2,502 research outputs found
Visual analytics for supply network management: system design and evaluation
We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip
Ecosystem Intelligence for AI-based Assistant Platforms
Digital assistants like Alexa, Google Assistant, or Siri have seen a large adoption over the past years. Using artificial intelligence (AI) technologies, they provide a vocal interface to physical devices as well as to digital services and have spurred an entire new eco-system. This comprises the big tech companies themselves, but also a strongly growing community of developers that make these functionalities available via digital platforms. At present, only few research is available to understand the structure and the value creation logic of these AI-based assistant platforms and their ecosystem. This research adopts ecosystem intelligence to shed light on their structure and dynamics. It combines existing data collection methods with an automated approach that proves useful in deriving a network-based conceptual model of Amazon's Alexa assistant platform and ecosystem. It shows that skills are a key unit of modularity in this ecosystem, which is linked to other elements such as service, data, and money flows. It also suggests that the topology of the Alexa ecosystem may be described using the criteria reflexivity, symmetry, variance, strength, and centrality of the skill coactivations. Finally, it identifies three ways to create and capture value on AI-based assistant platforms. Surprisingly only a few skills use a transactional business model by selling services and goods but many skills are complementary and provide information, configuration, and control services for other skill provider products and services. These findings provide new insights into the highly relevant ecosystems of AI-based assistant platforms, which might serve enterprises in developing their strategies in these ecosystems. They might also pave the way to a faster, data-driven approach for ecosystem intelligence
Impact in networks and ecosystems: building case studies that make a difference
open accessThis toolkit aims to support the building up of case studies that
show the impact of project activities aiming to promote innovation
and entrepreneurship. The case studies respond to the challenge
of understanding what kinds of interventions work in the Southern
African region, where, and why. The toolkit has a specific focus on entrepreneurial ecosystems and proposes a method of mapping out the actors and their relationships over time. The aim is to understand the changes that take place in the ecosystems. These changes are seen to be indicators of impact as
increased connectivity and activity in ecosystems are key enablers of innovation. Innovations usually happen together with matching social and institutional adjustments, facilitating the translation of inventions into new or improved products and services. Similarly, the processes supporting entrepreneurship are guided by policies implemented in the common framework provided by innovation systems. Overall, policies related to systems of innovation are by nature networking policies applied throughout the socioeconomic framework of society to pool scarce resources and make
various sectors work in coordination with each other. Most participating SAIS countries already have some kinds of identifiable systems of innovation in place both on national and regional levels, but the lack of appropriate institutions, policies, financial instruments, human resources, and support systems, together with underdeveloped markets, create inefficiencies and gaps in systemic cooperation and collaboration. In other words, we do not always know what works and what does not. On another level, engaging users and intermediaries at the local level and driving the development of local innovation
ecosystems within which local culture, especially in urban settings, has evident impact on how collaboration and competition is both seen and done. In this complex environment, organisations supporting entrepreneurship and innovation often find it difficult to create or apply relevant knowledge and appropriate networking tools, approaches, and methods needed to put their processes to work for broader developmental goals. To further enable these organisationsâ work, it is necessary to understand what works and why in a given environment. Enhanced local and regional cooperation promoted by SAIS Innovation Fund projects can generate new data on this little-explored area in Southern Africa. Data-driven knowledge on entrepreneurship and innovation support best practices as well as effective and efficient management of entrepreneurial ecosystems can support replication and inform policymaking, leading thus to a wider impact than just that of the immediate reported projects and initiatives
Data science: a game changer for science and innovation
AbstractThis paper shows data science's potential for disruptive innovation in science, industry, policy, and people's lives. We present how data science impacts science and society at large in the coming years, including ethical problems in managing human behavior data and considering the quantitative expectations of data science economic impact. We introduce concepts such as open science and e-infrastructure as useful tools for supporting ethical data science and training new generations of data scientists. Finally, this work outlines SoBigData Research Infrastructure as an easy-to-access platform for executing complex data science processes. The services proposed by SoBigData are aimed at using data science to understand the complexity of our contemporary, globally interconnected society
Patterns and Pathways: Applying Social Network Analysis to Understand User Behavior in the Tourism Industry Websites
The contemporary tourism landscape is undergoing rapid digitization,
necessitating a nuanced comprehension of online user behavior to guide
data-driven decision-making. This research bridges an existing gap by
investigating the tourism website ecosystem through social network analysis. It
focuses specifically on inter-website communication patterns based on user
navigation. Data mining facilitates the identification of 162 core Iranian
tourism websites, which are visualized as an interconnected network with
websites as nodes and user transitions as weighted directed edges. By
implementing community detection, eight key clusters are discerned,
encompassing domains like ticket/tour bookings, accommodations, location
services, and cuisine. Further analysis of inter-community relationships
reveals website groupings frequently accessed together by users, highlighting
complementary services sought during travel planning. The research derives
invaluable insights into user preferences and information propagation within
the tourism ecosystem. The methodology and findings contribute original
perspectives to academia while offering pragmatic strategic recommendations to
industry stakeholders like service providers, investors, and policymakers. This
pioneering exploration of latent user behavior patterns advances comprehension
of the evolving digital tourism landscape in Iran. It contributes pathways
toward a sustainable future vision of the ecosystem, guiding stakeholders in
targeted decision-making based on empirical evidence derived from social
network analysis of websites and consumption patterns. The innovative
methodology expands the toolkit for data-driven tourism research within
academia
Business Case and Technology Analysis for 5G Low Latency Applications
A large number of new consumer and industrial applications are likely to
change the classic operator's business models and provide a wide range of new
markets to enter. This article analyses the most relevant 5G use cases that
require ultra-low latency, from both technical and business perspectives. Low
latency services pose challenging requirements to the network, and to fulfill
them operators need to invest in costly changes in their network. In this
sense, it is not clear whether such investments are going to be amortized with
these new business models. In light of this, specific applications and
requirements are described and the potential market benefits for operators are
analysed. Conclusions show that operators have clear opportunities to add value
and position themselves strongly with the increasing number of services to be
provided by 5G.Comment: 18 pages, 5 figure
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Developing a Methodology to Characterize the Use of Emerging and Converging Technologies in Federal Agencies
Although some methodologies exist for the systematic and strategic consideration of emerging and converging technologies, they typically do not incorporate agency current use, strategies, or foresight. This research develops a methodology to characterize current and potential United States federal agency use of emerging and converging technologies to fulfill agency strategic plans and serve society.
Phase 1 of this research develops a methodology to fulfill criteria derived from a literature review and an assessment of best practices. Designed to be implemented in four phasesâdevelop, apply, evaluate, disseminateâthe steps of this methodology include definition, collection, organization, analysis, synthesis, evaluation, and dissemination. Within the analyze step, a mix of qualitative and quantitative analysis approaches are applied to answer the defined questions. Current agency use of emerging and converging technologies is characterized with content analysis of strategic documents; technology assessment analysis by experts; and individual interviews with government employees. Potential agency use of emerging and converging technologies is characterized with individual interviews with government employees; plausibility matrix analysis by experts; and crowd-sourced intelligence. The methodology is applied in Phase 2 to two cases, the Department of Commerce and the Department of Energy, then evaluated in Phase 3 versus the design criteria and visual analytics, and disseminated in Phase 4 to researchers, policymakers, and the general public.
Key findings, results, and meta-inferences of this research are that many more potential uses exist for using emerging and converging technologies to fulfill agency strategies and the research identifies some of the potential uses by technology and strategy. These potential uses also are presented in terms of comparable technical feasibility and societal benefit. Implications for policymakers are that governing with foresight is critical; encouraging systematic agency consideration of emerging and converging technologies is necessary; and it is important to implement a government-wide methodology that will characterize current and potential use of emerging and converging technologies for fulfilling agency strategies. This research contributes the criterion for such a methodology as well as the methodology and the results of its application to two agency cases
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