D. Jayasinghe, C. Pu, Fábio Oliveira, Florian Rosenberg, T. Eilam
Infrastructure-as-a-Service (IaaS) cloud environments expose to users the infrastructure of a data center while relieving them from the burden and costs associated with its management and maintenance. IaaS clouds provide an interface by means of which users can create, configure, and control a set of virtual machines that will typically host a composite software service. Given the increasing popularity of this computing paradigm, previous work has focused on modeling composite software services to automate their deployment in IaaS clouds. This work is concerned with the runtime state of composite services during and after deployment. We propose AESON, a deployment runtime that automatically detects node (virtual machine) failures and eventually brings the composite service to the desired deployment state by using information describing relationships between the service components. We have designed AESON as a decentralized peer-to-peer publish/subscribe system leveraging IBM's Bulletin Board (BB), a topic-based distributed shared memory service built on top of an overlay network.
{"title":"AESON: A Model-Driven and Fault Tolerant Composite Deployment Runtime for IaaS Clouds","authors":"D. Jayasinghe, C. Pu, Fábio Oliveira, Florian Rosenberg, T. Eilam","doi":"10.1109/SCC.2013.102","DOIUrl":"https://doi.org/10.1109/SCC.2013.102","url":null,"abstract":"Infrastructure-as-a-Service (IaaS) cloud environments expose to users the infrastructure of a data center while relieving them from the burden and costs associated with its management and maintenance. IaaS clouds provide an interface by means of which users can create, configure, and control a set of virtual machines that will typically host a composite software service. Given the increasing popularity of this computing paradigm, previous work has focused on modeling composite software services to automate their deployment in IaaS clouds. This work is concerned with the runtime state of composite services during and after deployment. We propose AESON, a deployment runtime that automatically detects node (virtual machine) failures and eventually brings the composite service to the desired deployment state by using information describing relationships between the service components. We have designed AESON as a decentralized peer-to-peer publish/subscribe system leveraging IBM's Bulletin Board (BB), a topic-based distributed shared memory service built on top of an overlay network.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125312944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In Application Management Services (AMS), high resource utilization, effective resource planning and optimal assignment of service requests to resources are critical to success. Meeting these objectives requires a systematic and repeatable approach for determining the best way of measuring resource utilization, assessing workload and assigning service requests. In this paper, we present a two-step approach to help achieve the above objectives. We first measure the actual amount of effort that each resource spends on handling each service request (SR) based on a metadata model and a set of SR handling priority rules. Then, we proceed to measure resource utilization and assess SR assignment process based on the effort data calculated in step one.
{"title":"Measuring and Applying Service Request Effort Data in Application Management Services","authors":"Ying Li, K. Katircioglu","doi":"10.1109/SCC.2013.64","DOIUrl":"https://doi.org/10.1109/SCC.2013.64","url":null,"abstract":"In Application Management Services (AMS), high resource utilization, effective resource planning and optimal assignment of service requests to resources are critical to success. Meeting these objectives requires a systematic and repeatable approach for determining the best way of measuring resource utilization, assessing workload and assigning service requests. In this paper, we present a two-step approach to help achieve the above objectives. We first measure the actual amount of effort that each resource spends on handling each service request (SR) based on a metadata model and a set of SR handling priority rules. Then, we proceed to measure resource utilization and assess SR assignment process based on the effort data calculated in step one.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116865425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Care processes in healthcare organizations are very complex and difficult to define precisely in advance. Furthermore, a specific process for the same disease can vary according to the characteristics of a patient and his or her situation at hand. In order to provide personalized healthcare services to patients, it is essential to identify the context of the patients. This paper presents an integrated architecture of context-aware business process management system based on ubiquitous computing technologies. By detecting the current health status of a patient using various ubiquitous devices such as RFID and smart sensors, the proposed system helps healthcare professionals provide personalized healthcare services.
{"title":"Context-Aware Business Process Management for Personalized Healthcare Services","authors":"Junho Moon, Dongsoo Kim","doi":"10.1109/SCC.2013.88","DOIUrl":"https://doi.org/10.1109/SCC.2013.88","url":null,"abstract":"Care processes in healthcare organizations are very complex and difficult to define precisely in advance. Furthermore, a specific process for the same disease can vary according to the characteristics of a patient and his or her situation at hand. In order to provide personalized healthcare services to patients, it is essential to identify the context of the patients. This paper presents an integrated architecture of context-aware business process management system based on ubiquitous computing technologies. By detecting the current health status of a patient using various ubiquitous devices such as RFID and smart sensors, the proposed system helps healthcare professionals provide personalized healthcare services.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121630161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
"Cloud of Things" (CoT) is a concept that provides smart things' functions as a service and allows them to be used by multiple applications. In the CoT, a single smart thing instance should efficiently host multiple applications, called multi-tenancy. However, since multiple applications may simultaneously access the same smart things, they may contend for uses of the same smart things, which is called resource conflicts. Moreover, smart things inherently form complex dependencies, examples of which are include a group of smart things in a room, a group of smart things owned by a person, etc. Since handling resource conflicts and complex dependencies at an application level is typically ad-hoc and error-prone, it results in exacerbating readability of application codes. To address these issues, we propose a middleware for Cloud of Things called ECO. The ECO middleware manages organizations to handle dependency among/between smart things and virtualizes physical smart things to enable isolation between/among multiple applications using shared smart things yet internally controls smart things's sharing to resolve resource conflicts. Also, it provides consolidation by harmonizing different smart things's execution contexts of multiple applications for efficient utilization of the shared smart things. As a result, ECO middleware facilitates development of multiple applications over heterogeneous smart things with efficient sharing. The ECO middleware is implemented with heterogeneous device frameworks like UPnP, ZigBee, and CoAP over 6LoWPAN. We show that ECO middleware provides efficient sharing controls and access controls with negligible virtualization overhead.
{"title":"Multi-tenancy Support with Organization Management in the Cloud of Things","authors":"S. Kim, Daeyoung Kim","doi":"10.1109/SCC.2013.61","DOIUrl":"https://doi.org/10.1109/SCC.2013.61","url":null,"abstract":"\"Cloud of Things\" (CoT) is a concept that provides smart things' functions as a service and allows them to be used by multiple applications. In the CoT, a single smart thing instance should efficiently host multiple applications, called multi-tenancy. However, since multiple applications may simultaneously access the same smart things, they may contend for uses of the same smart things, which is called resource conflicts. Moreover, smart things inherently form complex dependencies, examples of which are include a group of smart things in a room, a group of smart things owned by a person, etc. Since handling resource conflicts and complex dependencies at an application level is typically ad-hoc and error-prone, it results in exacerbating readability of application codes. To address these issues, we propose a middleware for Cloud of Things called ECO. The ECO middleware manages organizations to handle dependency among/between smart things and virtualizes physical smart things to enable isolation between/among multiple applications using shared smart things yet internally controls smart things's sharing to resolve resource conflicts. Also, it provides consolidation by harmonizing different smart things's execution contexts of multiple applications for efficient utilization of the shared smart things. As a result, ECO middleware facilitates development of multiple applications over heterogeneous smart things with efficient sharing. The ECO middleware is implemented with heterogeneous device frameworks like UPnP, ZigBee, and CoAP over 6LoWPAN. We show that ECO middleware provides efficient sharing controls and access controls with negligible virtualization overhead.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130819309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In e-commerce and e-service environments, transaction context is important when evaluating the trust level of a seller or a service provider in a forthcoming transaction. However, most existing trust evaluation models compute a single value to reflect the general trust level of a seller without taking any transaction context into account. In the literature, a trust vector approach has been proposed to resolve the above problem. In particular, the trust vector contains different sets of trust values (termed as CTT values) so as to outline a seller's reputation profile. As a result, buyers can identify the potential risk existing in a forthcoming transaction (e.g., value imbalance, i.e. a malicious seller may build up a high level of trust by selling cheap products and then deceive buyers by inducing them to purchase more expensive products) and thus avoid monetary losses. In computing CTT values, some approaches are proposed that store the precomputed aggregation results over large-scale ratings and transaction data of a seller, so as to deliver prompt responses to a buyer's query. Though these approaches allocate relatively small space to each seller for storing the aggregation results, if applied in a system with millions of sellers, space consumption will be intolerable. In this paper, we propose a novel model for CTT computation with fixed storage space, which provides a trade-off between aggregation detail and storage space. It is particular suitable for CTT computation where a request is regarding a seller's trust in recent time period, e.g., the latest six months, rather than six months plus one day. We have conducted experiments on both an eBay dataset and a synthetic dataset to illustrate its good efficiency in responding to buyers' CTT queries.
{"title":"A Novel Model for Contextual Transaction Trust Computation with Fixed Storage Space in E-Commerce and E-Service Environments","authors":"Haibin Zhang, Yan Wang","doi":"10.1109/SCC.2013.108","DOIUrl":"https://doi.org/10.1109/SCC.2013.108","url":null,"abstract":"In e-commerce and e-service environments, transaction context is important when evaluating the trust level of a seller or a service provider in a forthcoming transaction. However, most existing trust evaluation models compute a single value to reflect the general trust level of a seller without taking any transaction context into account. In the literature, a trust vector approach has been proposed to resolve the above problem. In particular, the trust vector contains different sets of trust values (termed as CTT values) so as to outline a seller's reputation profile. As a result, buyers can identify the potential risk existing in a forthcoming transaction (e.g., value imbalance, i.e. a malicious seller may build up a high level of trust by selling cheap products and then deceive buyers by inducing them to purchase more expensive products) and thus avoid monetary losses. In computing CTT values, some approaches are proposed that store the precomputed aggregation results over large-scale ratings and transaction data of a seller, so as to deliver prompt responses to a buyer's query. Though these approaches allocate relatively small space to each seller for storing the aggregation results, if applied in a system with millions of sellers, space consumption will be intolerable. In this paper, we propose a novel model for CTT computation with fixed storage space, which provides a trade-off between aggregation detail and storage space. It is particular suitable for CTT computation where a request is regarding a seller's trust in recent time period, e.g., the latest six months, rather than six months plus one day. We have conducted experiments on both an eBay dataset and a synthetic dataset to illustrate its good efficiency in responding to buyers' CTT queries.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122081903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Web service clustering is one of a very efficient approach to discover Web services efficiently. Current clustering approaches use traditional clustering algorithms such as agglomerative as the clustering algorithm. The algorithms have not provided visualization of service clusters that gives inspiration for a specific domain from visual feedback and failed to achieve higher noise isolation. Furthermore iterative steps of algorithms consider about the similarity of limited number of services such as similarity of cluster centers. This leads to reduce the cluster performance. In this paper we apply a spatial clustering technique called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic similarity values between services as the affinity values. Most of the current clustering approaches use similarity distance measurement such as keyword, ontology and information-retrieval-based methods. These approaches have problem of short of high quality ontology and loss of semantic information. In this paper, we calculate the service similarity by using hybrid term similarity method which uses ontology learning and information retrieval. Experimental results show our clustering approach is able to plot similar services into same area and aid to search Web services by visualization of the service data on a spherical surface.
{"title":"Clustering and Spherical Visualization of Web Services","authors":"B. Kumara, Y. Yaguchi, Incheon Paik, Wuhui Chen","doi":"10.1109/SCC.2013.90","DOIUrl":"https://doi.org/10.1109/SCC.2013.90","url":null,"abstract":"Web service clustering is one of a very efficient approach to discover Web services efficiently. Current clustering approaches use traditional clustering algorithms such as agglomerative as the clustering algorithm. The algorithms have not provided visualization of service clusters that gives inspiration for a specific domain from visual feedback and failed to achieve higher noise isolation. Furthermore iterative steps of algorithms consider about the similarity of limited number of services such as similarity of cluster centers. This leads to reduce the cluster performance. In this paper we apply a spatial clustering technique called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic similarity values between services as the affinity values. Most of the current clustering approaches use similarity distance measurement such as keyword, ontology and information-retrieval-based methods. These approaches have problem of short of high quality ontology and loss of semantic information. In this paper, we calculate the service similarity by using hybrid term similarity method which uses ontology learning and information retrieval. Experimental results show our clustering approach is able to plot similar services into same area and aid to search Web services by visualization of the service data on a spherical surface.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127916236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jia Zhang, Bob Iannucci, M. Hennessy, Kaushik Gopal, S. Xiao, Sumeet Kumar, David Pfeffer, Basmah Aljedia, Yuan Ren, M. Griss, Steven Rosenberg, J. Cao, Anthony G. Rowe
The Internet of Things (IoT) offers the promise of integrating the digital world of the Internet with the physical world in which we live. But realizing this promise necessitates a systematic approach to integrating the sensors, actuators, and information on which they operate into the Internet we know today. This paper reports the design and development of an open community-oriented platform aiming to support federated sensor data as a service, featuring interoperability and reusability of heterogeneous sensor data and data services. The concepts of virtual sensors and virtual devices are identified as central autonomic units to model scalable and context-aware configurable/reconfigurable sensor data and services. The decoupling of the storage and management of sensor data and platform-oriented metadata enables the handling of both discrete and streaming sensor data. A cloud computing-empowered prototyping system has been established as a proof of concept to host smart community-oriented sensor data and services.
{"title":"Sensor Data as a Service -- A Federated Platform for Mobile Data-centric Service Development and Sharing","authors":"Jia Zhang, Bob Iannucci, M. Hennessy, Kaushik Gopal, S. Xiao, Sumeet Kumar, David Pfeffer, Basmah Aljedia, Yuan Ren, M. Griss, Steven Rosenberg, J. Cao, Anthony G. Rowe","doi":"10.1109/SCC.2013.34","DOIUrl":"https://doi.org/10.1109/SCC.2013.34","url":null,"abstract":"The Internet of Things (IoT) offers the promise of integrating the digital world of the Internet with the physical world in which we live. But realizing this promise necessitates a systematic approach to integrating the sensors, actuators, and information on which they operate into the Internet we know today. This paper reports the design and development of an open community-oriented platform aiming to support federated sensor data as a service, featuring interoperability and reusability of heterogeneous sensor data and data services. The concepts of virtual sensors and virtual devices are identified as central autonomic units to model scalable and context-aware configurable/reconfigurable sensor data and services. The decoupling of the storage and management of sensor data and platform-oriented metadata enables the handling of both discrete and streaming sensor data. A cloud computing-empowered prototyping system has been established as a proof of concept to host smart community-oriented sensor data and services.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"35 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131859720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bo Hu, Yutao Ma, Liang-Jie Zhang, Chunxiao Xing, Jun Zou, Ping Xu
With the incredible popularity of cloud computing, the adoption of mass customization (MC) is significant for building a cloud computing system that could provide services provisioning in a manner of multi-tenancy. Because of lack of a standard architecture that supports MC development for cloud services, the existing metadata or model driven approaches have insufficient abilities to realize personalized requirements with mass production when applied to product development in large-scale enterprises. Aiming at these problems, this paper presents a novel MC-based development approach for enterprise-level business cloud services based on the specification of the Cloud Computing Reference Architecture (CCRA), and shares the practice about how the approach is applied to building Kingdee K/3 Collaboration Development Cloud (CDC). Successful practice has proved that by adopting our MC development approach, we can develop platforms and tools on the cloud at a low cost and more effectively.
{"title":"A CCRA Based Mass Customization Development for Cloud Services","authors":"Bo Hu, Yutao Ma, Liang-Jie Zhang, Chunxiao Xing, Jun Zou, Ping Xu","doi":"10.1109/SCC.2013.113","DOIUrl":"https://doi.org/10.1109/SCC.2013.113","url":null,"abstract":"With the incredible popularity of cloud computing, the adoption of mass customization (MC) is significant for building a cloud computing system that could provide services provisioning in a manner of multi-tenancy. Because of lack of a standard architecture that supports MC development for cloud services, the existing metadata or model driven approaches have insufficient abilities to realize personalized requirements with mass production when applied to product development in large-scale enterprises. Aiming at these problems, this paper presents a novel MC-based development approach for enterprise-level business cloud services based on the specification of the Cloud Computing Reference Architecture (CCRA), and shares the practice about how the approach is applied to building Kingdee K/3 Collaboration Development Cloud (CDC). Successful practice has proved that by adopting our MC development approach, we can develop platforms and tools on the cloud at a low cost and more effectively.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115384774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elasticity and economic considerations make Infrastructure-as-a-Service (IaaS) clouds attractive propositions for hosting enterprise IT applications. However, for prospective cloud customers, that potential is tempered by concerns, chief among them being security. We consider the problem of resource allocation in IaaS clouds while factoring in reachability and access control requirements of the cloud virtual machines (VMs). We describe a security-aware resource allocation framework that allows for effective enforcement of defense-in-depth for cloud VMs by determining (1) the grouping of VMs into security groups based on the similarity of their reachability requirements, and (2) the placement of virtual machines in a manner that reduces residual risks for individual VMs as well as security groups. We formalize security-aware resource allocation as a Constraint Satisfaction Problem (CSP), which can be solved using widely available Satisfiability Modulo Theories (SMT) solvers. Our experimental evaluation shows the effectiveness of our approach in reducing risk and improving manageability of security configurations for the cloud VMs.
{"title":"Security-Aware Resource Allocation in Clouds","authors":"Saeed Al-Haj, E. Al-Shaer, H. Ramasamy","doi":"10.1109/SCC.2013.36","DOIUrl":"https://doi.org/10.1109/SCC.2013.36","url":null,"abstract":"Elasticity and economic considerations make Infrastructure-as-a-Service (IaaS) clouds attractive propositions for hosting enterprise IT applications. However, for prospective cloud customers, that potential is tempered by concerns, chief among them being security. We consider the problem of resource allocation in IaaS clouds while factoring in reachability and access control requirements of the cloud virtual machines (VMs). We describe a security-aware resource allocation framework that allows for effective enforcement of defense-in-depth for cloud VMs by determining (1) the grouping of VMs into security groups based on the similarity of their reachability requirements, and (2) the placement of virtual machines in a manner that reduces residual risks for individual VMs as well as security groups. We formalize security-aware resource allocation as a Constraint Satisfaction Problem (CSP), which can be solved using widely available Satisfiability Modulo Theories (SMT) solvers. Our experimental evaluation shows the effectiveness of our approach in reducing risk and improving manageability of security configurations for the cloud VMs.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114529351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ke Ning, Zhangbing Zhou, Jianhua Zheng, Dong Liu, Liang-Jie Zhang
In the era of knowledge economy, knowledge resources have become the most valuable assets for enterprises. To better understand and reuse knowledge, it is necessary to relate it with the context in which the knowledge is generated and used. This is a process that usually occurs in an experienced knowledge-worker's mind and without efficient supporting tools. This paper proposes an approach for the acquisition and utilization of context for the enhancement of knowledge and with a particular focus on methods to enable context extraction from industrial settings. The approach adopts a knowledge context ontology, to correlate knowledge and its context in the high-level activities of a knowledge worker. Knowledge context are extracted by utilizing a combination of methods including context identification, context reasoning, and context similarity measurement. Based on the proposed approach, a set of services for context aware knowledge enhancement are developed and applied in The Chinese Enterprise Management Tank (CEMT), a knowledge sharing and reusing platform for business management knowledge workers in all around China.
{"title":"Services for Context Aware Knowledge Enhancement and Its Application in the Chinese Enterprise Management Tank (CEMT)","authors":"Ke Ning, Zhangbing Zhou, Jianhua Zheng, Dong Liu, Liang-Jie Zhang","doi":"10.1109/SCC.2013.32","DOIUrl":"https://doi.org/10.1109/SCC.2013.32","url":null,"abstract":"In the era of knowledge economy, knowledge resources have become the most valuable assets for enterprises. To better understand and reuse knowledge, it is necessary to relate it with the context in which the knowledge is generated and used. This is a process that usually occurs in an experienced knowledge-worker's mind and without efficient supporting tools. This paper proposes an approach for the acquisition and utilization of context for the enhancement of knowledge and with a particular focus on methods to enable context extraction from industrial settings. The approach adopts a knowledge context ontology, to correlate knowledge and its context in the high-level activities of a knowledge worker. Knowledge context are extracted by utilizing a combination of methods including context identification, context reasoning, and context similarity measurement. Based on the proposed approach, a set of services for context aware knowledge enhancement are developed and applied in The Chinese Enterprise Management Tank (CEMT), a knowledge sharing and reusing platform for business management knowledge workers in all around China.","PeriodicalId":370898,"journal":{"name":"2013 IEEE International Conference on Services Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129475407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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