Design of Computer Multimedia Intelligent Platform Using Big Data Analysis

Big data technology has become a hot research area and has expanded into the field of public safety. Public safety big data uses satellite data for large-scale detection, aerial data for regional abnormality inspections, and ground sensor data for abnormal target positioning, which can realize the prediction and early warning of public safety events. This article focuses on the application of skyland big data in the chemical park, and builds the chemical industry park’s personnel management system, vehicle management system, perimeter management system, and fire accident warning system. The construction of a big data system application demonstration in the chemical park assists the construction of the park. Become a smart chemical park.

Cloud computing and big data: Technologies and applications

Business operations in the power industry, as with every other industry, require payback for the resolution of process problems. In order to achieve this payback many plant monitoring systems are used such as Performance and Condition Monitoring Systems. Performance Monitoring systems use first principles calculations for a baseline and test performance case, both of these calculations can then be reconciled to give a cost associated with off design operation. Condition Monitoring systems operate on the Advanced Pattern Recognition Algorithm (APR) and can be used to identify slight variations in the performance of a system largely independent of the quality of the inputs. The process to identify deviations is as follows: a predicted value is calculated for every modeled parameter, a difference between the predicted value and actual value is calculated, the difference is compared to an allowed threshold, and then problems are reported. The purpose of the following report is to identify the strengths and weaknesses of each monitoring system and show how they may be used together for a more thorough analysis of off design operation. Performance monitoring systems provide a reliable and actionable assessment of Heat Rate deviations when they occur in the field. The calculations provided by these systems can lead directly to the diagnosis of real performance problems as well as instrument inaccuracies and provide the financial implications of each issue. Performance monitoring systems, however, are highly dependent upon input quality as their results are found using first principles calculations. Condition Monitoring systems can be used to identify smaller deviations from normal at an earlier time. The major strength of the APR process is its ability to identify these deviations regardless of input quality. As with Performance Monitoring Systems the APR process will identify real problems as well as instrument problems. However, it will not provide a financially quantifiable result as to the effect of the deviation. Monitoring systems should be able to provide quantifiable results with minimal personnel use in order to achieve a payback for more operational problems. This paper will discuss how the use of Performance Monitoring Systems in conjunction with Condition Monitoring Systems will provide the complete analysis needed by the power industry today.

Vehicle parking monitoring and management has become a big challenge for educational institutions with increasing enrollments, high percentage of vehicle ownership and decreasing parking supply which in result triggering blockage of vehicle, congestion, wastage of time and money. In university campuses particularly in Kingdom of Saudi Arabia, vehicle parking monitoring and management problem is getting worse and more frustrating due to the fact that majority of students, faculty and staff members own cars and drive through them to the university campuses. These common problems include finding out people (evidence) who are responsible for the damages (hitting, scraping, scratching and dents) to other cars and the blockage of car due to wrong car parking which takes much time to locate the owner of the car. Moreover, locating or forgetting their car park location another difficulty that is often faced by the students, faculty and staff members. The existing cameras located at the parking lots are only for video surveillance and cannot help in such situations as there is a lack of proper vehicle parking monitoring and management system. To cope with above mentioned problems and to ensure a better parking experience by accommodating increasing number of vehicles in a proper convenient manner, we propose a smart vehicle parking monitoring and management system called CampusSense. In CampusSense, Automatic Number Plate Recognition (ANPR) cameras and android based mobile application is developed to efficiently monitor, manage and protect the parking facilities at university campuses. Parking problems around the university campus faced by the students, faculty and staff members are analyzed by conducting a survey.

As the scale of online socialization continues to expand, the amount of data generated by people’s social networking continues to increase, and data has gradually become the invisible wealth of various industries, and data analysis, as the main method to control data, has become a skill that must be mastered in the development of talents in various industries. Big data technology is a data storage and processing method based on data analysis technology. Prediction of the law of development of a certain thing and analysis of the success rate of a certain decision. Big data technology has played an important role in the informatization of various industries, and its use in accounting informatization can bring more convenience to accounting work. Although artificial intelligence technology has only been born for less than a hundred years, the technical branches it has developed have been widely used in various industries. At present, the types of algorithms corresponding to artificial intelligence technology are still increasing, and the application of AI algorithms to the development of accounting informatization can make Accounting work is more intelligent, and its accuracy can be improved on the basis of greatly reducing labor costs. Putting the construction of accounting informatization in colleges and universities under the technical background of artificial intelligence and big data development can achieve more valuable breakthroughs in data collection and countermeasure optimization. This paper first analyzes the theories of big data, artificial intelligence and accounting informatization, and then obtains various problems existing in the development of accounting informatization construction in colleges and universities through questionnaires. By comparing the newly designed solutions with the traditional accounting informatization construction solutions, it is found that the accounting informatization construction solutions combined with big data and artificial intelligence can be used in accounting personnel training, accounting platform security hidden danger investigation and college accounting. Internal control and other directions are superior to the traditional accounting informatization construction plan.

The purpose of the work is to study the current problems and prospects of the solution for processing big data received or stored in the Internet (web data), as well as the possibility of practical realization of Data Mining technology for big web data on practical example. Materials and methods. The study included a review of bibliographic sources on big data analysis problems.Data Mining technology was used to analyze large web data, as well as computer modeling of a practical problem using the C # programming language and creating a DDL database structure for accumulating web data.Results. In the course of the work, the specifics of big data were described, the main characteristics of big data were highlighted, and modern approaches to processing big data were analyzed. A brief description of the horizontal-scalable architecture and the BI-solution architecture for big data processing is given. The problems of processing large web data are formulated: limiting the speed of access to data, providing access via network protocols through general-purpose networks.An example showing the approach to processing large web data was also implemented. Based on the idea of big data, the described complexities of web data processing and the methods of Data Mining, techniques were proposed for effectively solving the practical problem of processing and searching patterns in a large data array.The following classes have been developed in the C # programming language:Class of receiving web data via the Internet; Data conversion class;Intelligent data processing class;Created DDL script that creates a structure for the accumulation of web data.A single UML class diagram has been developed.The constructed system of data and classes allows to solve the main part of the problems of processing large web data and perform intelligent processing using Data Mining technology in order to solve the problem posed of identifying certain records in a large array. The combination of object-oriented approach, neural networks and BI-analysis to filter data will speed up the process of data processing and obtaining the result of the studyConclusion. According to the results of the study, it can be argued that the current state of technology for analyzing large web data allows you to efficiently process data objects, identify patterns, get hidden data and get full-fledged statistical data.The obtained results can be used both for the purpose of the initial study of big data processing technologies, and as a basis for developing an already real application for analyzing web data. The use of neural networks and the created universal classes-handlers makes the created architecture flexible and self-learning, and the class declarations and the base DDL structure will greatly simplify the development of program code.

With the advent of the big data era, the amount of various types of data is growing exponentially. Technologies such as big data, cloud computing, and artificial intelligence have achieved unprecedented development speed, and countries, regions, and multiple fields have included big data technology in their key development strategies. Big data technology has been widely applied in various aspects of society and has achieved significant results. Using data to speak, analyze, manage, make decisions, and innovate has become the development direction of various fields in society. Taxation is the main form of China’s fiscal revenue, playing an important role in improving the national economic structure and regulating income distribution, and is the fundamental guarantee for promoting social development. Re examining the tax administration of tax authorities in the context of big data can achieve efficient and reasonable application of big data technology in tax administration, and better serve tax administration. Big data technology has the characteristics of scale, diversity, and speed. The effect of tax big data on tax collection and management is becoming increasingly prominent, gradually forming a new tax collection and management system driven by tax big data. The key research content of this article is how to organically combine big data technology with tax management, how to fully leverage the advantages of big data, and how to solve the problems of insufficient application of big data technology, lack of data security guarantee, and shortage of big data application talents in tax authorities when applying big data to tax management.

This paper uses big data technologies to study base stations’ behaviors and activities and their predictability in mobile cellular networks. With new technologies quickly appearing, current cellular networks have become more larger, more heterogeneous, and more complex. This provides network managements and designs with larger challenges. How to use network big data to capture cellular network behavior and activity patterns and perform accurate predictions is recently one of main problems. To the end, first we exploit big data platform and technologies to analyze cellular network big data, i.e., Call Detail Records (CDRs). Our CDRs data set, which includes more than 1,000 cellular towers, more than million lines of CDRs, and several million users and sustains for more than 100 days, is collected from a national cellular network. Second, we propose our methodology to analyze these big data. The data pre-handling and cleaning approach is proposed to obtain the valuable big data sets for our further studies. The feature extraction and call predictability methods are presented to capture base stations’ behaviors and dissect their predictability. Third, based on our method, we perform the detailed activity pattern analysis, including call distributions, cross correlation features, call behavior patterns, and daily activities. The detailed analysis approaches are also proposed to dig out base stations’ activities. A series of findings are found and observed in the analysis process. Finally, a study case is proposed to validate the predictability of base stations’ behaviors and activities. Our studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.

Big data are one of the representative phenomena of the information era of human societies. A basic study on the cognitive foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data manipulations. It leads to a set of mathematical theories that rigorously describe the general patterns of big data across pervasive domains in sciences, engineering, and societies. A significant finding towards big data science is that big data systems in nature are a recursive n-dimensional typed hyperstructure (RNTHS). The fundamental topological property of big data system enables the inherited complexities and unprecedented challenges of big data to be formally dealt with as a set of denotational mathematical operations in big data engineering. The cognitive relationship and transformability between data, information, knowledge, and intelligence are formally revealed towards big data science.

Big data is a term used for a wide variety of large data that require high processing speed. Big data handling includes not only the data, but also data collection, transmission, storage, processing (conversion, calculation, analysis), visualization and use of information in various areas of human activities. The emergence of Big data is one of the steps to digitizing all information used in different spheres. In recent years, Big data are used in foreign countries in a number of industries, including nuclear energy. It can be assumed that the Big data use in the coming years may become the next revolutionary introduction of digital technologies for NPPs. The paper provides examples of using the Big data in various countries to create new units and improve operating units. The areas of Big data use in the operation of Ukrainian NPPs are as follows: - in intended creation of an industrial system for technical diagnostics of systems, structures and components of NPPs; - for improving activities on long-term operation of power units; - for improving safety becouse of using of information based on Big data: structured information on equipment failures and incidents at different NPPs stored in international and national databases; information obtained from drones, in particular in post-accident monitoring, etc.

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

With the advent of the era of network, video surveillance technology is more and more integrated into the IT emerging technologies. Big data technology in the field of video surveillance has shown a broad development path. The research of surveillance video data mining based on big data is committed to achieve a perfect fusion of the big data technology and video surveillance business, in order to create a solution of intelligent video surveillance in big data era. In this paper, based on the application requirements of video surveillance on big data, we designed the framework of big data analysis for surveillance video, and study the key technology of storage and processing for surveillance video data based on big data and the value of data mining. The study on real-time intelligence analysis and data mining, progresses the video surveillance from manual sampling to efficiently advance warning, post analysis, realization of intelligent information analysis, forecasting. It brings profound changes in the field of video surveillance business development.

The purpose of this paper is to examine the mediating effect of the rate of quality of accounting information systems on the relationship between big data technology and firms’ financial performance in firms listed on the Palestine Stock Exchange. The researchers conducted an account of the previous studies in this field. The researcher used the deductive approach in studying and analyzing previous studies related to big data by relying on books, periodicals, theses, and accounting standards related to the subject of the research. The researcher applied an inductive approach when conducting the field study and testing the statistical hypotheses related to the study of the relationship between the use of big data technology and firms’ financial performance. The findings show a correlation coefficient of (0.54) and a coefficient of determination of (48%), indicating that big data analytics positively affects the rate of return on assets, and that there is a statistically significant relationship between the advancement of accounting information systems and the enhancement of financial performance in big data technology, as measured by the rate of return on equity and the rate of return on assets, which have correlation rates of (0.53) and (42%), respectively. This relationship is reflected in the data on the existence of a statistically significant relationship between the use of big data technology and the enhancement of financial performance with big data technology. The intention of big data, as well as the absence of fundamental differences between the sample individuals, states that the use of big data technology leads to improved performance through the development of various accounting practices and good inventory management by predicting customer behaviour, thus increasing the competitiveness of competition and improving the reputation of the establishment on social media. This is reflected in the company’s sales and its survival in the market, as well as the development of analytical models and advanced methods of analysis that limit fraud and help control it, which is one of the establishment’s goals at present. This paper contributes to the literature by showing that the use of big data leads to a change in methods of preparing the final accounts, especially the financial position, and displaying them at fair value, which increases investor confidence. The study offers insights into the necessity of holding training courses for accountants concerning technology related to digital transformation and big data analysis for use in developing accounting practices.

Artificial intelligence refers to the intelligence expressed by machines made by humans and is a manifestation of human intelligence. On the whole, artificial intelligence includes two aspects: “artificial” and “intelligence.” Artificial is created by man, and information work covers a number of areas, for instance thinking and consciousness. The process from feeling memory to thinking is called “wisdom.” The result of wisdom is behavior and language. The expression process of behavior and language is called “ability,” and the two are collectively called “intelligence.” However, there is no unified explanation for intelligence at present, but there is no doubt about the widespread. Big data processing includes multiple data processing processes, but data quality is the most important link in the entire process, and each data processing link will have an impact on the quality of big data. The big data processing process mainly includes data collection, data preprocessing, data storage, data processing and analysis, data display/data visualization, data application, and other links. The aim of this paper is to explore the studies on the development of intercultural intercommunication skills in English language learning and teaching in the context of artificial intelligence and big data. It is hoped that this will be done in the light of AI and big data technologies. We can explore the current situation of cross-cultural communication, provide new learning directions for cross-cultural communication, and promote cultural exchange and dissemination. This paper investigates students’ attitudes towards cross-cultural communication by means of a questionnaire and then briefly tests students’ cross-cultural communication ability. The experimental results of this paper show that 4 students have mastered cross-cultural knowledge, accounting for 3%; 8 students have mastered cross-cultural knowledge very well, accounting for 4%, and 9 students have mastered cross-cultural knowledge, accounting for 7%. There are 12 people who are very knowledgeable about cross-cultural knowledge, accounting for 9%. These data show that the current students have very little knowledge of intercultural communication, and their intercultural communication skills need to be improved.

The non-weighing lysimeter system in the Minqin desert areas was firstly used in the 1970 s, and mainly performed the orientation research on the water consumption by the transpiration of several desert plants and the evaporation on the surface of sand, which aimed to provide the theoretical guide for the selection of afforestation density and the utilization of sand and water in Chinese sand areas. At present the system has been running for 30 years, most of the lysimeters showed the jam and leakage phenomenon, and the observation was by conventional artificial means. All the factors seriously affected the accuracy of the data monitoring. However, on the basis of the non-weighing lysimeter system structure and its testing principle, the evapotranspiration area could be extended, the capacity of testing soil column could be increased, more real natural environment conditions could be simulated and the limitations of weighing lysimeter could be overcome, in sum, the lysimeter's application field and adaptability were extended. Owing to the problems of low automation, and lack of scalability and reliability, the non-weighed lysimeter monitoring and control system in the Minqin desert areas has been retrofitted to improve its performance based on structure characteristics. The non-weighed lysimeter proving ground of 720 m2was designed in Minqin sand areas, where 30 individual lysimeters (120 cm×120 cm) of desert plant were upgraded and 12 small groups of desert plant lysimeters (400 cm×400 cm) were expanded and built, at the same time, the lysimeter groups in different types could be used to carry out the test of water supply for plant under the constant water level of 190, 290 and 390 cm. The lysimeter proving ground upgraded and expanding-built could realize synchronous observation for evapotranspiration test from the desert plant individual scale to small group scale, and the experiment platform was provided for studying water consumption of desert plants transpiration in different scale and its coupling relationship. The distributed monitoring scheme was adopted in the new automatic monitoring system, in which the superior industrial computer was set up as the center with multiple lower machine monitoring points, such as lysimeter constant water level control and water-supplying measurement system, drainage measurement system, atmospheric precipitation monitoring system, soil moisture monitoring system, environment temperature measurement and control system, SMS cat alarm system, data remote transmission and monitoring system, and UPS over-voltage protection system, and the RS485 bus, ISM wireless communication and TCP/IP network structure were used in the system. The automatic control of the lysimeter water level, water supply and drainage, the automatic monitoring of water metering, and the soil water content were designed, and the atmospheric rainfall and the temperature indoor were monitored in real time in the new system; the system also had the functions of real-time data collection and processing, information communication and data entering, historical data storage, data report printing and remote transmission download, and the power supply system was protected against over-voltage to enhance the reliability of system operation and the stability of data acquisition. Additionally, the internet-based remote monitoring and control system was used, and the users could process the abnormal situation. According to the monitoring data analysis by the regression method, we could know that the errors of data regression analysis were ± 2.2% for the pressure sensor, ± 2.0% for the drop counter and ± 12.5% for the moisture sensors. The research extended the scale for locating observation of the desert plant and implemented the automatic monitoring for evapotranspiration of the desert plant. © 2015, Chinese Society of Agricultural Engineering. All right reserved.