Insight Research Collection

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At Insight we undertake high impact research in data analytics that has significant impact on industry and society by enabling better decision making.

Insight brings together leading Irish academics from 5 of Ireland's leading research centres (DERI, CLARITY, CLIQUE, 4C, TRIL), previously established by Science Foundation Ireland (SFI) and the Irish Industrial Development Authority (IDA), in key areas of priority research including:

  • The Semantic Web
  • Sensors and the Sensor Web
  • Social network analysis
  • Decision Support and Optimization
  • Connected Health

For more information, please visit the official website.


Recent Submissions

Now showing 1 - 5 of 554
  • Publication
    The role of MNEs in the genesis and growth of a resilient entrepreneurial ecosystem
    This article reports on a longitudinal process study of the critical role of anchor MNEs in the metamorphosis of a high-tech industrial cluster into a local entrepreneurial ecosystem. It draws on entrepreneurial ecosystem and international business literatures to frame the study of the genesis and evolutionary processes of an entrepreneurial ecosystem that emerged from two MNE subsidiaries, both of which had evolved into advanced R&D centres of excellence around a technology specialism. It shows how multiple new venture spinouts by former MNE employees introduced technological heterogeneity that catalysed into a resilient entrepreneurial ecosystem. The theoretical and policy implications that can be drawn from this case study emphasize the existence of both technology specialism and heterogeneity for resilience in an entrepreneurial ecosystem, and that reaching such a position is evolutionary in nature.
    Scopus© Citations 29  68
  • Publication
    Regional knowledge spaces: the interplay of entry-relatedness and entry-potential for technological change and growth
    This paper aims to uncover the mechanism of how the network properties of regional knowledge spaces contribute to technological change from the perspective of regional knowledge entry-relatedness and regional knowledge entry-potential. Entry-relatedness, which has been previously employed to investigate the technology evolution of regional economies, is advanced by introducing a knowledge gravity model. The entry-potential of a newly acquired regional specialisation has been largely ignored in the relevant literature; surprisingly given the high relevance that is attributed to the recombination potential of new capabilities. In other words, just adding new knowledge domains to a system is not sufficient alone, it really depends on how these fit into the existing system and thus can generate wider economic benefits. Based on an empirical analysis of EU-15 Metro and non-Metro regions from 1981 to 2015, we find that entry-relatedness has a significant negative association with novel inventive activities, while entry-potential has a significant positive association with the development of novel products and processes of economic value. This highlights that regions’ capacity to venture into high-potential areas of technological specialization in the knowledge space outperforms purely relatedness driven diversification that is frequently promoted in the relevant literature.
      27Scopus© Citations 7
  • Publication
    The relevance of scientific knowledge externalities for technological change and resulting inventions across European metropolitan areas
    Contrary to perceptions in which technological development proceeds independently of scientific research, the interplay between science and technology has been recognized as an essential part in technological change, industrial competitiveness, and economic growth. While the process of knowledge exchanges between the nexus is conceptually well grounded in relevant literatures, the absence of quantitative measures and assessments of such linkages may underestimate the importance of scientific knowledge inputs for generating high-impact innovative outcomes. In this regard, we propose a large quantitative analysis on knowledge externalities from science to technology by investigating patent citations to science data across European metropolitan regions. First, we construct a dataset of patent citations to scientific knowledge that includes information on the spatial origins of knowledge spillovers. Subsequently, the ratio of internal scientific knowledge sourcing to external sources and its effect on patent citation impact is evaluated. Findings suggest that regions with a higher reliance on their internal scientific resources tend to generate inventions with higher technological impact, and that a strong connection between science and technology is even more effective in advanced industrial regions.
    Scopus© Citations 1  45
  • Publication
    Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation
    Nowadays we commonly have multiple sources of data associated with items. Users may provide numerical ratings, or implicit interactions, but may also provide textual reviews. Although many algorithms have been proposed to jointly learn a model over both interactions and textual data, there is room to improve the many factorization models that are proven to work well on interactions data, but are not designed to exploit textual information. Our focus in this work is to propose a simple, yet easily applicable and effective, method to incorporate review data into such factorization models. In particular, we propose to build the user and item embeddings within the topic space of a topic model learned from the review data. This has several advantages: we observe that initializing the user and item embeddings in topic space leads to faster convergence of the factorization algorithm to a model that out-performs models initialized randomly, or with other state-of-the-art initialization strategies. Moreover, constraining user and item factors to topic space allows for the learning of an interpretable model that users can visualise.
      36Scopus© Citations 19
  • Publication
    Estimation of respiration rate and sleeping position using a wearable accelerometer
    Wearable inertial sensors offer the possibility to monitor sleeping position and respiration rate during sleep, enabling a comfortable and low-cost method to remotely monitor patients. Novel methods to estimate respiration rate and position during sleep using accelerometer data are presented, with algorithm performance examined for two sensor locations, and accelerometer-derived respiration rate compared across sleeping positions. Eleven participants (9 male; aged: 47.82±14.14 years; BMI 30.9±5.27 kg/m 2 ; AHI 5.77±4.18) undergoing a scheduled clinical polysomnography (PSG) wore a tri-axial accelerometer on their chest and upper abdomen. PSG cannula flow and position data were used as benchmark data for respiration rate (breaths per minute, bpm) and position. Sleeping position was classified using logistic regression, with features derived from filtered acceleration and orientation. Accelerometer-derived respiration rate was estimated for 30 s epochs using an adaptive peak detection algorithm which combined filtered acceleration and orientation data to identify individual breaths. Sensor-derived and PSG respiration rates were then compared. Mean absolute error (MAE) in respiration rate did not vary between sensor locations (abdomen: 1.67±0.37 bpm; chest: 1.89±0.53 bpm; p=0.52), while reduced MAE was observed when participants lay on their side (1.58±0.54 bpm) compared to supine (2.43±0.95 bpm), p<; 0.01. MAE was less than 2 bpm for 83.6% of all 30 s windows across all subjects. The position classifier distinguished supine and left/right with a ROC AUC of 0.87, and between left and right with a ROC AUC of 0.94. The proposed methods may enable a low-cost solution for in-home, long term sleeping posture and respiration monitoring.
    Scopus© Citations 15  68