Complex industrial automation data stream mining algorithm based on random Internet of robotic things

In recent years, with the continuous development of computer application technology, network technology, data storage technology, and the large amount of investment in information technology, enterprises have accumulated a large amount of data while transforming and improving enterprise management modes and means. How to mine useful data, discover important knowledge and extract useful information has become a hot topic of current research. Industrial big data is significantly different from traditional big data. The traditional big data is based on the Internet environment. Although the data has a high degree of discretization and distribution, its association is relatively simple. The collection of industrial process data is relatively easy, but the mathematical and physical and chemical mechanism models involved make the inherent relationship of data complex, so it is difficult to use common analytical models and methods for processing. In this paper, we propose a complex industrial automation data stream Mining algorithm based on random internet of robotic things, and experimental results show that the proposed algorithm has higher data mining efficiency and robustness

Accelerated PSO Swarm Search Feature Selection with SVM for Data Stream Mining Big Data

In the modern world there is huge development in the field of networking technology which handles huge data at a time. This data can be structured, semi structured or unstructured. To perform efficient mining of valuable information from such type of data the big data technology is gaining importance nowadays. Data mining application is been used in public and private sectors of industry because of its advantage over conventional networking technology to analyze large real time data. Data mining mainly relies on 3 V’s namely, Volume, Varity and Velocity of processing data. Volume refers to the huge amount of data it collects, Velocity refers to the speed at which it process the data and Variety defines that multi-dimensional data which can be numbers, dates, strings, geospatial data, 3D data, audio files, video files, social files, etc. These data which is stored in big data will be from different source at different rate and of different type; hence it will not be synchronized. This is one of the biggest challenges in working with big data. Second challenge is related to mining the valuable and relevant information from such data adhering to 3rd V i.e. Velocity. Speed is highly important as it is associated with cost of processing. This paper focuses detailed study of accelerated PSO Swarm search feature selection and use of support vector machine

Application of Big Data Mining Technology in Blockchain Computing

Big data in the modern science and technology and social activities play an important role, on the one hand, a large number of new applications and technology into our lives, in the use of these new technologies to produce a large amount of data, on the other hand, big data as one of the most important digital assets, many of the development of new technologies also rely on large data as support. This paper focuses on the research and application of big data mining technology in blockchain computing. Firstly, this paper extracts the corresponding transaction data according to the Bitcoin address and constructs the transaction features to get the Bitcoin data set. Then, the data features are processed. Then, three algorithm models, SVM, Adaboost and Random Forest, are selected to model and analyze the preprocessed data combined with different sampling strategies. According to the comprehensive performance of the model and its shortcomings, the model is selected and improved

Power Outage Fault Judgment Method Based on Power Outage Big Data

INTRODUCTION: With the deepening of the application of big data technology, the power sector attaches great importance to power outage judgment. However, many factors affect the judgment result of power outage, and the analysis process is very complicated, which can not achieve the corresponding accuracy. OBJECTIVES: Aiming at the problem that it is impossible to accurately judge the result in judging power failure, a deep mining model of big data is proposed. METHODS: Firstly, the research data set is established using power outage big data technology to ensure the results meet the requirements. Then, the power failure judgment data are classified using big data theory, and different judgment methods are selected. Using big data theory, the accuracy of power failure judgment is verified. RESULTS: The deep mining model of big data can improve the accuracy of power failure judgment and shorten the judgment time of power failure under big data, and the overall result is better than the statistical method of power failure. CONCLUSION: The deep mining model based on power outage big data proposed can accurately judge the power outage fault and shorten the analysis time

Data Mining Algorithms for Internet Data: from Transport to Application Layer

Nowadays we live in a data-driven world. Advances in data generation, collection and storage technology have enabled organizations to gather data sets of massive size. Data mining is a discipline that blends traditional data analysis methods with sophisticated algorithms to handle the challenges posed by these new types of data sets. The Internet is a complex and dynamic system with new protocols and applications that arise at a constant pace. All these characteristics designate the Internet a valuable and challenging data source and application domain for a research activity, both looking at Transport layer, analyzing network tra c flows, and going up to Application layer, focusing on the ever-growing next generation web services: blogs, micro-blogs, on-line social networks, photo sharing services and many other applications (e.g., Twitter, Facebook, Flickr, etc.). In this thesis work we focus on the study, design and development of novel algorithms and frameworks to support large scale data mining activities over huge and heterogeneous data volumes, with a particular focus on Internet data as data source and targeting network tra c classification, on-line social network analysis, recommendation systems and cloud services and Big data

Air travel demand forecasting based on big data: A struggle against public anxiety

It is of great significance to accurately grasp the demand for air travel to promote the revival of long-distance travel and alleviate public anxiety. The main purpose of this study is to build a high-precision air travel demand forecasting framework by introducing effective Internet data. In the age of big data, passengers before traveling often look for reference groups in search engines and make travel decisions under their informational influence. The big data generated based on these behaviors can reflect the overall passenger psychology and travel demand. Therefore, based on big data mining technology, this study designed a strict dual data preprocessing method and an ensemble forecasting framework, introduced search engine data into the air travel demand forecasting process, and conducted empirical research based on the dataset composed of air travel volume of Shanghai Pudong International Airport. The results show that effective search engine data is helpful to air travel demand forecasting. This research provides a theoretical basis for the application of big data mining technology and data spatial information in air travel demand forecasting and tourism management, and provides a new idea for alleviating public anxiety

Big Data para el análisis de sentimientos en imágenes

During the last years the terms Big Data and data mining are being listened to a lot, so in this project they will be explained that they are, in which they differ, their importance at present and will see some of the tools that are implemented with these two terms. Also, in recent years is increasing the use of social networks, where users give their opinion on any subject and also can upload any multimedia file, either a video or an image. Big companies are able to make any kind of prediction and extract information from any text that users have written, using Big Data and data mining. But, could it extract information from the images that users upload to know their opinion on a topic? In this project, a web page will be created, which simulates a social network, where through the use of hashtags and images, the page can know the feeling that causes a specific topic. To do this, it will be necessary to perform an analysis of the images, specifically those in which faces are detected, to determine their feelings. This analysis would be done using a technology of Big Data and data mining, with which the application will obtain a pattern with which can determine the feeling of the face. The analysis of the data extracted from the images will follow a set of steps that are carried out in any data mining project, thanks to these steps, it can be posible made modifications to the data or the configuration of the Big Data tool and data mining to improve results. As for the web application, a design will be made, and will explain the technologies that will be necessary for its creation. Finally, you will see the final result, where the web application will be displayed with the extraction of the image sentiment, thus achieving the main objective of the project