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141.
目前高校内各部门的管理信息系统都拥有独立的记录登录日志和读取登录日志的模块,从而造成了业务逻辑的重复和数据的冗余。针对这种情况提出了一种基于WebServices的系统登录日志技术,使各异构系统能够统一调用登录日志的WebServices。这种方案充分利用了现有的网络资源,有效地避免数据与业务逻辑的冗余,实现了模块和数据的重用。 相似文献
142.
随着城市交通出行需求的不断增长.出行信息服务应运而生.介绍了出行信息服务产业的国内外发展状况,提出了出行信息服务技术平台的体系结构.分析了通讯平台、定位平台和地图平台的地位、作用及其发展趋势.从交通信息采集与处理、空间数据模型、路径规划算法、短时交通预测、信息可视化和服务标准规范等方面评述了关键技术的研究进展,分析了出行信息服务产业发展需要重点解决的科学与技术问题.以促进出行信息服务技术研究的进一步深化. 相似文献
143.
针对基于Web的有色金属物性数据库的访问特点,分析了有色金属物性数据库在多层架构体系中存在的非法入侵、越权访问、信息重放攻击等安全性问题,提出了适应其软件架构要求的安全访问控制模型.并对所提出的安全模型分别进行了访问性能和安全性测试,测试结果表明,访问模型安全性较好,性能稳定. 相似文献
144.
FENECIA: failure endurable nested-transaction based execution of composite Web services with incorporated state analysis 总被引:1,自引:0,他引:1
Neila Ben Lakhal Takashi Kobayashi Haruo Yokota 《The VLDB Journal The International Journal on Very Large Data Bases》2009,18(1):1-56
Interest in the Web services (WS) composition (WSC) paradigm is increasing tremendously. A real shift in distributed computing
history is expected to occur when the dream of implementing Service-Oriented Architecture (SOA) is realized. However, there
is a long way to go to achieve such an ambitious goal. In this paper, we support the idea that, when challenging the WSC issue,
the earlier that the inevitability of failures is recognized and proper failure-handling mechanisms are defined, from the
very early stage of the composite WS (CWS) specification, the greater are the chances of achieving a significant gain in dependability.
To formalize this vision, we present the FENECIA (Failure Endurable Nested-transaction based Execution of Composite Web services with Incorporated state Analysis) framework. Our framework approaches the WSC issue from different points of view to guarantee a high level of dependability.
In particular, it aims at being simultaneously a failure-handling-devoted CWS specification, execution, and quality of service
(QoS) assessment approach. In the first section of our framework, we focus on answering the need for a specification model
tailored for the WS architecture. To this end, we introduce WS-SAGAS, a new transaction model. WS-SAGAS introduces key concepts that are not part of the WS architecture pillars, namely, arbitrary nesting, state, vitality degree, and compensation, to specify failure-endurable CWS as a hierarchy of recursively nested transactions. In addition, to define the CWS execution
semantics, without suffering from the hindrance of an XML-based notation, we describe a textual notation that describes a
WSC in terms of definition rules, composability rules, and ordering rules, and we introduce graphical and formal notations. These rules provide the solid foundation needed to formulate the execution
semantics of a CWS in terms of execution correctness verification dependencies. To ensure dependable execution of the CWS, we present in the second section of FENECIA our architecture THROWS, in which the execution control of the resulting CWS is distributed among engines, discovered dynamically, that communicate
in a peer-to-peer fashion. A dependable execution is guaranteed in THROWS by keeping track of the execution progress of a
CWS and by enforcing forward and backward recovery. We concentrate in the third section of our approach on showing how the
failure consideration is trivial in acquiring more accurate CWS QoS estimations. We propose a model that assesses several
QoS properties of CWS, which are specified as WS-SAGAS transactions and executed in THROWS. We validate our proposal and show
its feasibility and broad applicability by describing an implemented prototype and a case study. 相似文献
145.
Sebastiano Battiato Giovanni Maria Farinella Giovanni Giuffrida Catarina Sismeiro Giuseppe Tribulato 《Multimedia Tools and Applications》2009,42(1):5-30
Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audience.
Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect
to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing
process is simple and widely used, recently the industry has been under increased pressure to further optimize learning, in
particular when facing severe time and learning space constraints. The main contribution of the present work is to demonstrate
that direct marketing firms can exploit the information on visual content to optimize the learning phase. This paper proposes
a two-phase learning strategy based on a cascade of regression methods that takes advantage of the visual and text features
to improve and accelerate the learning process. Experiments in the domain of a commercial Multimedia Messaging Service (MMS)
show the effectiveness of the proposed methods and a significant improvement over traditional learning techniques. The proposed
approach can be used in any multimedia direct marketing domain in which offers comprise both a visual and text component.
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
Giuseppe TribulatoEmail: |
Sebastiano Battiato was born in Catania, Italy, in 1972. He received the degree in Computer Science (summa cum laude) in 1995 and his Ph.D in Computer Science and Applied Mathematics in 1999. From 1999 to 2003 he has lead the “Imaging” team c/o STMicroelectronics in Catania. Since 2004 he works as a Researcher at Department of Mathematics and Computer Science of the University of Catania. His research interests include image enhancement and processing, image coding and camera imaging technology. He published more than 90 papers in international journals, conference proceedings and book chapters. He is co-inventor of about 15 international patents. He is reviewer for several international journals and he has been regularly a member of numerous international conference committees. He has participated in many international and national research projects. He is an Associate Editor of the SPIE Journal of Electronic Imaging (Specialty: digital photography and image compression). He is director of ICVSS (International Computer Vision Summer School). He is a Senior Member of the IEEE. Giovanni Maria Farinella is currently contract researcher at Dipartimento di Matematica e Informatica, University of Catania, Italy (IPLAB research group). He is also associate member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006. His research interests lie in the fields of computer vision, pattern recognition and machine learning. In 2004 he received his degree in Computer Science (egregia cum laude) from University of Catania. He was awarded a Ph.D. (Computer Vision) from the University of Catania in 2008. He has co-authored several papers in international journals and conferences proceedings. He also serves as reviewer numerous international journals and conferences. He is currently the co-director of the International Summer School on Computer Vision (ICVSS). Giovanni Giuffrida is an assistant professor at University of Catania, Italy. He received a degree in Computer Science from the University of Pisa, Italy in 1988 (summa cum laude), a Master of Science in Computer Science from the University of Houston, Texas, in 1992, and a Ph.D. in Computer Science, from the University of California in Los Angeles (UCLA) in 2001. He has an extensive experience in both the industrial and academic world. He served as CTO and CEO in the industry and served as consultant for various organizations. His research interest is on optimizing content delivery on new media such as Internet, mobile phones, and digital tv. He published several papers on data mining and its applications. He is a member of ACM and IEEE. Catarina Sismeiro is a senior lecturer at Imperial College Business School, Imperial College London. She received her Ph.D. in Marketing from the University of California, Los Angeles, and her Licenciatura in Management from the University of Porto, Portugal. Before joining Imperial College Catarina had been and assistant professor at Marshall School of Business, University of Southern California. Her primary research interests include studying pharmaceutical markets, modeling consumer behavior in interactive environments, and modeling spatial dependencies. Other areas of interest are decision theory, econometric methods, and the use of image and text features to predict the effectiveness of marketing communications tools. Catarina’s work has appeared in innumerous marketing and management science conferences. Her research has also been published in the Journal of Marketing Research, Management Science, Marketing Letters, Journal of Interactive Marketing, and International Journal of Research in Marketing. She received the 2003 Paul Green Award and was the finalist of the 2007 and 2008 O’Dell Awards. Catarina was also a 2007 Marketing Science Institute Young Scholar, and she received the D. Antonia Adelaide Ferreira award and the ADMES/MARKTEST award for scientific excellence. Catarina is currently on the editorial boards of the Marketing Science journal and the International Journal of Research in Marketing. Giuseppe Tribulato was born in Messina, Italy, in 1979. He received the degree in Computer Science (summa cum laude) in 2004 and his Ph.D in Computer Science in 2008. From 2005 he has lead the research team at Neodata Group. His research interests include data mining techniques, recommendation systems and customer targeting. 相似文献
146.
In map generalization various operators are applied to the features of a map in order to maintain and improve the legibility
of the map after the scale has been changed. These operators must be applied in the proper sequence and the quality of the
results must be continuously evaluated. Cartographic constraints can be used to define the conditions that have to be met
in order to make a map legible and compliant to the user needs. The combinatorial optimization approaches shown in this paper
use cartographic constraints to control and restrict the selection and application of a variety of different independent generalization
operators into an optimal sequence. Different optimization techniques including hill climbing, simulated annealing and genetic
deep search are presented and evaluated experimentally by the example of the generalization of buildings in blocks. All algorithms
used in this paper have been implemented in a web services framework. This allows the use of distributed and parallel processing
in order to speed up the search for optimized generalization operator sequences.
相似文献
Moritz NeunEmail: |
147.
基于SOA与Web服务的后勤信息资源整合框架研究 总被引:2,自引:0,他引:2
分析了后勤信息资源的现状及整合需求,以面向服务体系结构SOA为指导,设计了基于Web Services的后勤信息资源整合的SOA框架,通过对现有后勤信息系统功能组件和数据的服务封装,利用BEPL和UDDI实现服务的动态绑定,建立了从网络协议到用户角色、服务质量保证的安全体系,实现了后勤信息资源的综合集成。 相似文献
148.
基于SOA的空间信息资源整合与服务模式探讨 总被引:1,自引:0,他引:1
提高空间信息资源的整合与服务水平,在迈向信息和服务型社会的过程中具有十分重要的意义。文章根据作者近年来在该领域的相关应用研究,对空间信息资源整合与服务模式进行了探讨,总结了基于SOA实现空间信息资源整合与服务的模式,并通过实例阐述了空间信息资源整合与服务的组织、实施和应用。 相似文献
149.
150.