This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems,addressing the lack of depth and precision in existing literature.It proposes a two-pronged approach:a thorough anal...This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems,addressing the lack of depth and precision in existing literature.It proposes a two-pronged approach:a thorough analysis of current algorithms and a novel,hierarchical taxonomy for precise categorization.The taxonomy is based on a tri-level hierarchy,starting with the methodology category and narrowing down to specific techniques.Such a framework allows for a structured and comprehensive classification of algorithms,assisting researchers in understanding the interrelationships among diverse algorithms and techniques.Covering a wide range of algorithms,this taxonomy first categorizes algorithms into four main analysis types:user and item similarity based methods,hybrid and combined approaches,deep learning and algorithmic methods,and mathematical modeling methods,with further subdivisions into sub-categories and techniques.The paper incorporates both empirical and experimental evaluations to differentiate between the techniques.The empirical evaluation ranks the techniques based on four criteria.The experimental assessments rank the algorithms that belong to the same category,sub-category,technique,and sub-technique.Also,the paper illuminates the future prospects of big data techniques in recommendation systems,underscoring potential advancements and opportunities for further research in this fields.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This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems,addressing the lack of depth and precision in existing literature.It proposes a two-pronged approach:a thorough analysis of current algorithms and a novel,hierarchical taxonomy for precise categorization.The taxonomy is based on a tri-level hierarchy,starting with the methodology category and narrowing down to specific techniques.Such a framework allows for a structured and comprehensive classification of algorithms,assisting researchers in understanding the interrelationships among diverse algorithms and techniques.Covering a wide range of algorithms,this taxonomy first categorizes algorithms into four main analysis types:user and item similarity based methods,hybrid and combined approaches,deep learning and algorithmic methods,and mathematical modeling methods,with further subdivisions into sub-categories and techniques.The paper incorporates both empirical and experimental evaluations to differentiate between the techniques.The empirical evaluation ranks the techniques based on four criteria.The experimental assessments rank the algorithms that belong to the same category,sub-category,technique,and sub-technique.Also,the paper illuminates the future prospects of big data techniques in recommendation systems,underscoring potential advancements and opportunities for further research in this fields.
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.
