Güth Philipp (19 results)

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Seminar paper from the year 2013 in the subject Engineering - Computer Engineering, grade: 1.0, University of Heidelberg (Computer Graphics and Visualization), course: Seminar Computer Vision, language: English, abstract: Visualization of large data sets, especially the visualization of unstructured grids, is a challenge due to the unstructured nature of the data which oftentimes causes large overheads in memory as well as performance problems on large grids. Problems emerge because existing solutions generally presuppose properties like uniform point distributions for datasets which are usually not existent in unstructured grids. These issues become particularly problematic on large grids since the existing solutions, if they work at all for unstructured grids, do not scale well. In this paper I will present two innovative approaches to visualization in large, unstructured grids. The first approach was developed by Max Langbein, Gerik Scheuermann and Xavier Tricoche. It makes use of cell adjacency and a complete adaptive k-d tree and utilizes ray shooting to locate points for visualization. The second approach was developed by Christoph Garth and Kenneth I. Joy. They use an innovative data structure, the celltree which is based on a bounding interval hierarchy, in order to narrow down the number of cells that conceivably contain points for visualization. Both approaches present memoryecient and performant solutions for visualizing large unstructured grids, the approach of Garth and Joy further focuses on numerical robustness. The main difference between the two papers is that the work of Garth and Joy designs a data structure based on points and attempts to narrow down the number of cell candidates and subsequently performs a simple check for inclusion, whereas in the work of Langbein et al. the data structure design is based on the cells and uses ray tracing after making an educated guess for a cell close to the searched point. In other words, Garth and Joy present an approach to cell location, Langbein et al. present an approach for point location.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Seminar paper from the year 2014 in the subject Communications - Public Relations, Advertising, Marketing, Social Media, grade: 1.3, University of Heidelberg (Computer Science), course: Seminar - Social Media Network Analysis, language: English, abstract: With a predicted volume of EUR439.7Bn in 2014 in Germany alone, the retail market bears large potential for generating additional revenues from marketing. With the decreasing effectiveness of classical marketing and even relatively new phenomena like online ads it becomes more and more important to find new ways to recommend products to customers. In e-commerce it is generally easier to target specific audiences by for example selecting ad spaces according to thematically fitting web pages.The fundamental difference to classical marketing approaches is the availability of data about the respective customer. Currently the most common approach is to mine frequent item sets from the purchase history of the customer population and recommend products to customers based on what other customers bought. In order to obtain more specific product predictions for a particular customer, more data about the respective customer is needed. It seems like a natural choice to dig for data in the rich pool of data generated by each customer himself by assessing their respective actions and content generated, especially on social media websites. The available data there is much more user specific than general purchasing behaviors of user groups and can potentially lead to very precise predictions about what the user is interested in and will buy.This paper first gives a brief overview over the development and research conducted on social media recommendation and behavior of online shoppers in general. Then the work of Y. Zhang and M. Pennacchiotti is presented. Finally, several possibilities for subsequent research based on previous work and the work of Zhang and Pennacchiotti are presented. Since the work presented in this paper is very foundational, some emphasis is put on the outlook in order to underline the relevance of Zhang's and Pennacchiotti's work.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Bachelor Thesis from the year 2015 in the subject Computer Science - Applied, grade: 1.0, University of Heidelberg (Computer Science), language: English, abstract: Determining point similarity is a cornerstone for a wide variety of applications. Whenever data is being compared, the problem can be stated as a matter of comparing vectors. Dueto this fundamental importance, research has been conducted in this area for decades. For a long time, even for relatively low dimensions such as for example d = 10, complexity was far beyond of what was considered to be feasible in practice. A common approach is to soften the requirements for the accuracy of the neighbor search and look for points within a certain proximity to the query point instead of searching for exact neighbors.In this thesis we evaluate how well the SRS-12 algorithm works on real-world image data in order to lay the ground for future work such as image prediction. The SRS-12 algorithm is an approach to c-approximate nearest-neighbors and claims to have a tiny index and arbitrary approximation ratio while maintaining good theoretical guarantees.We first implement and verify the algorithm and subsequently examine the quality of its outputs when it is applied to image data by performing block matching. We find that the SRS-12algorithm is indeed very suitable for processing image data as long as the input images are cut into patches that are sufficiently large. However the parameters of the algorithm have to be tuned carefully because they significantly affect not only the quality of the results, but also the computation time which can easily exceed an exact nearest-neighbor query if the parameters are not set properly. We conclude our experiment with a recommendation for well-working parameter settings and propose approaches to enhance the quality and speed of approximate nearest-neighbor queries on image data made with the SRS-12 algorithm such that it can be used in real-time.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Seminar paper from the year 2013 in the subject Engineering - Computer Engineering, grade: 1.0, University of Heidelberg (Computer Graphics and Visualization), course: Seminar Computer Vision, language: English, abstract: Visualization of large data sets, especially the visualization of unstructured grids, is a challenge due to the unstructured nature of the data which oftentimes causes large overheads in memory as well as performance problems on large grids. Problems emerge because existing solutions generally presuppose properties like uniform point distributions for datasets which are usually not existent in unstructured grids. These issues become particularly problematic on large grids since the existing solutions, if they work at all for unstructured grids, do not scale well. In this paper I will present two innovative approaches to visualization in large, unstructured grids. The first approach was developed by Max Langbein, Gerik Scheuermann and Xavier Tricoche. It makes use of cell adjacency and a complete adaptive k-d tree and utilizes ray shooting to locate points for visualization. The second approach was developed by Christoph Garth and Kenneth I. Joy. They use an innovative data structure, the celltree which is based on a bounding interval hierarchy, in order to narrow down the number of cells that conceivably contain points for visualization. Both approaches present memoryecient and performant solutions for visualizing large unstructured grids, the approach of Garth and Joy further focuses on numerical robustness. The main difference between the two papers is that the work of Garth and Joy designs a data structure based on points and attempts to narrow down the number of cell candidates and subsequently performs a simple check for inclusion, whereas in the work of Langbein et al. the data structure design is based on the cells and uses ray tracing after making an educated guess for a cell close to the searched point. In other words, Garth and Joy present an approach to cell location, Langbein et al. present an approach for point location. 20 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Seminar paper from the year 2014 in the subject Communications - Public Relations, Advertising, Marketing, Social Media, grade: 1.3, University of Heidelberg (Computer Science), course: Seminar - Social Media Network Analysis, language: English, abstract: With a predicted volume of EUR439.7Bn in 2014 in Germany alone, the retail market bears large potential for generating additional revenues from marketing. With the decreasing effectiveness of classical marketing and even relatively new phenomena like online ads it becomes more and more important to find new ways to recommend products to customers. In e-commerce it is generally easier to target specific audiences by for example selecting ad spaces according to thematically fitting web pages.The fundamental difference to classical marketing approaches is the availability of data about the respective customer. Currently the most common approach is to mine frequent item sets from the purchase history of the customer population and recommend products to customers based on what other customers bought. In order to obtain more specific product predictions for a particular customer, more data about the respective customer is needed. It seems like a natural choice to dig for data in the rich pool of data generated by each customer himself by assessing their respective actions and content generated, especially on social media websites. The available data there is much more user specific than general purchasing behaviors of user groups and can potentially lead to very precise predictions about what the user is interested in and will buy.This paper first gives a brief overview over the development and research conducted on social media recommendation and behavior of online shoppers in general. Then the work of Y. Zhang and M. Pennacchiotti is presented. Finally, several possibilities for subsequent research based on previous work and the work of Zhang and Pennacchiotti are presented. Since the work presented in this paper is very foundational, some emphasis is put on the outlook in order to underline the relevance of Zhang's and Pennacchiotti's work. 24 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Bachelor Thesis from the year 2015 in the subject Computer Science - Applied, grade: 1.0, University of Heidelberg (Computer Science), language: English, abstract: Determining point similarity is a cornerstone for a wide variety of applications. Whenever data is being compared, the problem can be stated as a matter of comparing vectors. Dueto this fundamental importance, research has been conducted in this area for decades. For a long time, even for relatively low dimensions such as for example d = 10, complexity was far beyond of what was considered to be feasible in practice. A common approach is to soften the requirements for the accuracy of the neighbor search and look for points within a certain proximity to the query point instead of searching for exact neighbors.In this thesis we evaluate how well the SRS-12 algorithm works on real-world image data in order to lay the ground for future work such as image prediction. The SRS-12 algorithm is an approach to c-approximate nearest-neighbors and claims to have a tiny index and arbitrary approximation ratio while maintaining good theoretical guarantees.We first implement and verify the algorithm and subsequently examine the quality of its outputs when it is applied to image data by performing block matching. We find that the SRS-12algorithm is indeed very suitable for processing image data as long as the input images are cut into patches that are sufficiently large. However the parameters of the algorithm have to be tuned carefully because they significantly affect not only the quality of the results, but also the computation time which can easily exceed an exact nearest-neighbor query if the parameters are not set properly. We conclude our experiment with a recommendation for well-working parameter settings and propose approaches to enhance the quality and speed of approximate nearest-neighbor queries on image data made with the SRS-12 algorithm such that it can be used in real-time. 48 pp. Englisch.

Condition: New. Print on Demand pp. 24.

Condition: New. Print on Demand pp. 48.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Seminar paper from the year 2013 in the subject Engineering - Computer Engineering, grade: 1.0, University of Heidelberg (Computer Graphics and Visualization), course: Seminar Computer Vision, language: English, abstract: Visualization of large data sets, especially the visualization of unstructured grids, is a challenge due to the unstructured nature of the data which oftentimes causes large overheads in memory as well as performance problems on large grids. Problems emerge because existing solutions generally presuppose properties like uniform point distributions for datasets which are usually not existent in unstructured grids. These issues become particularly problematic on large grids since the existing solutions, if they work at all for unstructured grids, do not scale well. In this paper I will present two innovative approaches to visualization in large, unstructured grids. The first approach was developed by Max Langbein, Gerik Scheuermann and Xavier Tricoche. It makes use of cell adjacency and a complete adaptive k-d tree and utilizes ray shooting to locate points for visualization. The second approach was developed by Christoph Garth and Kenneth I. Joy. They use an innovative data structure, the celltree which is based on a bounding interval hierarchy, in order to narrow down the number of cells that conceivably contain points for visualization. Both approaches present memoryecient and performant solutions for visualizing large unstructured grids, the approach of Garth and Joy further focuses on numerical robustness. The main difference between the two papers is that the work of Garth and Joy designs a data structure based on points and attempts to narrow down the number of cell candidates and subsequently performs a simple check for inclusion, whereas in the work of Langbein et al. the data structure design is based on the cells and uses ray tracing after making an educated guess for a cell close to the searched point. In other words, Garth and Joy present an approach to cell location, Langbein et al. present an approach for point location.Books on Demand GmbH, Überseering 33, 22297 Hamburg 20 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Seminar paper from the year 2014 in the subject Communications - Public Relations, Advertising, Marketing, Social Media, grade: 1.3, University of Heidelberg (Computer Science), course: Seminar - Social Media Network Analysis, language: English, abstract: With a predicted volume of ¿439.7Bn in 2014 in Germany alone, the retail market bears large potential for generating additional revenues from marketing. With the decreasing effectiveness of classical marketing and even relatively new phenomena like online ads it becomes more and more important to find new ways to recommend products to customers. In e-commerce it is generally easier to target specific audiences by for example selecting ad spaces according to thematically fitting web pages.The fundamental difference to classical marketing approaches is the availability of data about the respective customer. Currently the most common approach is to mine frequent item sets from the purchase history of the customer population and recommend products to customers based on what other customers bought. In order to obtain more specific product predictions for a particular customer, more data about the respective customer is needed. It seems like a natural choice to dig for data in the rich pool of data generated by each customer himself by assessing their respective actions and content generated, especially on social media websites. The available data there is much more user specific than general purchasing behaviors of user groups and can potentially lead to very precise predictions about what the user is interested in and will buy.This paper first gives a brief overview over the development and research conducted on social media recommendation and behavior of online shoppers in general. Then the work of Y. Zhang and M. Pennacchiotti is presented. Finally, several possibilities for subsequent research based on previous work and the work of Zhang and Pennacchiotti are presented. Since the work presented in this paper is very foundational, some emphasis is put on the outlook in order to underline the relevance of Zhang's and Pennacchiotti's work.Books on Demand GmbH, Überseering 33, 22297 Hamburg 24 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Bachelor Thesis from the year 2015 in the subject Computer Science - Applied, grade: 1.0, University of Heidelberg (Computer Science), language: English, abstract: Determining point similarity is a cornerstone for a wide variety of applications. Whenever data is being compared, the problem can be stated as a matter of comparing vectors. Dueto this fundamental importance, research has been conducted in this area for decades. For a long time, even for relatively low dimensions such as for example d = 10, complexity was far beyond of what was considered to be feasible in practice. A common approach is to soften the requirements for the accuracy of the neighbor search and look for points within a certain proximity to the query point instead of searching for exact neighbors. In this thesis we evaluate how well the SRS-12 algorithm works on real-world image data in order to lay the ground for future work such as image prediction. The SRS-12 algorithm is an approach to c-approximate nearest-neighbors and claims to have a tiny index and arbitrary approximation ratio while maintaining good theoretical guarantees. We first implement and verify the algorithm and subsequently examine the quality of its outputs when it is applied to image data by performing block matching. We find that the SRS-12 algorithm is indeed very suitable for processing image data as long as the input images are cut into patches that are sufficiently large. However the parameters of the algorithm have to be tuned carefully because they significantly affect not only the quality of the results, but also the computation time which can easily exceed an exact nearest-neighbor query if the parameters are not set properly. We conclude our experiment with a recommendation for well-working parameter settings and propose approaches to enhance the quality and speed of approximate nearest-neighbor queries on image data made with the SRS-12 algorithm such that it can be used in real-time.Books on Demand GmbH, Überseering 33, 22297 Hamburg 48 pp. Englisch.