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The purpose of this paper is to evaluate the global progress and explore research areas and development trends of open government data (OGD) field from the Web of Science (WOS) database by applying the bibliometric visualization approach.

This paper conducted a bibliometric mapping study on OGD scientific research publications based on WOS from six aspects.

There are six research perspectives on OGD research. European countries and developed countries pay more attention to OGD movement. The 20 most cited and highly influential research documents were identified. What’s more, the analysis of journals level highlights the interdisciplinary and cross-disciplinary characteristics of OGD research. Current six research topics for OGD research that have been formed and two major emerging research priorities in OGD research fields were identified.

The limitation is that data retrieval result which decided to include only 180 publications in the WOS-indexed publications produced a bias against research publications published in non-WOS publication sources. A fuller research trend would be obtained with the more extensively used electronic databases.

By dint of bibliometric analysis, this paper may be able to quantify research patterns on OGD, to analyze what has been done in this field and to identify the main research hotspots. Therefore, it can aid academic researchers and practicing professionals in contributing to the field more effectively and advancing scientific progress in the field of OGD research.

The results can also promote the study on OGD movement in academia, government and industry and also enrich the theory of OGD and provide some new perspectives for research on OGD.

This is the first study to quantify and evaluate global research patterns and development trends in OGD research based on WOS database, which provides a quantitative perspective on OGD studies that may assist in advancing the development of the field.

The purpose of this paper is to further explore the co-citation and bibliographic-coupling relationship among the core authors in the field of Chinese information science (IS), to expose research activity and author impact, and to make induction analyses about Chinese IS research patterns and theme evolution.

The research data include 8,567 papers and 70,947 cited articles in the IS field indexed by Chinese Social Sciences Citation Index from 2000 to 2009. Author co-citation analysis, author bibliographic-coupling analysis, social network analysis, and factor analysis were combined to explore co-citation and bibliographic-coupling relationships and to identify research groups and subjects.

Scholars with greatest impact are different from the most active scholars of Chinese IS; there is no uniform impact pattern forming since authors’ impact subjects are scattered and not steady; while authors’ research activities present higher independence and concentration, there is still no steady research pattern due to no deep research existing. Furthermore, Chinese IS studies can be delineated by: foundation or extension. The research subjects of these two parts, as well as their corresponding/contributing authors, are different under different views. The general research status of core authors is concentrated, while their impact is broad.

The combined use of some related methods could enrich the development and methodology research of the discipline, and the results establish a reference point on the development of IS research.

The purpose of this paper is to meet the large demand for the new-generation intelligence monitoring systems that are used to detect targets within a dynamic background.

A dynamic target detection method based on the fusion of optical flow and neural network is proposed.

Simulation results verify the accuracy of the moving object detection based on optical flow and neural network fusion. The method eliminates the influence caused by the movement of the camera to detect the target and has the ability to extract a complete moving target.

It provides a powerful safeguard for target detection and targets the tracking application.

The proposed method represents the fusion of optical flow and neural network to detect the moving object, and it can be used in new-generation intelligent monitoring systems.