наук, академик НАН РК Уалиев Г., д.б.н. наук, член-корреспондент НАН РК Косов В.Н., канд. Также для определения параметров возможности их восстановления путем решения обратных задач в статье [7] авторы провели анализ идентифицируемости на основе чувствительности для разных моделей распространения пандемии COVID-19. В статье [10] используется полууправляемая нейронная сеть для исследования решения дифференциальных уравнений для различных параметров моделирования эволюции распространения COVID-19 и его начальных условий, а затем применяются методы решения обратных задач для оценки оптимальных условий. . соответствующих статистических данных.
Опыт с COVID-19 показывает, что двухнедельный инкубационный период может распространить вирус по всему миру и практически на все слои общества.
Генетический алгоритм
The stability study of numerical solution of Fredholm integral equations of the first kind with emphasis on its application in boundary element method. An efficient based on hybrid functions for Fredholm integral equation of the first kind with convergence analysis. The stability study of numerical solution of Fredholm integral equations of the first kind with emphasis on its application in boundary element method. 2017).
An efficient based on hybrid functions for the Fredholm integral equation of the first kind with convergence analysis. 2019).
ФИЗИКАЛЫҚ ПРОЦЕСТЕР МЕН МЕХАНИКАЛЫҚ ЖҮЙЕЛЕРДІ МОДЕЛЬДЕУ МОДЕЛИРОВАНИЕ ФИЗИЧЕСКИХ ПРОЦЕССОВ И МЕХАНИЧЕСКИХ СИСТЕМ
MODELING OF PHYSICAL PROCESSES AND MECHANICAL SYSTEMS
In this work, the mathematical modeling of the oscillation of an elastic plate will be discussed. Vibrations in two principal planes of an uneven beam of rectangular cross-section, one side of which varies as the square root of the axial coordinate // Journal of Sound and Vibration. News of the National Academy of Sciences of the Republic of Kazakhstan - Series of Geology and Technical Sciences.
News of the National Academy of Sciences of the Republic of Kazakhstan - Series of Geology and Technical Sciences.
ИНФОРМАТИКА COMPUTER SCIENCE
INTRODUCTION
Big data is defined as aggregated data sets that include structured and unstructured data that are significant in size and vary in structure. The rapidly growing volume of data presents us with new and challenging storage and processing challenges. Large-scale data processing challenges the development of business processes and the transformation of information flows into intelligent digital resources.
This concept describes an exponentially growing collection of data that is large, raw, and unstructured for analysis using relational database techniques. In his article "The Impact of Large-Scale Data Processing Technologies on the Future of Science" he showed that data is growing rapidly and that its processing is one of the most pressing problems in the future [3]. The transition from analog to digital has led to a daily increase in the volume of business data.
This data is driving the use of billions of phones, tens of billions of social media posts, an increase in the number of sensors connected to the internet in cars, the growth of POS terminals and more. Today, there is no one-size-fits-all method or algorithm for large-scale data processing that is suitable for every situation. Experts from around the world are studying the development of large-scale data processing methods and how this will affect the prospects of enterprises.
Long-term storage of data in a warehouse can not only cause various difficulties, but can also be beneficial if stored and processed correctly. EXISTING PRODUCTS FOR STORING AND PROCESSING LARGE AMOUNTS OF DATA Oracle Big Data Appliance is used to solve problems of processing, collecting and structuring data.
EXISTING PRODUCTS FOR STORING AND PROCESSING LARGE AMOUNTS OF DATA Oracle Big Data Appliance is used to solve problems of processing, collecting and structuring data. It is a
Graph stores are used to store information about data transmission networks such as social networks. Database key-value stores store each element in the database as a key along with its value. Some key-value stores, such as Redis, allow each value to be in integer form, which adds functionality.
NoSQL technology (for example, Cassandra) is not designed to replace a relational database, but to help solve problems when the amount of data is very large.
ANALYTICAL TECHNOLOGIES FOR LARGE-SCALE DATA PROCESSING
With this solution, you can reduce the cost of one gigabyte per second several times [11]. To take advantage of all the features of the architecture, programs must be written and defined individually for each task. It is recommended that a processor with multiple instruction streams and a single data stream often retains the advantage, as multiprogramming modules perform the same task on the same data, reducing the chance of success if one of the modules fails.
The MISD architecture allows you to compare the results of calculations to determine the defect. As the amount of data increases, our hard drives may experience problems and, more importantly, take time to access the required data. You can split the database by putting information from each class into its own database.
One way to reduce data access time is to put the database in random access memory. As the cost of memory is decreasing day by day, using it as storage can be effective in increasing the speed of data processing. However, the developers of Terradata were the first to perform an embedded analysis in the database [15].
The developers of these systems claim that this approach increases the performance of analytical workloads that are very demanding to read data similar to certain workloads, especially data warehouse applications [16]. Unlike a set of data mining algorithms and a database, analytics platforms are originally focused on data analysis and are designed to create out-of-the-box analytics solutions.
DOCUMENT PROCESSING FOR THE ENTERPRISE DIGITALIZATION SYSTEM BASED ON MONGODB
There are new types of databases for working with big data, such as analytical databases. Analytical platform is an information analytical system, as well as a specialized software solution that includes all the tools for obtaining templates from "raw" data, the process of obtaining some templates from the entire data array: means of consolidating information in one source, data collection, conversion, storage, information extraction algorithms, visualization, switching of simple and complex methods and models [17]. There are many algorithms for processing big data, but new algorithms must be developed to achieve certain goals.
But in the presence of a large number of documents and many links between them, such an operation can be quite complicated and, in case of an error, it can lead to the formation of false links to already deleted documents. In addition, there is always the possibility that the document was accidentally deleted, and no one is ever safe from that. To change, add and remove fields and arrays in documents, in MongoDB the db.collection.update() function is used.
USING MONGODB FOR BIG DATA STORAGE
As a result, the phrase goes through both levels mentioned above at the same time. Thus, achieving the avalanche effect is an important goal in cryptographic algorithm development. In the general case, the avalanche effect should be checked for each plaintext position.
For the numerical study of the developed algorithm, the software was compiled in the programming language Delphi 7. With the help of this software, studies were carried out to evaluate the spread of the avalanche effect. On approval of standard curricula for primary, elementary secondary and general secondary education of the Republic of Kazakhstan.
Order of the Minister of Education and Science of the Republic of Kazakhstan, dated November 8, 2012 No. 500 (amended by the Order of the Minister of Education and Science of the Republic of Kazakhstan No. On the approval of the standard curricula of primary education, basic secondary education, general secondary education of the Republic of Kazakhstan.
Decree of the Acting Minister of Education and Science of the Republic of Kazakhstan of October 25, No. Decree of the Minister of Education and Science of the Republic of Kazakhstan of April 3, 2013 No. 115 (as amended by the Decree of the Acting Minister of Education and Sciences of the Republic of Kazakhstan dated No.
23 Reference book on the updated content of general secondary education in the Republic of Kazakhstan.
МАТЕМАТИКА, ФИЗИКА ЖӘНЕ ИНФОРМАТИКАНЫ ОҚЫТУ ӘДІСТЕМЕСІ МЕТОДИКА ПРЕПОДАВАНИЯ МАТЕМАТИКИ, ФИЗИКИ И ИНФОРМАТИКИ
УДК 372.8
Аралас оқыту кезіндегі оқу процесінің негізі студенттің «мақсатты, қарқынды және бақыланатын өзіндік жұмысы» болып табылады. Электронды ортада аралас оқыту моделі негізінде және «Практикалық шет тілі» курсының мысалында іске асырылған ұқсас пәндер бойынша студенттердің өзіндік жұмысын бағалау және оқытудың табыстылығын болжау алгоритмі. Осы себепті клиентке арналған нүкте жүйе туралы білімдерін бағалауға қызмет етеді, бұл клиенттің бүкіл мерзім бойы жүйелі жұмысының жалғасуына әсер етеді.
Педагогикалық құбылыстың өзіндік жұмыс түсінігінде оқушының танымдық жағдайының бір бөлігі ретінде оқу-тәрбие процесі мен өзіндік жұмысты анықтауда қиын да өмірлік маңызы бар екенін көрдік. Бұл әдіс компанияның өзіндік жұмысында инновацияны қолданудың маңызды нүктесі болып саналады. Бірінші және екінші семестрдің аралық аттестаттау қорытындысы бойынша барлық студенттер қорытынды баға бойынша «Өте жақсы, Жақсы» деген көрсеткіштер бойынша топтарға бөлінді.
Ұсынылған инновациялық технология клиенттің өз жұмысын тиімді ұйымдастыруға негізделген. Практикалық дағдылар (ұйымдастырушылық-практикалық дағдылар, рефлексиялық дағдылар). ӘҚҰ контекстінде; .. проблемалық жағдайды талдау.
Үшінші топқа жататын барлық студенттер «тәуекел тобына» кіреді, олардың әрқайсысына LMS курсындағы қызметі туралы толық есеп жасалып, оқытушыға ұсынылады. 34 «Объектілі-бағытталған бағдарламалау» пәнін оқу нәтижесінде алынған теориялық білім және оқыту барысында инфографиканы пайдалану тәжірибесі объектілі-бағытталған бағдарламалау саласындағы білімді тереңдетуге негіз бола алады деп ойлаймыз.
![Figure 1. Example of data storage in Oracle Big Data Appliance [7]](https://thumb-ap.123doks.com/thumbv2/azdokorg/10355794.0/77.892.253.641.393.618/figure-example-data-storage-oracle-big-data-appliance.webp)
![Figure 2. Map-Reduce technology map [10]](https://thumb-ap.123doks.com/thumbv2/azdokorg/10355794.0/78.892.255.641.120.377/figure-map-reduce-technology-map.webp)
