Short Communication - (2022) Volume 9, Issue 3
Determination of Database Based on the Computer Science Education
Alice Walker*Received: 01-Jun-2022, Manuscript No. to social-22-69874; Editor assigned: 03-Jun-2022, Pre QC No. to social-22-69874 (PQ); Reviewed: 17-Jun-2022, QC No. to social-22-69874; Revised: 22-Jun-2022, Manuscript No. to social-22-69874 (R); Published: 29-Jun-2022
Introduction
The interdisciplinary field of data science, which applies computer science and statistical techniques to answer cross-domain questions, has recently gained significant growth and interest. This trend extends to undergraduate education, with an increasing number of institutions offering data science degree programs. However, there are significant differences in what this field really needs, and more broadly, how undergraduate programs prepare students for a data-intensive career.
Computer Science Education (CSEd) research from kindergarten to high school makes extensive use of empirical research in children. Insight into the demographics of these children is important for understanding the representativeness of the populations involved. This literature review examines the demographics of subjects enrolled in CSEd studies from kindergarten to high school.
Description
Motion analysis is important in video surveillance systems, and background subtraction helps detect moving objects in such systems. However, most of the existing background subtraction methods do not work well in evening surveillance systems because the objects are usually dark and the reflected light is usually strong. To solve these problems, we propose a framework that uses the Weber contrast descriptor, texture feature extractor, and photodetector to extract the features of the foreground object. We propose a local pattern improvement procedure.
With the steadily increasing trends in information and communication technology applications in the most diverse areas of life, networks are challenged to meet stringent performance requirements. Increased bandwidth is one of the most common solutions to ensure that adequate resources are available to achieve performance goals such as sustained high data rates, minimum latency, and limited latency variability. Guaranteed throughput, minimum latency, and minimum probability of packet loss guarantee the quality of service on your network. However, the amount of traffic that your network needs to handle is not fixed and depends on time, source, and other factors. The distribution of traffic usually follows some peak intervals, and in most cases the traffic remains at a moderate level. Network capacity, as determined by peak interval requirements, often requires higher capacity compared to the capacity required at medium intervals. Such an approach increases the cost of the network infrastructure and makes the network underutilized at reasonable intervals. Appropriate ways to increase network utilization at peak and medium intervals help operators keep network costs down in the state.
Due to the rapid increase in data on the Internet, the identification of related documents has been an active field of study for half a century. The traditional model for retrieving relevant documents is based on the following bibliographic information: B. Bibliographic combination, co-citation and direct citation. However, recently, the scientific community has begun to use text features to improve the accuracy of existing models. Previous research has shown that deep-level (ie, content-level) analysis of citations plays an important role in finding more relevant documents than the surface-level (that is, bibliographic details) [1-5].
Conclusion
Database systems play a central role in modern data-centric applications. Therefore, their performance is an important factor in the efficiency of the data processing pipeline. Modern database systems provide several parameters that users and database administrators can configure to tailor their database settings to the particular application under consideration. This task was traditionally performed manually, but several methods have recently been proposed to automatically find the optimal parameter configuration for the database. However, many of these methods use statistical models. Statistical models require large amounts of data and cannot represent all the factors that affect database performance or implement complex algorithmic solutions. This task explores the potential of a simple, general-purpose model-free configuration tool to automatically find the optimal parameter configuration for your database.
Acknowledgement
None.
Conflict of Interest
The author has declared no conflict of interest.
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Copyright: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.