Data volume is growing for each day as more and more is digitalized which puts the data management on test. The smart tools developed by Atlas Copco saves and transmits data to the cloud as a service to find errors in tightening's for their customers to review. A problem is the amount of data that is lost in this process. A tightening cycle usually contains thousands of data points and the storage space for it is too great for the tool's hardware. Today many of the data points are deleted and a small portion of scattered data of the cycle is saved and transmitted. To avoid overfilling the storage space the data need to be minimized. This study is focus on comparing data compression algorithms that could solve this problem.   In a literature study in the beginning, numerous data compression algorithms were found with their advantages and disadvantages. Two different types of compression algorithms are also defined as lossy compression, where data is compressed by losing data points or precision, and lossless compression, where no data is lost throughout the compression. Two lossy and two lossless algorithms are selected to be avaluated with respect to their compression ratio, speed and error tolerance. Poor Man's Compression - Midrange (PMC-MR) and SWING-filter are the lossy algorithms while Gorilla and Fixed-Point Compression (FPC) are the lossless ones.   The reached compression ratios, in percentage, could range from 39\% to 99\%. As combinations of a lossy and a lossless algorithm yields best compression ratios with lower error tolerance, PMC-MR with Gorilla is suggested to be the best suited for Atlas Copco's needs.

Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive. This paper proposes an anomaly detection method based on multiple streaming sensor data and performs anomaly detection on three data sets which are from the real company. First, this project proposes the state transition detection algorithm, state classification algorithm, and the correlation analysis method based on frequency. Then two algorithms were implemented in Python, and then make the correlation analysis using the results from the system to find some possible meaningful relations which can be used in the anomaly detection. Finally, calculate the accuracy and time complexity of the system, and then evaluated its feasibility and scalability. From the evaluation result, it is concluded that the method