A hybrid partitioning method for multimedia databases

Hybrid partitioning has been recognized as a technique to achieve query optimization in relational and object -oriented databases. Due to the increasing availability of multimedia applications, there is an interest in using partitioning techniques in multimedia da tabases in order to take advantage of the reduction in the number of pages required to answer a query and to minimize data exchange among sites. Never theless, until now only vertical and horizontal partitioning have been used in multimedia databases. This paper presents a hybrid partitioning method for multimedia databases. This method takes into account the size of the attributes and the selectivity of the predicates in order to generate hybrid partitioning schemes that reduce the execution cost of the que ries. A cost model for evaluating hybrid partitioning schemes in distributed multimedia databases was developed. Experiments in a multimedia database benchmark were performed in order to demonstrate the efficiency of our ap proach

Analysis of a Multilevel Diagnosis Decision Support System and Its Implications: A Case Study

Medical diagnosis can be performed in an automatic way with the use of computer-based systems or algorithms. Such systems are usually called diagnostic decision support systems (DDSSs) or medical diagnosis systems (MDSs). An evaluation of the performance of a DDSS called ML-DDSS has been performed in this paper. The methodology is based on clinical case resolution performed by physicians which is then used to evaluate the behavior of ML-DDSS. This methodology allows the calculation of values for several well-known metrics such as precision, recall, accuracy, specificity, and Matthews correlation coefficient (MCC). Analysis of the behavior of ML-DDSS reveals interesting results about the behavior of the system and of the physicians who took part in the evaluation process. Global results show how the ML-DDSS system would have significant utility if used in medical practice. The MCC metric reveals an improvement of about 30% in comparison with the experts, and with respect to sensitivity the system returns better results than the experts

Guest Editorial: Intelligent and Affective Learning Environments: New Trends and Challenges.

The authors reflect on the trends and challenges in intelligent and affective learning environments. They note the impact of traditional intelligent tutoring systems (ITS) on student learning which has been emerged with artificial intelligence techniques that can be helpful in human learning. The highlight the research papers included in the issue.4 Halama