02051nam a2200301 a 450000100080000000500110000800800410001902400350006010000200009524501000011526001840021530000150039949000610041450000960047552009090057165000180148065000190149865000250151765000220154265300300156465300190159465300250161365300220163870000240166070000210168470000230170570000210172820323222020-01-07       2015    bl uuuu     u00u1 u    #d7 a10.1007/978-3-319-22348-32DOI1 aCHINO, D. Y. T.  aDiscovering frequent patterns on agrometeorological data with TrieMotif.h[electronic resource]  aIn: INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, 16., 2014, Lisbon. Enterprise information systems: ICEIS 2014: revised selected papers. Switzerland: Springerc2015  ap. 91-107.  a(Lecture notes in business information processing, 227).  aEditores: José Cordeiro, Slimane Hammoudi, Leszek Maciaszek, Olivier Camp, Joaquim Filipe.  aThe "food safety" issue has concerned governments from several countries. The accurate monitoring of agriculture have become important specially due to climate change impacts. In this context, the development of new technologies for monitoring are crucial. Finding previously unknown patterns that frequently occur on time series, known as motifs, is a core task to mine the collected data. In this work we present a method that allows a fast and accurate time series motif discovery. From the experiments we can see that our approach is able to efficiently find motifs even when the size of the time series goes longer. We also evaluated our method using real data time series extracted from remote sensing images regarding sugarcane crops. Our proposed method was able to find relevant patterns, as sugarcane cycles and other land covers inside the same area, which are really useful for data analysis.  aData analysis  aRemote sensing  aTime series analysis  aAnálise de dados  aDados agrometeorológicos  aFrequent motif  aRemote sensing image  aSéries temporais1 aGONCALVES, R. R. V.1 aROMANI, L. A. S.1 aTRAINA JÚNIOR, C.1 aTRAINA, A. J. M.