Capacity forecasting is a common feature in popular storage systems. The storage system vendors use prediction methods based on the historical usage for forecasting future utilization. However, we observed that such methods do not perform well under situations when the storage usage may not follow the historical trend. To address the problem, we propose a prediction method based on the data observed over 18,000 storage system. An analysis of a large scale dataset allows us to identify the popular trends across the storage systems at various stages of their lifecycle and further improve the prediction. Our evaluation shows that using the population data reduces the prediction error by 45% over linear regression.

We study the indoor positioning problem by using multiple beacons inside a building and transforming the problem into a binary classification problem in which a person is detected if he or she is working nearby one beacon within a radius d > 0. By designing our own beacons and implementing a mobile application that can communicate with each beacon via Bluetooth Low Energy (BLE), we extract meaningful features from signals of beacons during a time window and generate training and testing datasets in order to learn an appropriate classifier which can achieve as high performance as possible. We consider six classification methods including linear SVM, logistic regression, random forest, decision tree, XGBoost, and neural networks for training the binary classification model and measuring the corresponding performance by precision, recall, F1-score, and AUC. The experimental results show that using random forest with a distance pre-filter can obtain the highest performance in terms of both F1-score and AUC. The outcome of the paper can give an additional contribution to the indoor positioning problem, especially in the inspection industry where the proposed algorithms can be applied for verifying if an inspector is working in the right factory and nearby important places inside the factory. It can also reduce the risk of fraudulent behaviors in important inspections.

Handheld and embedded devices are becoming increasingly popular and their uses are more versatile. Applications on these devices often need storing, retrieving and synchronizing of data. IBM DB2 Everyplace is a high performance small footprint DBMS that is targeted at this market segment. The authors describe the overall architecture, features and several design considerations.