ACM Transactions on Management Information System (TMIS)最新文献

{"title":"Introduction to the Special Issue on Pattern-Driven Mining, Analytics, and Prediction for Decision Making, Part 1","authors":"Chun-Wei Lin, Nachiketa Sahoo, Gautam Srivastava, Weiping Ding","doi":"10.1145/3486960","DOIUrl":"https://doi.org/10.1145/3486960","url":null,"abstract":"Data Mining is an analytic process to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new sets of data. More specifically, pattern-driven mining, analytics, and prediction have received a lot of attention in the last two decades since information discovered in data can be used to support decision and strategy making. The results can also be utilized in decision support or information management systems (IMS). Different types of patterns and knowledge can be mined (extracted) from various applications and domains. Many previous studies focused on designing and implementing new methodologies to handle different constraints and requirements. This special issue focuses on discovering the knowledge, rules, and information for decision support and management information systems. Innovative methodologies, principles, methods, techniques, framework, theory, and applications are thus considered to deal with the challenges for decision support and management information systems. In this special issue there were 47 submissions. For Part 1, we are publishing eight articles, with more planned for a future issue. All accepted manuscripts have made a significant scientific contribution and presented a rigorous evaluation of the Information Systems outcomes in real-world practices and analysis. The summary of the accepted papers is stated below. InDSSAE: Deep stacked sparse autoencoder analytical model for COVID-19 diagnosis by fractional Fourier entropy [1], the authors proposed a novel artificial intelligence model to diagnose COVID19 based on chest CT images. First, the two-dimensional fractional Fourier entropy was presented to extract features. A custom deep-stacked sparse autoencoder (DSSAE) model was then created to serve as the classifier. Improved multiple-way data augmentation was proposed to resist overfitting. Results showed that the designed DSSAE model obtains a micro-averaged F1 score of 92.32% in handling a four-class problem. In addition, the designed model outperforms 10 state-ofthe-art approaches. In TRG-DAtt: The target relational graph and double attention network based sentiment analysis and prediction for supporting decision making [2], the authors designed a TRG-DAtt model for sentiment analysis based on target relational graph (TRG) and double attention network (DAtt) to analyze the emotional information for supporting decision making. A dependency tree-based TRG is firstly introduced to independently and fully mine the semantic relationships. A dependency graph attention network (DGAT) is then designed, as well as the interactive attention network (IAT) to form the DAtt, and obtained the emotional features of the target words and reviews. DGAT models the dependency of the TRG by aggregating the semantic information, and the target emotional enhancement features are obtained by the DGAT as an input to the IAT.","PeriodicalId":157366,"journal":{"name":"ACM Transactions on Management Information System (TMIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114808379","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}

{"title":"TRG-DAtt: The Target Relational Graph and Double Attention Network Based Sentiment Analysis and Prediction for Supporting Decision Making","authors":"Fan Chen, Jiaoxiong Xia, Honghao Gao, Huahu Xu, Wei Wei","doi":"10.1145/3462442","DOIUrl":"https://doi.org/10.1145/3462442","url":null,"abstract":"The management of public opinion and the use of big data monitoring to accurately judge and verify all kinds of information are valuable aspects in the enterprise management decision-making process. The sentiment analysis of reviews is a key decision-making tool for e-commerce development. Most existing review sentiment analysis methods involve sequential modeling but do not focus on the semantic relationships. However, Chinese semantics are different from English semantics in terms of the sentence structure. Irrelevant contextual words may be incorrectly identified as cues for sentiment prediction. The influence of the target words in reviews must be considered. Thus, this paper proposes the TRG-DAtt model for sentiment analysis based on target relational graph (TRG) and double attention network (DAtt) to analyze the emotional information to support decision making. First, dependency tree-based TRG is introduced to independently and fully mine the semantic relationships. We redefine and constrain the dependency and use it as the edges to connect the target and context words. Second, we design dependency graph attention network (DGAT) and interactive attention network (IAT) to form the DAtt and obtain the emotional features of the target words and reviews. DGAT models the dependency of the TRG by aggregating the semantic information. Next, the target emotional enhancement features obtained by the DGAT are input to the IAT. The influence of each target word on the review can be obtained through the interaction. Finally, the target emotional enhancement features are weighted by the impact factor to generate the review's emotional features. In this study, extensive experiments were conducted on the car and Meituan review data sets, which contain consumer reviews on cars and stores, respectively. The results demonstrate that the proposed model outperforms the existing models.","PeriodicalId":157366,"journal":{"name":"ACM Transactions on Management Information System (TMIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134076194","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}

{"title":"A New Approach for Mining Correlated Frequent Subgraphs","authors":"Mohammad Ehsan Shahmi Chowdhury, Chowdhury Farhan Ahmed, C. Leung","doi":"10.1145/3473042","DOIUrl":"https://doi.org/10.1145/3473042","url":null,"abstract":"Nowadays graphical datasets are having a vast amount of applications. As a result, graph mining—mining graph datasets to extract frequent subgraphs—has proven to be crucial in numerous aspects. It is important to perform correlation analysis among the subparts (i.e., elements) of the frequent subgraphs generated using graph mining to observe interesting information. However, the majority of existing works focuses on complexities in dealing with graphical structures, and not much work aims to perform correlation analysis. For instance, a previous work realized in this regard, operated with a very naive raw approach to fulfill the objective, but dealt only on a small subset of the problem. Hence, in this article, a new measure is proposed to aid in the analysis for large subgraphs, mined from various types of graph transactions in the dataset. These subgraphs are immense in terms of their structural composition, and thus parallel the entire set of graphs in real-world. A complete framework for discovering the relations among parts of a frequent subgraph is proposed using our new method. Evaluation results show the usefulness and accuracy of the newly defined measure on real-life graphical datasets.","PeriodicalId":157366,"journal":{"name":"ACM Transactions on Management Information System (TMIS)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127340119","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}

{"title":"Mining High Utility Itemsets with Hill Climbing and Simulated Annealing","authors":"M. Nawaz, Philippe Fournier-Viger, Unil Yun, Youxi Wu, Wei Song","doi":"10.1145/3462636","DOIUrl":"https://doi.org/10.1145/3462636","url":null,"abstract":"High utility itemset mining (HUIM) is the task of finding all items set, purchased together, that generate a high profit in a transaction database. In the past, several algorithms have been developed to mine high utility itemsets (HUIs). However, most of them cannot properly handle the exponential search space while finding HUIs when the size of the database and total number of items increases. Recently, evolutionary and heuristic algorithms were designed to mine HUIs, which provided considerable performance improvement. However, they can still have a long runtime and some may miss many HUIs. To address this problem, this article proposes two algorithms for HUIM based on Hill Climbing (HUIM-HC) and Simulated Annealing (HUIM-SA). Both algorithms transform the input database into a bitmap for efficient utility computation and for search space pruning. To improve population diversity, HUIs discovered by evolution are used as target values for the next population instead of keeping the current optimal values in the next population. Through experiments on real-life datasets, it was found that the proposed algorithms are faster than state-of-the-art heuristic and evolutionary HUIM algorithms, that HUIM-SA discovers similar HUIs, and that HUIM-SA evolves linearly with the number of iterations.","PeriodicalId":157366,"journal":{"name":"ACM Transactions on Management Information System (TMIS)","volume":"83 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128760120","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}

{"title":"Privacy and Confidentiality in Process Mining: Threats and Research Challenges","authors":"Gamal Elkoumy, Stephan A. Fahrenkrog-Petersen, M. Sani, A. Koschmider, F. Mannhardt, Saskia Nuñez Von Voigt, Majid Rafiei, Leopold von Waldthausen","doi":"10.1145/3468877","DOIUrl":"https://doi.org/10.1145/3468877","url":null,"abstract":"Privacy and confidentiality are very important prerequisites for applying process mining to comply with regulations and keep company secrets. This article provides a foundation for future research on privacy-preserving and confidential process mining techniques. Main threats are identified and related to a motivation application scenario in a hospital context as well as to the current body of work on privacy and confidentiality in process mining. A newly developed conceptual model structures the discussion that existing techniques leave room for improvement. This results in a number of important research challenges that should be addressed by future process mining research.","PeriodicalId":157366,"journal":{"name":"ACM Transactions on Management Information System (TMIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125227503","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}