Performance evaluation of an RDB and an ORDB: A comparative study using the BUCKY benchmark

This paper highlights the functionality of object-based database systems by comparing the performance of relational database (RDB) and object-relational database (ORDB) systems. The study focuses on assessing the efficiency of database systems based on query processing and object complexity. We conducted an experiment that includes running the queries on the RDB and ORDB that were used in the BUCKY benchmark and implemented on Oracle 11g. The findings of this research show that the performance of both database systems depends on various factors, such as the size and type of databases, the schema and query structures, the number of tuples scanned in tables, indexes as well as the environment, in which the experiment was carried out

Zero-Shot Human Activity Recognition Using Non-Visual Sensors

Due to significant advances in sensor technology, studies towards activity recognition have gained interest and maturity in the last few years. Existing machine learning algorithms have demonstrated promising results by classifying activities whose instances have been already seen during training. Activity recognition methods based on real-life settings should cover a growing number of activities in various domains, whereby a significant part of instances will not be present in the training data set. However, to cover all possible activities in advance is a complex and expensive task. Concretely, we need a method that can extend the learning model to detect unseen activities without prior knowledge regarding sensor readings about those previously unseen activities. In this paper, we introduce an approach to leverage sensor data in discovering new unseen activities which were not present in the training set. We show that sensor readings can lead to promising results for zero-shot learning, whereby the necessary knowledge can be transferred from seen to unseen activities by using semantic similarity. The evaluation conducted on two data sets extracted from the well-known CASAS datasets show that the proposed zero-shot learning approach achieves a high performance in recognizing unseen (i.e., not present in the training dataset) new activities