Browsing by Author "Ugarte, Martin"

Now showing 1 - 3 of 3
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    A Formal Framework for Complex Event Recognition
    (2021) Grez, Alejandro; Riveros, Cristian; Ugarte, Martin; Vansummeren, Stijn
    Complex event recognition (CER) has emerged as the unifying field for technologies that require processing and correlating distributed data sources in real time. CER finds applications in diverse domains, which has resulted in a large number of proposals for expressing and processing complex events. Existing CER languages lack a clear semantics, however, which makes them hard to understand and generalize. Moreover, there are no general techniques for evaluating CER query languages with clear performance guarantees.
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    Efficient Query Processing for Dynamically Changing Datasets
    (2019) Idris, Muhammad; Ugarte, Martin; Vansummeren, Stijn; Voigt, Hannes; Lehner, Wolfgang
    The ability to efficiently analyze changing data is a key requirement of many real-time analytics applications. Traditional approaches to this problem were developed around the notion of Incremental View Maintenance (IVM), and are based either on the materialization of subresults (to avoid their recomputation) or on the recomputation of subresults (to avoid the space overhead of materialization). Both techniques are suboptimal: instead of materializing results and subresults, one may also maintain a data structure that supports efficient maintenance under updates and from which the full query result can quickly be enumerated. In two previous articles, we have presented algorithms for dynamically evaluating queries that are easy to implement, efficient, and can be naturally extended to evaluate queries from a wide range of application domains. In this paper, we discuss our algorithm and its complexity, explaining the main components behind its efficiency. Finally, we show experiments that compare our algorithm to a state-of-the-art (Higher-order) IVM engine, as well as to a prominent complex event recognition engine. Our approach outperforms the competitor systems by up to two orders of magnitude in processing time, and one order in memory consumption.
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    Screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test
    (CELL PRESS, 2021) Eyheramendy, Susana; Saa, Pedro A.; Undurraga, Eduardo A.; Valencia, Carlos; Lopez, Carolina; Mendez, Luis; Pizarro Berdichevsky, Javier; Finkelstein Kulka, Andres; Solari, Sandra; Salas, Nicolas; Bahamondes, Pedro; Ugarte, Martin; Barcelo, Pablo; Arenas, Marcelo; Agosin, Eduardo
    The sudden loss of smell is among the earliest and most prevalent symptoms of COVID-19 when measured with a clinical psychophysical test. Research has shown the potential impact of frequent screening for olfactory dysfunction, but existing tests are expensive and time consuming. We developed a low-cost ($0.50/test) rapid psychophysical olfactory test (KOR) for frequent testing and a model-based COVID-19 screening framework using a Bayes Network symptoms model. We trained and validated the model on two samples: suspected COVID-19 cases in five healthcare centers (n = 926; 33% prevalence, 309 RT-PCR confirmed) and healthy miners (n = 1,365; 1.1% prevalence, 15 RT-PCR confirmed). The model predicted COVID-19 status with 76% and 96% accuracy in the healthcare and miners samples, respectively (healthcare: AUC = 0.79 [0.75-0.82], sensitivity: 59%, specificity: 87%; miners: AUC = 0.71 [0.63-0.79], sensitivity: 40%, specificity: 97%, at 0.50 infection probability threshold). Our results highlight the potential for low-cost, frequent, accessible, routine COVID-19 testing to support society's reopening.