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Some popular Internet applications such as instant message, blog, twitter and Google buzz generate huge data of short text. These data can then be summarized, mined, and queried by other applications. To this end, suitable storage design with outstanding performance must be offered to address the question of real-time full text indexing and searching. This paper studies Lucene indexing and searching...
, interests and dislikes. If this data can be extracted and analyzed effectively, useful items, news or people can be recommended. There are high number of studies that extract keywords from texts in order to obtain such information, however, micro blogs have noisy text blocks, and hence regular text extraction algorithms fail
based clustering is the most appropriate clustering algorithm. Here we used tweets from Twitter, while the DBSCAN method is used for generating clusters. Here for finding similarity between tweets cosine similarity is used, but because of its low value we increase its value by adding weight to it by matching the keywords
parse to the filtering engine for filtering and cleaning the data sources using suitable keywords prior to store it in the large data repository. After that, the large data will be processed and analyzed using algorithm or mathematical calculation to determine the expected dengue cases. Then, the processed information will
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.