Text and structured data fusion in data tamer at scale

Gubanov, Michael; Bruckner, Daniel

doi:10.1109/icde.2014.6816755

Cited by 14 publications

(3 citation statements)

References 21 publications

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“…Recent work, such as by Ebraheem et al (2018), utilizes word embedding to create semantically similar clusters as well as recommend matched pairs. Data tamer (Gubanov et al, 2014) uses ML for entity consolidation by predicting which data item is most likely to be relevant.…”

Section: Machine Learning For Data Integrationmentioning

confidence: 99%

Artificial intelligence for ocean science data integration: current state, gaps, and way forward

Sagi

Lehahn²,

Bar

2020

Elem Sci Anth

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The study of the ocean is one of the biggest scientific challenges of the 21st century. It has a direct impact on our understanding of Earth's climate (Stocker et al., 2013) and biogeochemical cycling (Field et al., 1998), as well as on our ability to provide human society with food, chemicals, and energy (Lehahn et al., 2016). Oceanographic research strongly relies on in-situ and remotely-sensed observations, which describe physical, chemical, and biological seawater properties at a given time and place. These observations are collected from various crewed and autonomous platforms, including research vessels, floats (Roemmich et al., 2009), drifters (Lumpkin et al., 2017), autonomous vehicles (Eriksen et al., 2001), and satellites (Lehahn et al., 2018), providing an abundance of interdisciplinary information on processes occurring over a wide range of spatial (from micrometers to thousands of kilometers) and temporal (from seconds to decades) scales. Over the last century, numerous in-situ and remotelysensed measurements have been taken, resulting in the creation of an increasingly large amount of oceanic data. In recent years, with the enhanced utilization of satellites and autonomous observation platforms, these data are collected at a blistering rate. Improving the scientific community's ability to integrate, share, and explore this vast amount of data is an urgent task that will contribute substantially to our understanding of the ocean and its role in the Earth system. Several public data repositories have emerged to enable the archiving and sharing of data collected between researchers. For example, PANGEA (2020), a data repository for publishing and distributing georeferenced data

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Section: Machine Learning For Data Integrationmentioning

confidence: 99%

Artificial intelligence for ocean science data integration: current state, gaps, and way forward

Sagi

Lehahn²,

Bar

2020

Elem Sci Anth

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“…Additionally, normal EEG recording was conducted on one of the subjects four days in a row under controlled conditions to test the device reproducibility, followed by statistical analysis. In future EEG data can be integrated with other types of data thus increasing accuracy of identification [21][22][23][24][25].…”

Section: Data Collection and Experimental Proceduresmentioning

confidence: 99%

“…Electroencephalography (EEG -recording of electrical activity along the scalp) have been demonstrated effective for differentiation between different emotional states [3,5]. EEG signals are commonly described in terms of rhythmic activity across different frequency bands: a) delta (1-3 Hz), b) theta (4-7 Hz), c) alpha waves (8-13 Hz), d) beta waves (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), e) gamma (30-100+ Hz) [5][6][7][8]. Analyzing the distribution of power in respective frequency bands will reveal features that correspond to a particular state of the human mind [5,9].…”

Section: Introductionmentioning

confidence: 99%

Real Time Fear Detection Using Wearable Single Channel Electroencephalogram

Cheemalapati¹,

Adithya²

2016

Sensor Netw Data Commun

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Real time detection of emotional state has multiple applications for security, safety and identification of dangerous situations. Traditionally electroencephalogram (EEG) based emotion studies are conducted in controlled lab environment with multi-channel systems and large signal processing power. In order to be useful in real world situation the system for emotion detection has to be miniature, portable and working in real time supported by calculations that can be provided by a processor power of a mobile phone. Here we present our results on real time fear detection using portable single electrode EEG system conducted on 10 subjects. We studied possibility of translation of the markers previously identified for complex multi-electrode system-ratios of slow waves to fast waves into real time portable system. It was demonstrated using Student's t-test that the average value of the monitored parameter during normal state was significantly higher than that of during a scary stimulus with a P value of (0.027) ~ 0.03 for Theta/ Beta. The framework for portable fear detection together with the markers discussed in this study can enable many applications important for the soldiers in the battlefield or police officers while being under attack as an indicator that help is urgently needed.

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Generating Unified Famous Objects (UFOs) from the classified object tables

Kola

Soderman

et al. 2017

2017 IEEE International Conference on Big Data (Big Data)

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Text and structured data fusion in data tamer at scale

Cited by 14 publications

References 21 publications

Artificial intelligence for ocean science data integration: current state, gaps, and way forward

Artificial intelligence for ocean science data integration: current state, gaps, and way forward

Real Time Fear Detection Using Wearable Single Channel Electroencephalogram

Generating Unified Famous Objects (UFOs) from the classified object tables

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