Since the 1970s, China’s economy has been changing rapidly, especially after joining the World Economic and Trade Organization, and with the increase in multinational enterprises, the development of overseas markets has become more convenient, contributing to the further strengthening of our economic power. The financial industry plays an essential role in the development of a country, and commercial banks are the leaders in the financial sector. They have made significant contributions to national economic growth. Under the background of substantial economic development, the economic management decisions of commercial banks have become more complex, which makes the risks faced by commercial banks increase continuously. The rapid growth of global informatization has effectively boosted the development of all walks of life. Based on the intelligent characteristics of big data, this paper analyzes the influencing factors of economic management decisions of commercial banks based on wise choices of big data, hoping to enable commercial banks to achieve the ideal expectation of financial management of “reducing risk and creating value” to enhance the competitiveness of commercial banks in the whole market.

Flash-based storage subsystem is the key component that affects the system performance, reliability, and cost efficiency of Android-based smartphones. In this paper, we first introduce a trace collection tool specifically designed to capture the I/O requests with important content features in Android-based smartphones, which are critically important but rarely available in content-aware designs and optimizations, such as JProbe and Netlink. Based on the analysis of the traces collected from 15 popular mobile applications, we find that 20%-40% of the I/O requests on the I/O critical path of the storage stack are redundant and this data redundancy is minimally shared among different applications. Based on this key observation, we propose a content-aware optimization, called APP-Dedupe, that applies data deduplication on the I/O critical path to improve both performance and efficiency by reducing write amplification and improving GC efficiency of the flash storage on Android smartphones. The evaluation results show that APP-Dedupe reduces the GC overhead by an average of 41.5%, reduces the response times by up to 15.4% and reduces the amount of write data by an average of 45.2%.

Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to "zoom out" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.