UML Galaxy Diagram:An Approach of Conceptual Data Integration for OLAP

In e-commerce the multidimensional data analysis for OLAP (on-line analytical processing) based on the web data needs integrating various data sources such as XML (extensible markup language) data and relational data on the conceptual level. A conceptual data description approach of multidimensional data model was presented in order to conduct multidimensional data analysis of OLAP for multiple subjects. The UML (unified modeling language) galaxy diagram, describing the multidimensional structure of the conceptual integrating data at the conceptual level, was constructed. The approach was illuminated using a case of 2__roots UML galaxy diagram that takes one retailer and several suppliers of PC products into consideration.

The need for data integration to address the challenges of climate change on the Guyana coast

Guyana’s capacity to address the impacts of climate change on its coastal environment requires the ability to mon-itor,quantify and understand coastal change over short-,medium-and long-term.Understanding the drivers of change in coastal and marine environment can be achieved through the accurate measurement and critical anal-yses of morphologies,flows,processes and responses.This manuscript presents a strategy developed to create a central resource,database and web-based platform to integrate data and information on the drivers and the changes within Guyana coastal and marine environment.The strategy involves four complimentary work pack-ages including data collection,development of a platform for data integration,application of the data for coastal change analyses and consultation with stakeholders.The last aims to assess the role of the integrated data sys-tems to support strategic governance and sustainable decision-making.It is hoped that the output of this strategy would support the country’s climate-focused agencies,organisations,decision-makers,and researchers in their tasks and endeavours.

Comprehensive integration of single-cell transcriptomic data illuminates the regulatory network architecture of plant cell fate specification

Plant morphogenesis relies on precise gene expression programs at the proper time and position which is orchestrated by transcription factors(TFs)in intricate regulatory networks in a cell-type specific manner.Here we introduced a comprehensive single-cell transcriptomic atlas of Arabidopsis seedlings.This atlas is the result of meticulous integration of 63 previously published scRNA-seq datasets,addressing batch effects and conserving biological variance.This integration spans a broad spectrum of tissues,including both below-and above-ground parts.Utilizing a rigorous approach for cell type annotation,we identified 47 distinct cell types or states,largely expanding our current view of plant cell compositions.We systematically constructed cell-type specific gene regulatory networks and uncovered key regulators that act in a coordinated manner to control cell-type specific gene expression.Taken together,our study not only offers extensive plant cell atlas exploration that serves as a valuable resource,but also provides molecular insights into gene-regulatory programs that varies from different cell types.

Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

With the rapid development of information technology,IoT devices play a huge role in physiological health data detection.The exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge nodes.The storage capacity of edge nodes close to users is limited.We should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging problem.Our paper proposes a redundant data detection method that meets the privacy protection requirements.By scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot data.It has the same effect as zero-knowledge proof,and it will not reveal the privacy of users.In addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the data.We use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is deleted.The feasibility of the scheme is proved through safety analysis and efficiency analysis.

Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various benefits.However,the adoption of cloud storage poses significant risks to data secrecy and integrity.This article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic primitives.The proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced data.To preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing it.An extensive performance analysis is conducted to illustrate the efficiency of the proposed mechanism.Additionally,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running costs.The security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious entities.The proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Performance Analysis and Optimization of Energy Harvesting Modulation for Multi-User Integrated Data and Energy Transfer

Integrated data and energy transfer(IDET)enables the electromagnetic waves to transmit wireless energy at the same time of data delivery for lowpower devices.In this paper,an energy harvesting modulation(EHM)assisted multi-user IDET system is studied,where all the received signals at the users are exploited for energy harvesting without the degradation of wireless data transfer(WDT)performance.The joint IDET performance is then analysed theoretically by conceiving a practical time-dependent wireless channel.With the aid of the AO based algorithm,the average effective data rate among users are maximized by ensuring the BER and the wireless energy transfer(WET)performance.Simulation results validate and evaluate the IDET performance of the EHM assisted system,which also demonstrates that the optimal number of user clusters and IDET time slots should be allocated,in order to improve the WET and WDT performance.

A Semantic-Sensitive Approach to Indoor and Outdoor 3D Data Organization

Building model data organization is often programmed to solve a specific problem,resulting in the inability to organize indoor and outdoor 3D scenes in an integrated manner.In this paper,existing building spatial data models are studied,and the characteristics of building information modeling standards(IFC),city geographic modeling language(CityGML),indoor modeling language(IndoorGML),and other models are compared and analyzed.CityGML and IndoorGML models face challenges in satisfying diverse application scenarios and requirements due to limitations in their expression capabilities.It is proposed to combine the semantic information of the model objects to effectively partition and organize the indoor and outdoor spatial 3D model data and to construct the indoor and outdoor data organization mechanism of“chunk-layer-subobject-entrances-area-detail object.”This method is verified by proposing a 3D data organization method for indoor and outdoor space and constructing a 3D visualization system based on it.

Geospatial data ontology:the semantic foundation of geospatial data integration and sharing

Effective integration and wide sharing of geospatial data is an important and basic premise to facilitate the research and applications of geographic information science.However,the semantic heterogeneity of geospatial data is a major problem that significantly hinders geospatial data integration and sharing.Ontologies are regarded as a promising way to solve semantic problems by providing a formalized representation of geographic entities and relationships between them in a manner understandable to machines.Thus,many efforts have been made to explore ontology-based geospatial data integration and sharing.However,there is a lack of a specialized ontology that would provide a unified description for geospatial data.In this paper,with a focus on the characteristics of geospatial data,we propose a unified framework for geospatial data ontology,denoted GeoDataOnt,to establish a semantic foundation for geospatial data integration and sharing.First,we provide a characteristics hierarchy of geospatial data.Next,we analyze the semantic problems for each characteristic of geospatial data.Subsequently,we propose the general framework of GeoDataOnt,targeting these problems according to the characteristics of geospatial data.GeoDataOnt is then divided into multiple modules,and we show a detailed design and implementation for each module.Key limitations and challenges of GeoDataOnt are identified,and broad applications of GeoDataOnt are discussed.

Integration and implementation of precision medicine in the multifaceted inflammatory bowel disease

Inflammatory bowel disease(IBD)is a complex disease with variability in genetic,environmental,and lifestyle factors affecting disease presentation and course.Precision medicine has the potential to play a crucial role in managing IBD by tailoring treatment plans based on the heterogeneity of clinical and temporal variability of patients.Precision medicine is a population-based approach to managing IBD by integrating environmental,genomic,epigenomic,transcriptomic,proteomic,and metabolomic factors.It is a recent and rapidly developing medicine.The widespread adoption of precision medicine worldwide has the potential to result in the early detection of diseases,optimal utilization of healthcare resources,enhanced patient outcomes,and,ultimately,improved quality of life for individuals with IBD.Though precision medicine is promising in terms of better quality of patient care,inadequacies exist in the ongoing research.There is discordance in study conduct,and data collection,utilization,interpretation,and analysis.This review aims to describe the current literature on precision medicine,its multiomics approach,and future directions for its application in IBD.

Multi-Sinusoidal Waveform Shaping for Integrated Data and Energy Transfer in Aging Channels

Integrated data and energy transfer(IDET)is capable of simultaneously delivering on-demand data and energy to low-power Internet of Everything(Io E)devices.We propose a multi-carrier IDET transceiver relying on superposition waveforms consisting of multi-sinusoidal signals for wireless energy transfer(WET)and orthogonal-frequency-divisionmultiplexing(OFDM)signals for wireless data transfer(WDT).The outdated channel state information(CSI)in aging channels is employed by the transmitter to shape IDET waveforms.With the constraints of transmission power and WDT requirement,the amplitudes and phases of the IDET waveform at the transmitter and the power splitter at the receiver are jointly optimised for maximising the average directcurrent(DC)among a limited number of transmission frames with the existence of carrier-frequencyoffset(CFO).For the amplitude optimisation,the original non-convex problem can be transformed into a reversed geometric programming problem,then it can be effectively solved with existing tools.As for the phase optimisation,the artificial bee colony(ABC)algorithm is invoked in order to deal with the nonconvexity.Iteration between the amplitude optimisation and phase optimisation yields our joint design.Numerical results demonstrate the advantage of our joint design for the IDET waveform shaping with the existence of the CFO and the outdated CSI.

Blockchain and Data Integrity Authentication Technique for Secure Cloud Environment

Nowadays,numerous applications are associated with cloud and user data gets collected globally and stored in cloud units.In addition to shared data storage,cloud computing technique offers multiple advantages for the user through different distribution designs like hybrid cloud,public cloud,community cloud and private cloud.Though cloud-based computing solutions are highly con-venient to the users,it also brings a challenge i.e.,security of the data shared.Hence,in current research paper,blockchain with data integrity authentication technique is developed for an efficient and secure operation with user authentica-tion process.Blockchain technology is utilized in this study to enable efficient and secure operation which not only empowers cloud security but also avoids threats and attacks.Additionally,the data integrity authentication technique is also uti-lized to limit the unwanted access of data in cloud storage unit.The major objec-tive of the projected technique is to empower data security and user authentication in cloud computing environment.To improve the proposed authentication pro-cess,cuckoofilter and Merkle Hash Tree(MHT)are utilized.The proposed meth-odology was validated using few performance metrics such as processing time,uploading time,downloading time,authentication time,consensus time,waiting time,initialization time,in addition to storage overhead.The proposed method was compared with conventional cloud security techniques and the outcomes establish the supremacy of the proposed method.

Utilizing Machine Learning with Unique Pentaplet Data Structure to Enhance Data Integrity

Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative consequences.Intrusion detection systems are essential for keeping databases secure.Advancements in technology will lead to significant changes in the medical field,improving healthcare services through real-time information sharing.However,reliability and consistency still need to be solved.Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption.Dis-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is vital.This research presents a role-based access control architecture for an anomaly detection technique.Additionally,the Structured Query Language(SQL)queries are stored in a new data structure called Pentaplet.These pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual behavior.To identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).

Blockchain-Based Privacy-Preserving Public Auditing for Group Shared Data

Cloud storage has been widely used to team work or cooperation devel-opment.Data owners set up groups,generating and uploading their data to cloud storage,while other users in the groups download and make use of it,which is called group data sharing.As all kinds of cloud service,data group sharing also suffers from hardware/software failures and human errors.Provable Data Posses-sion(PDP)schemes are proposed to check the integrity of data stored in cloud without downloading.However,there are still some unmet needs lying in auditing group shared data.Researchers propose four issues necessary for a secure group shared data auditing:public verification,identity privacy,collusion attack resis-tance and traceability.However,none of the published work has succeeded in achieving all of these properties so far.In this paper,we propose a novel block-chain-based ring signature PDP scheme for group shared data,with an instance deployed on a cloud server.We design a linkable ring signature method called Linkable Homomorphic Authenticable Ring Signature(LHARS)to implement public anonymous auditing for group data.We also build smart contracts to resist collusion attack in group auditing.The security analysis and performance evalua-tion prove that our scheme is both secure and efficient.

Blockchain-based Privacy-Preserving Group Data Auditing with Secure User Revocation

Progress in cloud computing makes group data sharing in outsourced storage a reality.People join in group and share data with each other,making team work more convenient.This new application scenario also faces data security threats,even more complex.When a user quit its group,remaining data block signatures must be re-signed to ensure security.Some researchers noticed this problem and proposed a few works to relieve computing overhead on user side.However,considering the privacy and security need of group auditing,there still lacks a comprehensive solution to implement secure group user revocation,supporting identity privacy preserving and collusion attack resistance.Aiming at this target,we construct a concrete scheme based on ring signature and smart contracts.We introduce linkable ring signature to build a kind of novel meta data for integrity proof enabling anonymous verification.And the new meta data supports secure revocation.Meanwhile,smart contracts are using for resisting possible collusion attack and malicious re-signing computation.Under the combined effectiveness of both signature method and blockchain smart contracts,our proposal supports reliable user revocation and signature re-signing,without revealing any user identity in the whole process.Security and performance analysis compared with previous works prove that the proposed scheme is feasible and efficient.

Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.

UAV-Aided Data and Energy Integrated Network: System Design and Prototype Development

Terminal devices deployed in outdoor environments are facing a thorny problem of power supply.Data and energy integrated network(DEIN)is a promising technology to solve the problem,which simultaneously transfers data and energy through radio frequency signals.State-of-the-art researches mostly focus on theoretical aspects.By contrast,we provide a complete design and implementation of a fully functioning DEIN system with the support of an unmanned aerial vehicle(UAV).The UAV can be dispatched to areas of interest to remotely recharge batteryless terminals,while collecting essential information from them.Then,the UAV uploads the information to remote base stations.Our system verifies the feasibility of the DEIN in practical applications.

Data Integrity and Risk

Data Integrity is a critical component of Data lifecycle management. Its importance increases even more in a complex and dynamic landscape. Actions like unauthorized access, unauthorized modifications, data manipulations, audit tampering, data backdating, data falsification, phishing and spoofing are no longer restricted to rogue individuals but in fact also prevalent in systematic organizations and states as well. Therefore, data security requires strong data integrity measures and associated technical controls in place. Without proper customized framework in place, organizations are prone to high risk of financial, reputational, revenue losses, bankruptcies, and legal penalties which we shall discuss further throughout this paper. We will also explore some of the improvised and innovative techniques in product development to better tackle the challenges and requirements of data security and integrity.

Integrating multisource RS data and GIS techniques to assist the evaluation of resource-environment carrying capacity in karst mountainous area

The karst mountainous area is an ecologically fragile region with prominent humanland contradictions.The resource-environment carrying capacity(RECC)of this region needs to be further clarified.The development of remote sensing(RS)and geographic information system(GIS)provides data sources and processing platform for RECC monitoring.This study analyzed and established the evaluation index system of RECC by considering particularity in the karst mountainous area of Southwest China;processed multisource RS data(Sentinel-2,Aster-DEM and Landsat-8)to extract the spatial distributions of nine key indexes by GIS techniques(information classification,overlay analysis and raster calculation);proposed the methods of index integration and fuzzy comprehensive evaluation of the RECC by GIS;and took a typical area,Guangnan County in Yunnan Province of China,as an experimental area to explore the effectiveness of the indexes and methods.The results showed that:(1)The important indexes affecting the RECC of karst mountainous area are water resources,tourism resources,position resources,geographical environment and soil erosion environment.(2)Data on cultivated land,construction land,minerals,transportation,water conservancy,ecosystem services,topography,soil erosion and rocky desertification can be obtained from RS data.GIS techniques integrate the information into the RECC results.The data extraction and processing methods are feasible on evaluating RECC.(3)The RECC of Guangnan County was in the mid-carrying level in 2018.The midcarrying and low-carrying levels were the main types,accounting for more than 80.00%of the total study area.The areas with high carrying capacity were mainly distributed in the northern regions of the northwest-southeast line of the county,and other areas have a low carrying capacity comparatively.The coordination between regional resource-environment status and socioeconomic development is the key to improve RECC.This study explores the evaluation index system of RECC in karst mountainous area and the application of multisource RS data and GIS techniques in the comprehensive evaluation.The methods can be applied in related fields to provide suggestions for data/information extraction and integration,and sustainable development.

iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision therapies.AIM To investigate whether iCEMIGE(integration of CEll-morphometrics,MIcro-biome,and GEne biomarker signatures)improves risk stratification of breast cancer(BC)patients.METHODS We used our recently developed machine learning technique to identify cellular morphometric biomarkers(CMBs)from the whole histological slide images in The Cancer Genome Atlas(TCGA)breast cancer(TCGA-BRCA)cohort.Multivariate Cox regression was used to assess whether cell-morphometrics prognosis score(CMPS)and our previously reported 12-gene expression prognosis score(GEPS)and 15-microbe abundance prognosis score(MAPS)were independent prognostic factors.iCEMIGE was built upon the sparse representation learning technique.The iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS,GEPS,or MAPS alone.Nomogram models were created to predict overall survival(OS)and progress-free survival(PFS)rates at 5-and 10-year in the TCGA-BRCA cohort.RESULTS We identified 39 CMBs that were used to create a CMPS system in BCs.CMPS,GEPS,and MAPS were found to be significantly independently associated with OS.We then established an iCEMIGE scoring system for risk stratification of BC patients.The iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors(age,stage,and estrogen and progesterone receptor status)and PAM50-based molecular subtype.Importantly,the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS,GEPS,or MAPS alone.CONCLUSION Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients,which can be extended to other types of cancer.