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In today's technology-laden society human -computer interaction (HCI) is an important knowledge area for computer scientists and software engineers. This paper surveys existing approaches to incorporate HCI into computer science (CS) and such related issues as the perceived gap between the interests of the HCI community and the needs of CS educators. It presents several implementations of the HCI subset of the CC 0 01 curricular guidelines, targeting CS educators with varying degrees of HCI expertise. These implementations include course/module outlines from freshman to graduate levels, suggested texts, and project ideas and issues, such as programming languages and environments. Most importantly, each outline incorporates Bloom's taxonomy to identify the depth of knowledge to be mastered by students. This paper condenses collaborative contributions of 26 HCI/CS educators aiming to improve HCI coverage in mainstream CS curricula.

Abstract-In this study, a clustering algorithm that uses DCT transformed data is presented. The algorithm is a grid density-based clustering algorithm that can identify clusters of arbitrary shape. Streaming data are transformed and reconstructed as needed for clustering. Experimental results show that DCT is able to approximate a data distribution efficiently using only a small number of coefficients and preserve the clusters well. The grid based clustering algorithm works well with DCT transformed data, demonstrating the viability of DCT for data stream clustering applications.

In this article; the authors propose to use the grid file to store multi-dimensional data cubes and answer angesum queries. The grid file is enhanced with a dynamic splitting mechanism to accommodate insertions of data. It overcomes the drawback of the traditional grid file in storing uneven data while enjoying its advantages of simplicity and efficiency. The space requirement grows linearly with the dimension of the data cube; compared with the exponential growth of conventional methods that store pre-computed aggregate values for range-sum queries. The update cost is O (1); much faster than the pre-computed data cube approaches; which generally have exponential update cost. The grid file structure can also respond to range queries quickly. They compare it with an approach that uses the R*-tree structure to store the data cube. The experimental results show that the proposed method performs favorably in file size; update speed; construction time; and query response time for both evenly and unevenly distributed data.

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