On-the-fly tracing for data-centric computing : parallelization, workflow and applications

As data-centric computing becomes the trend in science and engineering, more and more hardware systems, as well as middleware frameworks, are emerging to handle the intensive computations associated with big data. At the programming level, it is crucial to have corresponding programming paradigms for dealing with big data. Although MapReduce is now a known programming model for data-centric computing where parallelization is completely replaced by partitioning the computing task through data, not all programs particularly those using statistical computing and data mining algorithms with interdependence can be re-factorized in such a fashion. On the other hand, many traditional automatic parallelization methods put an emphasis on formalism and may not achieve optimal performance with the given limited computing resources. In this work we propose a cross-platform programming paradigm, called on-the-fly data tracing , to provide source-to-source transformation where the same framework also provides the functionality of workflow optimization on larger applications. Using a big-data approximation computations related to large-scale data input are identified in the code and workflow and a simplified core dependence graph is built based on the computational load taking in to account big data. The code can then be partitioned into sections for efficient parallelization; and at the workflow level, optimization can be performed by adjusting the scheduling for big-data considerations, including the I/O performance of the machine. Regarding each unit in both source code and workflow as a model, this framework enables model-based parallel programming that matches the available computing resources. The techniques used in model-based parallel programming as well as the design of the software framework for both parallelization and workflow optimization as well as its implementations with multiple programming languages are presented in the dissertation. Then, the following experiments are performed to validate the framework: i) the benchmarking of parallelization speed-up using typical examples in data analysis and machine learning (e.g. naive Bayes, k-means) and ii) three real-world applications in data-centric computing with the framework are also described to illustrate the efficiency: pattern detection from hurricane and storm surge simulations, road traffic flow prediction and text mining from social media data. In the applications, it illustrates how to build scalable workflows with the framework along with performance enhancements

A survey on online active learning

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in the context of online active learning. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research. Our review aims to provide a comprehensive and up-to-date overview of the field and to highlight directions for future work