Images of Quantum Computing: Taking Stock and Moving Forward

2023 | European Conference On Information Systems | Citations: 0

Authors: Godoy-Descazeaux, Ignacio; Avital, Michel; Gleasure, Rob

Abstract: Quantum computing is attracting increasing attention due to both the threats an ... Expand

Abstract: Quantum computing is attracting increasing attention due to both the threats and the opportunities it may present. Nonetheless, the technology is still nascent and thus far lacks substantive commercial applications that can demonstrate, let alone validate, its potential impact. This uncertainty challenges organizations' ability to make strategic decisions concerning quantum computing. In this study, we explored the practitioners' discourse about quantum computing and the metaphors used to describe the technology and its prospective value. Building on a thematic analysis of 140 video presentations by quantum computing practitioners, we identified ten distinct metaphors of quantum computing. Subsequently, we sorted the metaphors based on the perceived feasibility and strategic potential of the technology to reveal four ways quantum computing may impact the prevailing digital logic. These findings shed light on the alternative development paths of quantum computing business applications and extend the theoretical foundation of this incipient discourse. Collapse

Topics: quantum computing YouTube enterprise information system high-performance computing artificial intelligence

Methods: qualitative content analysis

Green Data Analytics of Supercomputing from Massive Sensor Networks: Does Workload Distribution Matter?

2023 | Information Systems Research | Citations: 0

Authors: Guo, Zhiling; Li, Jin; Ramesh, Ram

Abstract: Energy costs represent a significant share of the total cost of ownership in hig ... Expand

Abstract: Energy costs represent a significant share of the total cost of ownership in high-performance computing (HPC) systems. Using a unique data set collected by massive sensor networks in a petascale national supercomputing center, we first present an explanatory model to identify key factors that affect energy consumption in supercomputing. Our analytic results show that, not only does computing node utilization significantly affect energy consumption, workload distribution among the nodes also has significant effects and could effectively be leveraged to improve energy efficiency. Next, we establish the high model performance using in-sample and out-of-sample analyses. We then develop prescriptive models for energy-optimal runtime workload management and extend the models to consider energy consumption and job performance tradeoffs. Specifically, we present four dynamic resource management methodologies (packing, load balancing, threshold-based switching, and energy optimization), model their application at two levels (purely within-rack and jointly cross-rack resource allocation), and explore runtime resource redistribution policies for jobs under the emergent principle of computational steering and comparatively evaluate strategies that use computational steering with those that do not. Our experimental studies show that packing is preferred when the total workload of a rack is higher than a threshold and load balancing is preferred when it is lower. These results lead to a threshold strategy that yields near-optimal energy efficiency under all workload conditions. We further calibrate the energy-optimal resource allocations over the full range of workloads and present a bicriteria evaluation to consider energy consumption and job performance tradeoffs. We demonstrate significant energy savings of our proposed strategies under various workload conditions. We conclude with implementation guidelines and policy insights into energy-efficient computing resource management in large supercomputing data centers. History: Olivia Liu Sheng, Senior Editor; Xiaobai Li, Associate Editor. Funding: The authors thank the National Supercomputing Center of Singapore for enabling and supporting this research. J. Li received support from the National Natural Science Foundation of China [Grants 71901169, 72032006, and T2293774]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2023.1208. Collapse

Topics: data center high-performance computing task productivity resource allocation analytical information system

Methods: optimization model experiment simulation descriptive statistic longitudinal research

Navigating the Use of ChatGPT in Classrooms: A Study of Student Experiences

2023 | International Conference on Information Systems | Citations: 0

Authors: Katavic, Rio; Pahuja, Aseem; Syed, Tahir Abbas

Abstract: ... Expand

Abstract: Collapse

Topics: problem solving artificial intelligence online learning system idea generation decision making

Methods: chatGPT qualitative interview literature study grounded theory synthesis

Cyber threat detection: Unsupervised hunting of anomalous commands (UHAC)

2023 | Decision Support Systems | Citations: 0

Authors: Kayhan, Varol O.; Agrawal, Manish; Shivendu, Shivendu

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive to ... Expand

Abstract: The cyber security industry is rapidly adopting threat hunting as a proactive tool for early and faster detection of suspected malicious actors. In this paper, we propose a machine learning-based method, Unsupervised Hunting of Anomalous Commands (UHAC), to detect text-based anomalous commands in security information and event management (SIEM) logs that are good candidates for threat hunting. A unique feature of the proposed method is that it first creates a feature set based on the augmentation of document-term and document-character matrices. Then, an autoencoder-based detector is trained on this feature set using a custom loss function. UHAC consis­ tently outperforms other feature sets and algorithms such as one-class support vector machine, density-based spatial clustering of applications with noise, and word-embedding based models such as word2vec. The UHAC detector identifies 84–89% of anomalies in the top 10% of the data. Findings have implications for cybersecurity analysts who perform threat hunting in SIEM logs for process auditing on endpoint devices. Collapse

Topics: IT security artificial intelligence Python decision support system email phishing

Methods: autoencoder data transformation DBSCAN anomaly detection machine learning

Pathways to developing information technology-enabled capabilities in born-digital new ventures

2023 | International Journal of Information Management | Citations: 0

Authors: Sadreddin, Arman; Chan, Yolande E.

Abstract: Despite significant business opportunities made possible through advancements in ... Expand

Abstract: Despite significant business opportunities made possible through advancements in technology, readily available digital technologies are often overlooked and not used by new ventures. To address this knowledge gap, this paper looks at the relationships between readily available digital technologies and born-digital new venture capabilities. We use an affordance lens to explore conditions in which born-digital new ventures interact with digital technologies to actualize digital affordances that facilitate the development of important capabilities. First, using the existing literature, we present a research model and its key elements. Then, using data on new ventures in a Canadian university incubator, we conduct fuzzy-set qualitative comparative analysis (fsQCA) and develop seven configurational research propositions as pathways to develop information technology-enabled organizational capabilities in new ventures. Using this retroductive approach, this research builds a contextspecific middle-range theory that explains complex interactions between readily available digital technologies and new venture characteristics, where resources are provided by incubators in dynamic environments, to facilitate the development of ITOCs in the new ventures. We close by describing the study’s theoretical and practical implications. Collapse

Topics: affordance user experience information technology infrastructure website social media

Methods: qualitative interview theory development case study machine learning longitudinal research

Theories: theory of affordance dynamic capabilities theory fuzzy set theory set theory

Availability-Aware Virtual Resource Provisioning for Infrastructure Service Agreements in the Cloud

2023 | Information Systems Frontiers | Citations: 0

Authors: Yuan, Shuai; Das, Sanjukta; Ramesh, Ram; Qiao, Chunming

Abstract: Service availability is a key construct in Service Level Agreements (SLA) betwee ... Expand

Abstract: Service availability is a key construct in Service Level Agreements (SLA) between a cloud service provider and a client. The provider typically allocates backup resources to mitigate the risk of violating the SLA-specified uptime guarantee. However, initial backups may need to be adjusted in response to real-time failure and recovery events. In this study, we first develop a recurrent intervention at fixed intervals (RIFI) strategy that allows the provider to adjust the allocation of backup resources such that the expected total cost is minimized. Next, we focus on the limit to number of interventions, starting from single intervention strategy, as frequent reallocations may be operationally disruptive. Particularly, we provide a cost minimization approach to guide the service providers in their virtual resources management, and a specific downtime minimization approach for more mission-critical applications as a more aggressive alternative. We present computational results exploring the impact of intervention on the likelihood of SLA violation for the rest of the contract period, and evaluate parameters such as time and quantum of resource level adjustment, penalty levels desired by clients, and their influences on the backup resource provisioning strategies. We also validate our models through the analysis of use cases from Amazon Elastic Compute Cloud. Finally, we summarize this study by providing key practical managerial implications for resource deployment in the availability-aware cloud. Collapse

Topics: backup cloud computing resource allocation price management high-performance computing

Methods: computational algorithm experiment literature study simulation

Technology pillars for digital transformation of cities based on open software architecture for end2end data streaming

2022 | HICSS | Citations: 0

Authors: Mazurek, Cezary; Stroinski, Maciej

Abstract: The goal of the paper is to expose the pillars of urban digital infrastructure a ... Expand

Abstract: The goal of the paper is to expose the pillars of urban digital infrastructure and their role in the development of smart and data driven applications in the e2e model. Today, no one contests the need for a general-purpose infrastructure in the city that improves the quality of life with respect to various types of socioeconomic activity of residents. However, the digital aspect of fostering this kind of activity is linked to a very dynamic and ubiquitous element of technology, and the quality and stability of solutions based on advanced technology is a key parameter of the quality of life and development that a city should provide to its residents. This pace of technology development means that our environment (including cities) are subject to a process of continuous digital transformation. Starting from examples of such transformation and top-trends in technology development, we focus on transformation pillars that can ensure sustainable growth of innovation supply in a city. We give three examples of platforms designed for City of Poznan. We conclude that a key factor benefiting this multidimensional transformation process is the provision of an open, data-transmission and processing infrastructure with the support of which all smart city stakeholders can pursue their social, scientific, economic, or political goals in a sustainable manner. Collapse

Topics: digital transformation smart city information technology infrastructure innovation management enterprise architecture

Methods: design artifact

Early Diagnosis of Mild Cognitive Impairment with 2-Dimensional Convolutional Neural Network Classification of Magnetic Resonance Images

2021 | HICSS | Citations: 0

Authors: Angelopoulos, Spyros

Abstract: We motivate and implement an Artificial Intelligence (AI) Computer Aided Diagnos ... Expand

Abstract: We motivate and implement an Artificial Intelligence (AI) Computer Aided Diagnosis (CAD) framework, to assist clinicians in the early diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Our framework is based on a Convolutional Neural Network (CNN) trained and tested on functional Magnetic Resonance Images datasets. We contribute to the literature on AI-CAD frameworks for AD by using a 2D CNN for early diagnosis of MCI. Contrary to current efforts, we do not attempt to provide an AI-CAD framework that will replace clinicians, but one that can work in synergy with them. Our framework is cheaper and faster as it relies on small datasets without the need of high-performance computing infrastructures. Our work contributes to the literature on digital transformation of healthcare, health Information Systems, and NeuroIS, while it opens novel avenues for further research on the topic. Collapse

Topics: artificial intelligence digital transformation health information system Python cloud computing

Methods: magnetic resonance imaging NeuroIS functional neuroimaging Keras machine learning

Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset using Big Data Analytics

2021 | HICSS | Citations: 0

Authors: Baembitov, Rashid; Dokic, Tatjana; Kezunovic, Mladen; Hu, Yi; Obradovic, Zoran

Abstract: A novel method for fast extraction of fundamental frequency events (FFE) based ... Expand

Abstract: A novel method for fast extraction of fundamental frequency events (FFE) based on measurements of frequency and rate of change of frequency by Phasor Measurement Units (PMU) is introduced. The method is designed to work with exceptionally large historical PMU datasets. Statistical analysis was used to extract the features and train Random Forest and Catboost classifiers. The method is capable of fast extraction of FFE from a historical dataset containing measurements from hundreds of PMUs captured over multiple years. The reported accuracy of the best algorithm for classification expressed as Area Under the receiver operating Characteristic curve reaches 0.98, which was obtained in out-of-sample evaluations on 109 system-wide events over 2 years observed at 43 PMUs. Then Minimum Volume Enclosing Ellipsoid Algorithm was used to further analyze the events. 93.72% events were correctly characterized, where average duration of the event as seen by the PMU was 9.93 sec. 1 Collapse

Topics: database system Python Apache Spark high-performance computing data quality

Methods: computational algorithm machine learning event log random forest classification gradient boosting

Machine Learning for Identifying Group Trajectory Outliers

2021 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Belhadi, Asma; Djenouri, Youcef; Djenouri, Djamel; Michalak, Tomasz; Lin, Jerry Chun-Wei

Abstract: Prior works on the trajectory outlier detection problem solely consider individu ... Expand

Abstract: Prior works on the trajectory outlier detection problem solely consider individual outliers. However, in real-world scenarios, trajectory outliers can often appear in groups, e.g., a group of bikes that deviates to the usual trajectory due to the maintenance of streets in the context of intelligent transportation. The current paper considers the Group Trajectory Outlier (GTO) problem and proposes three algorithms. The first and the second algorithms are extensions of the well-known DBSCAN and kNN algorithms, while the third one models the GTO problem as a feature selection problem. Furthermore, two different enhancements for the proposed algorithms are proposed. The first one is based on ensemble learning and computational intelligence, which allows for merging algorithms’ outputs to possibly improve the final result. The second is a general high-performance computing framework that deals with big trajectory databases, which we used for a GPU-based implementation. Experimental results on different real trajectory databases show the scalability of the proposed approaches. Collapse

Topics: database system climate change hurricane high-performance computing computer hardware

Methods: computational algorithm anomaly detection DBSCAN experiment machine learning