businesses that focus on customer value. Enterprise network (EN) is used to support social... Show More

Social business moves beyond linear, process-driven organizations to create new, dynamic, networked businesses that focus on customer value. Enterprise network (EN) is used to support social business by maximizing current and future opportunities and facilitate network-enabled processes, which can lead to value co-creation. EN is a multi-level hypergraph model with enterprises, employees, products and other related entities. In this paper the authors refine the EN model and present the foundation of EN to support social businesses. Then they introduce a case study on China automobile supply network (CASN). For the similarity with social networks, they verify power-law and small world theories in EN with statistical results on this data set. These theories are fitful in EN, but some new characteristics exist. The structure of EN consists of star-shaped clusters and the authors extract ego networks taking suppliers and manufacturers as the ego respectively. With the structure and distribution features of EN, they present the enterprise business similarity analysis method based on common-neighbors. And they also introduce the tentative work to detect Dunbar circles in EN. To analyze the data in a more intuitional and effective way, the authors use some data visualization tools to process the data in EN.

Moving objects gathering pattern represents a group events or incidents that involve congregation of moving objects, enabling the analysis of traffic system. However, effectively and efficiently... Show More

Moving objects gathering pattern represents a group events or incidents that involve congregation of moving objects, enabling the analysis of traffic system. However, effectively and efficiently discovering the specific gathering pattern turns to be a remaining challenging issue since the large number of moving objects will generate high volume of trajectory data. In order to address this issue, the authors propose a moving object gathering pattern retrieving method that aims to support the retrieving of gathering patterns based on spatio-temporal graph. In this method, firstly the authors use an improved R-tree based density clustering algorithm (RT-DBScan) to index the moving objects and collect clusters. Then, they maintain a spatio-temporal graph rather than storing the spatial coordinates to obtain the spatio-temporal changes in real time. Finally, a gathering retrieving algorithm is developed by searching the maximal complete graphs which meet the spatio-temporal constraints. To the best of their knowledge, effectiveness and efficiency of the proposed methods are outperformed other methods on both real and large trajectory data.