@unpublished{Nassourou2011,
  author    = {Nassourou, Mohamadou},
  title     = {Assisting Analysis and Understanding of Quran Search Results with Interactive Scatter Plots and Tables},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-55840},
  year      = {2011},
  abstract  = {The Quran is the holy book of Islam consisting of 6236 verses divided into 114 chapters called suras. Many verses are similar and even identical. Searching for similar texts (e.g verses) could return thousands of verses, that when displayed completely or partly as textual list would make analysis and understanding difficult and confusing. Moreover it would be visually impossible to instantly figure out the overall distribution of the retrieved verses in the Quran. As consequence reading and analyzing the verses would be tedious and unintuitive. In this study a combination of interactive scatter plots and tables has been developed to assist analysis and understanding of the search result. Retrieved verses are clustered by chapters, and a weight is assigned to each cluster according to number of verses it contains, so that users could visually identify most relevant areas, and figure out the places of revelation of the verses. Users visualize the complete result and can select a region of the plot to zoom in, click on a marker to display a table containing verses with English translation side by side.},
  subject      = {Text Mining},
  language  = {en}
}
@unpublished{Nassourou2012,
  author    = {Nassourou, Mohamadou},
  title     = {Towards a Knowledge-Based Learning System for The Quranic Text},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-70003},
  year      = {2012},
  abstract  = {In this research, an attempt to create a knowledge-based learning system for the Quranic text has been performed. The knowledge base is made up of the Quranic text along with detailed information about each chapter and verse, and some rules. The system offers the possibility to study the Quran through web-based interfaces, implementing novel visualization techniques for browsing, querying, consulting, and testing the acquired knowledge. Additionally the system possesses knowledge acquisition facilities for maintaining the knowledge base.},
  subject      = {Wissensbanksystem},
  language  = {en}
}
@unpublished{Nassourou2011,
  author    = {Nassourou, Mohamadou},
  title     = {Philosophical and Computational Approaches for Estimating and Visualizing Months of Revelations of Quranic Chapters},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-65784},
  year      = {2011},
  abstract  = {The question of why the Quran structure does not follow its chronology of revelation is a recurring one. Some Islamic scholars such as [1] have answered the question using hadiths, as well as other philosophical reasons based on internal evidences of the Quran itself. Unfortunately till today many are still wondering about this issue. Muslims believe that the Quran is a summary and a copy of the content of a preserved tablet called Lawhul-Mahfuz located in the heaven. Logically speaking, this suggests that the arrangement of the verses and chapters is expected to be similar to that of the Lawhul-Mahfuz. As for the arrangement of the verses in each chapter, there is unanimity that it was carried out by the Prophet himself under the guidance of Angel Gabriel with the recommendation of God. But concerning the ordering of the chapters, there are reports about some divergences [3] among the Prophet's companions as to which chapter should precede which one. This paper argues that Quranic chapters might have been arranged according to months and seasons of revelation. In fact, based on some verses of the Quran, it is defendable that the Lawhul-Mahfuz itself is understood to have been structured in terms of the months of the year. In this study, philosophical and mathematical arguments for computing chapters' months of revelation are discussed, and the result is displayed on an interactive scatter plot.},
  subject      = {Text Mining},
  language  = {en}
}
@article{SchmidSchindelinCardonaetal.2010,
  author    = {Schmid, Benjamin and Schindelin, Johannes and Cardona, Albert and Longair, Martin and Heisenberg, Martin},
  title     = {A high-level 3D visualization API for Java and ImageJ},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-67851},
  year      = {2010},
  abstract  = {Background: Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Results: Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. Conclusions: Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de.},
  subject      = {Visualisierung},
  language  = {en}
}
@phdthesis{Atzmueller2006,
  author    = {Atzm{\"u}ller, Martin},
  title     = {Knowledge-Intensive Subgroup Mining - Techniques for Automatic and Interactive Discovery},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-21004},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2006},
  abstract  = {Data mining has proved its significance in various domains and applications. As an important subfield of the general data mining task, subgroup mining can be used, e.g., for marketing purposes in business domains, or for quality profiling and analysis in medical domains. The goal is to efficiently discover novel, potentially useful and ultimately interesting knowledge. However, in real-world situations these requirements often cannot be fulfilled, e.g., if the applied methods do not scale for large data sets, if too many results are presented to the user, or if many of the discovered patterns are already known to the user. This thesis proposes a combination of several techniques in order to cope with the sketched problems: We discuss automatic methods, including heuristic and exhaustive approaches, and especially present the novel SD-Map algorithm for exhaustive subgroup discovery that is fast and effective. For an interactive approach we describe techniques for subgroup introspection and analysis, and we present advanced visualization methods, e.g., the zoomtable that directly shows the most important parameters of a subgroup and that can be used for optimization and exploration. We also describe various visualizations for subgroup comparison and evaluation in order to support the user during these essential steps. Furthermore, we propose to include possibly available background knowledge that is easy to formalize into the mining process. We can utilize the knowledge in many ways: To focus the search process, to restrict the search space, and ultimately to increase the efficiency of the discovery method. We especially present background knowledge to be applied for filtering the elements of the problem domain, for constructing abstractions, for aggregating values of attributes, and for the post-processing of the discovered set of patterns. Finally, the techniques are combined into a knowledge-intensive process supporting both automatic and interactive methods for subgroup mining. The practical significance of the proposed approach strongly depends on the available tools. We introduce the VIKAMINE system as a highly-integrated environment for knowledge-intensive active subgroup mining. Also, we present an evaluation consisting of two parts: With respect to objective evaluation criteria, i.e., comparing the efficiency and the effectiveness of the subgroup discovery methods, we provide an experimental evaluation using generated data. For that task we present a novel data generator that allows a simple and intuitive specification of the data characteristics. The results of the experimental evaluation indicate that the novel SD-Map method outperforms the other described algorithms using data sets similar to the intended application concerning the efficiency, and also with respect to precision and recall for the heuristic methods. Subjective evaluation criteria include the user acceptance, the benefit of the approach, and the interestingness of the results. We present five case studies utilizing the presented techniques: The approach has been successfully implemented in medical and technical applications using real-world data sets. The method was very well accepted by the users that were able to discover novel, useful, and interesting knowledge.},
  subject      = {Data Mining},
  language  = {en}
}