Data Mining with Linked Data: Past, Present, and Future

2018 ◽  
Author(s):  
Rohit Beniwal ◽  
Vikas Gupta ◽  
Manish Rawat ◽  
Rishabh Aggarwal
Keyword(s):  
Data Mining ◽  
Linked Data

Data Mining, Linked Data, and Library Service Delivery

2022 ◽  
pp. 60-72
Author(s):  
Blessing Babawale Amusan ◽  
Adepero Olajumoke Odumade
Keyword(s):  
Data Mining ◽  
Service Delivery ◽  
Linked Data ◽  
Service Improvement ◽  
Text And Image ◽  
Library Service ◽  
New Knowledge ◽  
Library Resources ◽  
Access To Data ◽  
Effective Service

There is no doubt that data mining and linked data can enhance library service delivery. Data mining aspects such as text and image mining will enable libraries to have access to data that can be used to discover new knowledge aid planning for effective service delivery or service improvement. Also, linked data will enable libraries connect with other libraries to share such data that can enhance job performance leading to enhanced productivity, improved service delivery, and wider visibility and access to library resources.

SEMDPA

2017 ◽  
Vol 13 (3) ◽  
pp. 1-21 ◽  
Author(s):  
Eliot Bytyçi ◽  
Besmir Sejdiu ◽  
Arten Avdiu ◽  
Lule Ahmedi
Keyword(s):  
Data Mining ◽  
Wireless Sensor Networks ◽  
Linked Data ◽  
Sensor Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Contextual Data ◽  
Hidden Knowledge ◽  
The Internet Of Things ◽  
The Web

The Internet of Things (IoT) vision is to connect uniquely identifiable devices that surround us to the Internet, which is best described through ontologies. Thereby, new emerging technologies such as wireless sensor networks (WSN) are recognized as an essential enabling component of the IoT today. Hence, given the increasing interest to provide linked sensor data through the Web either following the Semantic Web Enablement (SWE) standard or the Linked Data approach, there is a need to also explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture SEMDPA has been developed. It supports linking sensors and other devices, as well as people via a single web by mean of a device-person-activity (DPA) crossroad ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and Linked WSN data. SEMDPA could be easily extensible to capture semantics of input sensor data from other domains as well.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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