The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it produces.The decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesir...The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it produces.The decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of poor quality.A Data Warehouse(DW)is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions.The Extract,Transform,and Load(ETL)process is the backbone of a DW system,and it is responsible for moving data from source systems into the DW system.The more mature the ETL process the more reliable the DW system.In this paper,we propose the ETL Maturity Model(EMM)that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced.The EMM is made up of five levels of maturity i.e.,Chaotic,Acceptable,Stable,Efficient and Reliable.Each level of maturity contains Key Process Areas(KPAs)that have been endorsed by industry experts and include all critical features of a good ETL system.Quality Objectives(QOs)are defined procedures that,when implemented,resulted in a high-quality ETL process.Each KPA has its own set of QOs,the execution of which meets the requirements of that KPA.Multiple brainstorming sessions with relevant industry experts helped to enhance the model.EMMwas deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim.This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system.This model can also provide high-quality information to assist users inmaking better decisions and gaining their trust.展开更多
Universities collect and generate a considerable amount of data on students throughout their academic career. Currently in South Kivu, most universities have an information system in the form of a database made up of ...Universities collect and generate a considerable amount of data on students throughout their academic career. Currently in South Kivu, most universities have an information system in the form of a database made up of several disparate files. This makes it difficult to use this data efficiently and profitably. The aim of this study is to develop this transactional database-based information system into a data warehouse-oriented system. This tool will be able to collect, organize and archive data on the student’s career path, year after year, and transform it for analysis purposes. In the age of Big Data, a number of artificial intelligence techniques have been developed, making it possible to extract useful information from large databases. This extracted information is of paramount importance in decision-making. By way of example, the information extracted by these techniques can be used to predict which stream a student should choose when applying to university. In order to develop our contribution, we analyzed the IT information systems used in the various universities and applied the bottom-up method to design our data warehouse model. We used the relational model to design the data warehouse.展开更多
This paper describes the process of design and construction of a data warehouse(“DW”)for an online learning platform using three prominent technologies,Microsoft SQL Server,MongoDB and Apache Hive.The three systems ...This paper describes the process of design and construction of a data warehouse(“DW”)for an online learning platform using three prominent technologies,Microsoft SQL Server,MongoDB and Apache Hive.The three systems are evaluated for corpus construction and descriptive analytics.The case also demonstrates the value of evidence-centered design principles for data warehouse design that is sustainable enough to adapt to the demands of handling big data in a variety of contexts.Additionally,the paper addresses maintainability-performance tradeoff,storage considerations and accessibility of big data corpora.In this NSF-sponsored work,the data were processed,transformed,and stored in the three versions of a data warehouse in search for a better performing and more suitable platform.The data warehouse engines-a relational database,a No-SQL database,and a big data technology for parallel computations-were subjected to principled analysis.Design,construction and evaluation of a data warehouse were scrutinized to find improved ways of storing,organizing and extracting information.The work also examines building corpora,performing ad-hoc extractions,and ensuring confidentiality.It was found that Apache Hive demonstrated the best processing time followed by SQL Server and MongoDB.In the aspect of analytical queries,the SQL Server was a top performer followed by MongoDB and Hive.This paper also discusses a novel process for render students anonymity complying with Family Educational Rights and Privacy Act regulations.Five phases for DW design are recommended:1)Establishing goals at the outset based on Evidence-Centered Design principles;2)Recognizing the unique demands of student data and use;3)Adopting a model that integrates cost with technical considerations;4)Designing a comparative database and 5)Planning for a DW design that is sustainable.Recommendations for future research include attempting DW design in contexts involving larger data sets,more refined operations,and ensuring attention is paid to sustainability of operations.展开更多
Students’grades not only serve as an effective indicator of their learning achievements but also to some extent reflect the completion of teaching tasks by the instructors.Currently,many universities across the count...Students’grades not only serve as an effective indicator of their learning achievements but also to some extent reflect the completion of teaching tasks by the instructors.Currently,many universities across the country have collected and recorded various information about students and teachers in the school’s information management system,but it is only a simple storage record and has not effectively excavated hidden information,and data have not been fully utilized.Student performance information,enrolment information,course information,teaching plans,and teacher-related information are currently stored in separate databases,which are independent of each other,making it difficult to perform effective data analysis.Data warehousing technology can integrate various information and use data analysis software to excavate more high-value information,which is convenient for teaching evaluation and optimizing teaching strategies.Based on data warehousing technology,the article uses the hierarchical concept of data warehousing to construct the ODS layer,DWD layer,DWS layer and ETL layer.Facing the data warehousing topic,the article designs the data warehousing conceptual model,logical model,and physical model based on student performance,providing a model basis for later data mining.展开更多
In the era of Big Data,many NoSQL databases emerged for the storage and later processing of vast volumes of data,using data structures that can follow columnar,key-value,document or graph formats.For analytical contex...In the era of Big Data,many NoSQL databases emerged for the storage and later processing of vast volumes of data,using data structures that can follow columnar,key-value,document or graph formats.For analytical contexts,requiring a Big Data Warehouse,Hive is used as the driving force,allowing the analysis of vast amounts of data.Data models in Hive are usually defined taking into consideration the queries that need to be answered.In this work,a set of rules is presented for the transformation of multidimensional data models into Hive tables,making available data at different levels of detail.These several levels are suited for answering different queries,depending on the analytical needs.After the identification of the Hive tables,this paper summarizes a demonstration case in which the implementation of a specific Big Data architecture shows how the evolution from a traditional Data Warehouse to a Big Data Warehouse is possible.展开更多
The problem of storage and querying of large volumes of spatial grids is an issue to solve.In this paper,we propose a method to optimize queries to aggregate raster grids stored in databases.In our approach,we propose...The problem of storage and querying of large volumes of spatial grids is an issue to solve.In this paper,we propose a method to optimize queries to aggregate raster grids stored in databases.In our approach,we propose to estimate the exact result rather than calculate the exact result.This approach reduces query execution time.One advantage of our method is that it does not require implementing or modifying functionalities of database management systems.Our approach is based on a new data structure and a specific model of SQL queries.Our work is applied here to relational data warehouses.展开更多
Recently, due to the rapid growth increment of data sensors, a massive volume of data is generated from different sources. The way of administering such data in a sense storing, managing, analyzing, and extracting ins...Recently, due to the rapid growth increment of data sensors, a massive volume of data is generated from different sources. The way of administering such data in a sense storing, managing, analyzing, and extracting insightful information from the massive volume of data is a challenging task. Big data analytics is becoming a vital research area in domains such as climate data analysis which demands fast access to data. Nowadays, an open-source platform namely MapReduce which is a distributed computing framework is widely used in many domains of big data analysis. In our work, we have developed a conceptual framework of data modeling essentially useful for the implementation of a hybrid data warehouse model to store the features of National Climatic Data Center (NCDC) climate data. The hybrid data warehouse model for climate big data enables for the identification of weather patterns that would be applicable in agricultural and other similar climate change-related studies that will play a major role in recommending actions to be taken by domain experts and make contingency plans over extreme cases of weather variability.展开更多
Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is...Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is this idea that leads the integration of hierarchical dimensions into these responses. To meet this need, we propose, in this paper, a complete redefinition of the framework and the formal definition of traditional database analysis through the prism of hierarchical dimensions. After characterizing the hierarchical data cube lattice, we introduce the hierarchical data cube and its most concise reduced representation, the closed hierarchical data cube. It offers compact replication so as to optimize storage space by removing redundancies of strongly correlated data. Such data are typical of data warehouses, and in particular in video games, our field of study and experimentation, where hierarchical dimension attributes are widely represented.展开更多
Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to eac...Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process.However,evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way,is missing.It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored.To address this problem an integrated resource classification framework is presenting that identifies competence,suitability,and preference as the relationship of task with resources.But,only the competence relationship of human resources with a task is presented in this research as a resource competence model.Furthermore,the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence evaluation.The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process.Applicability of method is illustrated through a heathcare case study.展开更多
Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process an...Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task.In continuation of our previous work,we categorize resources into human and non-human resources.For instance,in the healthcare domain,human resources include doctors,nurses,and other associated staff responsible for the execution of healthcare activities;whereas the non-human resources include surgical and other equipment needed for execution.In this study,we contend that the two types of resources(human and non-human)have a different impact on the process performance,so their suitability should be measured differently.However,no work has been done to evaluate the suitability of non-human resources for the tasks of a process.Consequently,it becomes difficult to identify and subsequently overcome the inefficiencies caused by the non-human resources to the task.To address this problem,we present a three-step method to compute a suitability score of non-human resources for the task.As an evaluation of the proposed method,a healthcare case study is used to illustrate the applicability of the proposed method.Furthermore,we performed a controlled experiment to evaluate the usability of the proposed method.The encouraging response shows the usefulness of the proposed method.展开更多
The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum ass...The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum assistant decision-making function in the regulation and guidance of rural labors as well as in relevant programs.The assistant decision support system functions are discussed,the function modules of this system are introduced from four aspects,including the analysis of labor flow,the prediction of labor flow,the regulation of cross-regional flow and the configuration of decision support system;based on the data base obtained from dynamic tracking of the migrant workers and combining other data sources,the data warehouse model is established,for example,in the analysis of the labor migration times,a star multi-dimensional data model is designed from the time dimension,place dimension,the type of work dimension,accompaniers dimension and so on;the trans-regional flow of rural labor force is analyzed and predicted by using OLAP from the labor's migration times,migration places and other various perspectives.The operation principles of the assistant decision support system of trans-regional labor flow are introduced,it is pointed out that the system serves the policy-makers of the regulation of labor flow and other relevant enterprises,the system will play an important role in the tracking monitoring and cross-regional regulation of the rural labor flow.展开更多
In this article, our research aims to set up a geo-decisional system, more precisely we are particularly interested in the spatial analysis system of agricultural production in Madagascar. For this, we used the spatia...In this article, our research aims to set up a geo-decisional system, more precisely we are particularly interested in the spatial analysis system of agricultural production in Madagascar. For this, we used the spatial data warehouse technique based on the SOLAP spatial analysis tool. After having defined the concepts underlying these systems, we propose to address the research issues related to them from four points of view: needs study of the Malagasy Ministry of Agriculture, modeling of a multidimensional conceptual model according to the MultiDim model and the implementation of the system studied using GeoKettle, PostGIS, GeoServer, SPAGO BI and Géomondrian technologies. This new system helps improve the decision-making process for agricultural production in Madagascar.展开更多
The data warehouse is the most widely used database structure in many decision support systems around the world. This is the reason why a lot of research has been conducted in the literature over the last two decades ...The data warehouse is the most widely used database structure in many decision support systems around the world. This is the reason why a lot of research has been conducted in the literature over the last two decades on their design, refreshment and optimization. The manipulation of hypercubes (cubes) of data is a frequently used operation in the design of multidimensional data warehouses, due to their better adaptation to OLAP (On-Line Analytical Processing). However, the updating of these hypercubes is a very complicated process due mainly to the mass and complexity of the data presented. The purpose of this paper is to present the state of the art of works based on multidimensional modeling using the hypercube as a unit of presentation of data stores. It starts with the base of this process which is the choice of the views (cubes) forming our data warehouse base. The objective of this work is to describe the state of the art of research works dealing with the selection of materialized views in decision support systems.展开更多
Fall armyworm(Spodoptera frugiperda),a native insect species in the Americas,is rapidly becoming a major agricultural pest worldwide and is causing great damage to corn,rice,soybeans,and other crops.To control this pe...Fall armyworm(Spodoptera frugiperda),a native insect species in the Americas,is rapidly becoming a major agricultural pest worldwide and is causing great damage to corn,rice,soybeans,and other crops.To control this pest,scientists have accumulated a great deal of high-throughput data of fall armyworm,and nine versions of its genomes and transcriptomes have been published.However,easily accessing and performing integrated analysis of these omics data sets is challenging.Here,we developed the Fall Armyworm Genome Database(FAWMine,http://159.226.67.243:8080/fawmine/)to maintain genome sequences,structural and functional annotations,transcriptomes,co-expression,protein interactions,homologs,pathways,and single-nucleotide variations.FAWMine provides a powerful framework that helps users to perform flexible and customized searching,present integrated data sets using diverse visualization methods,output results tables in a range of file formats,analyze candidate gene lists using multiple widgets,and query data available in other InterMine systems.Additionally,stand-alone JBrowse and BLAST services are also established,allowing the users to visualize RNA-Seq data and search genome and annotated gene sequences.Altogether,FAWMine is a useful tool for querying,visualizing,and analyzing compiled data sets rapidly and efficiently.FAWMine will be continually updated to function as a community resource for fall armyworm genomics and pest control research.展开更多
Recent research has shown an increase in the number of extreme tornado outbreaks per year.The characterization of the spatio-temporal pattern of tornado events is therefore a critical task in the analysis of meteorolo...Recent research has shown an increase in the number of extreme tornado outbreaks per year.The characterization of the spatio-temporal pattern of tornado events is therefore a critical task in the analysis of meteorological data.Currently,there are a large number of available meteorological datasets that can be used for such analysis.However,much of these data are distributed across multiple websites and are not accessible in a central location.This poses a significant challenge for a scientist who is interested in exploring meteorological patterns associated with tornado events.This paper presents a novel system which uses cloud-based technology for integrating,storing,exploring,analyzing,and visualizing meteorological data associated with tornado outbreaks.The system employs a novel NoSQL database schema and web services architecture for data integration and provides a user friendly interface that allows scientists to explore the spatio-temporal pattern of tornado events.Furthermore,scientists can use this interface to analyze the relationship between different meteorological variables and properties of tornado outbreaks using a number of spatio-temporal statistical and data mining methods.The efficacy of the system is demonstrated on a use case centered on the analysis of climatic indicators of large spatio-temporally clustered tornado outbreaks.展开更多
基金King Saud University for funding this work through Researchers Supporting Project Number(RSP-2021/387),King Saud University,Riyadh,Saudi Arabia.
文摘The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it produces.The decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of poor quality.A Data Warehouse(DW)is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions.The Extract,Transform,and Load(ETL)process is the backbone of a DW system,and it is responsible for moving data from source systems into the DW system.The more mature the ETL process the more reliable the DW system.In this paper,we propose the ETL Maturity Model(EMM)that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced.The EMM is made up of five levels of maturity i.e.,Chaotic,Acceptable,Stable,Efficient and Reliable.Each level of maturity contains Key Process Areas(KPAs)that have been endorsed by industry experts and include all critical features of a good ETL system.Quality Objectives(QOs)are defined procedures that,when implemented,resulted in a high-quality ETL process.Each KPA has its own set of QOs,the execution of which meets the requirements of that KPA.Multiple brainstorming sessions with relevant industry experts helped to enhance the model.EMMwas deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim.This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system.This model can also provide high-quality information to assist users inmaking better decisions and gaining their trust.
文摘Universities collect and generate a considerable amount of data on students throughout their academic career. Currently in South Kivu, most universities have an information system in the form of a database made up of several disparate files. This makes it difficult to use this data efficiently and profitably. The aim of this study is to develop this transactional database-based information system into a data warehouse-oriented system. This tool will be able to collect, organize and archive data on the student’s career path, year after year, and transform it for analysis purposes. In the age of Big Data, a number of artificial intelligence techniques have been developed, making it possible to extract useful information from large databases. This extracted information is of paramount importance in decision-making. By way of example, the information extracted by these techniques can be used to predict which stream a student should choose when applying to university. In order to develop our contribution, we analyzed the IT information systems used in the various universities and applied the bottom-up method to design our data warehouse model. We used the relational model to design the data warehouse.
文摘This paper describes the process of design and construction of a data warehouse(“DW”)for an online learning platform using three prominent technologies,Microsoft SQL Server,MongoDB and Apache Hive.The three systems are evaluated for corpus construction and descriptive analytics.The case also demonstrates the value of evidence-centered design principles for data warehouse design that is sustainable enough to adapt to the demands of handling big data in a variety of contexts.Additionally,the paper addresses maintainability-performance tradeoff,storage considerations and accessibility of big data corpora.In this NSF-sponsored work,the data were processed,transformed,and stored in the three versions of a data warehouse in search for a better performing and more suitable platform.The data warehouse engines-a relational database,a No-SQL database,and a big data technology for parallel computations-were subjected to principled analysis.Design,construction and evaluation of a data warehouse were scrutinized to find improved ways of storing,organizing and extracting information.The work also examines building corpora,performing ad-hoc extractions,and ensuring confidentiality.It was found that Apache Hive demonstrated the best processing time followed by SQL Server and MongoDB.In the aspect of analytical queries,the SQL Server was a top performer followed by MongoDB and Hive.This paper also discusses a novel process for render students anonymity complying with Family Educational Rights and Privacy Act regulations.Five phases for DW design are recommended:1)Establishing goals at the outset based on Evidence-Centered Design principles;2)Recognizing the unique demands of student data and use;3)Adopting a model that integrates cost with technical considerations;4)Designing a comparative database and 5)Planning for a DW design that is sustainable.Recommendations for future research include attempting DW design in contexts involving larger data sets,more refined operations,and ensuring attention is paid to sustainability of operations.
基金This work was supported by the Hainan Provincial Natural Science Foundation of China(project number:622RC723)the Education Department of Hainan Province(project number:Hnky2023-72).
文摘Students’grades not only serve as an effective indicator of their learning achievements but also to some extent reflect the completion of teaching tasks by the instructors.Currently,many universities across the country have collected and recorded various information about students and teachers in the school’s information management system,but it is only a simple storage record and has not effectively excavated hidden information,and data have not been fully utilized.Student performance information,enrolment information,course information,teaching plans,and teacher-related information are currently stored in separate databases,which are independent of each other,making it difficult to perform effective data analysis.Data warehousing technology can integrate various information and use data analysis software to excavate more high-value information,which is convenient for teaching evaluation and optimizing teaching strategies.Based on data warehousing technology,the article uses the hierarchical concept of data warehousing to construct the ODS layer,DWD layer,DWS layer and ETL layer.Facing the data warehousing topic,the article designs the data warehousing conceptual model,logical model,and physical model based on student performance,providing a model basis for later data mining.
基金This work has been supported by COMPETE:POCI-01-0145-FEDER-007043 and FCT(Fundação para a Ciência e Tecnologia)within the Project Scope:UID/CEC/00319/2013This work has been funded by the SusCity project(MITP-TB/CS/0026/2013)by Portugal Incentive System for Research and Technological Development,Project in co-promotion no 002814/2015(iFACTORY 2015-2018).
文摘In the era of Big Data,many NoSQL databases emerged for the storage and later processing of vast volumes of data,using data structures that can follow columnar,key-value,document or graph formats.For analytical contexts,requiring a Big Data Warehouse,Hive is used as the driving force,allowing the analysis of vast amounts of data.Data models in Hive are usually defined taking into consideration the queries that need to be answered.In this work,a set of rules is presented for the transformation of multidimensional data models into Hive tables,making available data at different levels of detail.These several levels are suited for answering different queries,depending on the analytical needs.After the identification of the Hive tables,this paper summarizes a demonstration case in which the implementation of a specific Big Data architecture shows how the evolution from a traditional Data Warehouse to a Big Data Warehouse is possible.
基金This work is funded by Auvergne region,Feder,Agaetis,and Irstea.
文摘The problem of storage and querying of large volumes of spatial grids is an issue to solve.In this paper,we propose a method to optimize queries to aggregate raster grids stored in databases.In our approach,we propose to estimate the exact result rather than calculate the exact result.This approach reduces query execution time.One advantage of our method is that it does not require implementing or modifying functionalities of database management systems.Our approach is based on a new data structure and a specific model of SQL queries.Our work is applied here to relational data warehouses.
文摘Recently, due to the rapid growth increment of data sensors, a massive volume of data is generated from different sources. The way of administering such data in a sense storing, managing, analyzing, and extracting insightful information from the massive volume of data is a challenging task. Big data analytics is becoming a vital research area in domains such as climate data analysis which demands fast access to data. Nowadays, an open-source platform namely MapReduce which is a distributed computing framework is widely used in many domains of big data analysis. In our work, we have developed a conceptual framework of data modeling essentially useful for the implementation of a hybrid data warehouse model to store the features of National Climatic Data Center (NCDC) climate data. The hybrid data warehouse model for climate big data enables for the identification of weather patterns that would be applicable in agricultural and other similar climate change-related studies that will play a major role in recommending actions to be taken by domain experts and make contingency plans over extreme cases of weather variability.
文摘Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is this idea that leads the integration of hierarchical dimensions into these responses. To meet this need, we propose, in this paper, a complete redefinition of the framework and the formal definition of traditional database analysis through the prism of hierarchical dimensions. After characterizing the hierarchical data cube lattice, we introduce the hierarchical data cube and its most concise reduced representation, the closed hierarchical data cube. It offers compact replication so as to optimize storage space by removing redundancies of strongly correlated data. Such data are typical of data warehouses, and in particular in video games, our field of study and experimentation, where hierarchical dimension attributes are widely represented.
文摘Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process.However,evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way,is missing.It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored.To address this problem an integrated resource classification framework is presenting that identifies competence,suitability,and preference as the relationship of task with resources.But,only the competence relationship of human resources with a task is presented in this research as a resource competence model.Furthermore,the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence evaluation.The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process.Applicability of method is illustrated through a heathcare case study.
文摘Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task.In continuation of our previous work,we categorize resources into human and non-human resources.For instance,in the healthcare domain,human resources include doctors,nurses,and other associated staff responsible for the execution of healthcare activities;whereas the non-human resources include surgical and other equipment needed for execution.In this study,we contend that the two types of resources(human and non-human)have a different impact on the process performance,so their suitability should be measured differently.However,no work has been done to evaluate the suitability of non-human resources for the tasks of a process.Consequently,it becomes difficult to identify and subsequently overcome the inefficiencies caused by the non-human resources to the task.To address this problem,we present a three-step method to compute a suitability score of non-human resources for the task.As an evaluation of the proposed method,a healthcare case study is used to illustrate the applicability of the proposed method.Furthermore,we performed a controlled experiment to evaluate the usability of the proposed method.The encouraging response shows the usefulness of the proposed method.
基金Supported by the National Science & Technology Pillar Program(2006BAJ07B07)
文摘The framework of the assistant decision support system of cross-regional rural labor flow is established,the system combines the cross-regional rural labor flow with DSS,which provides the leaders with the maximum assistant decision-making function in the regulation and guidance of rural labors as well as in relevant programs.The assistant decision support system functions are discussed,the function modules of this system are introduced from four aspects,including the analysis of labor flow,the prediction of labor flow,the regulation of cross-regional flow and the configuration of decision support system;based on the data base obtained from dynamic tracking of the migrant workers and combining other data sources,the data warehouse model is established,for example,in the analysis of the labor migration times,a star multi-dimensional data model is designed from the time dimension,place dimension,the type of work dimension,accompaniers dimension and so on;the trans-regional flow of rural labor force is analyzed and predicted by using OLAP from the labor's migration times,migration places and other various perspectives.The operation principles of the assistant decision support system of trans-regional labor flow are introduced,it is pointed out that the system serves the policy-makers of the regulation of labor flow and other relevant enterprises,the system will play an important role in the tracking monitoring and cross-regional regulation of the rural labor flow.
文摘In this article, our research aims to set up a geo-decisional system, more precisely we are particularly interested in the spatial analysis system of agricultural production in Madagascar. For this, we used the spatial data warehouse technique based on the SOLAP spatial analysis tool. After having defined the concepts underlying these systems, we propose to address the research issues related to them from four points of view: needs study of the Malagasy Ministry of Agriculture, modeling of a multidimensional conceptual model according to the MultiDim model and the implementation of the system studied using GeoKettle, PostGIS, GeoServer, SPAGO BI and Géomondrian technologies. This new system helps improve the decision-making process for agricultural production in Madagascar.
文摘The data warehouse is the most widely used database structure in many decision support systems around the world. This is the reason why a lot of research has been conducted in the literature over the last two decades on their design, refreshment and optimization. The manipulation of hypercubes (cubes) of data is a frequently used operation in the design of multidimensional data warehouses, due to their better adaptation to OLAP (On-Line Analytical Processing). However, the updating of these hypercubes is a very complicated process due mainly to the mass and complexity of the data presented. The purpose of this paper is to present the state of the art of works based on multidimensional modeling using the hypercube as a unit of presentation of data stores. It starts with the base of this process which is the choice of the views (cubes) forming our data warehouse base. The objective of this work is to describe the state of the art of research works dealing with the selection of materialized views in decision support systems.
基金the National Natural Science Foundation of China(Grant No.31771452)The State Key Laboratory of Integrated Management of Pest Insects and Rodents(Grant No.IPM2008)The Key Research Program of the Chinese Academy of Sciences(Grant No.KJZD-SW-L07).
文摘Fall armyworm(Spodoptera frugiperda),a native insect species in the Americas,is rapidly becoming a major agricultural pest worldwide and is causing great damage to corn,rice,soybeans,and other crops.To control this pest,scientists have accumulated a great deal of high-throughput data of fall armyworm,and nine versions of its genomes and transcriptomes have been published.However,easily accessing and performing integrated analysis of these omics data sets is challenging.Here,we developed the Fall Armyworm Genome Database(FAWMine,http://159.226.67.243:8080/fawmine/)to maintain genome sequences,structural and functional annotations,transcriptomes,co-expression,protein interactions,homologs,pathways,and single-nucleotide variations.FAWMine provides a powerful framework that helps users to perform flexible and customized searching,present integrated data sets using diverse visualization methods,output results tables in a range of file formats,analyze candidate gene lists using multiple widgets,and query data available in other InterMine systems.Additionally,stand-alone JBrowse and BLAST services are also established,allowing the users to visualize RNA-Seq data and search genome and annotated gene sequences.Altogether,FAWMine is a useful tool for querying,visualizing,and analyzing compiled data sets rapidly and efficiently.FAWMine will be continually updated to function as a community resource for fall armyworm genomics and pest control research.
文摘Recent research has shown an increase in the number of extreme tornado outbreaks per year.The characterization of the spatio-temporal pattern of tornado events is therefore a critical task in the analysis of meteorological data.Currently,there are a large number of available meteorological datasets that can be used for such analysis.However,much of these data are distributed across multiple websites and are not accessible in a central location.This poses a significant challenge for a scientist who is interested in exploring meteorological patterns associated with tornado events.This paper presents a novel system which uses cloud-based technology for integrating,storing,exploring,analyzing,and visualizing meteorological data associated with tornado outbreaks.The system employs a novel NoSQL database schema and web services architecture for data integration and provides a user friendly interface that allows scientists to explore the spatio-temporal pattern of tornado events.Furthermore,scientists can use this interface to analyze the relationship between different meteorological variables and properties of tornado outbreaks using a number of spatio-temporal statistical and data mining methods.The efficacy of the system is demonstrated on a use case centered on the analysis of climatic indicators of large spatio-temporally clustered tornado outbreaks.
