Aziz Nasridinov

A two-phase data space partitioning for efficient skyline computation

The skyline has attracted a lot of attention due to its wide application in various fields. However, the skyline computation is a challenging issue as there is a high probability that today’s applications deal with large and high-dimensional data. As skyline computation for such huge amount of data consumes much time, parallel and distributed skyline computations are considered. State-of-the-art methods for parallel and distributed skyline computations use various data space partitioning techniques. However, these methods are not efficient, as in certain cases, these methods perform unnecessary skyline computations in a partitioned space, where local-skyline tuples do not contribute to the global-skyline. This may impose additional processing overload and enlarge the overall skyline computation time. In this paper, we propose a novel data space partitioning method for parallel and distributed skyline computation that consists of two-phases: diagonal and entropy score curve based partitioning. The proposed method produces a small set of local-skyline tuples and leads to a more sophisticated merging step. The experiment results demonstrate that the proposed method reduces the number of comparisons and processing time of skyline computation in large amount of data when compared with the existing state-of-the-art methods.

Adaptive convex skyline: a threshold-based project partitioned layer-based index for efficient-processing top-k queries in entrepreneurship applications

Many entrepreneurship applications use data as the core concept of their business to better understand the needs of their customers. However, as the size of databases used by these entrepreneurship applications grows and as more users access data through various interactive interfaces, obtaining the result for a top-k query may take long time if the query matches millions of the tuples in the database. Traditionally, layer-based indexing methods are representative for processing top-k queries efficiently. These methods form tuples into a list of layers where the ith layer holds the tuples that can be the top-i answer. Layer-based indexing methods enable us to obtain top-k answers by accessing at most k layers. Most of these methods achieve high accuracy of query answer at the expense of enlarged index construction time. However, we can adjust between accuracy and index construction time to achieve an optimal performance. Thus in this paper, we propose a method, called the adaptive convex skyline (AdaptCS) for efficient-processing top-k queries in entrepreneurship applications. AdaptCS first prunes the data with a virtual threshold point and finds skyline points over the pruned data. Here, by adjusting virtual threshold we are able to achieve optimal performance. Then, AdaptCS divides the skyline into m subregions with projection partitioning method and constructs the convex hull m times for each subregion with virtual objects. Lastly, AdaptCS combines the objects obtained by computing the convex hull. The experimental results show that the proposed method outperforms the existing methods.

A ballet posture education using IT techniques: a comparative study

Recently, there were numerous ballet posture education systems using various IT techniques. We can divide these systems into three categories, such as wearable devices based systems; Kinect based systems; and other systems. Typically, these systems have a high accuracy of joint recognition. However, in certain cases, they outputs erroneous joint position. In this paper, we first provide a detailed overview of basic ballet postures and movements, discuss the state-of-the-art ballet posture education systems and then describe their main features, advantages and drawbacks.

AA-DBSCAN: an approximate adaptive DBSCAN for finding clusters with varying densities

Clustering is a typical data mining technique that partitions a dataset into multiple subsets of similar objects according to similarity metrics. In particular, density-based algorithms can find clusters of different shapes and sizes while remaining robust to noise objects. DBSCAN, a representative density-based algorithm, finds clusters by defining the density criterion with global parameters,

Chentry: Automated Evaluation of Students’ Learning Progress for Entry Education Software

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Design of SaaS based health care system for disease detection and prediction

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A Statistical Correlation Analysis on Road Accidents in South Korea

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