Online targeting has been increasingly used to deliver ads to consumers. But discovering how to target the most valuable web visitors and generate a high response rate is still a challenge for advertising intermediaries and advertisers. The purpose of this study is to examine how behavioral targeting (BT) impacts users’ responses to online ads and particularly whether BT works better in combination with contextual targeting (CT). Using a large, individual-level clickstream data set of an automobile advertising campaign from an Internet advertising intermediary, this study examines the impact of BT and CT strategies on users’ click behavior. The results show that (1) targeting a user with behavioral characteristics that are closely related to ads does not necessarily increase the click through rates (CTRs); whereas, targeting a user with behavioral characteristics that are loosely related to ads leads to a higher CTR, and (2) BT and CT work better in combination. Our study contributes to online advertising design literature and provides important managerial implications for advertising intermediaries and advertisers on targeting individual users.
{"title":"Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?","authors":"Xianghua Lu, Xia Zhao, Ling Xue","doi":"10.1145/2883816","DOIUrl":"https://doi.org/10.1145/2883816","url":null,"abstract":"Online targeting has been increasingly used to deliver ads to consumers. But discovering how to target the most valuable web visitors and generate a high response rate is still a challenge for advertising intermediaries and advertisers. The purpose of this study is to examine how behavioral targeting (BT) impacts users’ responses to online ads and particularly whether BT works better in combination with contextual targeting (CT). Using a large, individual-level clickstream data set of an automobile advertising campaign from an Internet advertising intermediary, this study examines the impact of BT and CT strategies on users’ click behavior. The results show that (1) targeting a user with behavioral characteristics that are closely related to ads does not necessarily increase the click through rates (CTRs); whereas, targeting a user with behavioral characteristics that are loosely related to ads leads to a higher CTR, and (2) BT and CT work better in combination. Our study contributes to online advertising design literature and provides important managerial implications for advertising intermediaries and advertisers on targeting individual users.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122977953","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}
Online personalization has become quite prevalent in recent years, with firms able to derive additional profits from such services. As the adoption of such services grows, firms implementing such practices face some operational challenges. One important challenge lies in the complexity associated with the personalization process and how to deploy available resources to handle such complexity. The complexity is exacerbated when a site faces a large volume of requests in a short amount of time, as is often the case for e-commerce and content delivery sites. In such situations, it is generally not possible for a site to provide perfectly personalized service to all requests. Instead, a firm can provide differentiated service to requests based on the amount of profiling information available about the visitor. We consider a scenario where the revenue function is concave, capturing the diminishing returns from personalization effort. Using a batching approach, we determine the optimal scheduling policy (i.e., time allocation and sequence of service) for a batch that accounts for the externality cost incurred when a request is provided service before other waiting requests. The batching approach leads to sunk costs incurred when visitors wait for the next batch to begin. An optimal admission control policy is developed to prescreen new request arrivals. We show how the policy can be implemented efficiently when the revenue function is complex and there are a large number of requests that can be served in a batch. Numerical experiments show that the proposed approach leads to substantial improvements over a linear approximation of the concave revenue function. Interestingly, we find that the improvements in firm profits are not only (or primarily) due to the different service times that are obtained when using the nonlinear personalization function—there is a ripple effect on the admission control policy that incorporates these optimized service times, which contributes even more to the additional profits than the service time optimization by itself.
{"title":"Who's Next? Scheduling Personalization Services with Variable Service Times","authors":"Dengpan Liu, S. Sarkar, C. Sriskandarajah","doi":"10.1145/2764920","DOIUrl":"https://doi.org/10.1145/2764920","url":null,"abstract":"Online personalization has become quite prevalent in recent years, with firms able to derive additional profits from such services. As the adoption of such services grows, firms implementing such practices face some operational challenges. One important challenge lies in the complexity associated with the personalization process and how to deploy available resources to handle such complexity. The complexity is exacerbated when a site faces a large volume of requests in a short amount of time, as is often the case for e-commerce and content delivery sites. In such situations, it is generally not possible for a site to provide perfectly personalized service to all requests. Instead, a firm can provide differentiated service to requests based on the amount of profiling information available about the visitor. We consider a scenario where the revenue function is concave, capturing the diminishing returns from personalization effort. Using a batching approach, we determine the optimal scheduling policy (i.e., time allocation and sequence of service) for a batch that accounts for the externality cost incurred when a request is provided service before other waiting requests. The batching approach leads to sunk costs incurred when visitors wait for the next batch to begin. An optimal admission control policy is developed to prescreen new request arrivals. We show how the policy can be implemented efficiently when the revenue function is complex and there are a large number of requests that can be served in a batch. Numerical experiments show that the proposed approach leads to substantial improvements over a linear approximation of the concave revenue function. Interestingly, we find that the improvements in firm profits are not only (or primarily) due to the different service times that are obtained when using the nonlinear personalization function—there is a ripple effect on the admission control policy that incorporates these optimized service times, which contributes even more to the additional profits than the service time optimization by itself.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115596464","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}
Xiaohui Zhao, Chengfei Liu, Sira Yongchareon, M. Kowalkiewicz, Wasim Sadiq
The process view concept deploys a partial and temporal representation to adjust the visible view of a business process according to various perception constraints of users. Process view technology is of practical use for privacy protection and authorization control in process-oriented business management. Owing to complex organizational structure, it is challenging for large companies to accurately specify the diverse perception of different users over business processes. Aiming to tackle this issue, this article presents a role-based process view model to incorporate role dependencies into process view derivation. Compared to existing process view approaches, ours particularly supports runtime updates to the process view perceivable to a user with specific view merging operations, thereby enabling the dynamic tracing of process perception. A series of rules and theorems are established to guarantee the structural consistency and validity of process view transformation. A hypothetical case is conducted to illustrate the feasibility of our approach, and a prototype is developed for the proof-of-concept purpose.
{"title":"Role-Based Process View Derivation and Composition","authors":"Xiaohui Zhao, Chengfei Liu, Sira Yongchareon, M. Kowalkiewicz, Wasim Sadiq","doi":"10.1145/2744207","DOIUrl":"https://doi.org/10.1145/2744207","url":null,"abstract":"The process view concept deploys a partial and temporal representation to adjust the visible view of a business process according to various perception constraints of users. Process view technology is of practical use for privacy protection and authorization control in process-oriented business management. Owing to complex organizational structure, it is challenging for large companies to accurately specify the diverse perception of different users over business processes. Aiming to tackle this issue, this article presents a role-based process view model to incorporate role dependencies into process view derivation. Compared to existing process view approaches, ours particularly supports runtime updates to the process view perceivable to a user with specific view merging operations, thereby enabling the dynamic tracing of process perception. A series of rules and theorems are established to guarantee the structural consistency and validity of process view transformation. A hypothetical case is conducted to illustrate the feasibility of our approach, and a prototype is developed for the proof-of-concept purpose.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130985764","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}
Task coordination poses significant challenges for globally dispersed teams (GDTs). Although various task coordination mechanisms have been proposed for such teams, there is a lack of systematic examination of the appropriate coordination mechanisms for different teams based on the nature of their task and the context under which they operate. Prior studies on collocated teams suggest matching their levels of task dependence to specific task coordination mechanisms for effective coordination. This research goes beyond the earlier work by also considering additional contextual factors of GDT (i.e., temporal dispersion and time constraints) in deriving their optimal IT-mediated task coordination mechanisms. Adopting the structural contingency theory, we propose optimal IT-mediated task coordination portfolios to fit the different levels of task dependence, temporal dispersion, and perceived time constraint of GDTs. The proposed fit is tested through a survey and profile analysis of 95 globally dispersed software development teams in a large financial organization. We find, as hypothesized, that the extent of fit between the actual IT-mediated task coordination portfolios used by the surveyed teams and their optimal portfolios proposed here is positively related to their task coordination effectiveness that in turn impacts the team's efficiency and effectiveness. The implications for theory and practice are discussed.
{"title":"Investigating Task Coordination in Globally Dispersed Teams: A Structural Contingency Perspective","authors":"J. Sutanto, A. Kankanhalli, B. Tan","doi":"10.1145/2688489","DOIUrl":"https://doi.org/10.1145/2688489","url":null,"abstract":"Task coordination poses significant challenges for globally dispersed teams (GDTs). Although various task coordination mechanisms have been proposed for such teams, there is a lack of systematic examination of the appropriate coordination mechanisms for different teams based on the nature of their task and the context under which they operate. Prior studies on collocated teams suggest matching their levels of task dependence to specific task coordination mechanisms for effective coordination. This research goes beyond the earlier work by also considering additional contextual factors of GDT (i.e., temporal dispersion and time constraints) in deriving their optimal IT-mediated task coordination mechanisms. Adopting the structural contingency theory, we propose optimal IT-mediated task coordination portfolios to fit the different levels of task dependence, temporal dispersion, and perceived time constraint of GDTs. The proposed fit is tested through a survey and profile analysis of 95 globally dispersed software development teams in a large financial organization. We find, as hypothesized, that the extent of fit between the actual IT-mediated task coordination portfolios used by the surveyed teams and their optimal portfolios proposed here is positively related to their task coordination effectiveness that in turn impacts the team's efficiency and effectiveness. The implications for theory and practice are discussed.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133946695","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}
Rahul C. Basole, Martha G. Russell, Jukka Huhtamäki, Neil Rubens, Kaisa Still, Hyunwoo Park
Business ecosystems consist of a heterogeneous and continuously evolving set of entities that are interconnected through a complex, global network of relationships. However, there is no well-established methodology to study the dynamics of this network. Traditional approaches have primarily utilized a single source of data of relatively established firms; however, these approaches ignore the vast number of relevant activities that often occur at the individual and entrepreneurial levels. We argue that a data-driven visualization approach, using both institutionally and socially curated datasets, can provide important complementary, triangulated explanatory insights into the dynamics of interorganizational networks in general and business ecosystems in particular. We develop novel visualization layouts to help decision makers systemically identify and compare ecosystems. Using traditionally disconnected data sources on deals and alliance relationships (DARs), executive and funding relationships (EFRs), and public opinion and discourse (POD), we empirically illustrate our data-driven method of data triangulation and visualization techniques through three cases in the mobile industry Google’s acquisition of Motorola Mobility, the coopetitive relation between Apple and Samsung, and the strategic partnership between Nokia and Microsoft. The article concludes with implications and future research opportunities.
{"title":"Understanding Business Ecosystem Dynamics: A Data-Driven Approach","authors":"Rahul C. Basole, Martha G. Russell, Jukka Huhtamäki, Neil Rubens, Kaisa Still, Hyunwoo Park","doi":"10.1145/2724730","DOIUrl":"https://doi.org/10.1145/2724730","url":null,"abstract":"Business ecosystems consist of a heterogeneous and continuously evolving set of entities that are interconnected through a complex, global network of relationships. However, there is no well-established methodology to study the dynamics of this network. Traditional approaches have primarily utilized a single source of data of relatively established firms; however, these approaches ignore the vast number of relevant activities that often occur at the individual and entrepreneurial levels. We argue that a data-driven visualization approach, using both institutionally and socially curated datasets, can provide important complementary, triangulated explanatory insights into the dynamics of interorganizational networks in general and business ecosystems in particular. We develop novel visualization layouts to help decision makers systemically identify and compare ecosystems. Using traditionally disconnected data sources on deals and alliance relationships (DARs), executive and funding relationships (EFRs), and public opinion and discourse (POD), we empirically illustrate our data-driven method of data triangulation and visualization techniques through three cases in the mobile industry Google’s acquisition of Motorola Mobility, the coopetitive relation between Apple and Samsung, and the strategic partnership between Nokia and Microsoft. The article concludes with implications and future research opportunities.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"99 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113960641","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}
In this study, we present stakeholder analyses of firm-related web forums. Prior analyses of firm-related forums have considered all participants in the aggregate, failing to recognize the potential for diversity within the populations. However, distinctive groups of forum participants may represent various interests and stakes in a firm worthy of consideration. To perform the stakeholder analyses, the Stakeholder Analyzer system for firm-related web forums is developed following the design science paradigm of information systems research. The design of the system and its approach to stakeholder analysis is guided by two kernel theories, the stakeholder theory of the firm and the systemic functional linguistic theory. A stakeholder analysis identifies distinctive groups of forum participants with shared characteristics expressed in discussion and evaluates their specific opinions and interests in the firm. Stakeholder analyses are performed in six major firm-related forums hosted on Yahoo Finance over a 3-month period. The relationships between measures extracted from the forums and subsequent daily firm stock returns are examined using multiple linear regression models, revealing statistically significant indicators of firm stock returns in the discussions of the stakeholder groups of each firm with stakeholder-model-adjusted R2 values reaching 0.83. Daily stock return prediction is also performed for 31 trading days, and stakeholder models correctly predicted the direction of return on 67% of trading days and generated an impressive 17% return in simulated trading of the six firm stocks. These evaluations demonstrate that the stakeholder analyses provided more refined assessments of the firm-related forums, yielding measures at the stakeholder group level that better explain and predict daily firm stock returns than aggregate forum-level information.
{"title":"Stakeholder Analyses of Firm-Related Web Forums: Applications in Stock Return Prediction","authors":"David Zimbra, Hsinchun Chen, R. Lusch","doi":"10.1145/2675693","DOIUrl":"https://doi.org/10.1145/2675693","url":null,"abstract":"In this study, we present stakeholder analyses of firm-related web forums. Prior analyses of firm-related forums have considered all participants in the aggregate, failing to recognize the potential for diversity within the populations. However, distinctive groups of forum participants may represent various interests and stakes in a firm worthy of consideration. To perform the stakeholder analyses, the Stakeholder Analyzer system for firm-related web forums is developed following the design science paradigm of information systems research. The design of the system and its approach to stakeholder analysis is guided by two kernel theories, the stakeholder theory of the firm and the systemic functional linguistic theory. A stakeholder analysis identifies distinctive groups of forum participants with shared characteristics expressed in discussion and evaluates their specific opinions and interests in the firm. Stakeholder analyses are performed in six major firm-related forums hosted on Yahoo Finance over a 3-month period. The relationships between measures extracted from the forums and subsequent daily firm stock returns are examined using multiple linear regression models, revealing statistically significant indicators of firm stock returns in the discussions of the stakeholder groups of each firm with stakeholder-model-adjusted R2 values reaching 0.83. Daily stock return prediction is also performed for 31 trading days, and stakeholder models correctly predicted the direction of return on 67% of trading days and generated an impressive 17% return in simulated trading of the six firm stocks. These evaluations demonstrate that the stakeholder analyses provided more refined assessments of the firm-related forums, yielding measures at the stakeholder group level that better explain and predict daily firm stock returns than aggregate forum-level information.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130847753","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}
Document clustering is crucial to automated document management, especially for the fast-growing volume of textual documents available digitally. Traditional lexicon-based approaches depend on document content analysis and measure overlap of the feature vectors representing different documents, which cannot effectively address word mismatch or ambiguity problems. Alternative query expansion and local context discovery approaches are developed but suffer from limited efficiency and effectiveness, because the large number of expanded terms create noise and increase the dimensionality and complexity of the overall feature space. Several techniques extend lexicon-based analysis by incorporating latent semantic indexing but produce less comprehensible clustering results and questionable performance. We instead propose a concept-based document representation and clustering (CDRC) technique and empirically examine its effectiveness using 433 articles concerning information systems and technology, randomly selected from a popular digital library. Our evaluation includes two widely used benchmark techniques and shows that CDRC outperforms them. Overall, our results reveal that clustering documents at an ontology-based, concept-based level is more effective than techniques using lexicon-based document features and can generate more comprehensible clustering results.
{"title":"Ontology-Based Mapping for Automated Document Management: A Concept-Based Technique for Word Mismatch and Ambiguity Problems in Document Clustering","authors":"Yen-Hsien Lee, P. H. Hu, Ching-Yi Tu","doi":"10.1145/2688488","DOIUrl":"https://doi.org/10.1145/2688488","url":null,"abstract":"Document clustering is crucial to automated document management, especially for the fast-growing volume of textual documents available digitally. Traditional lexicon-based approaches depend on document content analysis and measure overlap of the feature vectors representing different documents, which cannot effectively address word mismatch or ambiguity problems. Alternative query expansion and local context discovery approaches are developed but suffer from limited efficiency and effectiveness, because the large number of expanded terms create noise and increase the dimensionality and complexity of the overall feature space. Several techniques extend lexicon-based analysis by incorporating latent semantic indexing but produce less comprehensible clustering results and questionable performance. We instead propose a concept-based document representation and clustering (CDRC) technique and empirically examine its effectiveness using 433 articles concerning information systems and technology, randomly selected from a popular digital library. Our evaluation includes two widely used benchmark techniques and shows that CDRC outperforms them. Overall, our results reveal that clustering documents at an ontology-based, concept-based level is more effective than techniques using lexicon-based document features and can generate more comprehensible clustering results.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130083816","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}
Shing-Han Li, Yucheng Kao, Zongshen Zhang, Ying-Ping Chuang, D. Yen
In today's world, Botnet has become one of the greatest threats to network security. Network attackers, or Botmasters, use Botnet to launch the Distributed Denial of Service (DDoS) to paralyze large-scale websites or steal confidential data from infected computers. They also employ “phishing” attacks to steal sensitive information (such as users’ accounts and passwords), send bulk email advertising, and/or conduct click fraud. Even though detection technology has been much improved and some solutions to Internet security have been proposed and improved, the threat of Botnet still exists. Most of the past studies dealing with this issue used either packet contents or traffic flow characteristics to identify the invasion of Botnet. However, there still exist many problems in the areas of packet encryption and data privacy, simply because Botnet can easily change the packet contents and flow characteristics to circumvent the Intrusion Detection System (IDS). This study combines Particle Swarm Optimization (PSO) and K-means algorithms to provide a solution to remedy those problems and develop, step by step, a mechanism for Botnet detection. First, three important network behaviors are identified: long active communication behavior (ActBehavior), connection failure behavior (FailBehavior), and network scanning behavior (ScanBehavior). These behaviors are defined according to the relevant prior studies and used to analyze the communication activities among the infected computers. Second, the features of network behaviors are extracted from the flow traces in the network layer and transport layer of the network equipment. Third, PSO and K-means techniques are used to uncover the host members of Botnet in the organizational network. This study mainly utilizes the flow traces of a campus network as an experiment. The experimental findings show that this proposed approach can be employed to detect the suspicious Botnet members earlier than the detection application systems. In addition, this proposed approach is easy to implement and can be further used and extended in the campus dormitory network, home networks, and the mobile 3G network.
{"title":"A Network Behavior-Based Botnet Detection Mechanism Using PSO and K-means","authors":"Shing-Han Li, Yucheng Kao, Zongshen Zhang, Ying-Ping Chuang, D. Yen","doi":"10.1145/2676869","DOIUrl":"https://doi.org/10.1145/2676869","url":null,"abstract":"In today's world, Botnet has become one of the greatest threats to network security. Network attackers, or Botmasters, use Botnet to launch the Distributed Denial of Service (DDoS) to paralyze large-scale websites or steal confidential data from infected computers. They also employ “phishing” attacks to steal sensitive information (such as users’ accounts and passwords), send bulk email advertising, and/or conduct click fraud. Even though detection technology has been much improved and some solutions to Internet security have been proposed and improved, the threat of Botnet still exists. Most of the past studies dealing with this issue used either packet contents or traffic flow characteristics to identify the invasion of Botnet. However, there still exist many problems in the areas of packet encryption and data privacy, simply because Botnet can easily change the packet contents and flow characteristics to circumvent the Intrusion Detection System (IDS). This study combines Particle Swarm Optimization (PSO) and K-means algorithms to provide a solution to remedy those problems and develop, step by step, a mechanism for Botnet detection. First, three important network behaviors are identified: long active communication behavior (ActBehavior), connection failure behavior (FailBehavior), and network scanning behavior (ScanBehavior). These behaviors are defined according to the relevant prior studies and used to analyze the communication activities among the infected computers. Second, the features of network behaviors are extracted from the flow traces in the network layer and transport layer of the network equipment. Third, PSO and K-means techniques are used to uncover the host members of Botnet in the organizational network. This study mainly utilizes the flow traces of a campus network as an experiment. The experimental findings show that this proposed approach can be employed to detect the suspicious Botnet members earlier than the detection application systems. In addition, this proposed approach is easy to implement and can be further used and extended in the campus dormitory network, home networks, and the mobile 3G network.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131304742","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}
Text analytic methods are often aimed at extracting useful information from the vast array of unstructured, free format text documents that are created by almost all organizational processes. The success of any text mining application rests on the quality of the underlying data being analyzed, including both predictive features and outcome labels. In this case study, some focused experiments regarding data quality are used to assess the robustness of Statistical Text Mining (STM) algorithms when applied to clinical progress notes. In particular, the experiments consider the impacts of task complexity (by removing signals), training set size, and target outcome quality. While this research is conducted using a dataset drawn from the medical domain, the data quality issues explored are of more general interest.
{"title":"A Case Study of Data Quality in Text Mining Clinical Progress Notes","authors":"D. Berndt, J. McCart, Dezon K. Finch, S. Luther","doi":"10.1145/2669368","DOIUrl":"https://doi.org/10.1145/2669368","url":null,"abstract":"Text analytic methods are often aimed at extracting useful information from the vast array of unstructured, free format text documents that are created by almost all organizational processes. The success of any text mining application rests on the quality of the underlying data being analyzed, including both predictive features and outcome labels. In this case study, some focused experiments regarding data quality are used to assess the robustness of Statistical Text Mining (STM) algorithms when applied to clinical progress notes. In particular, the experiments consider the impacts of task complexity (by removing signals), training set size, and target outcome quality. While this research is conducted using a dataset drawn from the medical domain, the data quality issues explored are of more general interest.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124203449","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}
The use of free and Open-Source Software (OSS) systems is gaining momentum. Organizations are also now adopting OSS, despite some reservations, particularly about the quality issues. Stability of software is one of the main features in software quality management that needs to be understood and accurately predicted. It deals with the impact resulting from software changes and argues that stable components lead to a cost-effective software evolution. Changes are most common phenomena present in OSS in comparison to proprietary software. This makes OSS system evolution a rich context to study and predict stability. Our objective in this work is to build stability prediction models that are not only accurate but also interpretable, that is, able to explain the link between the architectural aspects of a software component and its stability behavior in the context of OSS. Therefore, we propose a new approach based on classifiers combination capable of preserving prediction interpretability. Our approach is classifier-structure dependent. Therefore, we propose a particular solution for combining Bayesian classifiers in order to derive a more accurate composite classifier that preserves interpretability. This solution is implemented using a genetic algorithm and applied in the context of an OSS large-scale system, namely the standard Java API. The empirical results show that our approach outperforms state-of-the-art approaches from both machine learning and software engineering.
{"title":"Predicting Stability of Open-Source Software Systems Using Combination of Bayesian Classifiers","authors":"S. Bouktif, H. Sahraoui, F. Ahmed","doi":"10.1145/2555596","DOIUrl":"https://doi.org/10.1145/2555596","url":null,"abstract":"The use of free and Open-Source Software (OSS) systems is gaining momentum. Organizations are also now adopting OSS, despite some reservations, particularly about the quality issues. Stability of software is one of the main features in software quality management that needs to be understood and accurately predicted. It deals with the impact resulting from software changes and argues that stable components lead to a cost-effective software evolution. Changes are most common phenomena present in OSS in comparison to proprietary software. This makes OSS system evolution a rich context to study and predict stability. Our objective in this work is to build stability prediction models that are not only accurate but also interpretable, that is, able to explain the link between the architectural aspects of a software component and its stability behavior in the context of OSS. Therefore, we propose a new approach based on classifiers combination capable of preserving prediction interpretability. Our approach is classifier-structure dependent. Therefore, we propose a particular solution for combining Bayesian classifiers in order to derive a more accurate composite classifier that preserves interpretability. This solution is implemented using a genetic algorithm and applied in the context of an OSS large-scale system, namely the standard Java API. The empirical results show that our approach outperforms state-of-the-art approaches from both machine learning and software engineering.","PeriodicalId":178565,"journal":{"name":"ACM Trans. Manag. Inf. Syst.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125146409","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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