With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service...With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.展开更多
The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legisla...The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legislation to establish their data sovereignty,they are also actively engaging in the negotiation of cross-border data flow rules within international trade agreements to construct data sovereignty.During these negotiations,countries express differing regulatory claims,with some focusing on safeguarding sovereignty and protecting human rights,some prioritizing economic promotion and security assurance,and others targeting traditional and innovative digital trade barriers.These varied approaches reflect the tension between three pairs of values:collectivism and individualism,freedom and security,and tradition and innovation.Based on their distinct value pursuits,three representative models of data sovereignty construction have emerged globally.At the current juncture,when international rules for digital trade are still in their nascent stages,China should timely establish its data sovereignty rules,actively participate in global data sovereignty competition,and balance its sovereignty interests with other interests.Specifically,China should explore the scope of system-acceptable digital trade barriers through free trade zones;integrate domestic and international legal frameworks to ensure the alignment of China’s data governance legislation with its obligations under international trade agreements;and use the development of the“Digital Silk Road”as a starting point to prioritize the formation of digital trade rules with countries participating in the Belt and Road Initiative,promoting the Chinese solutions internationally.展开更多
To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of polici...To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of policies.However,the data landscape is changing.With emerging big data and cloud-based services,there are new opportunities for data collection,influencing both official data collection processes and the operation of the programmes they monitor.This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation(EO)data products for monitoring and tracking the SDGs.This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs,particularly for low-income countries.The paper also discusses the challenges of using cloud services and big data for SDG monitoring.Validation and quality control of public EO data is very important;otherwise,the user will be unable to assess the quality of the data or use it with confidence.展开更多
Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industr...Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.展开更多
Background:Given the importance of customers as the most valuable assets of organizations,customer retention seems to be an essential,basic requirement for any organization.Banks are no exception to this rule.The comp...Background:Given the importance of customers as the most valuable assets of organizations,customer retention seems to be an essential,basic requirement for any organization.Banks are no exception to this rule.The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention.Methods:Being based on existing information technologies which allow one to collect data from organizations’databases,data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data.In this research,the decision tree technique was applied to build a model incorporating this knowledge.Results:The results represent the characteristics of churned customers.Conclusions:Bank managers can identify churners in future using the results of decision tree.They should be provide some strategies for customers whose features are getting more likely to churner’s features.展开更多
This paper proposes a method of data-flow testing for Web services composition.Firstly,to facilitate data flow analysis and constraints collecting,the existing model representation of business process execution langua...This paper proposes a method of data-flow testing for Web services composition.Firstly,to facilitate data flow analysis and constraints collecting,the existing model representation of business process execution language(BPEL)is modified in company with the analysis of data dependency and an exact representation of dead path elimination(DPE)is proposed,which over-comes the difficulties brought to dataflow analysis.Then defining and using information based on data flow rules is collected by parsing BPEL and Web services description language(WSDL)documents and the def-use annotated control flow graph is created.Based on this model,data-flow anomalies which indicate potential errors can be discovered by traversing the paths of graph,and all-du-paths used in dynamic data flow testing for Web services composition are automatically generated,then testers can design the test cases according to the collected constraints for each path selected.展开更多
The aim of the work was to determine the spatial distribution of activity in the forest on the area of the Forest Promotional Complex“Sudety Zachodnie”using mobile phone data.The study identified the sites with the ...The aim of the work was to determine the spatial distribution of activity in the forest on the area of the Forest Promotional Complex“Sudety Zachodnie”using mobile phone data.The study identified the sites with the highest(hot spot)and lowest(cold spot)use.Habitat,stand,demographic,topographic and spatial factors affecting the distribution of activity were also analyzed.Two approaches were applied in our research:global and local Moran’s coefficients,and a machine learning technique,Boosted Regression Trees.The results show that 11,503,320 visits to forest areas were recorded in the“Sudety Zachodnie”in 2019.The most popular season for activities was winter,and the least popular was spring.Using global and local Moran’s I coefficients,three small hot clusters of activity and one large cold cluster were identified.Locations with high values with similar neighbours(hot-spots)were most often visited forest areas,averaging almost 200,000 visits over 2019.Significantly fewer visits were recorded in cold-spots,the average number of visits to these areas was about 4,500.The value of global Moran’s I was equal to 0.54 and proved significant positive spatial autocorrelation.Results of Boosted Regression Trees modeling of visits in forest,using tree stand habitat and spatial factors accurately explained 76%of randomly selected input data.The variables that had the greatest effect on the distribution of activities were the density of hiking and biking trails and diversity of topography.The methodology presented in this article allows delineation of Cultural Ecosystem Services hot spots in forest areas based on mobile phone data.It also allows the identification of factors that may influence the distribution of visits in forests.Such data are important for managing forest areas and adapting forest management to the needs of society while maintaining ecosystem stability.展开更多
Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security asses...Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security assessment and risk management,many countries have successively issued relevant laws,regulations,and assessment guidelines.This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement.From the perspective of a single organization,the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors.Then,the risk assessment index system of cross-border data flow is constructed.A case study of risk assessment in 358 biomedical organizations is carried out,and the suggestions for data management are offered.This study is condusive to improving security monitoring and the early warning of the cross-border data flow,thereby realizing the safe and orderly global flow of biomedical data.展开更多
In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong Unive...In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.展开更多
With the growing popularity of data-intensive services on the Internet, the traditional process-centric model for business process meets challenges due to the lack of abilities to describe data semantics and dependenc...With the growing popularity of data-intensive services on the Internet, the traditional process-centric model for business process meets challenges due to the lack of abilities to describe data semantics and dependencies, resulting in the inflexibility of the design and implement for the processes. This paper proposes a novel data-aware business process model which is able to describe both explicit control flow and implicit data flow. Data model with dependencies which are formulated by Linear-time Temporal Logic(LTL) is presented, and their satisfiability is validated by an automaton-based model checking algorithm. Data dependencies are fully considered in modeling phase, which helps to improve the efficiency and reliability of programming during developing phase. Finally, a prototype system based on j BPM for data-aware workflow is designed using such model, and has been deployed to Beijing Kingfore heating management system to validate the flexibility, efficacy and convenience of our approach for massive coding and large-scale system management in reality.展开更多
Ecosystem services(ES)mapping and models have advanced in recent years.Improvements were made,and the assessments have transitioned from qualitative to quantitative.Although this is an important advancement,the ES map...Ecosystem services(ES)mapping and models have advanced in recent years.Improvements were made,and the assessments have transitioned from qualitative to quantitative.Although this is an important advancement,the ES mapping and modelling validation step has been overlooked,and this raises an important question in the credibility of the outcomes.This has been an important and unsolved issue in the ES research community that needs to be tackled.This highlight paper discusses the importance of validating single ES mapping and models.Conducting this using field or proximal/remote sensing raw data and not data from other models or stakeholder evaluation is important.A validation step should be mandatory in ES frameworks since it can assess the models’veracity,contribute to identifying the model’s weaknesses/strengths and ultimately represent a scientific advance in the field.This is easier to apply to the biophysical mapping and models of regulating and provisioning ES than to cultural ES,as the latter rely more on perception and cultural contexts.Also,ES supply models are easier to validate than demand and flow models.Robust and well-grounded models are essential for ensuring the reliability of individual ES maps and models and should be integrated into decision-making processes.Although several challenges arise related to the costs of data collection,in several cases prohibitive,and the time and the expertise needed to conduct this sampling and analysis,this is likely an imperative step that needs to be considered in the future.This will be beneficial in establishing ES research and improving decision-making and wellbeing.展开更多
Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These...Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.展开更多
As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitati...As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational stability.In this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)mechanism.The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings.By integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment.Specifically,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading model.Experimental results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of consensus.Furthermore,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing algorithms.Additionally,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other algorithms.These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.展开更多
Public buses play an important role in public transportation in most parts of the world and it is still the dominant public transportation mode in some regions. Nowadays, as people switch to a mobile lifestyle, they s...Public buses play an important role in public transportation in most parts of the world and it is still the dominant public transportation mode in some regions. Nowadays, as people switch to a mobile lifestyle, they spend significant amount of time on the traveling to work, back and forth. However, not much research has been done on how to provide some on-board service for those commuters in the public bus. This paper presents a Bluetooth-based system which is inexpensive, yet flexible, and scalable to serve commuters in a personalized manner using Bluetooth enabled mobile phones. However, from the Bluetooth specification, one Bluetooth dongle can connect to at most seven other Bluetooth devices. As we expect more than 7 users to use the services provided in the Bluetooth-based system (a full double-deck bus can carry around 100 passengers), we need to work out an effective scheduler to schedule all the private services on the Bluetooth servers in the bus. This paper also describes a scheduling algorithm that exploits the park mode feature of the Bluetooth specification to allow more users to have access to the Bluetooth services on the bus.展开更多
To extract structured data from a web page with customized requirements,a user labels some DOM elements on the page with attribute names.The common features of the labeled elements are utilized to guide the user throu...To extract structured data from a web page with customized requirements,a user labels some DOM elements on the page with attribute names.The common features of the labeled elements are utilized to guide the user through the labeling process to minimize user efforts,and are also utilized to retrieve attribute values.To turn the attribute values into a structured result,the attribute pattern needs to be induced.For this purpose,a space-optimized suffix tree called attribute tree is built to transform the document object model(DOM) tree into a simpler form while preserving its useful properties such as attribute sequence order.The pattern is induced bottom-up on the attribute tree,and is further used to build the structured result.Experiments are conducted and show high performance of our approach in terms of precision,recall and structural correctness.展开更多
文摘With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.
基金This paper is a phased result of the“Research on the Issue of China’s Data Export System”(24SFB3035)a research project of the Ministry of Justice of China on the construction of the rule of law and the study of legal theories at the ministerial level in 2024.
文摘The Fourth Industrial Revolution has endowed the concept of state sovereignty with new era-specific connotations,leading to the emergence of the theory of data sovereignty.While countries refine their domestic legislation to establish their data sovereignty,they are also actively engaging in the negotiation of cross-border data flow rules within international trade agreements to construct data sovereignty.During these negotiations,countries express differing regulatory claims,with some focusing on safeguarding sovereignty and protecting human rights,some prioritizing economic promotion and security assurance,and others targeting traditional and innovative digital trade barriers.These varied approaches reflect the tension between three pairs of values:collectivism and individualism,freedom and security,and tradition and innovation.Based on their distinct value pursuits,three representative models of data sovereignty construction have emerged globally.At the current juncture,when international rules for digital trade are still in their nascent stages,China should timely establish its data sovereignty rules,actively participate in global data sovereignty competition,and balance its sovereignty interests with other interests.Specifically,China should explore the scope of system-acceptable digital trade barriers through free trade zones;integrate domestic and international legal frameworks to ensure the alignment of China’s data governance legislation with its obligations under international trade agreements;and use the development of the“Digital Silk Road”as a starting point to prioritize the formation of digital trade rules with countries participating in the Belt and Road Initiative,promoting the Chinese solutions internationally.
基金funded by the National Key Research and Development Program of China(Grant No.2016YFA0600304)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA19030201).
文摘To achieve the Sustainable Development Goals(SDGs),high-quality data are needed to inform the formulation of policies and investment decisions,to monitor progress towards the SDGs and to evaluate the impacts of policies.However,the data landscape is changing.With emerging big data and cloud-based services,there are new opportunities for data collection,influencing both official data collection processes and the operation of the programmes they monitor.This paper uses cases and examples to explore the potential of crowdsourcing and public earth observation(EO)data products for monitoring and tracking the SDGs.This paper suggests that cloud-based services that integrate crowdsourcing and public EO data products provide cost-effective solutions for monitoring and tracking the SDGs,particularly for low-income countries.The paper also discusses the challenges of using cloud services and big data for SDG monitoring.Validation and quality control of public EO data is very important;otherwise,the user will be unable to assess the quality of the data or use it with confidence.
文摘Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloudbased technologies,such as the Internet of Things.With increasing industry adoption and migration of traditional computing services to the cloud,one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies.This work proposes a Data Security Framework for cloud computing services(CCS)that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS.This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation.The paper presents the design details of the framework,which consists of three stages:identification of data security requirements,management of data security risks and evaluation of data security performance in CCS.
文摘Background:Given the importance of customers as the most valuable assets of organizations,customer retention seems to be an essential,basic requirement for any organization.Banks are no exception to this rule.The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention.Methods:Being based on existing information technologies which allow one to collect data from organizations’databases,data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data.In this research,the decision tree technique was applied to build a model incorporating this knowledge.Results:The results represent the characteristics of churned customers.Conclusions:Bank managers can identify churners in future using the results of decision tree.They should be provide some strategies for customers whose features are getting more likely to churner’s features.
基金the National Natural Science Foundation of China(60425206,60503033)National Basic Research Program of China(973 Program,2002CB312000)Opening Foundation of State Key Laboratory of Software Engineering in Wuhan University
文摘This paper proposes a method of data-flow testing for Web services composition.Firstly,to facilitate data flow analysis and constraints collecting,the existing model representation of business process execution language(BPEL)is modified in company with the analysis of data dependency and an exact representation of dead path elimination(DPE)is proposed,which over-comes the difficulties brought to dataflow analysis.Then defining and using information based on data flow rules is collected by parsing BPEL and Web services description language(WSDL)documents and the def-use annotated control flow graph is created.Based on this model,data-flow anomalies which indicate potential errors can be discovered by traversing the paths of graph,and all-du-paths used in dynamic data flow testing for Web services composition are automatically generated,then testers can design the test cases according to the collected constraints for each path selected.
基金Funded by the National Science Centre,Poland under the OPUS call in the Weave programme(project No.2021/43/I/HS4/01451)funded by Ministry of Education and Science(901503)。
文摘The aim of the work was to determine the spatial distribution of activity in the forest on the area of the Forest Promotional Complex“Sudety Zachodnie”using mobile phone data.The study identified the sites with the highest(hot spot)and lowest(cold spot)use.Habitat,stand,demographic,topographic and spatial factors affecting the distribution of activity were also analyzed.Two approaches were applied in our research:global and local Moran’s coefficients,and a machine learning technique,Boosted Regression Trees.The results show that 11,503,320 visits to forest areas were recorded in the“Sudety Zachodnie”in 2019.The most popular season for activities was winter,and the least popular was spring.Using global and local Moran’s I coefficients,three small hot clusters of activity and one large cold cluster were identified.Locations with high values with similar neighbours(hot-spots)were most often visited forest areas,averaging almost 200,000 visits over 2019.Significantly fewer visits were recorded in cold-spots,the average number of visits to these areas was about 4,500.The value of global Moran’s I was equal to 0.54 and proved significant positive spatial autocorrelation.Results of Boosted Regression Trees modeling of visits in forest,using tree stand habitat and spatial factors accurately explained 76%of randomly selected input data.The variables that had the greatest effect on the distribution of activities were the density of hiking and biking trails and diversity of topography.The methodology presented in this article allows delineation of Cultural Ecosystem Services hot spots in forest areas based on mobile phone data.It also allows the identification of factors that may influence the distribution of visits in forests.Such data are important for managing forest areas and adapting forest management to the needs of society while maintaining ecosystem stability.
基金support from the National Natural Science Foundation of China(Grant No.:71901169)the Shaanxi Province Innovative Talents Promotion Plan-Youth Science and Technology Nova Project(Grant No.:2022KJXX-50).
文摘Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security assessment and risk management,many countries have successively issued relevant laws,regulations,and assessment guidelines.This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement.From the perspective of a single organization,the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors.Then,the risk assessment index system of cross-border data flow is constructed.A case study of risk assessment in 358 biomedical organizations is carried out,and the suggestions for data management are offered.This study is condusive to improving security monitoring and the early warning of the cross-border data flow,thereby realizing the safe and orderly global flow of biomedical data.
文摘In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.
基金supported by the National Natural Science Foundation of China (No. 61502043, No. 61132001)Beijing Natural Science Foundation (No. 4162042)BeiJing Talents Fund (No. 2015000020124G082)
文摘With the growing popularity of data-intensive services on the Internet, the traditional process-centric model for business process meets challenges due to the lack of abilities to describe data semantics and dependencies, resulting in the inflexibility of the design and implement for the processes. This paper proposes a novel data-aware business process model which is able to describe both explicit control flow and implicit data flow. Data model with dependencies which are formulated by Linear-time Temporal Logic(LTL) is presented, and their satisfiability is validated by an automaton-based model checking algorithm. Data dependencies are fully considered in modeling phase, which helps to improve the efficiency and reliability of programming during developing phase. Finally, a prototype system based on j BPM for data-aware workflow is designed using such model, and has been deployed to Beijing Kingfore heating management system to validate the flexibility, efficacy and convenience of our approach for massive coding and large-scale system management in reality.
基金supported by the project Monetary valuation of soil ecosystem services and creation of initiatives to invest in soil health:setting a framework for the inclusion of soil health in business and in the policy making process(InBestSoil)(Horizon Europe)Grant agreement ID:101091099。
文摘Ecosystem services(ES)mapping and models have advanced in recent years.Improvements were made,and the assessments have transitioned from qualitative to quantitative.Although this is an important advancement,the ES mapping and modelling validation step has been overlooked,and this raises an important question in the credibility of the outcomes.This has been an important and unsolved issue in the ES research community that needs to be tackled.This highlight paper discusses the importance of validating single ES mapping and models.Conducting this using field or proximal/remote sensing raw data and not data from other models or stakeholder evaluation is important.A validation step should be mandatory in ES frameworks since it can assess the models’veracity,contribute to identifying the model’s weaknesses/strengths and ultimately represent a scientific advance in the field.This is easier to apply to the biophysical mapping and models of regulating and provisioning ES than to cultural ES,as the latter rely more on perception and cultural contexts.Also,ES supply models are easier to validate than demand and flow models.Robust and well-grounded models are essential for ensuring the reliability of individual ES maps and models and should be integrated into decision-making processes.Although several challenges arise related to the costs of data collection,in several cases prohibitive,and the time and the expertise needed to conduct this sampling and analysis,this is likely an imperative step that needs to be considered in the future.This will be beneficial in establishing ES research and improving decision-making and wellbeing.
文摘Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.
基金supported by the National Natural Science Foundation of China under Grant 62471493 and 62402257partially supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066 and 2023QF025+2 种基金partially supported by the Open Research Subject of State Key Laboratory of Intelligent Game(No.ZBKF-24-12)partially supported by the Foundation of Key Laboratory of Education Informatization for Nationalities(Yunnan Normal University),the Ministry of Education(No.EIN2024C006)partially supported by the Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE(No.202306).
文摘As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational stability.In this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)mechanism.The main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network settings.By integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC deployment.Specifically,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading model.Experimental results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of consensus.Furthermore,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing algorithms.Additionally,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other algorithms.These findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.
文摘为提升医院精细化管理,推动医疗机构科学地开展智慧医院建设,解决国家、省、市逐年增长的数据填报工作难题,本文构建了符合实际场景的智慧医疗数据管理平台。该平台利用Data Services技术将HANA数据库计算出的指标抽取到平台数据库,利用JAVA SSM框架完成平台开发,可实现各科室数据自动填报,同时实现了业务处理、数据核对、流程管理、统计分析等上报数据的精细化管理。以SAP Data Services为工具,实现平台指标的自动计算展示,优化流程,建设数据为驱动的高水平智慧医院,从而提升医院核心竞争力。
文摘Public buses play an important role in public transportation in most parts of the world and it is still the dominant public transportation mode in some regions. Nowadays, as people switch to a mobile lifestyle, they spend significant amount of time on the traveling to work, back and forth. However, not much research has been done on how to provide some on-board service for those commuters in the public bus. This paper presents a Bluetooth-based system which is inexpensive, yet flexible, and scalable to serve commuters in a personalized manner using Bluetooth enabled mobile phones. However, from the Bluetooth specification, one Bluetooth dongle can connect to at most seven other Bluetooth devices. As we expect more than 7 users to use the services provided in the Bluetooth-based system (a full double-deck bus can carry around 100 passengers), we need to work out an effective scheduler to schedule all the private services on the Bluetooth servers in the bus. This paper also describes a scheduling algorithm that exploits the park mode feature of the Bluetooth specification to allow more users to have access to the Bluetooth services on the bus.
基金Supported by the National High Technology Research and Development Programme of China(No.2009AA01 Z141)the National Natural Science Foundation of China(No.60573117)Beijing Natural Science Foundation(No.4131001)
文摘To extract structured data from a web page with customized requirements,a user labels some DOM elements on the page with attribute names.The common features of the labeled elements are utilized to guide the user through the labeling process to minimize user efforts,and are also utilized to retrieve attribute values.To turn the attribute values into a structured result,the attribute pattern needs to be induced.For this purpose,a space-optimized suffix tree called attribute tree is built to transform the document object model(DOM) tree into a simpler form while preserving its useful properties such as attribute sequence order.The pattern is induced bottom-up on the attribute tree,and is further used to build the structured result.Experiments are conducted and show high performance of our approach in terms of precision,recall and structural correctness.
