{"title":"信息价值驱动的近实时决策支持系统","authors":"Ying Yan, Wen-Syan Li, Jian Xu","doi":"10.1109/ICDCS.2009.17","DOIUrl":null,"url":null,"abstract":"In this paper, we focus on challenges of supporting a decision support system (DSS) based on a hybrid approach (i.e. a federation system with data placement) for agile business intelligence applications. A DSS needs to be designed to handle a workload of potentially complex queries for important decision-making processes. The response time requirement (and a realistic goal) for such a DSS is near real time. The users of a DSS care about not only the response time but also the time stamp of the business operation report since both of them introduce uncertainty and risks to business decision-making. In our proposed DSS, each report is assigned with a business value; denoting its importance to business decision-making. An Information Value (IV) is a business value of a report discounted by time to reflex the uncertainty and risks associated with the computational latency and synchronization latency. We propose a novel Information Value-driven Query Processing (IVQP) framework specific for near real time DSS applications. The framework enables dynamic query plan selection by taking into account of information value and adaptation for online-arrival ad hoc queries. The framework works with single query as well as a workload of queries. The experimental results based on synthetic data and TPC-H show the effectiveness of our approach in achieving optimal information values for the workloads.","PeriodicalId":387968,"journal":{"name":"2009 29th IEEE International Conference on Distributed Computing Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Information Value-Driven Near Real-Time Decision Support Systems\",\"authors\":\"Ying Yan, Wen-Syan Li, Jian Xu\",\"doi\":\"10.1109/ICDCS.2009.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we focus on challenges of supporting a decision support system (DSS) based on a hybrid approach (i.e. a federation system with data placement) for agile business intelligence applications. A DSS needs to be designed to handle a workload of potentially complex queries for important decision-making processes. The response time requirement (and a realistic goal) for such a DSS is near real time. The users of a DSS care about not only the response time but also the time stamp of the business operation report since both of them introduce uncertainty and risks to business decision-making. In our proposed DSS, each report is assigned with a business value; denoting its importance to business decision-making. An Information Value (IV) is a business value of a report discounted by time to reflex the uncertainty and risks associated with the computational latency and synchronization latency. We propose a novel Information Value-driven Query Processing (IVQP) framework specific for near real time DSS applications. The framework enables dynamic query plan selection by taking into account of information value and adaptation for online-arrival ad hoc queries. The framework works with single query as well as a workload of queries. The experimental results based on synthetic data and TPC-H show the effectiveness of our approach in achieving optimal information values for the workloads.\",\"PeriodicalId\":387968,\"journal\":{\"name\":\"2009 29th IEEE International Conference on Distributed Computing Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 29th IEEE International Conference on Distributed Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDCS.2009.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 29th IEEE International Conference on Distributed Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCS.2009.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Information Value-Driven Near Real-Time Decision Support Systems
In this paper, we focus on challenges of supporting a decision support system (DSS) based on a hybrid approach (i.e. a federation system with data placement) for agile business intelligence applications. A DSS needs to be designed to handle a workload of potentially complex queries for important decision-making processes. The response time requirement (and a realistic goal) for such a DSS is near real time. The users of a DSS care about not only the response time but also the time stamp of the business operation report since both of them introduce uncertainty and risks to business decision-making. In our proposed DSS, each report is assigned with a business value; denoting its importance to business decision-making. An Information Value (IV) is a business value of a report discounted by time to reflex the uncertainty and risks associated with the computational latency and synchronization latency. We propose a novel Information Value-driven Query Processing (IVQP) framework specific for near real time DSS applications. The framework enables dynamic query plan selection by taking into account of information value and adaptation for online-arrival ad hoc queries. The framework works with single query as well as a workload of queries. The experimental results based on synthetic data and TPC-H show the effectiveness of our approach in achieving optimal information values for the workloads.