Correlating Time-Related Data Sources with Co-clustering

2008 ◽  
pp. 264-279 ◽  
Author(s):  
Vassiliki Koutsonikola ◽  
Sophia Petridou ◽  
Athena Vakali ◽  
Hakim Hacid ◽  
Boualem Benatallah
Keyword(s):  
Data Sources ◽  
Related Data

scholarly journals Arabic Language and the Concept of I’jaz al-Qur’an (Critics to Louwis ‘Awad’s thoughts/Bahasa Arab dan Konsep I’jaz al-Qur’an (Kritik Pemikiran Louwis ‘Awad)

2021 ◽  
Vol 5 (1) ◽  
pp. 161
Author(s):  
Rahadian Kurniawan ◽  
Musda Asmara ◽  
H Hardivizon
Keyword(s):  
Literature Review ◽  
Arabic Language ◽  
Data Sources ◽  
Islamic Theology ◽  
Related Data

This article discusses the concept of I'jaz al-Qur'an and its relationship with Arabic as a form of criticism of the logos concept put forward by Louis Awad. This research is a literature review conducted by collecting related data sources to analyze the character's thought in question. The concept of I'jaz al-Qur'an is a study related to the faith of Muslims, in this case the existence of the al-Qur'an as Kalamullah. Among the Islamic scholars, two views have been very influential since this issue was raised, namely the Asy'ariyah and Mu'tazilah circles. These two groups agree to believe that the Qur'an is Kalamullah. However, in this case, Dr. Louwis expresses this opinion and relates it to the concept of Christian Logos, in which the concept of al-Qur'an, as the eternal Kalamullah (qadim) put forward by two major groups in Islamic Theology, is an adoption of the Christian Logos concept. The misappropriation of the information provided by Dr. Louwis shows his lack of understanding and mastery regarding the issues he addresses in his book. Not supported by historical facts and correct scientific studies, he conveyed in his book a form of hatred against Islam, the Koran and the Arabic language in particular and the Arab nation in general.

Semantic Integration and Knowledge Discovery for Environmental Research

2008 ◽  
pp. 213-235
Author(s):  
Zhiyuan Chen ◽  
Aryya Gangopadhyay ◽  
George Karabatis ◽  
Michael McGuire ◽  
Claire Welty
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Semantic Network ◽  
Semantic Integration ◽  
Environmental Data ◽  
Data Sources ◽  
Environmental Research ◽  
Related Data ◽  
Use Of Data ◽  
Data Semantics

Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantics-based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution browsing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental results.

Probe People and Vehicle-Based Data Sources Application in Smart Transportation

2020 ◽  
pp. 162-195
Author(s):  
Sina Dabiri ◽  
Kaveh Bakhsh Kelarestaghi ◽  
Kevin Heaslip
Keyword(s):  
Salient Feature ◽  
Data Sources ◽  
Positioning Systems ◽  
Smart Transportation ◽  
Operational Mechanism ◽  
Related Data ◽  
Information And Communication ◽  
Data Source ◽  
Location Based Social Networks ◽  
Acquisition Management

Smart transportation is a framework that leverages the power of Information and Communication Technology for acquisition, management, and mining of traffic-related data sources. This chapter categorizes them into probe people and vehicles based on Global Positioning Systems, mobile phone cellular networks, and Bluetooth, location-based social networks, and transit data with the focus on smart cards. For each data source, the operational mechanism of the technology for capturing the data is succinctly demonstrated. Secondly, as the most salient feature of this study, the transport-domain applications of each data source that have been conducted by the previous studies are reviewed and classified into the main groups. Possible research directions are provided for all types of data sources. Finally, authors briefly mention challenges and their corresponding solutions in smart transportation.

scholarly journals Data Analytics and Mining in Healthcare with Emphasis on Causal Relationship Mining

2019 ◽  
Vol 8 (4) ◽  
pp. 195-204
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Causal Relationship ◽  
Data Analytics ◽  
Statistical Tests ◽  
Analytical Techniques ◽  
Data Sources ◽  
Healthcare Personnel ◽  
Related Data ◽  
Healthcare Data

High volumes and varieties of data is piling every day from healthcare and related fields. This big data sources if managed and analysed properly will provide vital knowledge. Data mining and data analytics have been playing an important role in extracting useful information from healthcare and related data sources. The knowledge extracted from these data sources guiding patients and healthcare personnel towards improved health conditions. Analytical techniques from statistics, functionalities from data mining and machine learning already proved their capability with significant contributions to healthcare industry. The dominant functionality of data mining is classification which has been in use in mining healthcare data. Though classification is a good learning technique it may not provide a causation model which will be a reliable model for better decision making particularly in the medical field. The present models for causality have limitations in terms of scalability and reliability. The present study is targeted to study causal models for causal relationship mining. This study tried to conclude with some proposals for causal relationship discovery which are efficient, reliable and scalable. The proposed model is going to make use of some qualities of decision trees along with statistical tests and analytics. It is proposed to build the learning models on healthcare big data sources.

scholarly journals MultiSourcDSim: an integrated approach for exploring disease similarity

2019 ◽  
Vol 19 (S6) ◽  
Author(s):  
Lei Deng ◽  
Danyi Ye ◽  
Junmin Zhao ◽  
Jingpu Zhang
Keyword(s):  
Integrated Approach ◽  
Data Sources ◽  
Related Data ◽  
The Past ◽  
Multiple Data ◽  
Disease Similarity ◽  
Disease Associations ◽  
Novel Method ◽  
Similarity Networks ◽  
Few Data

Abstract Background A collection of disease-associated data contributes to study the association between diseases. Discovering closely related diseases plays a crucial role in revealing their common pathogenic mechanisms. This might further imply treatment that can be appropriated from one disease to another. During the past decades, a number of approaches for calculating disease similarity have been developed. However, most of them are designed to take advantage of single or few data sources, which results in their low accuracy. Methods In this paper, we propose a novel method, called MultiSourcDSim, to calculate disease similarity by integrating multiple data sources, namely, gene-disease associations, GO biological process-disease associations and symptom-disease associations. Firstly, we establish three disease similarity networks according to the three disease-related data sources respectively. Secondly, the representation of each node is obtained by integrating the three small disease similarity networks. In the end, the learned representations are applied to calculate the similarity between diseases. Results Our approach shows the best performance compared to the other three popular methods. Besides, the similarity network built by MultiSourcDSim suggests that our method can also uncover the latent relationships between diseases. Conclusions MultiSourcDSim is an efficient approach to predict similarity between diseases.

A graph-based approach for representing, integrating and analysing neuroscience data: the case of the murine basal ganglia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maren Parnas Gulnes ◽  
Ahmet Soylu ◽  
Dumitru Roman
Keyword(s):  
User Interface ◽  
Basal Ganglia ◽  
Ad Hoc ◽  
Graph Model ◽  
Third Party ◽  
Data Sources ◽  
Data Set ◽  
Content Type ◽  
Related Data ◽  
Neuroanatomical Data

PurposeNeuroscience data are spread across a variety of sources, typically provisioned through ad-hoc and non-standard approaches and formats and often have no connection to the related data sources. These make it difficult for researchers to understand, integrate and reuse brain-related data. The aim of this study is to show that a graph-based approach offers an effective mean for representing, analysing and accessing brain-related data, which is highly interconnected, evolving over time and often needed in combination.Design/methodology/approachThe authors present an approach for organising brain-related data in a graph model. The approach is exemplified in the case of a unique data set of quantitative neuroanatomical data about the murine basal ganglia––a group of nuclei in the brain essential for processing information related to movement. Specifically, the murine basal ganglia data set is modelled as a graph, integrated with relevant data from third-party repositories, published through a Web-based user interface and API, analysed from exploratory and confirmatory perspectives using popular graph algorithms to extract new insights.FindingsThe evaluation of the graph model and the results of the graph data analysis and usability study of the user interface suggest that graph-based data management in the neuroscience domain is a promising approach, since it enables integration of various disparate data sources and improves understanding and usability of data.Originality/valueThe study provides a practical and generic approach for representing, integrating, analysing and provisioning brain-related data and a set of software tools to support the proposed approach.

scholarly journals Making Use of Comparable Health Data to Improve Quality of Care and Outcomes in Diabetes: The EUBIROD Review of Diabetes Registries and Data Sources in Europe

2021 ◽  
Vol 2 ◽  
Author(s):  
Fabrizio Carinci ◽  
Iztok Štotl ◽  
Scott G. Cunningham ◽  
Tamara Poljicanin ◽  
Ivan Pristas ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Target Population ◽  
Population Based ◽  
Service Planning ◽  
Data Sources ◽  
Improve Quality ◽  
Related Data ◽  
Health Project ◽  
Quality And Outcomes

BackgroundRegistries and data sources contain information that can be used on an ongoing basis to improve quality of care and outcomes of people with diabetes. As a specific task of the EU Bridge Health project, we carried out a survey of diabetes-related data sources in Europe.ObjectivesWe aimed to report on the organization of different sources of diabetes information, including their governance, information infrastructure and dissemination strategies for quality control, service planning, public health, policy and research.MethodsSurvey using a structured questionnaire to collect targeted data from a network of collaborating institutions managing registries and data sources in 17 countries in the year 2017.ResultsThe 18 data sources participating in the study were most frequently academic centres (44.4%), national (72.2%), targeting all types of diabetes (61.1%) covering no more than 10% of the target population (44.4%). Although population-based in over a quarter of cases (27.8%), sources relied predominantly on provider-based datasets (38.5%), fewer using administrative data (16.6%). Data collection was continuous in the majority of cases (61.1%), but 50% could not perform data linkage. Public reports were more frequent (72.2%) as well as quality reports (77.8%), but one third did not provide feedback to policy and only half published ten or more peer reviewed papers during the last 5 years.ConclusionsThe heterogeneous implementation of diabetes registries and data sources hampers the comparability of quality and outcomes across Europe. Best practices exist but need to be shared more effectively to accelerate progress and deliver equitable results for people with diabetes.

scholarly journals Integrated Infodemic Surveillance System: The Case of COVID-19 in South Korea

2021 ◽  
Author(s):  
Gil-sung Park ◽  
Jintae Bae ◽  
Jong Hun Lee ◽  
Byung Yeon Yun ◽  
Byunghwee Lee ◽  
...  
Keyword(s):  
Social Media ◽  
Social Network ◽  
South Korea ◽  
Surveillance System ◽  
Data Sources ◽  
Management Policy ◽  
Multiple Sources ◽  
Related Data ◽  
Social Concerns ◽  
Different Sources

This study merges multiple COVID-19 data sources from news articles and social media to propose an integrated infodemic surveillance system (IISS) that implements infodemiology for a well-tailored epidemic management policy. IISS is an à-la-carte infodemic surveillance solution that enables users to gauge the epidemic related consensus, which compiles epidemic-related data from multiple sources and equipped with various methodological toolkits – topic modeling, Word2Vec, and social network analysis. IISS can provide reliable empirical evidence for proper policymaking. We demonstrate the heuristic utilities of IISS using empirical data from the first wave of COVID-19 in South Korea. Measuring discourse congruence allows us to gauge the distance between the discourse corpus from different sources, which can highlight consensus and conflicts in epidemic discourse. Furthermore, IISS detects discrepancies between social concerns and main actors.

scholarly journals Agricultural Sector Investment Need in Increasing Economic Growth

JEJAK ◽  
2017 ◽  
Vol 10 (2) ◽  
pp. 372-384
Author(s):  
Muhammad Nasir ◽  
Nurul Faizun ◽  
Mohd. Nur Syechalad
Keyword(s):  
Economic Growth ◽  
Agricultural Sector ◽  
Secondary Data ◽  
Data Sources ◽  
Research Results ◽  
Related Data ◽  
Aceh Province ◽  
Output Ratio ◽  
Secondary Data Sources ◽  
Regional Product

This paper is aimed to analyze the need of investment in agricultural sector in increasing economic growth in Aceh Province. The priority in developing agricultural commodities increases economic growth in Aceh Province. This research used secondary data sources from Indonesian Statistic Board (BPS) and other related data sources. Meanwhile, the research method used is Incremental Capital Output Ratio (ICOR) Analysis. Based on the research results, it is found that the ICOR in crop plantation, livestock, forestry, and fisheries sub sectors are 2.926, 0.000, 0.108, and 0.298. This means that in achieving economic growth by 1 percent in all four commodities, its need the growth of investment in crop plantation, livestock, forestry, and fisheries sub sectors as much as 2.926, 0.000, 0.108, and 0.298 percent each. Furthermore, in achieving 4 percent economic growths, the developing of commodities that have the effect on economic welfare, it needs the investment for the five years period as much as 286 billion rupiah or equivalent with 58.1 percent of Gross Domestic Regional Product (GDRP) of Aceh Province. Thus, based on the research results, it is recommended that the Aceh Government should promote investment in agricultural sector in promoting economic growth in Aceh Province.

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