Auflistung nach Autor:in "Zhang, Ying"
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- ZeitschriftenartikelGenome sequence analysis with MonetDB(Datenbank-Spektrum: Vol. 15, No. 3, 2015) Cijvat, Robin; Manegold, Stefan; Kersten, Martin; Klau, Gunnar W.; Schönhuth, Alexander; Marschall, Tobias; Zhang, YingNext-generation sequencing (NGS) technology has led the life sciences into the big data era. Today, sequencing genomes takes little time and cost, but yields terabytes of data to be stored and analyzed. Biologists are often exposed to excessively time consuming and error-prone data management and analysis hurdles. In this paper, we propose a database management system (DBMS) based approach to accelerate and substantially simplify genome sequence analysis. We have extended MonetDB, an open-source column-based DBMS, with a BAM module, which enables easy, flexible, and rapid management and analysis of sequence alignment data stored as Sequence Alignment/Map (SAM/BAM) files. We describe the main features of MonetDB/BAM using a case study on Ebola virusgenomes.
- KonferenzbeitragGenome sequence analysis with monetdb: a case study on ebola virus diversity(Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2015) Cijvat, Robin; Manegold, Stefan; Kersten, Martin; Klau, Gunnar W.; Schönhuth, Alexander; Marschall, Tobias; Zhang, YingNext-generation sequencing (NGS) technology has led the life sciences into the big data era. Today, sequencing genomes takes little time and cost, but results in terabytes of data to be stored and analysed. Biologists are often exposed to excessively time consuming and error-prone data management and analysis hurdles. In this paper, we propose a database management system (DBMS) based approach to accelerate and substantially simplify genome sequence analysis. We have extended MonetDB, an open-source column-based DBMS, with a BAM module, which enables easy, flexible, and rapid management and analysis of sequence alignment data stored as Sequence Alignment/Map (SAM/BAM) files. We describe the main features of MonetDB/BAM using a case study on Ebola virus genomes.
- ZeitschriftenartikelRobust visualization of trajectory data(it - Information Technology: Vol. 64, No. 4-5, 2022) Zhang, Ying; Klein, Karsten; Deussen, Oliver; Gutschlag, Theodor; Storandt,SabineThe analysis of movement trajectories plays a central role in many application areas, such as traffic management, sports analysis, and collective behavior research, where large and complex trajectory data sets are routinely collected these days. While automated analysis methods are available to extract characteristics of trajectories such as statistics on the geometry, movement patterns, and locations that might be associated with important events, human inspection is still required to interpret the results, derive parameters for the analysis, compare trajectories and patterns, and to further interpret the impact factors that influence trajectory shapes and their underlying movement processes. Every step in the acquisition and analysis pipeline might introduce artifacts or alterate trajectory features, which might bias the human interpretation or confound the automated analysis. Thus, visualization methods as well as the visualizations themselves need to take into account the corresponding factors in order to allow sound interpretation without adding or removing important trajectory features or putting a large strain on the analyst. In this paper, we provide an overview of the challenges arising in robust trajectory visualization tasks. We then discuss several methods that contribute to improved visualizations. In particular, we present practical algorithms for simplifying trajectory sets that take semantic and uncertainty information directly into account. Furthermore, we describe a complementary approach that allows to visualize the uncertainty along with the trajectories.