Approximated and User Steerable tSNE for Progressive Visual Analytics

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results. One key method for data analysis is dimensionality reduction, for example, to produce 2D embeddings that can be visualized and analyzed efficiently. t-Distributed Stochastic Neighbor Embedding (tSNE) is a well-suited technique for the visualization of several high-dimensional data. tSNE can create meaningful intermediate results but suffers from a slow initialization that constrains its application in Progressive Visual Analytics. We introduce a controllable tSNE approximation (A-tSNE), which trades off speed and accuracy, to enable interactive data exploration. We offer real-time visualization techniques, including a density-based solution and a Magic Lens to inspect the degree of approximation. With this feedback, the user can decide on local refinements and steer the approximation level during the analysis. We demonstrate our technique with several datasets, in a real-world research scenario and for the real-time analysis of high-dimensional streams to illustrate its effectiveness for interactive data analysis

A Survey on Visual Analytics of Social Media Data

The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, ha..

Doctor of Philosophy

dissertationThe embedded system space is characterized by a rapid evolution in the complexity and functionality of applications. In addition, the short time-to-market nature of the business motivates the use of programmable devices capable of meeting the conflicting constraints of low-energy, high-performance, and short design times. The keys to achieving these conflicting constraints are specialization and maximally extracting available application parallelism. General purpose processors are flexible but are either too power hungry or lack the necessary performance. Application-specific integrated circuits (ASICS) efficiently meet the performance and power needs but are inflexible. Programmable domain-specific architectures (DSAs) are an attractive middle ground, but their design requires significant time, resources, and expertise in a variety of specialties, which range from application algorithms to architecture and ultimately, circuit design. This dissertation presents CoGenE, a design framework that automates the design of energy-performance-optimal DSAs for embedded systems. For a given application domain and a user-chosen initial architectural specification, CoGenE consists of a a Compiler to generate execution binary, a simulator Generator to collect performance/energy statistics, and an Explorer that modifies the current architecture to improve energy-performance-area characteristics. The above process repeats automatically until the user-specified constraints are achieved. This removes or alleviates the time needed to understand the application, manually design the DSA, and generate object code for the DSA. Thus, CoGenE is a new design methodology that represents a significant improvement in performance, energy dissipation, design time, and resources. This dissertation employs the face recognition domain to showcase a flexible architectural design methodology that creates "ASIC-like" DSAs. The DSAs are instruction set architecture (ISA)-independent and achieve good energy-performance characteristics by coscheduling the often conflicting constraints of data access, data movement, and computation through a flexible interconnect. This represents a significant increase in programming complexity and code generation time. To address this problem, the CoGenE compiler employs integer linear programming (ILP)-based 'interconnect-aware' scheduling techniques for automatic code generation. The CoGenE explorer employs an iterative technique to search the complete design space and select a set of energy-performance-optimal candidates. When compared to manual designs, results demonstrate that CoGenE produces superior designs for three application domains: face recognition, speech recognition and wireless telephony. While CoGenE is well suited to applications that exhibit a streaming behavior, multithreaded applications like ray tracing present a different but important challenge. To demonstrate its generality, CoGenE is evaluated in designing a novel multicore N-wide SIMD architecture, known as StreamRay, for the ray tracing domain. CoGenE is used to synthesize the SIMD execution cores, the compiler that generates the application binary, and the interconnection subsystem. Further, separating address and data computations in space reduces data movement and contention for resources, thereby significantly improving performance compared to existing ray tracing approaches

Designing and evaluating the usability of a machine learning API for rapid prototyping music technology

To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable

PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that support this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.Comment: Submitted to Parallel Computing, Elsevie

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining