The Experts below are selected from a list of 384 Experts worldwide ranked by ideXlab platform
Qingfeng Zhuge - One of the best experts on this subject based on the ideXlab platform.
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LCN - Towards Real-Time and Temporal Information Services in Vehicular Networks via Multi-Objective Optimization
2016 IEEE 41st Conference on Local Computer Networks (LCN), 2016Co-Authors: Liang Feng, Qingfeng ZhugeAbstract:Real-time and Temporal information services are intrinsic characteristics in vehicular networks, where the timeliness of Data dissemination and the maintenance of Data quality interplay with each other and influence overall system performance. In this work, we present the system architecture where multiple road side units (RSUs) are cooperated to provide information services, and the vehicles can upload up-to-date information to RSUs via vehicle-to-infrastructure (V2I) communication. On this basis, we formulate the distributed Temporal Data Management (DTDM) problem as a two-objective problem, which aims to enhance overall system performance on both the service quality and the service ratio simultaneously. Further, we propose a multiobjective evolutionary algorithm called MO-DTDM to obtain a set of pareto solutions and analyze how to fulfill given requirements on system performance with obtained pareto solutions. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed optimization method.
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Towards Real-Time and Temporal Information Services in Vehicular Networks via Multi-Objective Optimization
2016 IEEE 41st Conference on Local Computer Networks (LCN), 2016Co-Authors: Liang Feng, Qingfeng ZhugeAbstract:Real-time and Temporal information services are intrinsic characteristics in vehicular networks, where the timeliness of Data dissemination and the maintenance of Data quality interplay with each other and influence overall system performance. In this work, we present the system architecture where multiple road side units (RSUs) are cooperated to provide information services, and the vehicles can upload up-to-date information to RSUs via vehicle-to-infrastructure (V2I) communication. On this basis, we formulate the distributed Temporal Data Management (DTDM) problem as a two-objective problem, which aims to enhance overall system performance on both the service quality and the service ratio simultaneously. Further, we propose a multiobjective evolutionary algorithm called MO-DTDM to obtain a set of pareto solutions and analyze how to fulfill given requirements on system performance with obtained pareto solutions. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed optimization method.
Richard T. Snodgrass - One of the best experts on this subject based on the ideXlab platform.
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Temporal Data Management
IEEE Transactions on Knowledge and Data Engineering, 1999Co-Authors: Christian S Jensen, Richard T. SnodgrassAbstract:A wide range of Database applications manage time-varying information. Existing Database technology currently provides little support for managing such Data. The research area of Temporal Databases has made important contributions in characterizing the semantics of such information and in providing expressive and efficient means to model, store, and query Temporal Data. This paper introduces the reader to Temporal Data Management, surveys state-of-the-art solutions to challenging aspects of Temporal Data Management, and points to research directions.
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Report on the 1995 international workshop on Temporal Databases
ACM SIGMOD Record, 1995Co-Authors: Arie Segev, Christian S Jensen, Richard T. SnodgrassAbstract:This paper provides an overview of the 1995 International Workshop on Temporal Databases. It summarizes the technical papers and related discussions, and three panels: “Wither TSQL3?”, “Temporal Data Management in Financial Applications,” and “Temporal Data Management Infrastructure & Beyond.”
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panel the state of the art in Temporal Data Management perspectives from the research and financial applications communities
Proceedings of the International Workshop on Temporal Databases: Recent Advances in Temporal Databases, 1995Co-Authors: James Clifford, Richard T. Snodgrass, Christian S Jensen, Michael H Bohlen, Hasan Dewan, Duri SchmidtAbstract:In the first part of the panel, several well-known researchers from the Temporal Database field discussed ongoing efforts to create an infrastructure to support Temporal Data Management. What infrastructure now exists, or should exist soon?
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Temporal Databases - Panel: The State-of-the-Art in Temporal Data Management: Perspectives from the Research and Financial Applications Communities
Workshops in Computing, 1995Co-Authors: James Clifford, Richard T. Snodgrass, Christian S Jensen, Michael H Bohlen, Hasan Dewan, Duri SchmidtAbstract:In the first part of the panel, several well-known researchers from the Temporal Database field discussed ongoing efforts to create an infrastructure to support Temporal Data Management. What infrastructure now exists, or should exist soon?
Christian S Jensen - One of the best experts on this subject based on the ideXlab platform.
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eBISS - Temporal Data Management – An Overview
Lecture Notes in Business Information Processing, 2018Co-Authors: Michael H Bohlen, Anton Dignos, Johann Gamper, Christian S JensenAbstract:Despite the ubiquity of Temporal Data and considerable research on the effective and efficient processing of such Data, Database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in the processing of Temporal Data that captures multiple states of reality. The SQL:2011 standard incorporates some Temporal support, and commercial DBMSs have started to offer Temporal functionality in a step-by-step manner, such as the representation of Temporal intervals, Temporal primary and foreign keys, and the support for so-called time-travel queries that enable access to past states.
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Temporal Data Management an overview
European Business Intelligence and Big Data Summer School, 2017Co-Authors: Michael H Bohlen, Anton Dignos, Johann Gamper, Christian S JensenAbstract:Despite the ubiquity of Temporal Data and considerable research on the effective and efficient processing of such Data, Database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in the processing of Temporal Data that captures multiple states of reality. The SQL:2011 standard incorporates some Temporal support, and commercial DBMSs have started to offer Temporal functionality in a step-by-step manner, such as the representation of Temporal intervals, Temporal primary and foreign keys, and the support for so-called time-travel queries that enable access to past states.
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BNCOD - Towards General Temporal Aggregation
Lecture Notes in Computer Science, 2008Co-Authors: Michael H Bohlen, Johann Gamper, Christian S JensenAbstract:Most Database applications manage time-referenced, or Temporal, Data. Temporal Data Management is difficult when using conventional Database technology, and many contributions have been made for how to better model, store, and query Temporal Data. Temporal aggregation illustrates well the problems associated with the Management of Temporal Data. Indeed, Temporal aggregation is complex and among the most difficult, and thus interesting, Temporal functionality to support. This paper presents a general framework for Temporal aggregation that accommodates existing kinds of aggregation, and it identifies open challenges within Temporal aggregation.
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Temporal Data Management
IEEE Transactions on Knowledge and Data Engineering, 1999Co-Authors: Christian S Jensen, Richard T. SnodgrassAbstract:A wide range of Database applications manage time-varying information. Existing Database technology currently provides little support for managing such Data. The research area of Temporal Databases has made important contributions in characterizing the semantics of such information and in providing expressive and efficient means to model, store, and query Temporal Data. This paper introduces the reader to Temporal Data Management, surveys state-of-the-art solutions to challenging aspects of Temporal Data Management, and points to research directions.
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Report on the 1995 international workshop on Temporal Databases
ACM SIGMOD Record, 1995Co-Authors: Arie Segev, Christian S Jensen, Richard T. SnodgrassAbstract:This paper provides an overview of the 1995 International Workshop on Temporal Databases. It summarizes the technical papers and related discussions, and three panels: “Wither TSQL3?”, “Temporal Data Management in Financial Applications,” and “Temporal Data Management Infrastructure & Beyond.”
B. Pernici - One of the best experts on this subject based on the ideXlab platform.
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Temporal Data Management systems: a comparative view
IEEE Transactions on Knowledge and Data Engineering, 1991Co-Authors: R. Maiocchi, B. PerniciAbstract:Characteristics and requirements of systems for Temporal Data Management in the areas of Data and knowledge bases, artificial intelligence, and software engineering are investigated and discussed on the basis of a case study. Six representative approaches were selected for this analysis, with the goal of identifying particular features of systems proposed in different areas. The six approaches are: Allen's interval-based logic, Dean and McDermott's time map Management, Kowalski and Sergot's event calculus, Maiocchi and Pernici's TSOS. Snodgrass' TQuel, and Hagelstein's ERAE. The characteristics of each system are classified and compared. On the basis of this analysis, a framework for the evaluation of Temporal systems and for the specification of Temporal Data Management systems is proposed.
Sheng Lin - One of the best experts on this subject based on the ideXlab platform.
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developing an oracle based spatio Temporal information Management system
Database Systems for Advanced Applications, 2011Co-Authors: Lei Zhao, Peiquan Jin, Lanlan Zhang, Huaishuai Wang, Sheng LinAbstract:In this paper, we present an extension of Oracle, named STOC (Spatio-Temporal Object Cartridge), to support spatio-Temporal Data Management in a practical way. The extension is developed as a PL/SQL package and can be integrated into Oracle to offer spatio-Temporal Data types as well as spatio-Temporal operations for various applications. Users are allowed to use standard SQL to access spatio-Temporal Data and functions. After an overview of the general features of STOC, we discuss the architecture and implementation of STOC. And finally, a case study of STOC is presented, which shows that STOC is effective to represent and query spatio-Temporal Data on the basis of Oracle.
