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Streaming data applications are becoming more common due to the ability ofdifferent information sources to continuously capture or produce data, such assensors and social media. Despite recent advances, most visualizationapproaches, in particular, multidimensional projection or dimensionalityreduction techniques, cannot be directly applied in such scenarios due to thetransient nature of streaming data. Currently, only a few methods address thislimitation using online or incremental strategies, continuously processingdata, and updating the visualization. Despite their relative success, most ofthem impose the need for storing and accessing the data multiple times, notbeing appropriate for streaming where data continuously grow. Others do notimpose such requirements but are not capable of updating the position of thedata already projected, potentially resulting in visual artifacts. In thispaper, we present Xtreaming, a novel incremental projection technique thatcontinuously updates the visual representation to reflect new emergingstructures or patterns without visiting the multidimensional data more thanonce. Our tests show that Xtreaming is competitive in terms of global distancepreservation if compared to other streaming and incremental techniques, but itis orders of magnitude faster. To the best of our knowledge, it is the firstmethodology that is capable of evolving a projection to faithfully representnew emerging structures without the need to store all data, providing reliableresults for efficiently and effectively projecting streaming data.

Xtreaming: an incremental multidimensional projection technique and its application to streaming data