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Initially I had been working on semantics of programming languages and later on merging algorithms. Later, I proposed two theoretically as well as practically significant in-place merging algorithms. First, there is the SymMerge algorithm originating from 2004 that is now already used within the libraries of the Go programming language for in-place merging. Second, there is the Block-Rotation-Merge algorithm from 2008, which represented the first working fully optimal in-place merging algorithm. There is a nice presentation of this algorithm in Wikipedia: https://en.wikipedia.org/wiki/Block_sort
In late 2013 I met Klaus Heese from the Graduate School of Biomedical Science & Engineering and we decided to join our forces. I changed my research focus to bioinformatics and we established a prolific interdisciplinary research group, where Klaus is focused on biology and genetics and I look into the computational aspects of genetics. In the context of our joined work we realized the tremendous significance of knowledge about the evolutionary development of genes. As result I initiated the project “Taxonomy Driven Gene Prediction”, which is supported by the National Research Foundation of Korea. Aside of the work on gene prediction we have been working on tools for cancer-data analysis and visualization. Here we collect data from several publicly available data-sources and post-process them for storage in a local database that is in turn used for analysis and visualization. Further, we have been working on clustering of genes within a taxonomic context. Using such a clustering it is possible to get an impression of the evolutionary behavior of a single gene or class of genes.
The following keywords can be associated to the above research focus:
Computational Genetics, Gene Prediction, Taxonomies, Big Data Analysis, Algorithms and Complexity
November 2013. The day when the guys from TV5 québec canada visited us on campus …
Old stuff …
In-place Merging Algorithm Benchmarking Tool
My former research interest was into merging algorithms and their complexity. In the context of this research I developed prototype implementations of several algorithms and used these prototypes for benchmarking. Below you can download a small Visual C++ project that comprises the code used for benchmarking. Some Notes:
- The zip file contains a Visual C++ 2008 project. However, the C++ code should be easily portable to other platforms/compilers as e.g. gcc.
- I created this project for getting an impression of the run-time behavior of my published in-place merging algorithms. Any use of this code in productive environments is, at least currently, not advisable.
- There comes a small documentation with the benchmarking tool. Some LNCS papers on algorithms implemented as part of the tool can be found here.
- A comprehensive description of the fully optimal in-place algorithms can be found here.
Download (Version 0.2 – 2011/08/03):
- ZIP-archive with Visual C++ 2008 project: benchmarking-v0.2.zip