scholarly journals An efficient integration and indexing method based on feature patterns and semantic analysis for big data

Array ◽  
2020 ◽  
Vol 7 ◽  
pp. 100033
Author(s):  
Madhu Mahesh Nashipudimath ◽  
Subhash K. Shinde ◽  
Jayshree Jain
Keyword(s):  
Big Data ◽  
Semantic Analysis ◽  
Indexing Method ◽  
Efficient Integration

scholarly journals FORESIGHT AS A TOOL OF TECHNOLOGICAL PLANNING IN THE MANAGEMENT OF PUBLIC JOINT STOCK COMPANY “GAZPROM” IN THE ERA OF DIGITALIZATION

2020 ◽  
pp. 54-62
Author(s):  
V. V. Degtyareva ◽  
D. A. Lozhnikova
Keyword(s):  
Big Data ◽  
Strategic Planning ◽  
Technology Assessment ◽  
Semantic Analysis ◽  
Graphical Model ◽  
Joint Stock Company ◽  
Stock Company ◽  
Technological Developments ◽  
Information Input

The issues of presenting the basic prerequisites for forecasting and planning tools for managing an organization based on the foresight method, – have been highlighted. The strategic planning mechanism of PJSC Gazprom has been described, the place of foresight research in the formation of a long-term strategy has been reflected, and interaction with the innovation environment has been reflected. Five stages of foresight research and their filling have been presented. The sequence of stages of foresight research has been described. A generalized picture of collecting the necessary information for conducting a foresight study and forming a pool of experts from the preliminary registry on thematic selected areas has been presented. A list of criteria for assessing the prospects of technologies, as well as the sequence of their selection in accordance with the system of prospects indexes of technological developments for further updating the organization’s strategy, – has been considered. A graphical model of the results of technology assessment for their use in the strategic planning of the organization. A digital model that makes a decision on the choice of the necessary technologies based on semantic analysis of big data – IFORA has been considered. A comparison of information input and applied methods has been made. When preparing the article, such research methods as analysis, synthesis, and generalization were used.

scholarly journals Big data integration enhancement based on attributes conditional dependency and similarity index method

2021 ◽  
Vol 18 (6) ◽  
pp. 8661-8682
Author(s):  
Vishnu Vandana Kolisetty ◽  
◽  
Dharmendra Singh Rajput ◽  
Keyword(s):  
Big Data ◽  
Semantic Analysis ◽  
Similarity Index ◽  
Data Sources ◽  
Sources Of Information ◽  
Index Method ◽  
Related Information ◽  
Multiple Data ◽  
Integration Techniques ◽  
Conditional Dependency

<abstract> <p>Big data has attracted a lot of attention in many domain sectors. The volume of data-generating today in every domain in form of digital is enormous and same time acquiring such information for various analyses and decisions is growing in every field. So, it is significant to integrate the related information based on their similarity. But the existing integration techniques are usually having processing and time complexity and even having constraints in interconnecting multiple data sources. Many of these sources of information come from a variety of sources. Due to the complex distribution of many different data sources, it is difficult to determine the relationship between the data, and it is difficult to study the same data structures for integration to effectively access or retrieve data to meet the needs of different data analysis. In this paper, proposed an integration of big data with computation of attribute conditional dependency (ACD) and similarity index (SI) methods termed as ACD-SI. The ACD-SI mechanism allows using of an improved Bayesian mechanism to analyze the distribution of attributes in a document in the form of dependence on possible attributes. It also uses attribute conversion and selection mechanisms for mapping and grouping data for integration and uses methods such as LSA (latent semantic analysis) to analyze the content of data attributes to extract relevant and accurate data. It performs a series of experiments to measure the overall purity and normalization of the data integrity, using a large dataset of bibliographic data from various publications. The obtained purity and NMI ratio confined the clustered data relevancy and the measure of precision, recall, and accurate rate justified the improvement of the proposal is compared to the existing approaches.</p> </abstract>

scholarly journals BIG DATA ANALYSIS FOR INNOVATION DEVELOPMENT DECISION MAKING IN ENERGY SECTOR

2020 ◽  
Author(s):  
А.В. Михеев
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Semantic Analysis ◽  
Big Data Analysis ◽  
Energy Sector ◽  
Innovative Development ◽  
Innovation Development ◽  
Bibliometric Review ◽  
Scopus Database

В статье рассматриваются возможности применения методов анализа больших данных для принятия решений по инновационному развитию в энергетике. Выполнен библиометрический обзор научных исследований по использованию анализа больших данных для задач в сфере энергетики на основе публикаций международной базы Scopus за 2010-2020 гг. Приведены содержательные задачи мониторинга, прогнозирования и оценки перспективности технологических решений в энергетике на основе семантического анализа больших данных. The article discusses the feasibility and possible applications of big data analysis for making decisions on innovative development in the energy sector. A bibliometric review of scientific research on the use of big data analysis for problems in the energy sector was carried out based on publications of Scopus database for 2010-2020. The substantive tasks of monitoring, forecasting and evaluating the prospects of technological solutions in the energy sector based on semantic analysis of big data are presented.

scholarly journals Demand Analysis of Online Chinese Behavior Expression in Wireless Sensor Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zheng Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Data Analysis ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Semantic Analysis ◽  
Big Data Analysis ◽  
Wireless Sensor ◽  
Data Set ◽  
Analysis Models

Due to the common progress and interdependence of wireless sensor networks and language, Chinese semantic analysis under wireless sensor networks has become more and more important. Although there are many research results on wireless networks and Chinese semantics, there are few researches on the influence and relationship between them. Wireless sensor networks have strong application relevance, and the key technologies that need to be solved are also different for different application backgrounds. In order to reveal the basic laws and development trends of online Chinese semantic behavior expression in the context of wireless sensor networks, this paper adopts big data analysis methods and semantic model analysis methods and constructs semantic analysis models through PLSA method calculations, so that the λ construction process conforms to this research topic. Research the accuracy and applicability of the semantic analysis model. Through word extraction of 1.05 million word data of 1,103 documents on Baidu Tieba, HowNet, and citeulike websites, the data set was integrated into a data set, and the PLSA model was verified with this data set. In addition, through the construction of the wireless sensor network, the semantic analysis results in the expression of Chinese behavior are obtained. The results show that the accuracy of the data set extracted from 1103 documents increases with the increase of the number of documents. Second, after using the PLSA model to perform semantic analysis on the data set, the accuracy of the data set is improved. Compared with traditional semantic analysis, the model and the big data analysis framework have obvious advantages. With the continuous development of Internet big data, the big data methods used to count Chinese semantics are also constantly updated, and their efficiency is constantly improving. These updated semantic analysis models and statistical methods are constantly eliminating the uncertainty of modern online Chinese. The basic laws and development trends of statistical Chinese semantics also provide new application scenarios for online Chinese behavior. It also laid a ladder for subsequent scholars.

scholarly journals A study on Predictive Modeling of Users’ Parasocial Relationship Types based on Social Media Text Big Data

2022 ◽  
Vol 16 ◽  
pp. 171-180
Author(s):  
Jiatong Meng ◽  
Yucheng Chen
Keyword(s):  
Social Media ◽  
Big Data ◽  
Prediction Model ◽  
Social Relationship ◽  
Performance Test ◽  
Semantic Analysis ◽  
Processing Efficiency ◽  
Relationship Type ◽  
Social Media Text ◽  
User Data

The traditional quasi-social relationship type prediction model obtains prediction results by analyzing and clustering the direct data. The prediction results are easily disturbed by noisy data, and the problems of low processing efficiency and accuracy of the traditional prediction model gradually appear as the amount of user data increases. To address the above problems, the research constructs a prediction model of user quasi-social relationship type based on social media text big data. After pre-processing the collected social media text big data, the interference data that affect the accuracy of non-model prediction are removed. The interaction information in the text data is mined based on the principle of similarity calculation, and semantic analysis and sentiment annotation are performed on the information content. On the basis of BP neural network, we construct a prediction model of user’s quasi-social relationship type. The performance test data of the model shows that the average prediction accuracy of the constructed model is 89.84%, and the model has low time complexity and higher processing efficiency, which is better than other traditional models.

Decomposition tree: a spatio-temporal indexing method for movement big data

2015 ◽  
Vol 18 (4) ◽  
pp. 1481-1492 ◽  
Author(s):  
Zhenwen He ◽  
Chonglong Wu ◽  
Gang Liu ◽  
Zufang Zheng ◽  
Yiping Tian
Keyword(s):  
Big Data ◽  
Decomposition Tree ◽  
Indexing Method ◽  
Spatio Temporal

scholarly journals Big data in IBD: big progress for clinical practice

Gut ◽  
2020 ◽  
Vol 69 (8) ◽  
pp. 1520-1532 ◽  
Author(s):  
Nasim Sadat Seyed Tabib ◽  
Matthew Madgwick ◽  
Padhmanand Sudhakar ◽  
Bram Verstockt ◽  
Tamas Korcsmaros ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Treatment Options ◽  
Health And Safety ◽  
Fine Tuning ◽  
Molecular Networks ◽  
Data Generation ◽  
Intrinsic Factors ◽  
Challenges And Opportunities ◽  
Efficient Integration

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation sequencing, high-throughput omics data generation and molecular networks have catalysed IBD research. The advent of artificial intelligence, in particular, machine learning, and systems biology has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically translatable knowledge. In this narrative review, we discuss how big data integration and machine learning have been applied to translational IBD research. Approaches such as machine learning may enable patient stratification, prediction of disease progression and therapy responses for fine-tuning treatment options with positive impacts on cost, health and safety. We also outline the challenges and opportunities presented by machine learning and big data in clinical IBD research.

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