While existing theoretical frameworks describe collective technology adoption patterns, they provide little insight regarding the expected patterns of wiki activity within projects. Another impediment to the study of wiki sustainability is the absence of time-series analysis methods that are suitable for the unique patterns of wiki activity logs. The primary goals of this study are to: (i) develop a novel method for analyzing wiki edit activity logs, (ii) reveal the temporal patterns of corporate wiki edit activity, and (iii) study the factors impacting wikis' sustainability. A validation of our proposed method demonstrates that it is superior to the baseline algorithm in the face of noisy data. Our empirical study combines wiki system edit activity logs with a survey of users' perceptions, and explores 33277 distinct wiki applications within one global organization over the first 5 years of wiki operation. Our results reveal six different prototypical wiki activity patterns, and show that most corporate wikis become inactive after a relatively short period. Findings from the user survey show that users of sustainable wikis are more satisfied with the wiki system and its contents, and feel that the wiki provides them with a sense of community and productivity enhancements.
{"title":"The sustainability of corporate wikis: A time-series analysis of activity patterns","authors":"Ofer Arazy, A. Croitoru","doi":"10.1145/1877725.1877731","DOIUrl":"https://doi.org/10.1145/1877725.1877731","url":null,"abstract":"While existing theoretical frameworks describe collective technology adoption patterns, they provide little insight regarding the expected patterns of wiki activity within projects. Another impediment to the study of wiki sustainability is the absence of time-series analysis methods that are suitable for the unique patterns of wiki activity logs. The primary goals of this study are to: (i) develop a novel method for analyzing wiki edit activity logs, (ii) reveal the temporal patterns of corporate wiki edit activity, and (iii) study the factors impacting wikis' sustainability. A validation of our proposed method demonstrates that it is superior to the baseline algorithm in the face of noisy data. Our empirical study combines wiki system edit activity logs with a survey of users' perceptions, and explores 33277 distinct wiki applications within one global organization over the first 5 years of wiki operation. Our results reveal six different prototypical wiki activity patterns, and show that most corporate wikis become inactive after a relatively short period. Findings from the user survey show that users of sustainable wikis are more satisfied with the wiki system and its contents, and feel that the wiki provides them with a sense of community and productivity enhancements.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131698189","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}
Changes in the business environment such as turbulent market forces, rapidly evolving system requirements, and advances in technology demand agility in the development of software systems. Though agile approaches have received wide attention, empirical research that evaluates their effectiveness and appropriateness is scarce. Further, research to-date has investigated individual practices in isolation rather than as an integrated system. Addressing these concerns, we develop a system dynamics simulation model that considers the complex interdependencies among the variety of practices used in agile development. The model is developed on the basis of an extensive review of the literature as well as quantitative and qualitative data collected from real projects in nine organizations. We present the structure of the model focusing on essential agile practices. The validity of the model is established based on extensive structural and behavioral validation tests. Insights gained from experimentation with the model answer important questions faced by development teams in implementing two unique practices used in agile development. The results suggest that due to refactoring, the cost of implementing changes to a system varies cyclically and increases during later phases of development. Delays in refactoring also increase costs and decrease development productivity. Also, the simulation shows that pair programming helps complete more tasks and at a lower cost. The systems dynamics model developed in this research can be used as a tool by IS organizations to understand and analyze the impacts of various agile development practices and project management strategies.
{"title":"Modeling dynamics in agile software development","authors":"Lan Cao, B. Ramesh, T. Abdel-Hamid","doi":"10.1145/1877725.1877730","DOIUrl":"https://doi.org/10.1145/1877725.1877730","url":null,"abstract":"Changes in the business environment such as turbulent market forces, rapidly evolving system requirements, and advances in technology demand agility in the development of software systems. Though agile approaches have received wide attention, empirical research that evaluates their effectiveness and appropriateness is scarce. Further, research to-date has investigated individual practices in isolation rather than as an integrated system. Addressing these concerns, we develop a system dynamics simulation model that considers the complex interdependencies among the variety of practices used in agile development. The model is developed on the basis of an extensive review of the literature as well as quantitative and qualitative data collected from real projects in nine organizations. We present the structure of the model focusing on essential agile practices. The validity of the model is established based on extensive structural and behavioral validation tests. Insights gained from experimentation with the model answer important questions faced by development teams in implementing two unique practices used in agile development. The results suggest that due to refactoring, the cost of implementing changes to a system varies cyclically and increases during later phases of development. Delays in refactoring also increase costs and decrease development productivity. Also, the simulation shows that pair programming helps complete more tasks and at a lower cost. The systems dynamics model developed in this research can be used as a tool by IS organizations to understand and analyze the impacts of various agile development practices and project management strategies.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125477464","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}
N. Pervin, Fang Fang, Anindya Datta, K. Dutta, Debra E. VanderMeer
Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both real-world events and cultural trends. Such networks are often the best sources of up-to-the-minute information, and are therefore of considerable commercial and consumer interest. The trending topics that appear first on these networks represent an answer to the age-old query “what are people talking about?” Given the incredible volume of posts (on the order of 45,000 or more per minute), and the vast number of stories about which users are posting at any given time, it is a formidable problem to extract trending stories in real time. In this article, we describe a method and implementation for extracting trending topics from a high-velocity real-time stream of microblog posts. We describe our approach and implementation, and a set of experimental results that show that our system can accurately find “hot” stories from high-rate Twitter-scale text streams.
{"title":"Fast, Scalable, and Context-Sensitive Detection of Trending Topics in Microblog Post Streams","authors":"N. Pervin, Fang Fang, Anindya Datta, K. Dutta, Debra E. VanderMeer","doi":"10.1145/2407740.2407743","DOIUrl":"https://doi.org/10.1145/2407740.2407743","url":null,"abstract":"Social networks, such as Twitter, can quickly and broadly disseminate news and memes across both real-world events and cultural trends. Such networks are often the best sources of up-to-the-minute information, and are therefore of considerable commercial and consumer interest. The trending topics that appear first on these networks represent an answer to the age-old query “what are people talking about?” Given the incredible volume of posts (on the order of 45,000 or more per minute), and the vast number of stories about which users are posting at any given time, it is a formidable problem to extract trending stories in real time. In this article, we describe a method and implementation for extracting trending topics from a high-velocity real-time stream of microblog posts. We describe our approach and implementation, and a set of experimental results that show that our system can accurately find “hot” stories from high-rate Twitter-scale text streams.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130449958","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}
Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving products and services, achieving better operational efficiency, and fostering customer relationships. In this article, we will categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics. The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications. For each research direction, we will also determine a few important questions to be addressed in future research.
{"title":"Business Intelligence and Analytics: Research Directions","authors":"Ee-Peng Lim, Hsinchun Chen, Guoqing Chen","doi":"10.1145/2407740.2407741","DOIUrl":"https://doi.org/10.1145/2407740.2407741","url":null,"abstract":"Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving products and services, achieving better operational efficiency, and fostering customer relationships. In this article, we will categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics. The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications. For each research direction, we will also determine a few important questions to be addressed in future research.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134324942","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}
Enabled by Web 2.0 technologies social media provide an unparalleled platform for consumers to share their product experiences and opinions---through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics thereof are related to consumer purchases and product sales. By integrating network analysis with text sentiment mining techniques, we propose product comparison networks as a novel construct, computed from consumer product reviews. To test the validity of these product ranking measures, we conduct an empirical study based on a digital camera dataset from Amazon.com. The results demonstrate significant linkage between network-based measures and product sales, which is not fully captured by existing review measures such as numerical ratings. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that WOM in social media also constitutes a competitive landscape for firms to understand and manipulate.
{"title":"Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth","authors":"Zhu Zhang, Chenhui Guo, Paulo B. Góes","doi":"10.1145/2407740.2407744","DOIUrl":"https://doi.org/10.1145/2407740.2407744","url":null,"abstract":"Enabled by Web 2.0 technologies social media provide an unparalleled platform for consumers to share their product experiences and opinions---through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics thereof are related to consumer purchases and product sales. By integrating network analysis with text sentiment mining techniques, we propose product comparison networks as a novel construct, computed from consumer product reviews. To test the validity of these product ranking measures, we conduct an empirical study based on a digital camera dataset from Amazon.com. The results demonstrate significant linkage between network-based measures and product sales, which is not fully captured by existing review measures such as numerical ratings. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that WOM in social media also constitutes a competitive landscape for firms to understand and manipulate.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115987197","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}
Makiya Nakashima, A. Sim, Youngsoo Kim, Jong-Hoi Kim, Jinoh Kim
Variable selection (also known as feature selection ) is essential to optimize the learning complexity by prioritizing features, particularly for a massive, high-dimensional dataset like network traffic data. In reality, however, it is not an easy task to effectively perform the feature selection despite the availability of the existing selection techniques. From our initial experiments, we observed that the existing selection techniques produce different sets of features even under the same condition (e.g., a static size for the resulted set). In addition, individual selection techniques perform inconsistently, sometimes showing better performance but sometimes worse than others, thereby simply relying on one of them would be risky for building models using the selected features. More critically, it is demanding to automate the selection process, since it requires laborious efforts with intensive analysis by a group of experts otherwise. In this article, we explore challenges in the automated feature selection with the application of network anomaly detection. We first present our ensemble approach that benefits from the existing feature selection techniques by incorporating them, and one of the proposed ensemble techniques based on greedy search works highly consistently showing comparable results to the existing techniques. We also address the problem of when to stop to finalize the feature elimination process and present a set of methods designed to determine the number of features for the reduced feature set. Our experimental results conducted with two recent network datasets show that the identified feature sets by the presented ensemble and stopping methods consistently yield comparable performance with a smaller number of features to conventional selection techniques.
{"title":"Automated Feature Selection for Anomaly Detection in Network Traffic Data","authors":"Makiya Nakashima, A. Sim, Youngsoo Kim, Jong-Hoi Kim, Jinoh Kim","doi":"10.1145/3446636","DOIUrl":"https://doi.org/10.1145/3446636","url":null,"abstract":"\u0000 Variable selection (also known as\u0000 feature selection\u0000 ) is essential to optimize the learning complexity by prioritizing features, particularly for a massive, high-dimensional dataset like network traffic data. In reality, however, it is not an easy task to effectively perform the feature selection despite the availability of the existing selection techniques. From our initial experiments, we observed that the existing selection techniques produce different sets of features even under the same condition (e.g., a static size for the resulted set). In addition, individual selection techniques perform inconsistently, sometimes showing better performance but sometimes worse than others, thereby simply relying on one of them would be risky for building models using the selected features. More critically, it is demanding to automate the selection process, since it requires laborious efforts with intensive analysis by a group of experts otherwise. In this article, we explore challenges in the automated feature selection with the application of network anomaly detection. We first present our ensemble approach that benefits from the existing feature selection techniques by incorporating them, and one of the proposed ensemble techniques based on greedy search works highly consistently showing comparable results to the existing techniques. We also address the problem of when to stop to finalize the feature elimination process and present a set of methods designed to determine the number of features for the reduced feature set. Our experimental results conducted with two recent network datasets show that the identified feature sets by the presented ensemble and stopping methods consistently yield comparable performance with a smaller number of features to conventional selection techniques.\u0000","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125515526","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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