scholarly journals Exploiting Semantics for Big Data Integration

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 25-38 ◽  
Author(s):  
Craig A. Knoblock ◽  
Pedro Szekely
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Cultural Heritage ◽  
Use Case ◽  
Heterogeneous Datasets ◽  
Dataset Size ◽  
Integrate Data

There is a great deal of interest in big data, focusing mostly on dataset size. An equally important dimension of big data is variety, where the focus is to process highly heterogeneous datasets. We describe how we use semantics to address the problem of big data variety.  We also describe Karma, a system that implements our approach and show how Karma can be applied to integrate data in the cultural heritage domain. In this use case, Karma integrates data across many museums even though the datasets from different museums are highly heterogeneous.

scholarly journals Foreword to the Special Issue: “Semantics for Big Data Integration”

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 68 ◽  
Author(s):  
Domenico Beneventano ◽  
Maurizio Vincini
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Semantic Technologies ◽  
Special Issue ◽  
Innovative Solutions ◽  
Challenges And Opportunities ◽  
Dataset Size ◽  
Data Context

In recent years, a great deal of interest has been shown toward big data. Much of the work on big data has focused on volume and velocity in order to consider dataset size. Indeed, the problems of variety, velocity, and veracity are equally important in dealing with the heterogeneity, diversity, and complexity of data, where semantic technologies can be explored to deal with these issues. This Special Issue aims at discussing emerging approaches from academic and industrial stakeholders for disseminating innovative solutions that explore how big data can leverage semantics, for example, by examining the challenges and opportunities arising from adapting and transferring semantic technologies to the big data context.

scholarly journals Leveraging national claim and hospital big data integration: a cohort study on a statin-drug interaction use case. (Preprint)

10.2196/29286 ◽  
2021 ◽  
Author(s):  
Aurélie Bannay ◽  
Mathilde Bories ◽  
Pascal Le Corre ◽  
Christine Riou ◽  
Pierre Lemordant ◽  
...  
Keyword(s):  
Big Data ◽  
Cohort Study ◽  
Drug Interaction ◽  
Data Integration ◽  
Use Case ◽  
Statin Drug

Using ISO and Semantic Web standard for building a multilingual terminology e-Dictionary: A use case of Chinese ceramic vases

2021 ◽  
pp. 016555152110221
Author(s):  
Tong Wei ◽  
Christophe Roche ◽  
Maria Papadopoulou ◽  
Yangli Jia
Keyword(s):  
Semantic Web ◽  
Cultural Heritage ◽  
Knowledge Sharing ◽  
Use Case ◽  
Knowledge Infrastructure ◽  
Ming And Qing Dynasties ◽  
New Perspective ◽  
Digital Protection ◽  
Protection Of Cultural Heritage

Cultural heritage is the legacy of physical artefacts and intangible attributes of a group or society that is inherited from past generations. Terminology is a tool for the dissemination and communication of cultural heritage. The lack of clearly identified terminologies is an obstacle to communication and knowledge sharing. Especially, for experts with different languages, it is difficult to understand what the term refers to only through terms. Our work aims to respond to this issue by implementing practices drawn from the Semantic Web and ISO Terminology standards (ISO 704 and ISO 1087-1) and more particularly, by building in a W3C format ontology as knowledge infrastructure to construct a multilingual terminology e-Dictionary. The Chinese ceramic vases of the Ming and Qing dynasties are the application cases of our work. The method of building ontology is the ‘term-and-characteristic guided method’, which follows the ISO principles of Terminology. The main result of this work is an online terminology e-Dictionary. The terminology e-Dictionary could help archaeologists communicate and understand the concepts denoted by terms in different languages and provide a new perspective based on ontology for the digital protection of cultural heritage. The e-Dictionary was published at http://www.dh.ketrc.com/e-dictionary.html .

scholarly journals Big Data Analytics for Search Engine Optimization

2020 ◽  
Vol 4 (2) ◽  
pp. 5 ◽  
Author(s):  
Ioannis C. Drivas ◽  
Damianos P. Sakas ◽  
Georgios A. Giannakopoulos ◽  
Daphne Kyriaki-Manessi
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Search Engine ◽  
Data Analytics ◽  
User Behavior ◽  
Big Data Analytics ◽  
Data Types ◽  
Search Engine Optimization ◽  
The Impact ◽  
The Web

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.

Evaluation Model for Big Data Integration Tools

2019 ◽  
pp. 601-610 ◽  
Author(s):  
Ângela Alpoim ◽  
Tiago Guimarães ◽  
Filipe Portela ◽  
Manuel Filipe Santos
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Evaluation Model

scholarly journals Big Data in HEP: A comprehensive use case study

2017 ◽  
Vol 898 ◽  
pp. 072012
Author(s):  
Oliver Gutsche ◽  
Matteo Cremonesi ◽  
Peter Elmer ◽  
Bo Jayatilaka ◽  
Jim Kowalkowski ◽  
...  
Keyword(s):  
Big Data ◽  
Use Case

scholarly journals Big Data Usage Patterns in the Health Care Domain: A Use Case Driven Approach Applied to the Assessment of Vaccination Benefits and Risks

2014 ◽  
Vol 23 (01) ◽  
pp. 27-35 ◽  
Author(s):  
S. de Lusignan ◽  
S-T. Liaw ◽  
C. Kuziemsky ◽  
F. Mold ◽  
P. Krause ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Predictive Analytics ◽  
Regulatory Compliance ◽  
Use Cases ◽  
Data Sources ◽  
Use Case ◽  
Crowd Sourcing ◽  
Big Data Processing

Summary Background: Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive. Objective: To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. Method: We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars. Results: We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowd-sourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the “internet of things”, and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources. Conclusions: Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.

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