Lingjiu Software: Big data mining technology is more important than data

　　Data and information are important factors of production and strategic assets, and there is a global consensus. However, out-of-control and disorganized data and information do not function well as strategic assets. Information mining is the process of classifying, indexing, describing, and revealing information resource objects such as documents and data to make them orderly and systematic. information resources to ensure the effective acquisition and utilization of users. Therefore, information mining plays an important role in the management and utilization of big data resources.
　　At the same time, the current big data environment has brought a huge impact on information mining. Correctly identifying these impacts is of great significance for condensing the research direction of information mining, adapting it to the current development environment, connecting with the country's major needs, and providing intellectual support for the implementation of the national big data strategy.
　　NLPIR Big Data Semantic Intelligence Teaching and Research Platform is a comprehensive teaching and research platform for big data semantic intelligence analysis major. The platform is centered on natural language understanding, combined with the BIT team's years of scientific research and front-line teaching experience, and is committed to improving the level of students' big data and artificial intelligence teaching and training, scientific research and engineering practice in a scientific and rigorous way.
　　The NLPIR Big Data Semantic Intelligent Teaching and Research Platform has a complete and rich teaching system, including course materials, video teaching, practical training platform, experimental verification and project cases.
　　The NLPIR Big Data Semantic Intelligence Teaching and Research Platform has rich teaching content, mainly focusing on the three core areas of big data, artificial intelligence and natural language understanding. The core content includes the following aspects:
　　1) The scientific concept of big data: the definition of big data, The origin of scientific development; how to treat big data scientifically? How to grasp big data, and expound the scientific big data view from three levels of "knowledge", "microscopic" and "xiaoyi".
　　2) Big data technology platform and architecture: cloud computing technology and open source platform construction; Hadoop, Spark and other data architectures, computing paradigms and application practices; TensorFlow deep learning platform.
　　3) Machine learning and common data mining: common machine learning algorithms: Bayes, SVM, deep neural network, etc.; common data mining techniques: association rule mining, classification, clustering, singularity analysis; deep learning: CNN, RNN, LSTM, Attention model, seq2seq model.
　　4) Big data semantic precision search: the contradiction between general search engine and big data vertical business; the basic technology of big data precision search: fast incremental inverted index, structured and unstructured data fusion, big data sorting algorithm, semantic association , automatic caching and optimization mechanism; big data precise search syntax: proximity search, compound search, sentiment search, precise search;
　　5) unstructured big data semantic mining
　　Semantic understanding basis: ICTCLAS and Chinese word segmentation; content key semantic automatic indexing and Automatic generation of word cloud; big data clustering; big data classification and information filtering; big data deduplication and automatic summarization; sentiment analysis and sentiment calculation
　　; Concept; automatic discovery of knowledge points; knowledge big data generation based on bootstrapping;
　　7) NLPIR intelligent semantic platform: NLPIR intelligent semantic analysis online cloud service; NLPIR Parser semantic analysis platform training; NLPIR intelligent semantic secondary development interface and tutorial.
　　8) Analysis and review of big data application cases: State Grid big data application case; new media communication innovation and headline application; unstructured big data mining.
　　It is gradually clear that the application of new technologies of big data will become the focus of development in the next stage. With the increasing application of new technologies, more new concepts and methods will appear in various fields in the future. Aiming at the pain points and problems in the current Internet big data mining and analysis, a deeper change will appear in the Internet field, and a new era will eventually come.