Hai T. Mai
ABSTRACT
Recently, the world gets more and more distributed, big data now not only come from websites as Google, Bing, Yahoo having now or social networking service as Facebook etc.; there are sensors everywhere reporting millions of data each second. Among all the types of big data, data from sensors which is the most widespread is referred as time-series data. There are many attempts have been taken to recognize or retrieve the pattern of time-series such as recommender system, machine learning with pattern recognition and classification but all of them are push model. Once the expected patterns change, the whole system must be trained again it is great pain and it takes a huge of time. In other words, the existing systems cannot support dynamic expected patterns for retrieving the information. This paper proposes a novel pattern search engine for time-series which allows us to use any expected pattern or the combination of them as a query for searching information in a very short period of time without being trained or indexed again.
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