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

Cost-based Optimization of Multistore Query Plans

2023 | Information Systems Frontiers | Citations: 0

Authors: Forresi, Chiara; Francia, Matteo; Gallinucci, Enrico; Golfarelli, Matteo

Abstract: Multistores are data management systems that enable query processing across diff ... Expand

Abstract: Multistores are data management systems that enable query processing across different and heterogeneous databases; besides the distribution of data, complexity factors like schema heterogeneity and data replication must be resolved through integration and data fusion activities. Our multistore solution relies on a dataspace to provide the user with an integrated view of the available data and enables the formulation and execution of GPSJ queries. In this paper, we propose a technique to optimize the execution of GPSJ queries by formulating and evaluating different execution plans on the multistore. In particular, we outline different strategies to carry out joins and data fusion by relying on different schema representations; then, a self-learning black-box cost model is used to estimate execution times and select the most efficient plan. The experiments assess the effectiveness of the cost model in choosing the best execution plan for the given queries and exploit multiple multistore benchmarks to investigate the factors that influence the performance of different plans. Collapse

Topics: database system MongoDB PostgreSQL document database relational database

Methods: computational algorithm experiment case study regression analysis method longitudinal research

Application Massive Data Processing Platform for Smart Manufacturing Based on Optimization of Data Storage

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ren, Bin; Chen, Yuquiang; Wang, Fujie

Abstract: The aim of smart manufacturing is to reduce manpower requirements of the product ... Expand

Abstract: The aim of smart manufacturing is to reduce manpower requirements of the production line by applying technology of huge amounts of data to the manufacturing industry. Smart manufacturing is also called Industry 4.0, and the platform for processing huge amounts of data has an indispensable role. The massive data processing platform is like the brain of the entire factory, receiving all data from production line sensors via edge computing, processing, and analyzing, and finally making feedback decisions. With the innovation of production technology, the data that the platform needs to process has become diverse and complex, and the amount has become increasingly large. As well, many precision manufacturing industries have begun to enter the field of Industry 4.0. In addition to the accuracy and availability of data processing, there is emphasis on the real-time nature of data processing. After the sensor receives the data, the platform must provide feedback within a short period of time. This article proposes a massive data processing platform based on the Lambda architecture, which has the coexistence of stream processing and batch processing to meet real-time feedback needs of high-precision manufacturing. To verify the effectiveness of the optimization, it is based on real data from the manufacturing industry. To generate a large amount of test data to confirm the optimization of the storage of pictures. The results show that it optimizes the storage and optimization of the image data generated by the Automated Optical Inspection technology used in manufacturing today and optimizes the query for data storage. It also reduces the consumption of a large amount of memory as expected, and the query for Hive reduced the time spent. Collapse

Topics: Apache HBase Apache Hadoop Hadoop Distributed File System stream processing Python

Methods: experiment

A Complex Adaptive Systems Perspective of Software Reuse in the Digital Age: An Agenda for IS Research

2023 | Information Systems Research | Citations: 0

Authors: Vial, Gregory

Abstract: Software is instrumental to the accelerated pace of digital innovation, and our ... Expand

Abstract: Software is instrumental to the accelerated pace of digital innovation, and our ability to rapidly develop and deliver digital products and services is largely based on the reuse of existing software. In recent years, packaged software reuse has emerged as an important phenomenon driving the creation of new software, both proprietary and open source, as well as the emergence and evolution of entire software ecosystems. Arguing that our theoretical understanding of the nature and the magnitude of current packaged software reuse practice remains limited, our objective with this research commentary is twofold. First, we draw attention to the importance of packaged software reuse and its relevance as a promising phenomenon of interest for information systems (IS) researchers. Second, we mobilize the concept of generativity to conceptualize packaged software as a form of technological innovation that fosters large-scale (re)combination and propose complex adaptive systems (CAS) as a theoretical foundation to help us engage with the current nature of the phenomenon. Using key principles of CAS as the generative foundation for our conceptual scaffolding, we offer a research framework for packaged software reuse and develop an agenda for IS research organized across three main axes. For each axis, we outline relevant research themes and research questions leveraging the nature of software as objects constituted of other pieces of software developed and maintained by heterogeneous groups of software developers. Shedding light on the renewed role of software reuse, our work contributes to ongoing conversations on generativity and software ecosystems as well as the design of digital products and services. History: Suprateek Sarker, Senior Editor; Ning Su, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1200. Collapse

Topics: digital ecosystem packaged software digital platform systems development Java

Methods: theory development TensorFlow theoretical contribution

Theories: complex adaptive system co-evolution theory media synchronicity theory

Software Components and Product Variety in a Platform Ecosystem: A Dynamic Network Analysis of WordPress

2023 | Information Systems Research | Citations: 0

Authors: Um, Sungyong; Zhang, Bin; Wattal, Sunil; Yoo, Youngjin

Abstract: Software components, such as application programming interfaces (APIs), provided ... Expand

Abstract: Software components, such as application programming interfaces (APIs), provided by both the platform owner and external developers in a platform ecosystem are vital to online digital platforms. These APIs are associated with functionality provided by the platform. A larger number of APIs used is generally associated with an increase in the platform ecosystem’s variety of products. However, the precise nature of the relationship between the use of APIs and product variety in a platform ecosystem is not yet known because of the lack of appropriate methodologies to analyze the complex data generated in such platform ecosystems. Drawing on a recombinatorial design perspective, we use a dynamic network model to create metrics for product variety in a digital ecosystem and study how the use of different types of APIs affects product variety. In particular, we theorize that, contrary to existing models, the structure of APIs in a large platform ecosystem is not limited to a core–periphery structure, but includes an additional third layer that we refer to as the regular core. We further hypothesize that external APIs in the regular core play a crucial role in increasing product variety in the ecosystem. To test our hypothesis, we conduct an empirical study using longitudinal data of all available digital product (i.e., plug-in) source codes on WordPress.org, the world’s largest content management system and one of the most extensive digital platform ecosystems. By analyzing all of the plug-ins on WordPress over 10 years from its inception, we find the support of our three-layer structure of APIs in a platform ecosystem: complete core, regular core, and periphery. Our empirical analysis further supports the role of external APIs in the regular core in increasing product variety. We, however, find that the strength of this effect diminishes in a newly created product category. History: Param Singh, Senior Editor; Pallab Sanyal, Associate Editor. Funding: This work was supported by the Ministry of Education Singapore [Grants R-253-000-122-133 and T1-251RES1916], the National Science Foundation [Grants 1261977 and 1120966], and Club Informatique des Grandes Entreprises Françaises. Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2022.1172. Collapse

Topics: application programming interface digital ecosystem digital product innovation management website

Methods: doc2vec hierarchical clustering longitudinal research cluster analysis chi squared test

Context-Aware Marketing Attribution Based on Survival Analysis

2023 | International Conference on Business Informatics | Citations: 0

Authors: Züllig, Kilian; Napirata, Stefan; Zimmermann, Steffen

Abstract: Companies increasingly invest in digital marketing channels to promote their pr ... Expand

Abstract: Companies increasingly invest in digital marketing channels to promote their products and services. While the expenditures for each marketing channel are known, the contribution of marketing channels to a successful conversion, and therefore the value they generate, is unknown, but highly relevant for strategic decision-making. In this paper, we develop a novel, context-aware additive hazard marketing attribution (CAHMA) model based on survival analysis to address this problem. In addition to channel-specific, time-decaying effects of marketing on the users' conversion rate, we control for the effects of contextual features, such as the device or country from which users interact with marketing channels. Based on a prototypical implementation, we demonstrate the model's applicability and evaluate it on real-world data from the industry. We find that CAHMA outperforms other models in terms of accuracy while offering unique interpretability of the results and hence, providing deep insights for practitioners into the effects of marketing. Collapse

Topics: marketing management iOS Android mobile system MapReduce

Methods: machine learning survival analysis hazard function design artifact Markov chain Monte Carlo

Theories: probability theory

Impact of digital leadership capability on innovation performance: The role of platform digitization capability

2022 | Information & Management | Citations: 1

Authors: Benitez, Jose; Arenas, Alvaro; Castillo, Ana; Esteves, Jose

Abstract: Following a mixed-methods approach, we theorized that digital leadership influen ... Expand

Abstract: Following a mixed-methods approach, we theorized that digital leadership influences innovation performance by digitalizing the firm’s platform. A multiple case study of ten companies was deployed to derive a theoretical model relating digital leadership and innovation performance. The resulting model was empirically tested on a sample of 117 European firms. We find that digital leadership improves a firm’s innovation performance by digitalizing the firm’s platform. We contribute to IS research by theoretically developing the concepts of digital leadership capability and platform digitization capability and empirically analyzing the relationship of these two critical IT capabilities and their impact on innovation performance. Collapse

Topics: innovation management IT leadership business process management information technology infrastructure data integration

Methods: qualitative interview survey mixed method theory development statistical power analysis

Strategic business value from big data analytics: An empirical analysis of the mediating effects of value creation mechanisms

2022 | Information & Management | Citations: 3

Authors: Elia, Gianluca; Raguseo, Elisabetta; Solazzo, Gianluca; Pigni, Federico

Abstract: Big data are a prominent source of value capable of generating competitive advan ... Expand

Abstract: Big data are a prominent source of value capable of generating competitive advantage and superior business performance. This paper represents the first empirical investigation of the theoretical model proposed by Grover et al. (2018), considering the mediating effects of four value creation mechanisms on the relationship between big data analytics capabilities (BDAC) and four value targets. The four value creation mechanisms investigated (the source of the value being pursued) are transparency, access, discovery, and proactive adaptation, while the four value targets (the impacts of the value creation process) are organization performance, business process improvement, customer experience and market enhancement, and product and service innovation. The proposed empirical validation of Grover et al.’s (2018) model adopts an econometric analysis applied to data gathered through a survey involving 256 BDA experts. The results reveal that transparency mediates the relationship for all the value targets, while access and proactive adaptation mediate only in case of some value targets, and discovery does not have any mediating effect. Theoretical and practical implications are discussed at the end of the paper. Collapse

Topics: value creation big data innovation management organizational productivity business process improvement

Methods: survey theory development company material exploratory data analysis longitudinal research

Theories: theory of economic development dynamic capabilities theory resource based view of the firm

Semantic Framework for Practicing Data Science in Public Health Organizations during the Covid-19 Pandemics

2022 | HICSS | Citations: 0

Authors: Juric, Radmila; Ronchieri, Elisabetta

Abstract: This paper proposes a semantic framework based on software architectures for acc ... Expand

Abstract: This paper proposes a semantic framework based on software architectures for accommodating data science practices to the needs of Public Health Organizations (PHO), during the covid-19 pandemics. The goal is to create an environment suitable for deploying data science on an ad-hoc basis, upon the selection of data generated by governments, nongovernment organizations, public databases and social media, but guided by PHO own needs and expertise. It is important to run predictions, through learning technologies, which may depend on circumstances and situations relevant for PHO in the particular moment and thus enable better decision making in the time of the pandemic. The proposed software architecture relies on its deployment within integrated development environments and plug-ins/APIs towards software tools, and libraries for (a) data gathering and preprocessing, (b) predictions with learning technologies (c) reasoning with semantic technologies and (d) including human intervention to aid in understanding the situation in which PHO questions may be answered. The illustration of the proposal is uses the sentiment analysis of twitter data relevant to covid-19 and classification of tweets with machine learning. Collapse

Topics: Twitter Python application programming interface decision making enterprise architecture

Methods: experiment computational algorithm natural language processing sentiment analysis ontological modelling

Digital Platform Ecosystem Dynamics: The Roles of Product Scope, Innovation, and Collaborative Network Centrality

2022 | Management Information Systems Quarterly | Citations: 6

Authors: Li, He; Zhang, Chen; Kettinger, William

Abstract: This research highlights the circulative nature of digital platform ecosystem dy ... Expand

Abstract: This research highlights the circulative nature of digital platform ecosystem dynamics. Investigating these dynamics, we examine the mutual influence between participants’ product scope and product innovation over time and probe the moderating role of co-created collaborative networks. We distinguish between two types of product innovation: new product development and existing product updates. Our longitudinal analysis of the Hadoop software ecosystem indicates that participants covering a broader scope of the platform’s technological layers are less likely to develop new products but more likely to update existing products. In turn, participants with more frequent new product development are more likely to expand their product scope, whereas those with more frequent existing product updates are less likely to pursue scope expansion. Participants’ centrality in the ecosystem’s collaborative network amplifies the bidirectional link between product scope and existing product updates but weakens the link between product scope and new product development. Our findings offer a theoretical and practical understanding of temporal dynamics between participants’ product scope choices and different forms of product innovations in the co-created collaborative network environment. Collapse

Topics: innovation management digital ecosystem Apache Hadoop new product development open source software project

Methods: qualitative content analysis longitudinal data analysis statistical hypothesis test qualitative coding theory development