Knowledge discovery from sensor data (SensorKDD)
نویسندگان
چکیده
منابع مشابه
Knowledge Discovery from Sensor Data For Scientific Applications
Wireless sensor networks, in-situ sensor infrastructures and remote sensors offer new opportunities for pervasive monitoring of the built and natural environments. The ability to generate actionable predictive insights from raw sensor data is critical for many scientific applications of high societal priority. One example is sensor-based early warning systems for geophysical extremes like tsuna...
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Evolving threat situations in a post 9/11 world demand faster and more reliable decisions to thwart the adversary. One critical path to enhanced threat cognizance is through online knowledge discovery based on dynamic, heterogeneous data available from strategically placed wide-area sensor networks. The knowledge discovery process needs to coordinate adaptive predictive analysis with real-time ...
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(KDD) field draws on findings from statistics, databases, and artificial intelligence to construct tools that let users gain insight from massive data sets. People in business, science, medicine, academia, and government collect such data sets, and several commercial packages now offer general-purpose KDD tools. An important KDD goal is to “turn data into knowledge.” For example, knowledge acqu...
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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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Linked Data has been increasing rapidly by publishing machine readable structured data. DBpedia and YAGO are cross-domain data sets, which provide semantic knowledge of things. Although both data sets contain millions of entities, there are still missing knowledge exist in each data set. In this paper, we analyze graph patterns of Linked Data entities to discover missing knowledge in the data s...
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ژورنال
عنوان ژورنال: ACM SIGKDD Explorations Newsletter
سال: 2008
ISSN: 1931-0145,1931-0153
DOI: 10.1145/1540276.1540297