Approximate Data Analytics Systems

Today, most modern online services make use of big data analytics systems to extract useful information from the raw digital data. The data normally arrives as a continuous data stream at a high speed and in huge volumes. The cost of handling this massive data can be significant. Providing interactive latency in processing the data is often impractical due to the fact that the data is growing exponentially and even faster than Moore’s law predictions. To overcome this problem, approximate computing has recently emerged as a promising solution. Approximate computing is based on the observation that many modern applications are amenable to an approximate, rather than the exact output. Unlike traditional computing, approximate computing tolerates lower accuracy to achieve lower latency by computing over a partial subset instead of the entire input data. Unfortunately, the advancements in approximate computing are primarily geared towards batch analytics and cannot provide low-latency guarantees in the context of stream processing, where new data continuously arrives as an unbounded stream. In this thesis, we design and implement approximate computing techniques for processing and interacting with high-speed and large-scale stream data to achieve low latency and efficient utilization of resources. To achieve these goals, we have designed and built the following approximate data analytics systems: • StreamApprox—a data stream analytics system for approximate computing. This system supports approximate computing for low-latency stream analytics in a transparent way and has an ability to adapt to rapid fluctuations of input data streams. In this system, we designed an online adaptive stratified reservoir sampling algorithm to produce approximate output with bounded error. • IncApprox—a data analytics system for incremental approximate computing. This system adopts approximate and incremental computing in stream processing to achieve high-throughput and low-latency with efficient resource utilization. In this system, we designed an online stratified sampling algorithm that uses self-adjusting computation to produce an incrementally updated approximate output with bounded error. • PrivApprox—a data stream analytics system for privacy-preserving and approximate computing. This system supports high utility and low-latency data analytics and preserves user’s privacy at the same time. The system is based on the combination of privacy-preserving data analytics and approximate computing. • ApproxJoin—an approximate distributed joins system. This system improves the performance of joins — critical but expensive operations in big data systems. In this system, we employed a sketching technique (Bloom filter) to avoid shuffling non-joinable data items through the network as well as proposed a novel sampling mechanism that executes during the join to obtain an unbiased representative sample of the join output. Our evaluation based on micro-benchmarks and real world case studies shows that these systems can achieve significant performance speedup compared to state-of-the-art systems by tolerating negligible accuracy loss of the analytics output. In addition, our systems allow users to systematically make a trade-off between accuracy and throughput/latency and require no/minor modifications to the existing applications

Active Teaching Techniques for Engineering Students to Ensure The Learning Outcomes of Training Programs by CDIO Approach

Recent research results show that students' ability to absorb and apply lessons increases when they are actively learning. In the innovative teaching method, learners - objects of teaching activities as well as subjects of learning activities - are attracted to active learning activities organized and instructed by teachers. By this way, learners are self-reliant to discover what they do not know, not passively absorb the knowledge arranged by the teacher. Placed in situations of real life, learners experience, directly observe, discuss, experiment, solve problems posed by their thinking, both working in groups, thereby gaining new knowledge, new skills, promoting creative potential. Depending on the objectives of the specific subject, which level of knowledge or skill needs to be achieved according to the CDIO approach, the lecturer will organize appropriate activities to help students actively learn to achieve the goals. The paper explores and evaluates innovative teaching methods to help students actively learn and experience to achieve the subject’s goals and training program following the CDIO approach, as well as meet the requirements of society

Essential oil of Citrus hystrix DC.: A mini-review on chemical composition, extraction method, bioactivities, and potential applications in food and pharmaceuticals

Citrus hystrix DC. is a common herb in tropical regions. Its essential oils are now widely researched and applied because of their high economic value and safety for humans and are interesting materials for future trends. This review provides an extensive overview of the biological activities of C. hystrix essential oil, characterized predominantly by citronellal, ?-Pinene, sabinene, limonene, and terpinene-4-ol, which are deciding factors in antimicrobial, antioxidant, insect repellent, anti-tumor, and anti-inflammatory properties. Therefore, it is applied in the fields of food preservation and pharmaceuticals. However, these applications should consider the ratio of these components in the essential oil, which is variable when using materials from different parts of the plant and depending on the original location of the plant, growth stages, traditional or modern extraction methods, and pre-treatment methods

The Impact of Supporting Industries on Attracting Foreign Direct Investment: A Case Study in Vinh Phuc Province, Vietnam

 Summary: This study focuses on explaining the theoretical basis of the impact of supporting industries (SI) on attracting foreign direct investment (FDI); assessing the state of the impact of SI on FDI attraction into Vietnam in general and Vinh Phuc province in particular. Quantitative analysis results show that, in the field of SI in Vietnam, import suppliers are dominating domestic suppliers, the factor that most affects FDI enterprises' satisfaction level is labor, especially hard-work and progressiveness, followed by quality and attitude of discipline compliance of labor resources. The domestic SI still face difficulties in approaching customers, quality assurance, with outdated technology, lack of high-tech manpower, poor innovative research capabilities... In the coming time, to contribute to FDI attraction into Vietnam, to become a supplier for FDI enterprises, domestic enterprises working in supporting industries (SI enterprises) need to increase the rate of capital investment on technology, improve the quality of human resources, and promote information exchange with FDI enterprises