Database can accommodate a very large number of users on an on-demand basis. The main limitations with conventional relational database management systems (RDBMS) are that they are hard to scale with Data warehousing, Grid, Web 2.0 and Cloud applications, have non-linear query execution time, have unstable query plans and have static schema. Even though RDBMS's have provided database users with the best mix of simplicity, robustness, flexibility, performance, scalability and compatibility but they are not able to satisfy the present day users and applications for the reasons mentioned above. The next generation NonSQL (NoSQL) databases are mostly non-relational, distributed and horizontally scalable and are able to satisfy most of the needs of the present day applications. The main characteristics of these databases are schema-free, no join, nonrelational, easy replication support, simple API and eventually consistent. The aim of this paper is to illustrate how a problem being solved using MySQL will perform when MongoDB is used on a Big data dataset. The results are encouraging and clearly showcase the comparisons made. Queries are executed on a big data airlines database using both MongoDB and MySQL. Select, update, delete and insert queries are executed and performance is evaluated.

No abstract

No abstract

No abstract

Emotion detection using facial images is a technique that researchers have been using for the last two decades to try to analyze a person's emotional state given his/her image. Detection of various kinds of emotion using facial expressions of students in educational environment is useful in providing insight into the effectiveness of tutoring sessions. Robust recognition with conventional 2D cameras is still not possible under realistic conditions, ie, in the presence of variation in lighting and illumination and pose variations of the participants. This paper discusses in brief how facial expression recognition from 3D models could be achieved for applications under various educational environments. 3D Face Model dataset for various facial expressions of about 95 undergraduate students of the Department of Information Science and Engineering was collected. The new dataset produced comprise 3D models of emotions represented by five groups of facial expressions, namely: normal, happy, sad, surprised and angry, which are mainly seen among students during their college days. These data were collected using a Kinect camera, which also gives the depth map for the facial expressions. The dataset collected is a point cloud. The Kinect camera measures a large number of points on the face of the person and this output is stored as a point cloud data (PCD) file. The point cloud thus represents the sets of points that the camera has measured. The data are stored in the PCD file format, which supports the 3D point cloud data, and which can be viewed using any point cloud library (PCL) viewer. IntroductionThe face, body, posture and action contribute in conveying the emotional state of the individual. These are treated as nonlinguistic symbols, which compliment the linguistic style of the person.