Integration Of Data Resources Research Articles

Data Integration to Support Child Care Business Sustainability: Iowa’s Child Care Connect

Working parents need access to childcare, and the unprecedented COVID-19 pandemic highlighted critical gaps in data system capacities to meet this need. This was particularly acute in the US where many child care providers closed their doors permanently because they could not sustain their business. The state of Iowa estimates losses over $930 million per year to businesses and individuals due to a lack of childcare. Childcare businesses are not sustainable if they cannot fill slots and be consistently paid for care – and families need to be able to find care when they need it to fill those slots. Iowa is addressing this gap by developing a real-time, integrated data solution that includes historically disparate data systems to better identify childcare supply and demand. Through intensive community engagement processes including leadership from state agencies, the development team created a system that includes multiple technology solutions for real-time data sharing; a secure cloud-based system for storage, integration, and analysis; and two public-facing applications that allow data to be aggregated and shared for strategic decision-making as well as used by families to identify available care when and where they need it. This presentation will discuss challenges in cross-sector data linkage with a focus on child care providers, families, and population demand. It will include an overview of the development process, governance structure, and analytic uses of the system. Highlights include identification of real-time vacancies by age group, geography, quality rating, and in relation to estimated potential demand using additional integrated data resources.

TE-TSS: an integrated data resource of human and mouse transposable element (TE)-derived transcription start site (TSS).

Transposable elements (TEs) are abundant in the genome and serve as crucial regulatory elements. Some TEs function as epigenetically regulated promoters, and these TE-derived transcription start sites (TSSs) play a crucial role in regulating genes associated with specific functions, such as cancer and embryogenesis. However, the lack of an accessible database that systematically gathers TE-derived TSS data is a current research gap. To address this, we established TE-TSS, an integrated data resource of human and mouse TE-derived TSSs (http://xozhanglab.com/TETSS). TE-TSS has compiled 2681 RNA sequencing datasets, spanning various tissues, cell linesand developmental stages. From these, we identified 5768 human TE-derived TSSs and 2797 mouse TE-derived TSSs, with 47% and 38% being experimentally validated, respectively. TE-TSS enables comprehensive exploration of TSS usage in diverse samples, providing insights into tissue-specific gene expression patterns and transcriptional regulatory elements. Furthermore, TE-TSS compares TE-derived TSS regions across 15 mammalian species, enhancing our understanding of their evolutionary and functional aspects. The establishment of TE-TSS facilitates further investigations into the roles of TEs in shaping the transcriptomic landscape and offers valuable resources for comprehending their involvement in diverse biological processes.

IP for Sharing and Opening Government Data Based on Big Data

With the continuous application and popularization of big data technology, all government departments are aware of the importance of data as a national basic strategic resource, and all over the country have been promoting the sharing and opening (SOA) of local government data (GA) one after another, improving the organization, formulating government regulations, and building a GA SOA platform. At present, GA are kept separately in each department, while there is no good DS scheme, resulting in low data sharing (DS), lack of authenticity of shared data, easy leakage of data, and difficulty in data tracking. Through the analysis of the above problems, an integrated platform(IP) of GA SOA based on big data is designed, aiming to realize safe and controlled sharing of GA. The application of this platform helps to efficiently integrate data resources and maximize the realistic value of GA for people's services in order to achieve sustainable social development.

Opportunities for improving data sharing and FAIR data practices to advance global mental health.

It is crucial to optimize global mental health research to address the high burden of mental health challenges and mental illness for individuals and societies. Data sharing and reuse have demonstrated value for advancing science and accelerating knowledge development. The FAIR (Findable, Accessible, Interoperable, and Reusable) Guiding Principles for scientific data provide a framework to improve the transparency, efficiency, and impact of research. In this review, we describe ethical and equity considerations in data sharing and reuse, delineate the FAIR principles as they apply to mental health research, and consider the current state of FAIR data practices in global mental health research, identifying challenges and opportunities. We describe noteworthy examples of collaborative efforts, often across disciplinary and national boundaries, to improve Findability and Accessibility of global mental health data, as well as efforts to create integrated data resources and tools that improve Interoperability and Reusability. Based on this review, we suggest a vision for the future of FAIR global mental health research and suggest practical steps for researchers with regard to study planning, data preservation and indexing, machine-actionable metadata, data reuse to advance science and improve equity, metrics and recognition.

Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado

The use of social media, such as Twitter, has changed the information landscape for citizens’ participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise, such as how the usage of Twitter by a large population may change during the development of a major drought alongside how the changing usage facilitates drought detection. For this reason, contemporary analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Three different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is found that the integration of data resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Eight of the ten models tested showed quantifiable improvements in the performance over the control run model, suggesting that the Twitter-based model was superior in predicting drought severity. Future work lies in expanding this method to depict drought in the western U.S.

Data Analysis of Building Microrenovation Based on City Information Modeling and Smart Sensor Networks

Building microrenovation refers to the building renewal method implemented through partial demolition, repair, preservation, function replacement, renovation, improvement, protection, decoration, and activation under the premise of maintaining the current construction pattern basically unchanged. City information modeling (CIM) technology provides a three-dimensional space floor with all elements for refined city governance and smart city construction; the data layers of smart sensor network (SSN) technology can aggregate multisource data by docking with facility layers and can classify integrated data resources. On the basis of summarizing and analyzing the previous research results, this study expounded the research status and significance of data analysis of building microrenovation, elaborated the development background, current status, and future outlooks of CIM and SSN, introduced the methods and principles of the aggregation and segmentation of information resources and the aggregation and interconnection of multisource data, performed the feature extraction and data storage of the original buildings, conducted the scheme design and information classification of microrenovation, implemented the data extraction and model construction of building microrenovation based on CIM and SSN, performed the data analysis of building appearance improvement, conducted the data analysis of building function replacement, implemented the database establishment and data analysis of building microrenovation based on CIM and SSN, and finally carried out a case application and its result analysis by taking a middle school two-story teaching building as an example. The study results show that the CIM technology can provide buildings’ actual data information such as geometry, physics, structure, and rules for building microrenovation; the SSN technology greatly reduces a lot of work caused by the rewiring of control system in the construction of building microrenovation. The combination of CIM and SSN can effectively connect various entity elements in building microrenovation to form a theme library through various data spatial relationships and spatial coding, realizing the connection of spatial data and nonspatial data in multiple dimensions. The data analysis of building microrenovation based on CIM and SSN includes multiple dimensions of achievements, processes, resources, characteristics, and applications, which is more in line with CIM integration applications and is easier for realistic information exchange and sharing applications.

Research on the Protection of Personal Data Rights——From the perspective of the application of privacy computing technology

With the development of marketization of data elements, privacy computing arises at the right moment in order to solve the contradiction between data circulation and its security risks. It is widely used in financial market, medical service, government management and other scenarios. It provides a new way for the identification of personal data rights, subject self-determination, safe circulation and corporate compliance, but also brings new challenges. From the perspective of the path and application scenarios of the key technologies of privacy computing technology, we can find the problems of personal data protection put forward by the current technology; privacy computing technology still has security risks in the process of data processing, and its industrial business model limits the benefits of individuals to their own data. Meanwhile, laws and regulations related to this technology also need to be refined and implemented. In order to solve these problems, it is necessary to promote the optimization of key technologies and the formulation of standards, build self-discipline rules and associations of the industry, strengthen legislation and special supervision of personal data, and build a global governance framework for the protection of personal data rights in privacy computing. It can enhance the use value of data, promote the integration of data resources and international circulation and sharing under the condition that the rights of personal data are fully protected.

Research on management informatization construction of electric power enterprise based on big data technology

Background:The energy industry is the leader of the third industrial revolution, and the field of big data is the concrete embodiment of the Internet. With the advent of the era of big data, data is growing at a very fast speed, forming a large number of digital resources including text data, pictures, audio, video and other multimedia data. The application of big data will bring changes in technology application, service mode and development ideas to the power grid. Objective:The purpose of this paper is to improve the data analysis and data mining ability in the related fields of power energy enterprise management, and improve the service ability and service quality of power marketing business through the informatization construction of power energy enterprises. Methods:According to the increasingly urgent scientific decision-making needs of large-scale energy enterprises, the paper combines big data technology with cloud computing technology to construct the management information system of electric power and energy enterprises. Conclusion:The electricity consumption information collection system adopts distributed architecture to collect electricity consumption data, establishes a big data cloud platform, and presents the operation and maintenance information to managers with vivid and intuitive three-dimensional graphics, which facilitates the integration of data resources of multiple heterogeneous operation and maintenance systems, and improves the intuitive perception and judgment ability of operation and maintenance managers on the operation state of data centers to a certain extent.

Effect Evaluation of Eco-Environmental Big Data Resource Integration and Data Sharing Construction

The application of big data in the field of ecological environment has achieved remarkable results, but there are still problems such as insufficient innovation and lack of shared service ability. This research takes the big data platform as the technical support and makes full use of advanced technologies such as internet of things, cloud computing, and big data to realize the integration of ecological environment big data resources and real-time data sharing. Through network technology, the ecological environment metadata can be automatically obtained from the internet and stored. When sharing ecological environment metadata, realize the data response and declassification of geospatial data for different user access levels, and finally safely feedback the ecological environment spatial big data integrated with the target ecological environment metadata and declassified geospatial data to the access initiator, so as to realize the centralized sharing of decentralized environmental information resources and give full play to the supporting role of big data, so that big data can be effectively applied in environmental monitoring. Find out the changes of pollution sources and pollutants through massive data, so as to provide necessary data support for the whole environmental monitoring.

Research on Optimization and Application of University Student Development and Management Strategy Driven by Multidimensional Big Data

The purpose of education is to enable students to develop fully, freely, comprehensively, and harmoniously. The development of college students is a social problem that must be focused on in higher education under the background of China’s powerful human resources. It is an important subject that higher education must attach great importance to and solve to find out the way and method to solve and avoid the development of college students to help them face risks correctly, meet challenges, and grow healthily. Under the background in the era of big data, Internet and mobile intelligent terminal has great popularity in colleges and universities, especially in the digital library, the official platform construction, and is widely used in Internet multimedia technology in college classroom, although for college students and the teacher provides vast amounts of information data, exploring the college students’ horizons, enriching the students’ knowledge structure. But the explosive growth of data and information security threat to the development of college students management work still bring the severe test of this; education workers in colleges and universities should comply with the development of The Times, the big data organic blend in the current education system, and the specific practice of education, for modern education practice and college students’ all-round development’s age characteristic new development mode and effective way. In order to adapt to the new form of the development of The Times, the management of colleges and universities should keep pace with The Times, and integrate the concept of big data and advanced technology into the education management work, so as to realize the national deployment of the education system innovation development strategy, explore teaching rules, students’ growth and development tasks, and other realistic needs of the times. In addition, making big data the most powerful internal driving force for educational development helps colleges and universities to realize the frontier, timeliness, interactivity, and individuation of educational management. Colleges and universities should actively apply the idea of big data, improve the data resource integration and use ability of the staff in the field of education management through diversified means, improve the management level in multiple dimensions, and promote the updating and upgrading of the management structure of higher education and teaching. By analyzing the background of big data era, this paper analyzes the challenges of college students’ management in the era of big data and puts forward the strategies of college students’ management in the era of big data, so as to improve the quality and efficiency of college students’ management.

Informatics Ecosystems to Advance the Biology of Glycans.

Glycomics researchers have identified the need for integrated database systems for collecting glycomics information in a consistent format. The goal is to create a resource for knowledge discovery and dissemination to wider research communities. This has the potential and has exhibited initial success, to extend the research community to include biologists, clinicians, chemists, and computer scientists. This chapter discusses the technology and approach needed to create integrated data resources and informatics ecosystems to empower the broader community to leverage extant glycomics data. The focus is on glycosaminoglycan (GAGs) and proteoglycan research, but the approach can be generalized. The methods described span the development of glycomics standards from CarbBank to Glyco Connection Tables. Integrated data sets provide a foundation for novel methods of analysis such as machine learning and deep learning for knowledge discovery. The implications of predictive analysis are examined in relation to disease biomarker to expand the target audience of GAG and proteoglycan research.

Particular matter prediction using synergy of multiple source urban big data in smart cities

At present, the issue of air quality in populated urban areas is recognized as an environmental crisis. Air pollution affects the sustainability of the city. In controlling air pollution and protecting its hazards from humans, air quality data are very important. However, the costs of constructing and maintaining air quality registration infrastructure are very expensive and high, and air quality data recording at one point will not be generalizable to even a few kilometers. Some of the gains come from the integration of multiple data sources, which can never be achieved through independent single-source processing. Urban organizations in each city independently produce and record data relevant to the organization’s goals and objectives. These issues create separate data silos associated with an urban system. These data are varied in model and structure, and the integration of such data provides an appropriate opportunity to discover knowledge that can be useful in urban planning and decision making. This paper aims to show the generality of our previous research, which proposed a novel model to predict Particulate Matter (PM) as the main factor of air quality in the regions of the cities where air quality sensors are not available through urban big data resources integration, by extending the model and experiments with various configuration for different settings in smart cities. This work extends the evaluation scenarios of the model with the extended dataset of city of Aarhus, in Denmark, and compare the model performance against various specified baselines. Details of removing the heterogeneity of multiple data sources in the Multiple Data Set Aggregator & Heterogeneity Remover (MDA&HR) and improving the operation of Train Data Splitter (TDS) part of the model by focusing on the finding more similar pattern of air quality also are presented in this paper. The acceptable accuracy of the results shows the generality of the model.

The Managing Epilepsy Well (MEW) network database: Lessons learned in refining and implementing an integrated data tool in service of a national U.S. Research Collaborative

Epilepsy self-management (ESM) is the summative set of behaviors that people with epilepsy use to cope with seizures and optimize health. This report describes the implementation and evolution of the Managing Epilepsy Well Network Database (MEW DB), an integrated data resource intended to advance knowledge on ESM. The MEW DB utilizes a three-tiered (Tier 1–3) system of data organization, with tiers of data generally increasing in ascending complexity or collection burden. A MEW DB Steering Committee (SC) establishes consensus on planned analyses using a standardized new analysis request template. The data management structure facilitates harmonization and integration of additional data, or to update the database as new data become available. The current MEW DB comprises 1,563 people with epilepsy. Mean age was 39.9 years, 64.9% women (N = 1006), 12.8% African American (N = 170), 22.2% Hispanic (N = 306). On average, individuals have lived with epilepsy since their early 20s and are prescribed between 1 and 2 antiepileptic drugs. The MEW DB spans multiple socio-ecological levels to provide a robust multi-tiered framework for studying ESM. A total of 41 common data elements have been identified through iterative consensus. This integrated database takes advantage of an extensive collective background of archival evidence in ESM and brings together engaged investigators to build a dataset that represents diverse types of individuals with epilepsy, targets health domains important to ESM, and facilitates analyses that would not be possible with sites operating independently. Overall, the MEW DB serves the greater mission of this research collaborative and has potential to advance ESM research.

Analysis of clinicopathologic characteristics of gastric cancer patients undergoing gastrectomy based on a single-center gastric cancer database with above 10 000 cases

Objective: The storage of medical data has been digitized in China, but a unified and structured model has not yet been established. The standardized collection, analysis and sorting of tumor clinical data is the foundation of improving the standard of tumor diagnosis and treatment. Therefore, establishing a database platform of gastric cancer (GC) is an urgent need to integrate data resources and improve the level of diagnosis and treatment. The population economics indexes of GC patients in the last 20 years are analyzed in a single-center GC database. The medical records were structured by natural language processing technology. Authors aim to investigate the clinical pathological characteristics, staging and survival of the GC patients with gastrectomy. Method: A retrospective cohort study was carried out. Clinicopatological data of patients receiving surgical treatment from 2000 to 2019 were retrospectively collected. According to the gastric cancer TNM staging guidelines from the Union for International Cancer Control and the American Joint Committee on Cancer (UICC/AJCC) 8th edition, the structured gastric cancer clinicopathological data were re-evaluated and interpreted. The Kaplan-Meier method and the log-rank test were used to compare survival rate among different groups of patients with complete follow-up data of 2010-2016. Results: Clinicopathological data of 13 492 GC patients were enrolled. The ratio of men to women in the whole group was 3.25:1.00, including 10 320 men with average onset age of 59.68 years, which was basically stable in recent 20 years, and 3172 women with average onset age of 55.93 years, which presented a trend of average increasement of 0.17 year per year. The average hospitalization duration for GC patients showed a decreasing trend year by year, which was 13.87 days in 2019. Average hospitalization cost for GC patients was increasing year by year, with a peak of 83 600 CNY in 2017 and 75 400 CNY in 2019. By natural language identification and exclusion criteria screening, a total of 7218 GC patients obtained structured clinicopathological information. Analysis on clinicopathological characteristics of 3626 GC patients in the last 5 years showed that the average diameter of tumor was (4.44±2.61) cm; the average number of harvested lymph node was 24.30±13.29; the proportion of surgical methods were as following: open surgery in 1398 cases (38.55%), laparoscopic surgery in 1856 cases (51.19%) and robotic surgery in 372 cases (10.26%). The postoperative pathological stage was as following: IA in 658 cases (18.15%), IB in 318 cases (8.77%), IIA in 559 cases (15.42%), IIB in 543 (14.98%), III A in 632 (17.43%), III B in 612 cases (16.88%), III C in 276 cases (7.61%), and IV in 28 cases (0.77%). Complete follow-up data of 3431 patients from 2010 to 2016 were presented. The 1-, 3- and 5-year survival rates were 82%, 69% and 60%, respectively for the whole group. The 1-, 3- and 5-year survival rates for patients undergoing laparoscopic surgery were 83%, 70% and 64%, respectively, and for those undergoing open surgery were 81%, 67% and 56%, respectively, and the difference between the two groups was not statistically significant (P=0.109). The 5-year survival rate of GC patients with different AJCC stages was as following: 88% in IA, 77% in IB, 70% in II A, 62% in II B, 44% in III A, 32% in III B, 22% in III C, and 17% in IV. Conclusion: This study provides basic data for the establishment of comprehensive diagnosis and treatment model of multicenter, shedding light on the improvement of comprehensive treatment of GC in China.

Research on Urban and Rural Planning and Smart City Construction in Big Data Era

Recent years have witnessed faster information development and the wide application of big data, Internet of Things (IoT) and other intelligent technologies, which has laid a solid foundation for the development of new technologies and new environment. [1] Countries and regions around the world are promoting the digital smart development strategy with the hope of building smart urban and rural areas. In big data era, the main task of smart city planning and construction is the integration of data resources and the open sharing of data information. Therefore, information technology is indispensable for the planning and construction. Proceeding from the big data era, the paper explores technical details related to smart city planning and construction, aiming to create a more comfortable and livable environment for people and promote the overall improvement of China’s urban construction. [2]

Construction and Practice of Public Service Platform Based on Trusted Blockchain

In recent years, the digital economy has experienced the era of digital economy 1.0, the era of all-round electronization brought about by the rise of personal computers, and the era of 2.0, the era of all-round data brought about by the popularity of the Internet and mobile Internet, and entered the era of 3.0, the era of “digital right economy”. In the era of digital rights economy, all aspects of social and economic life will create new ecology and models. Digital identity and digital governance, digital asset transaction circulation, digital payment and settlement, and digital industry integration constitute the core sector of digital rights economic development. Blockchains has become the preferred model for establishing digital identity and governance due to its characteristics of multi-party co-governance, distributed storage, transparent rules, and permanent and true data persistence. This paper proposes to solve the problem of economic governance derived from the era of digital economy by building a blockchain service platform. The main achievements of this paper are to create new business models, actively explore the deepening application of blockchain technology in the field of energy and power, break through data barriers, activate data value, develop digital economy, and promote data fusion and data resource integration in the field of energy and power.

IRESbase: A Comprehensive Database of Experimentally Validated Internal Ribosome Entry Sites

Internal ribosome entry sites (IRESs) are functional RNA elements that can directly recruit ribosomes to an internal position of the mRNA in a cap-independent manner to initiate translation. Recently, IRES elements have attracted much attention for their critical roles in various processes including translation initiation of a new type of RNA, circular RNA (circRNA), with no 5′ cap to support classical cap-dependent translation. Thus, an integrative data resource of IRES elements with experimental evidence will be useful for further studies. In this study, we present IRESbase, a comprehensive database of IRESs, by curating the experimentally validated functional minimal IRES elements from literature and annotating their host linear and circular RNAs. The current version of IRESbase contains 1328 IRESs, including 774 eukaryotic IRESs and 554 viral IRESs from 11 eukaryotic organisms and 198 viruses, respectively. As IRESbase collects only IRES of minimal length with functional evidence, the median length of IRESs in IRESbase is 174 nucleotides. By mapping IRESs to human circRNAs and long non-coding RNAs (lncRNAs), 2191 circRNAs and 168 lncRNAs were found to contain at least one entire or partial IRES sequence. IRESbase is available at http://reprod.njmu.edu.cn/cgi-bin/iresbase/index.php.

Network connectivity of the creative brain: Current knowledge and future directions

Creativity is an important manifestation of human wisdom and is generally defined as the ability to generate something both novel and useful. Emerging evidence suggests that the complex cognitive processes of creative thinking involve the interaction of multiple brain regions and networks and may not depend on the specific function of a single brain region. The study of the human connectome provides a chance to reveal the brain network basis of creativity, and the focus in the neural mechanisms of creativity has gradually expanded from the local region to the brain network. A growing number of functional brain network studies have shown a consistent pattern of functional network connectivity underlying creativity that has been characterized by interactions between the default mode network, the salience network, and the fronto-parietal control network. Dynamic functional connectivity analysis further suggested that the coupling of the network hubs dynamically changes at different stages of creative thinking. The spontaneous neural activity of the creative brain has also been characterized by flexibility and variability, even during the resting state. On the other hand, evidence from brain lesion research has confirmed that the default mode network and the fronto-parietal control network are responsible for different aspects of creative cognitive abilities, such as the generation of remote associates and the combination of remote associates. According to the dual-process theory of creative thinking, researchers have suggested that the default mode network retrieves and extracts novel information from the semantic and episodic memory systems through spontaneous associations. The fronto-parietal control network assesses and selects current views according to specific task goals. Moreover, the salience network may be responsible for the flexible switching between the default mode network and the fronto-parietal control network based on the novelty of the associative content. Future research needs to continuously integrate data resources to identify the main components of creativity and to develop advanced assessment tools with more ecological validity, which is a critical and fundamental endeavor for scientific research on creativity. Equally important, it is necessary to combine multimodal brain imaging to provide new insight into the dynamic collaborative mode of functional networks and the specific role of structural networks in creative thinking that would contribute to deepening the understanding of the cognitive processes and neural mechanisms of creativity. In addition, due to the poor predictive power of single modality data, future studies could take advantage of multi-omics and multimodal data to achieve the goal of accurate prediction and assessment of individuals’ creativity. Furthermore, it is important to explore how to improve individuals’ creative abilities and develop effective promotion methods, such as cognitive training and neuromodulation. Although specific interventions have led to some improvements in individuals’ creativity, the dynamic underlying neural mechanisms of brain plasticity during the intervention are not clear and should be addressed in the future.

RESEARCH AND APPLICATION OF FOLK SPORTS HERITAGE BASED ON BIG DATA

In order to study the challenges faced by folk sports inheritance in the context of big data and explore the scientific and rational ways of inheritance, this article started with the development status of folk sports and the characteristics of big data, and analyzed the issues of insufficient digital resources, serious data barriers, and lack of comprehensive talents in the inheritance of folk sports in the context of big data, and then attempted to build a legacy of folk sports in the age of big data. The results showed that building a folk sports database, constructing a folk sports data platform, integrating data resources, and cultivating big data talents were all reasonable approaches for the inheritance and development of folk sports. In the context of the era of big data, folk sports want survival and development must be combined with the times to achieve the scientific integration of tradition and modernization.

Research on Virtual and Identity Identity, Correlation and Analysis Technology of Internet-related Economic Crimes Based on Big Data

In recent years, social activities on the Internet have been frequent, and people have carried out network social activities by registering different IDs on various platforms, which has formed a new identity - Internet virtual identity. A large number of virtual identity exchanges form a social network relationship diagram, but the network virtual identity does not require real-name authentication, which increases the security maintenance and supervision of the network. Therefore, the investigation of network-related economic crimes under big data faces new challenges. In response to this situation, a combination of virtual and real identity, association and analysis techniques based on big data is proposed. Firstly, through the integration and processing of heterogeneous data, the data resources integration under the condition of decentralized data source and multi-database data source is realized. Secondly, the verification and analysis platform of Internet information is built by Solr which is an open source framework. Finally, using full-text search technology to achieve target search and virtual identity association analysis under big data.