Multi-domain and Sub-role Oriented Software Architecture for Managing Scientific Big Data

2019 ◽  
pp. 111-122 ◽  
Author(s):  
Qi Sun ◽  
Yue Liu ◽  
Wenjie Tian ◽  
Yike Guo ◽  
Jiawei Lu
Keyword(s):  
Big Data ◽  
Software Architecture

A Distributed and Scalable Solution for Applying Semantic Techniques to Big Data

Big Data ◽  
2016 ◽  
pp. 1091-1109 ◽  
Author(s):  
Alba Amato ◽  
Salvatore Venticinque ◽  
Beniamino Di Martino
Keyword(s):  
Big Data ◽  
Cultural Heritage ◽  
Software Architecture ◽  
Application Domain ◽  
Proof Of Concept ◽  
Functional Requirements ◽  
Original Formulation ◽  
Digital Revolution ◽  
Big Data Technologies ◽  
Semantic Techniques

The digital revolution changes the way culture and places could be lived. It allows users to interact with the environment creating an immense availability of data, which can be used to better understand the behavior of visitors, as well as to learn about their thoughts on what the visit creates excitement or disappointment. In this context, Big Data becomes immensely important, making possible to turn this amount of data in information, knowledge, and, ultimately, wisdom. This paper aims at modeling and designing a scalable solution that integrates semantic techniques with Cloud and Big Data technologies to deliver context aware services in the application domain of the cultural heritage. The authors started from a baseline framework that originally was not conceived to scale when huge workloads, related to big data, must be processed. They provide an original formulation of the problem and an original software architecture that fulfills both functional and not-functional requirements. The authors present the technological stack and the implementation of a proof of concept.

scholarly journals Towards Specification of a Software Architecture for Cross-Sectoral Big Data Applications

2019 ◽  
Author(s):  
Ioannis Arapakis ◽  
Yolanda Becerra ◽  
Omer Boehm ◽  
George Bravos ◽  
Vassilis Chatzigiannakis ◽  
...  
Keyword(s):  
Big Data ◽  
Software Architecture ◽  
Big Data Applications

A Distributed and Scalable Solution for Applying Semantic Techniques to Big Data

2014 ◽  
Vol 6 (2) ◽  
pp. 50-67
Author(s):  
Alba Amato ◽  
Salvatore Venticinque ◽  
Beniamino Di Martino
Keyword(s):  
Big Data ◽  
Software Architecture ◽  
Context Aware ◽  
Application Domain ◽  
Proof Of Concept ◽  
Functional Requirements ◽  
Original Formulation ◽  
Digital Revolution ◽  
Big Data Technologies ◽  
Semantic Techniques

The digital revolution changes the way culture and places could be lived. It allows users to interact with the environment creating an immense availability of data, which can be used to better understand the behavior of visitors, as well as to learn about their thoughts on what the visit creates excitement or disappointment. In this context, Big Data becomes immensely important, making possible to turn this amount of data in information, knowledge, and, ultimately, wisdom. This paper aims at modeling and designing a scalable solution that integrates semantic techniques with Cloud and Big Data technologies to deliver context aware services in the application domain of the cultural heritage. The authors started from a baseline framework that originally was not conceived to scale when huge workloads, related to big data, must be processed. They provide an original formulation of the problem and an original software architecture that fulfills both functional and not-functional requirements. The authors present the technological stack and the implementation of a proof of concept.

Software Architecture Patterns in Big Data

2020 ◽  
pp. 90-110
Author(s):  
Serkan Ayvaz ◽  
Yucel Batu Salman
Keyword(s):  
Big Data ◽  
Software Architecture ◽  
Software Systems ◽  
Software Components ◽  
Entire System ◽  
Service Oriented Architectures ◽  
Tightly Coupled ◽  
Service Oriented

Traditional monolithic systems are composed of software components that are tightly coupled and composed into one unit. Monolithic systems have scalability issues as all components of the entire system need to be compiled and deployed even for simple modifications. In this chapter, the evolution of the software systems used in big data from monolithic systems to service-oriented architectures was explored. More specifically, the challenges and strengths of implementing service-oriented architectures of microservices and serverless computing were investigated in detail. Moreover, the advantages of migrating to service-oriented architectures and the patterns of migration were discussed.

scholarly journals Big data solutions for CMS computing monitoring and analytics

2020 ◽  
Vol 245 ◽  
pp. 03022
Author(s):  
Christian Ariza-Porras ◽  
Valentin Kuznetsov ◽  
Federica Legger
Keyword(s):  
Big Data ◽  
Software Architecture ◽  
Open Source ◽  
Performance Studies ◽  
Web Applications ◽  
Predictive Analytics ◽  
Data Sources ◽  
It Infrastructure ◽  
Monitoring Applications ◽  
Computing Infrastructure

The CMS computing infrastructure is composed of several subsystems that accomplish complex tasks such as workload and data management, transfers, submission of user and centrally managed production requests. Till recently, most subsystems were monitored through custom tools and web applications, and logging information was scattered over several sources and typically accessible only by experts. In the last year, CMS computing fostered the adoption of common big data solutions based on open-source, scalable, and no-SQL tools, such as Hadoop, InfluxDB, and ElasticSearch, available through the CERN IT infrastructure. Such systems allow for the easy deployment of monitoring and accounting applications using visualisation tools such as Kibana and Grafana. Alarms can be raised when anomalous conditions in the monitoring data are met, and the relevant teams are automatically notified. Data sources from different subsystems are used to build complex workflows and predictive analytics (such as data popularity, smart caching, transfer latency), and for performance studies. We describe the full software architecture and data flow, the CMS computing data sources and monitoring applications, and show how the stored data can be used to gain insights into the various subsystems by exploiting scalable solutions based on Spark.

scholarly journals Software Architecture of Fogcloud Computing for Big Data in Ubiquitous Cyberspace

2019 ◽  
Vol 21 (6) ◽  
pp. 114
Author(s):  
Yan Jia ◽  
Binxing Fang ◽  
Xiang Wang ◽  
Yongheng Wang ◽  
Jingbin An ◽  
...  
Keyword(s):  
Big Data ◽  
Software Architecture
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