Analysis of Technology Trends Based on Big Data

Segev, Aviv; Jung, Chihoon; Jung, Sukhwan

doi:10.1109/bigdata.congress.2013.65

Cited by 9 publications

(4 citation statements)

References 2 publications

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“…This suggests the entities related to both technologies are experiencing initial hype with the public while the researchers have already passed this stage and show diminished interests in the same entities. Such differences are validated by the authors' previous research on the different time windows for technology growth curves in different datasets, where the technology's development starts with the academic domain and the public inherits the changes afterward [1], [18].…”

Section: Extracting Technology-related Entity Trendsmentioning

confidence: 60%

“…The trend curves are generated by connecting the discrete data points into a series of line graphs. The trend curves are not normalized as in the previous research [18] since the process searches not only for curves with a specific growth pattern but also curves with overall elevated values. All entities are deemed related to the technology in question and are treated equally regardless of their distances from it, or the number of see-also sections between them.…”

Section: Extracting Technology-related Entity Trendsmentioning

confidence: 99%

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Untitled

2020

IJWesT

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Technological developments are not isolated and are influenced not only by similar technologies but also by many entities, which are sometimes unforeseen by the experts in the field. The authors propose a method for identifying technology-relevant entities with trend curve analysis. The method first utilizes the tangential connection between terms in the encyclopedic dataset to extract technology-related entities with varying relation distances. Changes in their term frequencies within 389 million academic articles and 60 billion web pages are then analyzed to identify technology-relevant entities, incorporating the degrees and changes in both academic interests and public recognitions. The analysis is performed to find entities both significant and relevant to the technology of interest, resulting in the discovery of 40 and 39 technologyrelevant entities, respectively, for unmanned aerial vehicle and hyperspectral imaging with 0.875 and 0.5385 accuracies. The case study showed the proposed method can capture hidden relationships between seemingly unrelated entities.

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Section: Extracting Technology-related Entity Trendsmentioning

confidence: 60%

Section: Extracting Technology-related Entity Trendsmentioning

confidence: 99%

Untitled

2020

IJWesT

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“…Multiple applications of the predictive topic evolution have been proposed. A semi-manual technology trend analysis was done to identify the roots of new technologies with their projected impact on the research field [36]. A semantically enhanced technology-topic model for interdisciplinary knowledge sharing was also proposed, integrating topic models and their similarities to calculate the likelihood of a technology being adapted to a specific research area without extensive presence in the field [28].…”

Section: Identifying and Predicting New Topicsmentioning

confidence: 99%

Analyzing the generalizability of the network-based topic emergence identification method

Jung

Segev

2022

Self Cite

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Topic evolution helps the understanding of current research topics and their histories by automatically modeling and detecting the set of shared research fields in academic publications as topics. This paper provides a generalized analysis of the topic evolution method for predicting the emergence of new topics, which can operate on any dataset where the topics are defined as the relationships of their neighborhoods in the past by extrapolating to the future topics. Twenty sample topic networks were built with various fields-of-study keywords as seeds, covering domains such as business, materials, diseases, and computer science from the Microsoft Academic Graph dataset. The binary classifier was trained for each topic network using 15 structural features of emerging and existing topics and consistently resulted in accuracy and F1 over 0.91 for all twenty datasets over the periods of 2000 to 2019. Feature selection showed that the models retained most of the performance with only one-third of the tested features. Incremental learning was tested within the same topic over time and between different topics, which resulted in slight performance improvements in both cases. This indicates there is an underlying pattern to the neighbors of new topics common to research domains, likely beyond the sample topics used in the experiment. The result showed that network-based new topic prediction can be applied to various research domains with different research patterns.

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“…There is plenty of literature, however, giving examples of how big data use cases could look like in enterprises [33], public administration bodies [34] or research institutions [35]. Segev et al [36], for instance, explain how technology trends can be analyzed based on big data. Making the identification of use cases less challenging requires a combination of expert guidance, to compensate a potential lack of big data experience, and a systemic view of the organization, to ensure that the use cases create added value and encounter organizational demand.…”

Section: Research Directionsmentioning

confidence: 99%

Big data challenges: Impact, potential responses and research needs

Bachlechner

Leimbach

2016

2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societi

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Although reports on big data success stories have been accumulating in the media, most organizations dealing with high-volume, high-velocity and high-variety information assets still face challenges. Only a thorough understanding of these challenges puts organizations into a position in which they can make an informed decision for or against big data, and, if the decision is positive, overcome the challenges smoothly. The combination of a series of interviews with leading experts from enterprises, associations and research institutions, and focused literature reviews allowed not only identifying and describing eight key challenges but also characterizing their impact, outlining potential responses to them and proposing directions for future research. The challenges faced were found to be not only technological in nature. Organizational and people-related matters as well as the legislative framework are also relevant. For large enterprises and startups specialized in big data, it is typically easier to overcome the challenges than it is for other enterprises and public administration bodies

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Analysis of Technology Trends Based on Big Data

Cited by 9 publications

References 2 publications

Untitled

Untitled

Analyzing the generalizability of the network-based topic emergence identification method

Big data challenges: Impact, potential responses and research needs

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