{"id":14849,"date":"2015-07-30T12:30:20","date_gmt":"2015-07-30T16:30:20","guid":{"rendered":"http:\/\/www.montclair.edu\/news\/article.php?ArticleID=14849"},"modified":"2021-04-29T15:38:58","modified_gmt":"2021-04-29T19:38:58","slug":"14849_ship-student-selected-to-present-at-njtc-event","status":"publish","type":"post","link":"https:\/\/www.montclair.edu\/csam\/2015\/07\/30\/14849_ship-student-selected-to-present-at-njtc-event\/","title":{"rendered":"SHIP student selected to present at NJTC event"},"content":{"rendered":"
The objective of this research is two-fold. First, this research will investigate the challenges and issues with developing an application using a data science approach. Second, a specific sensor network will be developed at 精品成人福利在线 University (MSU) for collecting potentially large volumes of unstructured data that will be stored and analyzed by the application to identify patterns and trends on campus. The primary patterns I intend to investigate for this research are vehicular traffic and parking trends on campus. Sensor networks aim to collect physical and environmental data using a wide array of spatially distributed sensors. Data collected from these sensors are often unstructured and may contain text, images, and video. Conventional relational database management systems (RDBMS) were originally designed to store and analyze only structured data. Consequently, a RDBMS cannot glean the wealth of hidden information generated by sensor networks, and so this research will explore alternative methods for the analysis of unstructured data.<\/p>\n
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