Conceptual model of operational–analytical data marts for big data processing

Operational data marts that basically constitute slices of thematic narrowly-focused information are designed to provide operational access to big data sources due to consolidation and ranking of information resources based on their relevance. Unlike operational data marts dependent on the sources, analytical data marts are considered as independent data sources created by users to provide structuring of data for the tasks being solved. Therefore, the conceptual model of operational-analytical data marts allows combining the concepts of operational and analytical data marts to generate an analytical cluster that shall act as the basis for quick designing, development and implementation of data models.

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

Yearbook of Medical Informatics ◽

10.15265/iy-2014-0016 ◽

2014 ◽

Vol 23 (01) ◽

pp. 27-35 ◽

Cited By ~ 16

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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A Power Big Data Processing Method for Heterogeneous Data Sources

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/512/1/012156 ◽

2020 ◽

Vol 512 ◽

pp. 012156

Author(s):

Du Hongjun ◽

Li Wei ◽

Wang Ou ◽

Zhang Wenjie

Keyword(s):

Big Data ◽

Data Processing ◽

Heterogeneous Data ◽

Processing Method ◽

Data Sources ◽

Big Data Processing ◽

Heterogeneous Data Sources ◽

Data Processing Method

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Review of Big Data and Processing Frameworks for Disaster Response Applications

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090387 ◽

2019 ◽

Vol 8 (9) ◽

pp. 387 ◽

Cited By ~ 3

Author(s):

Silvino Pedro Cumbane ◽

Gyozo Gidófalvi

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Processing ◽

Disaster Management ◽

Disaster Response ◽

Human Life ◽

Streaming Data ◽

Data Sources ◽

Big Data Processing ◽

Processing Framework

Natural hazards result in devastating losses in human life, environmental assets and personal, and regional and national economies. The availability of different big data such as satellite imageries, Global Positioning System (GPS) traces, mobile Call Detail Records (CDRs), social media posts, etc., in conjunction with advances in data analytic techniques (e.g., data mining and big data processing, machine learning and deep learning) can facilitate the extraction of geospatial information that is critical for rapid and effective disaster response. However, disaster response systems development usually requires the integration of data from different sources (streaming data sources and data sources at rest) with different characteristics and types, which consequently have different processing needs. Deciding which processing framework to use for a specific big data to perform a given task is usually a challenge for researchers from the disaster management field. Therefore, this paper contributes in four aspects. Firstly, potential big data sources are described and characterized. Secondly, the big data processing frameworks are characterized and grouped based on the sources of data they handle. Then, a short description of each big data processing framework is provided and a comparison of processing frameworks in each group is carried out considering the main aspects such as computing cluster architecture, data flow, data processing model, fault-tolerance, scalability, latency, back-pressure mechanism, programming languages, and support for machine learning libraries, which are related to specific processing needs. Finally, a link between big data and processing frameworks is established, based on the processing provisioning for essential tasks in the response phase of disaster management.

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