Microservice-Based Platform for Space Situational Awareness Data Analytics

Lu, Wanjie; Xu, Qing; Lan, Chaozhen; Lyu, Liang; Zhou, Yang; Shi, Qunshan; Zhao, Y. H.

doi:10.1155/2020/8149034

Cited by 6 publications

(4 citation statements)

References 49 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We can observe many industrial applications of the data platforms, manufacturing systems, geospatial analytics, power grids, and eCommerce [12]- [15]. Enterprise data platforms have layers for data ingestion, storage, a data lake, and data consumption, including further processing and analytics applications.…”

Section: B Data Analytics Platformmentioning

confidence: 99%

“…The data consumption's uneasiness and usability are directly linked with the business value the entire data platform brings to the organization [17]. For instance, the authors of [12] describe a complete cloud-based data platform architected with microservices. It can operate on various data sources and has many data processing capabilities, e.g., batch processing, stream processing.…”

Section: B Data Analytics Platformmentioning

confidence: 99%

“…The objective of exposing data for cross-domain analysis to drive value becomes a challenge. Building a data-driven organization and successfully adopting artificial intelligence in software products and business decisions needs foundations in stable, findable, and accessible data products 12 .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Trip to The Enterprise Gourmet Data Product Marketplace through a Self-service Data Platform

Zasadzinski,

Theodoulou,

Thurner

et al. 2021

Preprint

View full text Add to dashboard Cite

Data Analytics provides core business reporting needs in many software companies, acts as a source of truth for key information, and enables building advanced solutions, e.g., predictive models, machine learning, real-time recommendations, to grow the business. Typically, companies ingest data to data lakes, have elaborate ETL/ELT processes, and consume the data. Organizations have challenges in becoming data-driven at scale, with centralized teams and modules acting as bottlenecks, responsible for many aspects such as data governance, ingestion, or engineering. Placing the tools in the hands of developers and empowering engineers across all business domains truly unlocks the ability for a company to become data-driven, at scale, both in culture and practice.A self-service, multi-tenant, API-first, and scalable data platform is the foundational requirement in creating an enterprise data marketplace, which enables the creation, publishing, and exchange of data products. Such a marketplace enables the exploration and discovery of data products, further providing highlevel data governance and oversight on marketplace contents.In this paper, we describe our way to the gourmet data product marketplace. We cover the design principles, the implementation details, technology choices, and the journey to build an enterprise data platform that meets the above characteristics. The platform consists of ingestion, streaming, storage, transformation, schema generation, fail-safe, data sharing, access management, PII data automatic identification, self-service storage optimization recommendations, and CI/CD integration. We then show how the platform enables and operates the data marketplace, facilitating the exchange of stable data products across users and tenants. We motivate and show how we run scalable decentralized data governance.All of this is built and run for Cimpress Technology (CT), which operates the Mass Customization Platform for Cimpress and its businesses. The CT data platform serves 1000s of users from different platform participants, with data sourced from heterogeneous sources. Data is ingested at a rate of well over 1000 individual messages per second and serves more than 100k analytical queries daily.

show abstract

Section: B Data Analytics Platformmentioning

confidence: 99%

Section: B Data Analytics Platformmentioning

confidence: 99%

See 1 more Smart Citation

The Trip to The Enterprise Gourmet Data Product Marketplace through a Self-service Data Platform

Zasadzinski,

Theodoulou,

Thurner

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, getting a reliable and precise orbital propagation model is very important to avoid such things recurring. As the number of space objects increases, the risk of conflict will indirectly increase [5], [6]. Figure 1 shows the trend of space objects in Earth Orbit according to the types of objects that increase each year, which require attention from all parties involved [7].…”

Section: Introductionmentioning

confidence: 99%

Modeling Orbital Propagation Using Regression Technique and Artificial Neural Network

Salleh¹,

Yuhaniz²,

Azmi³

2022

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

Orbital propagation models are used to predict the position and velocity of natural and artificial objects orbiting the Earth. It is crucial to get accurate predictions to ensure proper satellite operational planning and early detection of possible disasters. It became critical as the number of space objects grew due to many countries scrambling to explore space for various purposes such as communications, remote sensing, scientific mission, and many more. Physical-based and mathematical expression approaches provide orbital propagation with high accuracy. However, these approaches require substantial expenditure to provide suitable facilities and are complicated for those with no expertise in this field. The orbital propagation model is developed using regression techniques and artificial neural networks in this study. The aim is to have a reliable and precise orbital propagation model with minimal computational and cost savings. The past orbital data is used instead of complicated numerical equations and expensive tools. As a result, the trained orbital propagation model with accuracy up to 99.49% with a distance error of 18.73km per minute is achievable. The trained model can be improved further by modifying the network model and various input data. This model is also expected to provide vital information for organizations and anyone interested. Finally, this research can help organizations with insufficient resources to have their orbit propagation model without special tools or rely on other countries with satellite data at a lower cost.

show abstract

Pipeline risk big data intelligent decision-making system based on machine learning and situation awareness

Zhong

Zhang

2022

Neural Comput & Applic

View full text Add to dashboard Cite

Microservice-Based Platform for Space Situational Awareness Data Analytics

Cited by 6 publications

References 49 publications

The Trip to The Enterprise Gourmet Data Product Marketplace through a Self-service Data Platform

The Trip to The Enterprise Gourmet Data Product Marketplace through a Self-service Data Platform

Modeling Orbital Propagation Using Regression Technique and Artificial Neural Network

Pipeline risk big data intelligent decision-making system based on machine learning and situation awareness

Contact Info

Product

Resources

About