During the late years there has been an ever-increasing focus on the possibilities to change the building process to raise quality on the final building products as well as the activities of actors involved in the bui...During the late years there has been an ever-increasing focus on the possibilities to change the building process to raise quality on the final building products as well as the activities of actors involved in the building process. One reason for this interest is the new opportunities evolving due to introduction of advanced information and communication technology (ICT). The paper focuses on creative changes of the building process powered by user driven innovation activities. An overview of existing user driven innovation methodologies is given as well experiences from the ongoing Virtual Innovation in Construction (VIC) project. One important driving force for change is the opportunity for users to develop and articulate real needs concerning for example different functionalities of a building and its parts, but also on artifacts supporting the actual needs capture and requirements formulation during building design. A general methodological framework and meta ontology for Virtual Innovation in Construction is presented as well as findings from implementation of the method.展开更多
Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data...Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data mining are to discover knowledge of interest to user needs.Data mining is really a useful tool in many domains such as marketing, decision making, etc. However, some basic issues of data mining are ignored. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? Is there any rule we should obey in a data mining process? In order to discover patterns and knowledge really interesting and actionable to the real world Zhang et al proposed a domain-driven human-machine-cooperated data mining process.Zhao and Yao proposed an interactive user-driven classification method using the granule network. In our work, we find that data mining is a kind of knowledge transforming process to transform knowledge from data format into symbol format. Thus, no new knowledge could be generated (born) in a data mining process. In a data mining process, knowledge is just transformed from data format, which is not understandable for human, into symbol format,which is understandable for human and easy to be used.It is similar to the process of translating a book from Chinese into English.In this translating process,the knowledge itself in the book should remain unchanged. What will be changed is the format of the knowledge only. That is, the knowledge in the English book should be kept the same as the knowledge in the Chinese one.Otherwise, there must be some mistakes in the translating proces, that is, we are transforming knowledge from one format into another format while not producing new knowledge in a data mining process. The knowledge is originally stored in data (data is a representation format of knowledge). Unfortunately, we can not read, understand, or use it, since we can not understand data. With this understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.展开更多
Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data main...Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.展开更多
This article describes a sampling and estimation scheme for estimating the size of an injecting drug user (IDU) population by combining classical sampling and respondent-driven sampling procedures. It is designed to u...This article describes a sampling and estimation scheme for estimating the size of an injecting drug user (IDU) population by combining classical sampling and respondent-driven sampling procedures. It is designed to use the information from harm reduction programs, especially, Needle Exchange Programs (NEPs). The approach involves using respondent-driven sampling design to collect a sample of injecting drug users who appear at site of NEP in a certain period of time and to obtain retrospective self-report data on the number of friends among the IDUs and number of needles exchanged for each sampled injecting drug user. A methodology is developed to estimate the size of injecting drug users who have ever used the NEP during the fixed period of time, and which allows us to estimate the proportion of injecting drug users in using NEP. The size of the IDU population is estimated by dividing the total number of IDUs who using NEPs during the period of time by the estimated proportion of IDUs in the group. The technique holds promise for providing data needed to answer questions such as “What is the size of an IDU population in a city?” and “Is that size changing?” and better understand the dynamics of the IDU population. The methodology described here can also be used to estimate size of other hard-to-reach population by using information from harm reduction programs.展开更多
用户界面描述语言是实现模型驱动的用户界面开发的重要方式.当前的用户界面描述语言一方面在对不同物理特性的交互设备上的用户界面的描述能力不足;另一方面,缺乏可扩展性及界面描述的组成部分的可复用性.针对上述问题,设计出一种界面...用户界面描述语言是实现模型驱动的用户界面开发的重要方式.当前的用户界面描述语言一方面在对不同物理特性的交互设备上的用户界面的描述能力不足;另一方面,缺乏可扩展性及界面描述的组成部分的可复用性.针对上述问题,设计出一种界面描述语言——E-UIDL(extensible user interface description language).该语言遵循层次化、模块化的设计原则,能够支持多设备、多通道的用户界面的描述,并通过实例说明描述语言对笔式用户界面开发、多设备界面自动生成以及自适应用户界面开发的支持,深入地阐述了E-UIDL的特性.展开更多
文摘During the late years there has been an ever-increasing focus on the possibilities to change the building process to raise quality on the final building products as well as the activities of actors involved in the building process. One reason for this interest is the new opportunities evolving due to introduction of advanced information and communication technology (ICT). The paper focuses on creative changes of the building process powered by user driven innovation activities. An overview of existing user driven innovation methodologies is given as well experiences from the ongoing Virtual Innovation in Construction (VIC) project. One important driving force for change is the opportunity for users to develop and articulate real needs concerning for example different functionalities of a building and its parts, but also on artifacts supporting the actual needs capture and requirements formulation during building design. A general methodological framework and meta ontology for Virtual Innovation in Construction is presented as well as findings from implementation of the method.
文摘Data mining (also known as Knowledge Discovery in Databases - KDD) is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The aims and objectives of data mining are to discover knowledge of interest to user needs.Data mining is really a useful tool in many domains such as marketing, decision making, etc. However, some basic issues of data mining are ignored. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? Is there any rule we should obey in a data mining process? In order to discover patterns and knowledge really interesting and actionable to the real world Zhang et al proposed a domain-driven human-machine-cooperated data mining process.Zhao and Yao proposed an interactive user-driven classification method using the granule network. In our work, we find that data mining is a kind of knowledge transforming process to transform knowledge from data format into symbol format. Thus, no new knowledge could be generated (born) in a data mining process. In a data mining process, knowledge is just transformed from data format, which is not understandable for human, into symbol format,which is understandable for human and easy to be used.It is similar to the process of translating a book from Chinese into English.In this translating process,the knowledge itself in the book should remain unchanged. What will be changed is the format of the knowledge only. That is, the knowledge in the English book should be kept the same as the knowledge in the Chinese one.Otherwise, there must be some mistakes in the translating proces, that is, we are transforming knowledge from one format into another format while not producing new knowledge in a data mining process. The knowledge is originally stored in data (data is a representation format of knowledge). Unfortunately, we can not read, understand, or use it, since we can not understand data. With this understanding of data mining, we proposed a data-driven knowledge acquisition method based on rough sets. It also improved the performance of classical knowledge acquisition methods. In fact, we also find that the domain-driven data mining and user-driven data mining do not conflict with our data-driven data mining. They could be integrated into domain-oriented data-driven data mining. It is just like the views of data base. Users with different views could look at different partial data of a data base. Thus, users with different tasks or objectives wish, or could discover different knowledge (partial knowledge) from the same data base. However, all these partial knowledge should be originally existed in the data base. So, a domain-oriented data-driven data mining method would help us to extract the knowledge which is really existed in a data base, and really interesting and actionable to the real world.
文摘Using Louisiana’s Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana’s Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state’s Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.
文摘This article describes a sampling and estimation scheme for estimating the size of an injecting drug user (IDU) population by combining classical sampling and respondent-driven sampling procedures. It is designed to use the information from harm reduction programs, especially, Needle Exchange Programs (NEPs). The approach involves using respondent-driven sampling design to collect a sample of injecting drug users who appear at site of NEP in a certain period of time and to obtain retrospective self-report data on the number of friends among the IDUs and number of needles exchanged for each sampled injecting drug user. A methodology is developed to estimate the size of injecting drug users who have ever used the NEP during the fixed period of time, and which allows us to estimate the proportion of injecting drug users in using NEP. The size of the IDU population is estimated by dividing the total number of IDUs who using NEPs during the period of time by the estimated proportion of IDUs in the group. The technique holds promise for providing data needed to answer questions such as “What is the size of an IDU population in a city?” and “Is that size changing?” and better understand the dynamics of the IDU population. The methodology described here can also be used to estimate size of other hard-to-reach population by using information from harm reduction programs.
文摘用户界面描述语言是实现模型驱动的用户界面开发的重要方式.当前的用户界面描述语言一方面在对不同物理特性的交互设备上的用户界面的描述能力不足;另一方面,缺乏可扩展性及界面描述的组成部分的可复用性.针对上述问题,设计出一种界面描述语言——E-UIDL(extensible user interface description language).该语言遵循层次化、模块化的设计原则,能够支持多设备、多通道的用户界面的描述,并通过实例说明描述语言对笔式用户界面开发、多设备界面自动生成以及自适应用户界面开发的支持,深入地阐述了E-UIDL的特性.
