Automating Research Data Management Using Machine-Actionable Data Management Plans

2022 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Miksa, Tomasz; Oblasser, Simon; Rauber, Andreas

Abstract: Many research funders mandate researchers to create and maintain data management ... Expand

Abstract: Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs. Collapse

Topics: data management business process management enterprise architecture recommender system user experience

Methods: design artifact business process model and notation modeling language case study design science

Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Samtani, Sagar; Chai, Yidong; Chen, Hsinchun

Abstract: Black hat hackers use malicious exploits to circumvent security controls and tak ... Expand

Abstract: Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)- based exploit-vulnerability attention deep structured semantic model (EVA-DSSM) that includes bidirectional processing and attention mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel device vulnerability severity metric (DVSM) that incorporates the exploit post date and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-theart non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and denial of service. Results of these evaluations indicate that the proposed EVA-DSSM achieves precision at 1 scores 20% - 41% higher than non-DL approaches and 4% - 10% higher than DL-based approaches. We demonstrated the EVA-DSSM’s and DVSM’s practical utility with two CTI case studies: openly accessible systems in the top eight U.S. hospitals and over 20,000 Supervisory Control and Data Acquisition (SCADA) systems worldwide. A complementary user evaluation of the case study results indicated that 45 cybersecurity professionals found the EVADSSM and DVSM results more useful for exploit-vulnerability linking and risk prioritization activities than those produced by prevailing approaches. Given the rising cost of cyberattacks, the EVA-DSSM and DVSM have important implications for analysts in security operations centers, incident response teams, and cybersecurity vendors. Collapse

Topics: IT security web application information retrieval darknet PHP

Methods: computational algorithm case study experiment usability testing Okapi BM25

Detecting Android Malware and Classifying Its Families in Large-scale Datasets

2022 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Sun, Bo; Takahashi, Takeshi; Ban, Tao; Inoue, Daisuke

Abstract: To relieve the burden of security analysts, Android malware detection and its fa ... Expand

Abstract: To relieve the burden of security analysts, Android malware detection and its family classification need to be automated. There are many previous works focusing on using machine (or deep) learning technology to tackle these two important issues, but as the number of mobile applications has increased in recent years, developing a scalable and precise solution is a new challenge that needs to be addressed in the security field. Accordingly, in this article, we propose a novel approach that not only enhances the performance of both Android malware and its family classification, but also reduces the running time of the analysis process. Using large-scale datasets obtained from different sources, we demonstrate that our method is able to output a high F-measure of 99.71% with a low FPR of 0.37%. Meanwhile, the computation time for processing a 300K dataset is reduced to nearly 3.3 hours. In addition, in classification evaluation, we demonstrate that the F-measure, precision, and recall are 97.5%, 96.55%, 98.64%, respectively, when classifying 28 malware families. Finally, we compare our method with previous studies in both detection and classification evaluation. We observe that our method produces better performance in terms of its effectiveness and efficiency. Collapse

Topics: malware Android mobile application mobile application market application programming interface

Methods: machine learning computational algorithm scale validation artificial neural network doc2vec

Just DM Me (Politely): Direct Messaging, Politeness, and Hiring Outcomes in Online Labor Markets

2021 | Information Systems Research | Citations: 1

Authors: Hong, Yili; Peng, Jing; Burtch, Gordon; Huang, Ni

Abstract: This study examines the role of text-based direct messaging systems in online la ... Expand

Abstract: This study examines the role of text-based direct messaging systems in online labor markets, which provide a communication channel between workers and employers, adding a personal touch to the exchange of online labor. We propose the effect of workers’ use of the direct messaging system on employers’ hiring decisions and conceptualize the information role of direct messaging. To empirically evaluate the information role of the direct messaging system, we leverage data on the direct messaging activities between workers and employers across more than 470,000 job applications on a leading online labor market. We report evidence that direct messaging with a prospective employer increases a worker’s probability of being hired by 8.9%. However, the degree to which workers benefit from direct messaging is heterogeneous, and the effect amplifies for workers approaching employers from a position of disadvantage (lacking tenure or fit with the job) and attenuates as more workers attempt to message the same prospective employer. The effects also depend on message content. In particular, we find that the benefits of direct messaging for workers depend a great deal on the politeness of the workers, and this “politeness effect” depends on several contextual factors. The beneficial effects are amplified for lower-status workers (i.e., workers lacking tenure and job fit) and workers who share a common language with the employer. At the same time, the beneficial effects weaken in the presence of typographical errors. These findings provide important insights into when and what to message to achieve favorable hiring outcomes in online employment settings. Collapse

Topics: online labor market information overload platform worker database system PHP

Methods: data imputation instrumental variables estimation machine learning simulation XGBoost

Theories: lemon market theory

Online to Offline: The Impact of Social Media on Offline Sales in the Automobile Industry

2021 | Information Systems Research | Citations: 16

Authors: Wang, Yen-Yao; Guo, Chenhui; Susarla, Anjana; Sambamurthy, Vallabh

Abstract: Given the limited research into the impact of social media on offline sales of d ... Expand

Abstract: Given the limited research into the impact of social media on offline sales of durable goods, this study examines the dynamic relationships between firm-generated content (FGC), user-generated content (UGC), traditional media, and offline light vehicle sales. Data were collected from the official Facebook and Twitter pages of 30 U.S. car brands from 2009 to 2015. We utilized a panel vector autoregressive model to investigate the dynamic relationships among multiple time series variables while controlling for influential durable goods characteristics. The empirical results suggest that Facebook and Twitter have heterogeneous effects on offline vehicle sales. Moreover, FGC is more effective than UGC for influencing offline light vehicle sales. Viral impressions from Facebook and Twitter are essential, although the effects vary by social media platform (Facebook versus Twitter) and content type (FGC versus UGC). The result of the impulse response function analysis indicates that both the firm’s marketing efforts (FGC and traditional media) and UGC have a long-term effect on offline sales, with the long-term effect of a firm’s marketing efforts outlasting that of UGC. Incorporating FGC and UGC from Facebook and Twitter and traditional media could improve the performance of offline sales prediction. We also documented the within-Twitter synergistic effect between FGC and UGC for offline car sales and cross-channel substitution relationships (FGC on both Facebook and Twitter serves as a substitution to traditional media). Finally, we provide guidance for managers seeking to leverage multichannel marketing to boost the offline sales of durable goods. Collapse

Topics: Twitter Facebook social media marketing management database system

Methods: longitudinal research Granger causality Monte Carlo simulation time series analysis cross sectional research

Applying Text Analytics to Examination of End Users’ Mental Models of Cybersecurity

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Cotoranu, Andreea; Chen, Li-Chiou

Abstract: Mental models can explain how end users perceive their interactions with inform ... Expand

Abstract: Mental models can explain how end users perceive their interactions with information systems, and inform cybersecurity awareness training. In this study, we used text analytic techniques to extract mental models representing cybersecurity concepts in learners at different levels of expertise. We applied these analytic techniques to text data collected from open-ended questions designed to capture learners' understanding of cybersecurity concepts. We analyzed similarities and differences between learner groups using frequency, entropy and cosine similarity measures applied to n-gram features of their written responses. Our analysis showed that there is a difference in mental models between learners with informal exposure to cybersecurity topics and those with formal exposure. Furthermore, as a proof to demonstrate the predictive power of mental models, we correlated end users mental models with their perceived security. Finally, this study validated text analytics as a tool for capturing the mental models of end users without influencing the models. KeywordsMental models, perceived security, content analysis, declarative and procedural knowledge. Collapse

Topics: IT security electronic commerce hypertext password knowledge representation

Methods: natural language processing survey personal interview linear regression analysis nonparametric test

Responding to Cybersecurity Challenges: Securing Vulnerable U.S. Emergency Alert Systems

2020 | Communications of the Association for Information Systems | Citations: 0

Authors: Green, Andrew W.; Woszczynski, Amy B.; Dodson, Kelly; Easton, Peter

Abstract: Emergency alert systems (EASs) in the United States (US) form part of the nation ... Expand

Abstract: Emergency alert systems (EASs) in the United States (US) form part of the nation’s critical infrastructure. These systems rely on aging platforms and suffer from a fragmented interconnected network of partnerships. Some EASs have an easily identifiable vulnerability: one can access their management website via the Internet. Authorities must secure these systems quickly. Other concerns also exist, such as the lack of policies for reporting vulnerabilities. To begin to assess EASs in the US, we used Shodan to evaluate the availability of these websites in six southeastern states. We found 18 such websites that one could access via the Internet and that required only requiring user credentials to login into. Next, we searched for published policies on reporting vulnerabilities; we found no vulnerability-disclosure policies for any system we identified. To identify, prioritize, and address EAS vulnerabilities, we present a list of technical and management strategies to reduce cybersecurity threats. We recommend integrated policies and procedures at all levels of the public-private-government partnerships and system resilience as lines of defense against cybersecurity threats. By implementing these strategies, EASs in the US will be positioned to update critical infrastructure, notify groups of emergencies, and ensure the distribution of valid and reliable information to atrisk populations. Collapse

Topics: website IT security internet technology data encryption strategic management

Methods: survey

Does one Creative Tool Fit All? Initial Evidence on Creativity Support Systems and Wikipedia-based Stimuli

2020 | International Conference on Information Systems | Citations: 0

Authors: Klein, Hans Christian; Weber, Sebastian; Schlechtinger, Michael; Oschinsky, Frederike Marie

Abstract: Creativity is important to all kind of organizations, because creative capacity ... Expand

Abstract: Creativity is important to all kind of organizations, because creative capacity can help tackle complex challenges and navigate through the ambiguity of wicked problems. In Information Systems (IS) research, this topic is addressed by studies on creativity support systems (CSS). One promising approach is to provide (context related-) stimuli to individuals in order to inspire new and useful ideas. The relatedness of a stimuli, which means the degree to which a stimulus is related to a topic (i.e., to the creative task), is a vital characteristic. We investigated in the relationship between Wikipedia-based searching results (computational relatedness) and individual cognition (individual relatedness). Our initial findings show that there can be differences between individuals based on demographic variables. We further suggest a laboratory experiment in order to contribute to a CSS that takes individual relatedness and thus custom-fit stimuli into account. Collapse

Topics: Wikipedia Apple iPhone IT skill knowledge representation systems design

Methods: parametric test two-way ANOVA experiment laboratory experiment longitudinal research

Theories: cognitive network model of creativity theory adaptive control of thought theory

Predicting Labor Market Competition: Leveraging Interfirm Network and Employee Skills

2020 | Information Systems Research | Citations: 8

Authors: Liu, Yuanyang; Pant, Gautam; Sheng, Olivia R. L.

Abstract: Human capital is a key component of the knowledge economy that firms compete for ... Expand

Abstract: Human capital is a key component of the knowledge economy that firms compete for in the labor market. Compared with the product market competition, the identification and prediction of labor market competitors have garnered little attention in the literature. In this study, we perform an interfirm labor market competitor analysis with a unique longitudinal employer-employee matched data set derived from online profiles of 89,943 employees, tracking their careers in 3,467 public firms from the years 2000 to 2014. Using employee migrations across firms, we derive and analyze a human capital flow network. We leverage this network to extract global cues about interfirm human capital overlap through structural equivalence and community classification. The online employee profiles also provide rich data on the explicit knowledge base of firms. In particular, they allow us to represent firms in the space of the skills possessed by their employees and measure the interfirm human capital overlap in terms of similarity in their employees’ skills. We validate our proposed human capital overlap metrics in a predictive analytics framework using future employee migrations as an indicator of labor market competition. The results show that our proposed metrics have superior predictive power over conventional firm-level economic and human resource measures. We also demonstrate how our proposed metrics and the prediction framework can be incorporated into a comprehensive competitor analysis that includes both product and labor overlap between firms. Collapse

Topics: human resource management LinkedIn database system implicit knowledge information retrieval

Methods: machine learning Compustat experiment computational algorithm latent dirichlet allocation

Theories: resource based view of the firm

Interactive Videos vs. Hypertext Documents – The Effect on Learning Quality and Time Effort when Acquiring Procedural Knowledge

2019 | HICSS | Citations: 0

Authors: Keller, Alexander; Langbauer, Michael; Fritsch, Thomas; Lehner, Franz

Abstract: The use of information systems and the rise of new learning concepts have change ... Expand

Abstract: The use of information systems and the rise of new learning concepts have changed the way individuals are acquiring knowledge in organizational, educational and private contexts. Recently, video tutorials have become a widely-used instrument for learning and successful platforms emerged, offering massive open online courses based on video content. With the existence of different learning technologies the question arises: How these media formats affect the learning performance of individuals? We introduce interactive videos as a new media format and compare this technology to hypertext documents in an educational context. Our results from an experiment with 130 participants reveal that the learning quality can be significantly increased when interactive videos are used to acquire procedural knowledge. However, we did not observe any effect on time effort. Collapse

Topics: hypertext intrinsic motivation data quality user satisfaction system quality

Methods: experiment survey laboratory experiment experimental group simple linear regression

Theories: dual-coding theory cognitive load theory