I will present an algorithm that tries to handle reflection accurately in the general case for real-time rendering. The algorithm uses a database of prerendered environment maps to render both the original object itself and an additional bidirectional reflection distribution function (BRDF). The algorithm performs image-based rendering in reflection space in order to achieve accurate results. It also uses graphics processing unit (GPU) features to accelerate rendering.

XBench is a family of XML benchmarks which recognizes that the XML data that DBMSs manage are quite varied and no one database schema and workload can properly capture this variety. Thus, the members of this benchmark family have been defined for capturing diverse application domains. In this report we briefly discuss the XBench XML benchmark and report on the relative performance of three commercial DBMSs: X-Hive, DB2 and SQL Server. We also describe the potential issues when storing XML documents into relational DBMSs.

In this thesis, we want to support set-at-a-time extensions for XML through DOM. Through extending some powerful functions to get summary information from different groups of related document elements, and filter, extract and transform this information as a sequence of nodes, the extended DOM can reduce the communications overhead and response time between the client and the server and therefore provide applications
with more convenience. Our work is to explore the ideas for set-at-a-time processing, define and implement some suitable methods, and code some query application examples query using the DOM and our extensions to make comparisons through a test system. Finally, future work will be given.

In this paper, we look at the problem of traffic prediction in the presence of self-similarity. We briefly describe a number of short-memory and long-memory stochastic traffic models and talk about non-model-based predictors, particularly minimum mean square error and its normalized version. Numerical results of our experimental comparison between the so-called fractional predictors and the simple minimum mean square error predictor show that this simple method can achieve accuracy within $5\%$ of the best fractional predictor while it is much simpler than any model-based predictor and is easily used in an on-line fashion.