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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 52380 (05)
- University: Sharif University of Technology
- Department: Electrical Engineering
- Advisor(s): Gholampour, Iman
- Abstract:
- Nowadays, using different types of data has shown significant impacts on analyzing the related systems. Growth in data volume, systems complexity and existence of error and obscurity in collecting the data, increased the necessity of inventing new data analysis methods. Location-based data is an important data type for such analyses which are collected from sensors in different places. These data besides other official organization's information like municipality or Google … provide us a bulk volume of raw data. Such collections of raw data are mostly diverse, heterogeneous, bulk and outspread. Inspite of that, raw data with machine learning algorithms lead to considerable practical results. For example, analyzing Google maps using image processing and transferring the data from image to maps, produces invaluable traffic information. By using software platforms like Spark or Knime along with other programming languages such as Python and R, we have analyzed such data and extracted various results. To summarize, the goals of processing such data are: evaluation, classification and comparing variuous conditions and events, anomaly detection, finding appropriate locations for measurements, and predicting the future. Detecting important patterns in data, determining the correlation between the measurements in different points, and optimation placement of IoT devices are amongst the results gained from our analyses. Results are expandable to any system which is related to location-based measurements. A vast set of applications is imaginable for the methods proposed in this thesis, from IoT placement and data analysis problems to smart cities and evaluating distributed control systems
- Keywords:
- Internet of Things ; Data Mining ; Machine Learning
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