Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

2023 | Information Systems Frontiers | Citations: 0

Authors: Alahakoon, Damminda; Nawaratne, Rashmika; Xu, Yan; De Silva, Daswin; Sivarajah, Uthayasankar; Gupta, Bhumika

Abstract: The emerging information revolution makes it necessary to manage vast amounts of ... Expand

Abstract: The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications. Collapse

Topics: artificial intelligence smart city big data cloud computing distributed system

Methods: computational algorithm experiment unsupervised learning machine learning longitudinal research

Designing a Digital Marketing Analytics Operations Platform using Action Design Research

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Babu, Xavier; Motiwalla, Luvai

Abstract: Digital environment has transformed the work environment of marketing firms. Tr ... Expand

Abstract: Digital environment has transformed the work environment of marketing firms. Traditional marketing processes are repetitive, lack coordination, and are not dynamic or data-driven. To compete effectively in the digital world, marketing firms need a digital marketing analytics operations platform (DMAOP) optimized for data analytics, workflow, team coordination, and quick decision-making. We developed a DMAOP using a design science research model known as ADR. DMAOP uses data analytics leading to marketing decisions that create value with intelligent robotic processes assisting marketing practitioners in executing marketing actions. DMAOP is customized to a user's skill level, supporting marketing functions, advanced analytics, preserving privacy, and episodic reporting for users without technical skills. This study contributes to descriptive knowledge problem space by identifying the key aspects of DMAO. It also contributes to the "elaborated ADR" model with a new cycle, "personalization". Finally, it contributes to prescriptive knowledge and DMAOP instantiation with design science for digital platforms. Collapse

Topics: marketing management usability personalization marketing intelligence distributed computing

Methods: design science case study action design research design artifact design principle

Defining Information Systems Concepts for Uncharted Waters

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Hassan, Nik Rushdi

Abstract: This article raises the level of discourse about the role of concepts in the in ... Expand

Abstract: This article raises the level of discourse about the role of concepts in the information systems (IS) field. Concepts are the irreducible and indispensable elements of theory. It is through the invention of new concepts that disciplines are able to navigate uncharted waters. Without new concepts, not only will we be unable to comprehend unprecedented phenomena, we will not be able to tame their less than desirable implications. Concepts are distinguished from constructs, which IS researchers are more familiar with, but which enter the research process too late for any creative theorizing. A brief example of how concepts are defined illustrates the usefulness of concept analysis. Collapse

Topics: cloud computing distributed computing Wikipedia cloud storage computer system

Navigating Workload Compatibility Between a Recommender System and a NoSQL Database: An Interactive Tutorial

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Kayhan, Varol; Berndt, Donald

Abstract: Follow this and additional works at: https://aisel.aisnet.org/cais Recommended ... Expand

Abstract: Follow this and additional works at: https://aisel.aisnet.org/cais Recommended Citation Recommended Citation Abstract:In this tutorial, the issue of compatibility between a big data storage technology and an analytic workload is explored using a fictitious streaming company as an example. The tutorial offers an interactive approach to help students understand the importance of considering workload compatibility when adopting new technologies. We provide instructors with the following: two Jupyter Notebooks that analyze compatibility, a detailed instructor guide on how to execute these notebooks, lessons learned, and appendices containing solutions and explanations. This tutorial provides a valuable resource for instructors teaching courses in database systems, big data, and analytic concepts, helping students develop practical skills to navigate effectively the complexities of big data technologies. Collapse

Topics: database system document database recommender system collaborative filtering nosql database

Methods: database modeling design science survey

Introduction to IT Architectures and Implementations in Healthcare Environments Minitrack

2022 | HICSS | Citations: 0

Authors: Juric, Radmila; Suh, Sang; Klein, Jörn

Abstract: Environments has attracted a variety of papers, but we have selected only two.T ... Expand

Abstract: Environments has attracted a variety of papers, but we have selected only two.The selected papers talk about implementations of software technologies in the public health sector and in neuroscience diagnostics, but push forward / create different software architectures and paradigms. The first paper focuses on the field of human-machine augmentation and the development of brain-computer interfaces, and the second paper shows a generic software architectural model for running predictions in the public health sector, where the predictive inference is enhanced by human interactions.The paper titled "The feedback dynamics of brain-computer interfaces in a distributed processing environment" comes from the US and describes a paradigm for creating human brain-computer interfaces. The research enhances the field of human-machine augmentation. However the authors went one step further and investigated scenarios in which machines, i.e. Machine Learning (ML) algorithms, can change their perceptual stimulus based on the real-time brain response. The scope of such "Brain-in-the-Loop" systems has been very narrow, therefore the authors introduce a generic client-server-based, distributed Brain-in-the-Loop (BitL) paradigm and study the issue of round-trip latency under various conditions. Apart from the undisputed help in neuroscience and its diagnostics, the results of this research also show something interesting: any machine learning algorithm analyzing the real-time EEG data stream has at least 32 ms to do its processing and generate its feedback message without impacting the closed-loop system performance in any way.The paper "Semantic Framework for Practicing Data Science in Public Health Organizations during the Covid-19 Pandemics" comes from Europe and focuses on two issues. The first one is to create a reusable and generic software architectural style. It hosts ML algorithms within component based and layered software applications, generated from the architecture and creates an operational environment for running predictions in the public health sector. The application would not need a special expertise in data science, but requires expert knowledge from the public health sector professionals. The second issue is focused on the proposed human intervention in this application. It would directly enhance the semantic of managing feature selection when running ML algorithms and as such, allow experiments which could allow healthcare professionals to predict the spread infectious diseases. Experiments showed that the semantic management of features selections with human involvement helped in frequent monitoring of the results of predictions with ML, during the covid pandemic. Collapse

Topics: pandemic IT productivity enterprise architecture distributed computing

Methods: machine learning computational algorithm experiment electroencephalography

Creative Appeals in Firm-Generated Content and Product Performance

2022 | Information Systems Research | Citations: 0

Authors: Mu, Jifeng; Zhang, Jonathan; Borah, Abhishek; Qi, Jiayin

Abstract: The authors of this research investigate the effects of hedonic and utilitarian ... Expand

Abstract: The authors of this research investigate the effects of hedonic and utilitarian message appeals in firm-generated content on product performance measured by daily sales. The results from a multiproduct category, large-scale empirical study with data collected from a variety of sources show that hedonic appeals have a higher positive impact than utilitarian appeals on product performance. The authors also illustrate that message appeals have positive effects on product performance only when message quality and message goal consistency surpass a certain threshold and that these positive effects are stronger for hedonic than for utilitarian appeals. The authors further demonstrate that message complexity negatively moderates the effects of hedonic and utilitarian appeals on product performance with a stronger negative moderating effect for hedonic appeals. The research provides guidance on the proper combination of message styles and suggests that firms’ creative capabilities to curate online messages that resonate with consumers can produce significant performance advantages. Collapse

Topics: Twitter Facebook social media personality marketing management

Methods: Student's t-test parametric test instrumental variables estimation sentiment analysis descriptive statistic

An Innovative Approach to Modeling Aviation Safety Incidents

2022 | HICSS | Citations: 0

Authors: Shi, Donghui; Cao, Shuai; Zurada, Jozef; Guan, Jian

Abstract: Due to the complexity of aviation safety operations, the number of flight incide ... Expand

Abstract: Due to the complexity of aviation safety operations, the number of flight incidents continues to rise. The Aviation Safety Reporting System (ASRS) contains the largest collection of such incidents. Efficient and effective analysis of these incidents remains a challenge. This paper proposes a new approach to analyze aviation safety records using deep learning methods to improve incident classification. The proposed approach, CNNLSTM, combines the characteristics of convolutional neural network (CNN) and long short-term memory (LSTM) neural network, and a distributed computing method to model aviation safety data. The five machine learning methods Logistic Regression, Naive Bayes, Random Forest, Support Vector Machine, Multi-layer Perceptron were used to compare with CNN-LSTM. The results show that CNN-LSTM model can significantly improve the accuracy rates of classification for aviation safety incident reports using Word2Vec. The distributed platform in Spark with clusters can make full use of computing resources when processing textual data from ASRS, reducing time-consumption greatly when compared with machine learning algorithms running on a standalone computer. Timely and accurate identification of causes of reported incidents is important. The results of this study demonstrate a new approach to improve both accuracy and efficiency in incident cause identification. Collapse

Topics: usability ergonomics logistics management distributed computing Python

Methods: word2vec term frequency–inverse document frequency support vector machine classification method machine learning

Deep Learning of Spatiotemporal Patterns for Urban Mobility Prediction Using Big Data

2022 | Information Systems Research | Citations: 0

Authors: Wang, Yun; Currim, Faiz; Ram, Sudha

Abstract: Timely and accurate prediction of human movement in urban areas offers instructi ... Expand

Abstract: Timely and accurate prediction of human movement in urban areas offers instructive insights into transportation management, public safety, and location-based services, to name a few. Yet, modeling urban mobility is challenging and complex because of the spatiotemporal dynamics of movement behavior and the influence of exogenous factors such as weather, holidays, and local events. In this paper, we use bus transportation as a proxy to mine spatiotemporal travel patterns. We propose a deep-learning-based urban mobility prediction model that collectively forecasts passenger flows between pairs of city regions in an origin-destination (OD) matrix. We first process OD matrices in a convolutional neural network to capture spatial correlations. Intermediate results are reconstructed into three multivariate time series: hourly, daily, and weekly time series. Each time series is aggregated in a long short-term memory (LSTM) network with a novel attention mechanism to guide the aggregation. In addition, our model is context-aware by using contextual embeddings learned from exogenous factors. We dynamically merge results from LSTM components and context embeddings in a late fusion network to make a final prediction. The proposed model is implemented and evaluated using a large-scale transportation data set of more than 200 million bus trips with a suite of Big Data technologies developed for data processing. Through performance comparison, we show that our approach achieves sizable accuracy improvements in urban mobility prediction. Our work has major implications for efficient transportation system design and performance improvement. The proposed deep neural network structure is generally applicable for sequential graph data prediction. Collapse

Topics: big data global navigation satellite system distributed computing Apache Hadoop transportation management

Methods: longitudinal research time series analysis deep learning artificial neural network cluster analysis

Stewardship of personal data on social networking sites

2021 | International Journal of Information Management | Citations: 12

Authors: Angelopoulos, Spyros; Brown, Michael; McAuley, Derek; Merali, Yasmin; Mortier, Richard; Price, Dominic

Abstract: The extant Information Management literature highlights the asymmetric distribut ... Expand

Abstract: The extant Information Management literature highlights the asymmetric distribution of power between users and online platforms, while the issues related to the stewardship of personal data on such platforms remain problematic and largely unresolved. To address that lacuna, we propose a conceptual design that can help to overcome many of the challenges related to storage, analysis, and integrity associated with the stewardship of personal data on online platforms. We adopt a systemic perspective and propose a shift from the current userplatform relationship to one in which users control the level of access to their data, organisations are relieved from the burden of maintaining personal data, and the data are not decoupled from information about their provenance and context of origin. We apply our conceptual design to the context of social networking sites, where we specifically address issues related to privacy, and identity and pave the path to a broader set of possible applications. We discuss the significance and timeliness of our proposed conceptual design for the stewardship of personal data, and the importance of our findings for future research, as well as for the design of online platforms. Collapse

Topics: privacy Twitter Facebook electronic mail LinkedIn

Methods: archival research survey design science multivariate analysis of variance design artifact

Theories: theory of affordance

Breakthroughs on Cross-Cutting Data Management, Data Analytics, and Applied Data Science

2021 | Information Systems Frontiers | Citations: 0

Authors: Chiusano, Silvia; Cerquitelli, Tania; Wrembel, Robert; Quercia, Daniele

Abstract: ... Expand

Abstract: Collapse

Topics: mobile system internet of things Twitter MongoDB application programming interface

Methods: network analysis