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6 results for “data driven architecture”
From a Monolithic Big Data System to a Microservices Event-Driven Architecture
<p>[Context] Data-intensive systems, a.k.a. big data systems (BDS), are software systems that handle a large volume of data in the presence of performance quality attributes, such as scalability and availability. Before the advent of big data management systems (e.g. Cassandra) and frameworks (e.g. Spark), organizations had to cope with large data volumes with custom-tailored solutions. In particular, a decade ago, Tecgraf/PUC-Rio developed a system to monitor truck fleet in real-time and proactively detect events from the positioning data received. Over the years, the system evolved into a complex and large obsolescent code base involving a hard maintenance process. [Goal] We report our experience on replacing a legacy BDS with a microservice-based event-driven system. [Method] We applied action research, investigating the reasons that motivate the adoption of a microservice-based event-driven architecture, intervening to define the new architecture, and documenting the challenges and lessons learned. [Results] We perceived that the resulting architecture enabled easier maintenance and fault-isolation. However, the myriad of technologies and the complex data flow were perceived as drawbacks. Based on the challenges faced, we highlight opportunities to improve the design of big data reactive systems. [Conclusions] We believe that our experience provides helpful takeaways for practitioners modernizing systems with data-intensive requirements.</p>
Figure 4. Portal Logical Architecture-A Proposed Data Driven Architecture for Cardiology Network Application
<p>The applications that are based on a SOA environment can be configured to work both<br> as services exchanging data in point to point protocol and as a communication channel<br> between two entities that call a service provided by an entity that has the role of mediator or<br> broker. The letter can be seen as intermediary level that represents an “enterprise service bus”<br> (ESB) between service producers and consumers, offering messaging exchange services.</p>
Figure 2. System General View-A Proposed Data Driven Architecture for Cardiology Network Application
<p>Figure 2 presents a general schematic overview of the medical informational system,<br> underlying the main roles in the system together with their interactions. From an architectural<br> point of view one can identify the following main components:<br> • Host systems, named local (medical data) production systems<br> • General Practitioner system<br> • Analysis Laboratory system<br> • Hospital system<br> • Client system (interface to remote devices)<br> • Local portal (regional, national) for medical assistance, and long term data storage<br> Host systems consist of database servers connected through access points with<br> fixed/mobile medical devices. The portal will supply the functionality necessary to collect<br> information from the local interconnected systems into a centralized repository. Medical<br> services will be provided at portal level using specialized web-services that will allow access<br> to data stored in the repository.</p>
Figure 1. Schema for a data-integration solution-A Proposed Data Driven Architecture for Cardiology Network Application
<p>Data integration has favored loosening the coupling between data. This may involve<br> providing a uniform query interface over a mediated schema (see figure 1), thus transforming<br> a query into specialized queries over the original databases. One can also term this process<br> "view-based query-answering" because each of the data sources functions as a view over the<br> (nonexistent) mediated schema.</p>
Figure 3. Data Integration Sample-A Proposed Data Driven Architecture for Cardiology Network Application
<p>Pentaho Data Integration has implemented a metadata-driven approach where you<br> only specify the data you want integrated, but you do not specify the way you want it done.<br> One of the most important advantages of Pentaho is that one can create complex<br> transformations and jobs in a graphical, drag-and-drop environment without having to create<br> proprietary custom code that will work only with some proprietary application.</p>
LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling
<p>This repository contains LaMEM source code and input files for the models presented in Kumar, A., Cacace, M., Scheck-Wenderoth, M., Götze, H.-J., & Kaus, B. J. P. (2022). Present-day upper-mantle architecture of the Alps: Insights from data-driven dynamic modeling. Geophysical Research Letters, 49, e2022GL099476. https://doi. org/10.1029/2022GL099476</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.