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

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

Skipping class: improving human-driven data exploration and querying through instances

2022 | European Journal of Information Systems | Citations: 0

Authors: Saghafi, Arash; Wand, Yair; Parsons, Jeffrey

Abstract: With the growing focus on business analytics and data-driven decision-making, th ... Expand

Abstract: With the growing focus on business analytics and data-driven decision-making, there is a greater need for humans to interact effectively with data. We propose that presenting data to human users in terms of instances and attributes provides a more flexible and usable structure for querying, exploring, and analysing data. Compared to a traditional representation, an instance-based representation does not impose any predefined classification schema over the data when it is presented to users. This paper examines the potential utility of instance-based data through two laboratory experiments – the first focusing on exploration of data for pattern discovery (open-ended tasks) and the second on retrieval of information (closed-ended tasks). In both cases, participants were able to achieve better results in tasks using instance-based data than using class-based representations. Given the growing need for self-service analytics, as well as using information for purposes not anticipated when it was collected, we show that instance-based representations can be an effective way to satisfy the emerging needs of information users. Collapse

Topics: database system decision making Tableau software business intelligence information retrieval

Methods: experiment parametric test Student's t-test experimental group data modeling

Theories: schema theory

Teaching NoSQL Data Models: A Tutorial

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Greiner, Martina

Abstract: This tutorial discusses the four most popular categories of NoSQL databases: ke ... Expand

Abstract: This tutorial discusses the four most popular categories of NoSQL databases: key-value databases, document databases, graph databases, and wide column databases. For each NoSQL database type, we explain differences to traditional relational database management systems, how data is organized using Airbnb as an example, and benefits and drawbacks. Collapse

Topics: Airbnb nosql database database system relational database Cassandra

Methods: data modeling conceptual modelling design science entity relationship modeling

Introduction to Big Data and Analytics: Pathways to Maturity “The Original Big Data and Analytics Minitrack”

2021 | HICSS | Citations: 0

Authors: Kaisler, Stephen H; Armour, Frank J; Espinosa, J Alberto

Abstract: ... Expand

Abstract: Collapse

Topics: IT project big data web page marketing management online advertising

Methods: qualitative interview

How Data Analytics Competencies Can Foster Business Value– A Systematic Review and Way Forward

2021 | Information Systems Management | Citations: 0

Authors: Klee, Sven; Janson, Andreas; Leimeister, Jan Marco

Abstract: Several authors have illustrated the benefits of data in organizations. For real ... Expand

Abstract: Several authors have illustrated the benefits of data in organizations. For realizing benefits, we see two major challenges for organizations. First, there are necessary investments, which have to be justified. Second, adequate data analytics competencies can be seen as enablers for realizing benefits. We aim to support organizations by showing relevant competencies and achievable business values. We present theoretical propositions and a research agenda on how to move the field of data analytics competencies forward. Collapse

Topics: analytical information system organizational value data management database system programming language

Methods: qualitative interview structured literature research computational algorithm literature sample machine learning

Applying Multi-Criteria Decision-Making for the Selection of Big Data Technologies

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Volk, Matthias; Staegemann, Daniel; Bischoff, Dennis; Turowski, Klaus

Abstract: For many years now big data presents itself as a technical starting point for a ... Expand

Abstract: For many years now big data presents itself as a technical starting point for a multitude of different trends, including data science, artificial intelligence, the internet of things, industry 4.0, and others. However, many decision makers are still challenged by the realization of related projects. One of the major problems arises from the multitude of existing technologies as well their selection and application. Due to that, in this paper, applying the design science research methodology, a comprehensive investigation about the application of multi-criteria decision-making (MCDM) methods is conducted. Apart from a structured literature review that seeks to identify relevant requirements for the technology selection, a conceptual model, as well as a prototypical implementation in the form of a decision support system, are presented. In the future, further MCDM approaches shall be tested and the system implemented in larger processes, including general sensemaking, system architecture modeling and rapid deployment. Collapse

Topics: big data decision support decision support system Apache Hadoop electronic authentication

Methods: design artifact structured literature research experiment design science multiple criteria decision making

MergePoint: A Graphical Web-App for merging HTTP-Endpoints and IoT- Platform Models

2020 | HICSS | Citations: 0

Authors: Burzlaff, Fabian; Hammen, Johannes; Bongarth, Benedikt; Grottker, Sven; Bartelt, Christian

Abstract: More and more devices are connected to Internet of Things Platforms in various a ... Expand

Abstract: More and more devices are connected to Internet of Things Platforms in various application domains. The resulting device integration effort is moderated by the concrete integration syntax and the technical abilities of the device integrator. Therefore, researchers from various communities have been investigating and designing component coupling architectures to achieve interoperability for more than 30 years. Emerging Smart Home scenarios challenges classical integration approaches as no single formal integration standard exists. In this paper we introduce a reference architecture called MergePoint that automates HTTPEndpoint integration with smart home platforms such as openHAB in a plug-and-play manner. Based on a prototypical system implementation, our empirical evaluation demonstrates that average integration time can be reduced by 78% and average tool usability score is increased by 65% compared to textual integration approaches. MergePoint can serve as a reference implementation for practitioners that want to automate the integration between HTTP-Endpoints and IoT Platform Models. Collapse

Topics: usability smart home internet of things mobile system application programming interface

Methods: data modeling design science survey case study cognitive walkthrough

Sentiment aggregation of targeted features by capturing their dependencies: Making sense from customer reviews

2020 | International Journal of Information Management | Citations: 8

Authors: Mirtalaie, Monireh Alsadat; Hussain, Omar Khadeer

Abstract: Ideation is an important phase in the new product development process at which p ... Expand

Abstract: Ideation is an important phase in the new product development process at which product designers innovate and select novel ideas that can be added as features to an existing product. One way to find novel ideas is to transfer uncommon features of products of other domains and integrate them into the product to be improved. However, before incorporating such targeted features into the product, they need to be evaluated against the customers’ acceptance in social media using sentiment aggregation tools. Despite the many studies in sentiment analysis, mapping the customers’ opinions towards both high-level and technical features of a product extracted from social media to their best corresponding component in that product is still a challenge. Furthermore, none of the existing approaches ascertains the sentiment value of a targeted feature by capturing its dependencies on other features. In this paper, to address these drawbacks, we propose the sentiment aggregation framework for targeted features (SA-TF). SA-TF determines the sentiment of a targeted feature by assisting product designers in the tasks of mapping the features discussed in the reviews to the right product components, sentiment aggregation and considering feature dependencies to determine their polarity. The superiority of the different phases of SA-TF is demonstrated with experiments and comparing it with an existing approach. Collapse

Topics: database system knowledge representation social capital innovation management decision making

Methods: sentiment analysis computational algorithm literature study experiment ontological modelling

The role of information governance in big data analytics driven innovation

2020 | Information & Management | Citations: 24

Authors: Mikalef, Patrick; Boura, Maria; Lekakos, George; Krogstie, John

Abstract: The age of big data analytics is now here, with companies increasingly investing ... Expand

Abstract: The age of big data analytics is now here, with companies increasingly investing in big data initiatives to foster innovation and outperform competition. Nevertheless, while researchers and practitioners started to examine the shifts that these technologies entail and their overall business value, it is still unclear whether and under what conditions they drive innovation. To address this gap, this study draws on the resource-based view (RBV) of the firm and information governance theory to explore the interplay between a firm’s big data analytics capabilities (BDACs) and their information governance practices in shaping innovation capabilities. We argue that a firm’s BDAC helps enhance two distinct types of innovative capabilities, incremental and radical capabilities, and that information governance positively moderates this relationship. To examine our research model, we analyzed survey data collected from 175 IT and business managers. Results from partial least squares structural equation modelling analysis reveal that BDACs have a positive and significant effect on both incremental and radical innovative capabilities. Our analysis also highlights the important role of information governance, as it posi­ tively moderates the relationship between BDAC’s and a firm’s radical innovative capability, while there is a nonsignificant moderating effect for incremental innovation capabilities. Finally, we examine the effect of en­ vironmental uncertainty conditions in our model and find that information governance and BDACs have am­ plified effects under conditions of high environmental dynamism. Collapse

Topics: data governance innovation management big data environmental uncertainty logistics management

Methods: partial least squares regression survey computational algorithm partial least squares path modeling field study

Theories: resource based view of the firm