Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining

An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithmsFrequent Pattern Mining, Parallel Computing, Dynamic Load Balancing, Temporal Data Mining, CUDA, GPU, Fermi, Thread

Improving Software Performance in the Compute Unified Device Architecture

This paper analyzes several aspects regarding the improvement of software performance for applications written in the Compute Unified Device Architecture CUDA). We address an issue of great importance when programming a CUDA application: the Graphics Processing Unit’s (GPU’s) memory management through ranspose ernels. We also benchmark and evaluate the performance for progressively optimizing a transposing matrix application in CUDA. One particular interest was to research how well the optimization techniques, applied to software application written in CUDA, scale to the latest generation of general-purpose graphic processors units (GPGPU), like the Fermi architecture implemented in the GTX480 and the previous architecture implemented in GTX280. Lately, there has been a lot of interest in the literature for this type of optimization analysis, but none of the works so far (to our best knowledge) tried to validate if the optimizations can apply to a GPU from the latest Fermi architecture and how well does the Fermi architecture scale to these software performance improving techniques.Compute Unified Device Architecture, Fermi Architecture, Naive Transpose, Coalesced Transpose, Shared Memory Copy, Loop in Kernel, Loop over Kernel

Optimization Solutions for Improving the Performance of the Parallel Reduction Algorithm Using Graphics Processing Units

In this paper, we research, analyze and develop optimization solutions for the parallel reduction function using graphics processing units (GPUs) that implement the Compute Unified Device Architecture (CUDA), a modern and novel approach for improving the software performance of data processing applications and algorithms. Many of these applications and algorithms make use of the reduction function in their computational steps. After having designed the function and its algorithmic steps in CUDA, we have progressively developed and implemented optimization solutions for the reduction function. In order to confirm, test and evaluate the solutions' efficiency, we have developed a custom tailored benchmark suite. We have analyzed the obtained experimental results regarding: the comparison of the execution time and bandwidth when using graphic processing units covering the main CUDA architectures (Tesla GT200, Fermi GF100, Kepler GK104) and a central processing unit; the data type influence; the binary operator's influence

Algorithms for Extracting Frequent Episodes in the Process of Temporal Data Mining

An important aspect in the data mining process is the discovery of patterns having a great influence on the studied problem. The purpose of this paper is to study the frequent episodes data mining through the use of parallel pattern discovery algorithms. Parallel pattern discovery algorithms offer better performance and scalability, so they are of a great interest for the data mining research community. In the following, there will be highlighted some parallel and distributed frequent pattern mining algorithms on various platforms and it will also be presented a comparative study of their main features. The study takes into account the new possibilities that arise along with the emerging novel Compute Unified Device Architecture from the latest generation of graphics processing units. Based on their high performance, low cost and the increasing number of features offered, GPU processors are viable solutions for an optimal implementation of frequent pattern mining algorithm

Improving Software Performance in the Compute Unified Device Architecture

This paper analyzes several aspects regarding the improvement of software performance for applications written in the Compute Unified Device Architecture CUDA). We address an issue of great importance when programming a CUDA application: the Graphics Processing Unit’s (GPU’s) memory management through ranspose ernels. We also benchmark and evaluate the performance for progressively optimizing a transposing matrix application in CUDA. One particular interest was to research how well the optimization techniques, applied to software application written in CUDA, scale to the latest generation of general-purpose graphic processors units (GPGPU), like the Fermi architecture implemented in the GTX480 and the previous architecture implemented in GTX280. Lately, there has been a lot of interest in the literature for this type of optimization analysis, but none of the works so far (to our best knowledge) tried to validate if the optimizations can apply to a GPU from the latest Fermi architecture and how well does the Fermi architecture scale to these software performance improving techniques

Making history and overcoming challenges: The career pathways and career advancement experiences of female provosts in the California State University system

The California State University (CSU) system is the largest public university system in United Sates. In 2014, female student population was 56% and 42% were male. Overall, there are higher percentage of female students than male students in the CSU system, yet there are only 10 female Provosts in the CSU system. The purpose of this qualitative study was to explore and describe the career journeys of women Provosts in the CSU system in order to learn more about: (a) what factors have motivated these women to executive leadership roles in the CSU system, (b) their career pathways, (c) any obstacles they may have encountered and addressed on their career pathways, and, (d) any mentoring support they may have received on their career pathways. This study described the career journeys of seven female CSU Provosts. This study was a qualitative portraiture design. Interviews consisting of 16 questions were conducted in person or over the phone. Nine themes emerged from the analysis of the interview. The themes were prior leadership experience, the mission of the CSU system, traditional and non-traditional career pathways, being female, balancing family and career, gender-based obstacles, formal and informal mentoring, and lastly female mentoring. This study had four conclusions. First, CSU women Provosts concluded that the CSU mission and vision motivated women to their current role and the connection to the system. Second, the CSU system supports both traditional and non-traditional pathways to the Provost position. Third, like other women leaders, CSU Provost continue to face challenges in their executive career pathways. The main conclusion for the challenges was balancing family and career, in addition to gender-based obstacles. Finally, the study concluded that women CSU Provosts had role models and different styles of mentorship throughout their education and career pathway towards leadership roles

Servo-Controlled 5-Axis 3D Printer from an Open-Source Kit

3D printers can serve as a remarkable tool for rapid prototyping of items due to its relatively low cost of materials and operation, which makes it more accessible to the public, and its being easier to use to print objects. However, some designs cannot easily be printed due to overhanging parts and thus require support structures to be printed. This leads to wasted material and more time spent due to having to print these support structures. Thus, as a solution to this problem, this paper proposes a 5-axis 3D printer design that uses servomotors to give 3-axis 3D printers the additional rotational axes, which can lessen printing time and can print overhanging structures without any additional support. From one test print that used 29 grams of material with support, the trials show a reduction of 7 grams of material using the 5-axis system. However, the print time increased by 1 h and 35 min due to the slow z movement of the printer