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The basic issues involved in the mashup creation process are data retrieval, source modeling, data cleaning, data integration, and data visualization. With Karma, all of these issues are addressed in one seamless interactive process and the user indirectly solves each issue by providing only examples. In this demo, we create a mashup that gathers data from different kinds of data sources such as Excel spreadsheet, Google News website, CSV file and a database in an emergency management scenario.
Video | Project Paper
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Generating plans to automatically integrate data across sources. We can utilize various extraction techniques to extract data from a wide variety of sources. However, different sources often have different schemas, access methods, and coverage.
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To exploit the road network in raster maps, the first step is to extract the pixels that constitute the roads and then vectorize the road pixels. Identifying colors that represent roads in raster maps for extracting road pixels is difficult.
We implemet an approach that minimizes the required user input for identifying the road colors representing the road network in a raster map.
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Creating a Mashup, a web application that integrates data from multiple web sources to provide a unique service, involves solving multiple problems, such as extracting data from multiple web sources, cleaning it, and combining it together.
Existing work relies on a widget paradigm where users address those problems during a Mashup building process by selecting, customizing, and connecting widgets together. While these systems claim that their users do not have to write a single line of code, merely abstracting programming methods into widgets has several disadvantages.
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In many scenarios, such as emergency response or ad hoc collaboration, it is critical to reduce the overhead in integrating data. Here, the goal is often to rapidly integrate ÒenoughÓ data to answer a specific question.
Ideally, one could perform the entire process interactively under one unified interface: defining extractors and wrappers for sources, creating a mediated schema, and adding schema mappings Ñ while seeing how these impact the integrated view of the data, and refining the design accordingly.
Video | Project Paper
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