SIGMOD acceptance rates have generally been in the narrow range of between 14 to 18 percent during the past decade. However, for given individuals the range is much wider. Some people have a zero percent acceptance rate, after five or six frustratingly unsuccessful attempts they set their sights lower (or, more pessimistically, they fail to get tenure and stop trying). Many people have acceptance rates that reflect the SIGMOD average of about 20%. Are there people that have perfect acceptance rates? In this talk I argue that while a perfect acceptance rate is essentially impossible to achieve year after year, an 80% acceptance rate is possible for top conferences. I will show how ten simple "tricks" allow you to significantly increase your odds of acceptance. As proof of utility I note that in the last ten years these ideas have allowed me to achieve 80%+ acceptance rates for many competitive conferences, including ICDM (22 papers), SIGKDD (19 papers), SDM (16 papers), VLDB (6) papers etc.
{"title":"Getting your acceptance rate to 80%: a checklist for publishing","authors":"Eamonn J. Keogh","doi":"10.1145/2213598.2213600","DOIUrl":"https://doi.org/10.1145/2213598.2213600","url":null,"abstract":"SIGMOD acceptance rates have generally been in the narrow range of between 14 to 18 percent during the past decade. However, for given individuals the range is much wider. Some people have a zero percent acceptance rate, after five or six frustratingly unsuccessful attempts they set their sights lower (or, more pessimistically, they fail to get tenure and stop trying). Many people have acceptance rates that reflect the SIGMOD average of about 20%. Are there people that have perfect acceptance rates?\u0000 In this talk I argue that while a perfect acceptance rate is essentially impossible to achieve year after year, an 80% acceptance rate is possible for top conferences. I will show how ten simple \"tricks\" allow you to significantly increase your odds of acceptance. As proof of utility I note that in the last ten years these ideas have allowed me to achieve 80%+ acceptance rates for many competitive conferences, including ICDM (22 papers), SIGKDD (19 papers), SDM (16 papers), VLDB (6) papers etc.","PeriodicalId":335125,"journal":{"name":"PhD '12","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115299401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As cloud computing environments become explosively popular, dealing with unpredictable changes, uncertainties, and disturbances in both systems and environments turns out to be one of the major challenges facing the concurrent computing industry. My research goal is to dynamically manage resources and workloads for RDBMS in cloud computing environments in order to achieve ``better performance but lower cost", i.e., better service level compliance but lower consumption of virtualized computing resource(s). Nowadays, although control theory offers a principled way to deal with the challenge based on feedback mechanisms, a controller is typically designed based on the system designer's domain knowledge and intuition instead of the behavior of the system being controlled. My research approach is based on the essence of control theory but transcends state-of-the-art control-theoretic approaches by leveraging interdisciplinary areas, especially from machine learning. While machine learning is often viewed merely as a toolbox that can be deployed for many data-centric problems, my research makes efforts to incorporate machine learning as a full-fledged engineering discipline into control-theoretic approaches for realizing my research goal. My PhD thesis work implements two solid systems by leveraging machine learning techniques, namely, ActiveSLA and SmartSLA. ActiveSLA is an automatic controller featuring risk assessment admission control to obtain the most profitable service-level compliance. SmartSLA is an automatic controller featuring cost-sensitive adaptation to achieve the lowest total cost. The experimental results show that both of the two systems outperform the state-of-the-art methods.
{"title":"Dynamic management of resources and workloads for RDBMS in cloud: a control-theoretic approach","authors":"Pengcheng Xiong","doi":"10.1145/2213598.2213614","DOIUrl":"https://doi.org/10.1145/2213598.2213614","url":null,"abstract":"As cloud computing environments become explosively popular, dealing with unpredictable changes, uncertainties, and disturbances in both systems and environments turns out to be one of the major challenges facing the concurrent computing industry. My research goal is to dynamically manage resources and workloads for RDBMS in cloud computing environments in order to achieve ``better performance but lower cost\", i.e., better service level compliance but lower consumption of virtualized computing resource(s).\u0000 Nowadays, although control theory offers a principled way to deal with the challenge based on feedback mechanisms, a controller is typically designed based on the system designer's domain knowledge and intuition instead of the behavior of the system being controlled. My research approach is based on the essence of control theory but transcends state-of-the-art control-theoretic approaches by leveraging interdisciplinary areas, especially from machine learning. While machine learning is often viewed merely as a toolbox that can be deployed for many data-centric problems, my research makes efforts to incorporate machine learning as a full-fledged engineering discipline into control-theoretic approaches for realizing my research goal.\u0000 My PhD thesis work implements two solid systems by leveraging machine learning techniques, namely, ActiveSLA and SmartSLA. ActiveSLA is an automatic controller featuring risk assessment admission control to obtain the most profitable service-level compliance. SmartSLA is an automatic controller featuring cost-sensitive adaptation to achieve the lowest total cost. The experimental results show that both of the two systems outperform the state-of-the-art methods.","PeriodicalId":335125,"journal":{"name":"PhD '12","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129297187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Regular expressions can be found in a wide array of technology for data processing on the web. We are motivated by two such technologies: schema languages for XML and query languages for graph-structured or linked data. Our focus is on theoretical aspects of regular expressions in these contexts.
{"title":"Foundations of regular expressions in XML schema languages and SPARQL","authors":"Katja Losemann","doi":"10.1145/2213598.2213609","DOIUrl":"https://doi.org/10.1145/2213598.2213609","url":null,"abstract":"Regular expressions can be found in a wide array of technology for data processing on the web. We are motivated by two such technologies: schema languages for XML and query languages for graph-structured or linked data. Our focus is on theoretical aspects of regular expressions in these contexts.","PeriodicalId":335125,"journal":{"name":"PhD '12","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123709750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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