Discovery of Loose Group Companion From Trajectory Data Streams
نویسندگان
چکیده
منابع مشابه
A A Framework of Traveling Companion Discovery on Trajectory Data Streams
The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data stream. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management and military surve...
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Traditional pratice in machine learning algorithms involve fixed data sets and static models. Most of the times, all the data is loaded into memory and the learning task is solved by performing multiple scans over the training data. These assumptions fail with the advent of new application areas, like ubiquitous computing, sensor networks, e-commerce, etc., where data flows continuously, eventu...
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Much of current data mining research is focused on discovering sets of attributes that discriminate data entities into classes, such as shopping trends for a particular demographic group. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is ...
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Mining data streams has raised a number of research challenges for the data mining community. These challenges include the limitations of computational resources, especially because mining streams of data most likely be done on a mobile device with limited resources. Also due to the continuality of data streams, the algorithm should have only one pass or less over the incoming data elements. In...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2992596