为适应时代的发展与现代战争的需求,以提高装备作战效能为目标,构建一种基于认知测试性设计(design for cognitive testability,DFCT)的装备保障大数据建设理念。注重“信息”在装备保障中的作用,认知测试性扩展传统测试性内涵。为评估...为适应时代的发展与现代战争的需求,以提高装备作战效能为目标,构建一种基于认知测试性设计(design for cognitive testability,DFCT)的装备保障大数据建设理念。注重“信息”在装备保障中的作用,认知测试性扩展传统测试性内涵。为评估认知测试性设计的水平,提出以信息获取性能、故障诊断性能、预判决策性能为主的评价指标体系,并阐述其具体含义。结果表明,该设计可为装备保障数据库的建立提供设计支撑。展开更多
With the growing popularity of Internet applications and the widespread use of mobile Internet, Internet traffic has maintained rapid growth over the past two decades. Internet Traffic Archival Systems（ITAS） for pac...With the growing popularity of Internet applications and the widespread use of mobile Internet, Internet traffic has maintained rapid growth over the past two decades. Internet Traffic Archival Systems（ITAS） for packets or flow records have become more and more widely used in network monitoring, network troubleshooting, and user behavior and experience analysis. Among the three key technologies in ITAS, we focus on bitmap index compression algorithm and give a detailed survey in this paper. The current state-of-the-art bitmap index encoding schemes include： BBC, WAH, PLWAH, EWAH, PWAH, CONCISE, COMPAX, VLC, DF-WAH, and VAL-WAH. Based on differences in segmentation, chunking, merge compress, and Near Identical（NI） features, we provide a thorough categorization of the state-of-the-art bitmap index compression algorithms. We also propose some new bitmap index encoding algorithms, such as SECOMPAX, ICX, MASC, and PLWAH＋, and present the state diagrams for their encoding algorithms. We then evaluate their CPU and GPU implementations with a real Internet trace from CAIDA. Finally, we summarize and discuss the future direction of bitmap index compression algorithms. Beyond the application in network security and network forensic, bitmap index compression with faster bitwise-logical operations and reduced search space is widely used in analysis in genome data, geographical information system, graph databases, image retrieval, Internet of things, etc. It is expected that bitmap index compression will thrive and be prosperous again in Big Data era since 1980s.展开更多
文摘为适应时代的发展与现代战争的需求,以提高装备作战效能为目标,构建一种基于认知测试性设计(design for cognitive testability,DFCT)的装备保障大数据建设理念。注重“信息”在装备保障中的作用,认知测试性扩展传统测试性内涵。为评估认知测试性设计的水平,提出以信息获取性能、故障诊断性能、预判决策性能为主的评价指标体系,并阐述其具体含义。结果表明,该设计可为装备保障数据库的建立提供设计支撑。
基金supported by the National Key Basic Research and Development (973) Program of China (Nos. 2012CB315801 and 2013CB228206)the National Natural Science Foundation of China A3 Program (No. 61140320)+2 种基金the National Natural Science Foundation of China (Nos. 61233016 and 61472200)supported by the National Training Program of Innovation and Entrepreneurship for Undergraduates (Nos. 201410003033 and 201410003031)Hitachi (China) Research and Development Corporation
文摘With the growing popularity of Internet applications and the widespread use of mobile Internet, Internet traffic has maintained rapid growth over the past two decades. Internet Traffic Archival Systems（ITAS） for packets or flow records have become more and more widely used in network monitoring, network troubleshooting, and user behavior and experience analysis. Among the three key technologies in ITAS, we focus on bitmap index compression algorithm and give a detailed survey in this paper. The current state-of-the-art bitmap index encoding schemes include： BBC, WAH, PLWAH, EWAH, PWAH, CONCISE, COMPAX, VLC, DF-WAH, and VAL-WAH. Based on differences in segmentation, chunking, merge compress, and Near Identical（NI） features, we provide a thorough categorization of the state-of-the-art bitmap index compression algorithms. We also propose some new bitmap index encoding algorithms, such as SECOMPAX, ICX, MASC, and PLWAH＋, and present the state diagrams for their encoding algorithms. We then evaluate their CPU and GPU implementations with a real Internet trace from CAIDA. Finally, we summarize and discuss the future direction of bitmap index compression algorithms. Beyond the application in network security and network forensic, bitmap index compression with faster bitwise-logical operations and reduced search space is widely used in analysis in genome data, geographical information system, graph databases, image retrieval, Internet of things, etc. It is expected that bitmap index compression will thrive and be prosperous again in Big Data era since 1980s.
