In the cases when public health authorities confirm patients with highly contagious diseases, they release summaries about patient locations and travel information to alert the public. Due to privacy concerns, these releases do not include the detailed data, and typically comprise the information about commercial facilities and public transportation used by the patients. This paper proposes a framework to represent the information in Resource Description Framework (RDF). The open data would then be used by residents for automated estimation of the potential risks of contacts, combined with the location information stored in their mobile phones.

Despite recent open data initiatives in many countries, a significant percentage of the data provided is in non-machine-readable formats like image format rather than in a machine-readable electronic format, thereby restricting their usability. This paper describes the first unified framework for converting legacy open data in image format into a machine-readable and reusable format by using crowdsourcing. Crowd workers are asked not only to extract data from an image of a chart but also to reproduce the chart objects in spreadsheets. The properties of the reconstructed chart objects give their data structures including series names and values, which are useful for automatic processing of data by computer. Since results produced by crowdsourcing inherently contain errors, a quality control mechanism was developed that improves the accuracy of extracted tables by aggregating tables created by different workers for the same chart image and by utilizing the data structures obtained from the reproduced chart objects. Experimental results demonstrated that the proposed framework and mechanism are effective.

We propose a collaborative approach for personal task management which is modeled as an integration of alliance and human-in-the-loop model. Alliance model is based on information sharing and collaboration of several persons. They disclose their task condition and maintain to be updatable by their friends. To avoid privacy issues we propose emergent group discovery algorithm to control the level of disclosure. Human-in-the-loop model consists of three subsystems to support decision-making activities. Visualizer indicates the attributes associated with each task such as the deadline, the subjective priority, and the workload, which are determined by the user. Optimizer generates executable schedules from these tasks by active scheduler and multiobjective genetic algorithm. Recommender evaluates these alternatives by analytic hierarchy process. We implement client/server system called social scheduler on cell-phones environment. We remark the advantages of our approach with an experiment.