The evolution of data architecture has seen the rise of data lakes, aiming to solve the bottlenecks of data management and promote intelligent decision-making. However, this centralized architecture is limited by the proliferation of data sources and the growing demand for timely analysis and processing. A new data paradigm, Data Mesh, is proposed to overcome these challenges. In this decentralized architecture where data is locally preserved by each domain team, traditional centralized machine learning cannot conduct effective analysis across multiple domains, especially for security-sensitive organizations. To this end, we introduce a pioneering approach that incorporates Federated Learning into Data Mesh. This applied research article emphasizes the benefits of combining two distinct domains to achieve the best outcomes for industrial use cases.

With the rapid development of big data and cloud computing, data management has become increasingly challenging. A possible solution is to use an intelligent hierarchical (multi-tier) storage system (HSS). An HSS is a meta solution that consists of different storage frameworks organized as a jointly constructed storage pool. A built-in data migration policy that determines the optimal placement of the datasets in the hierarchy is essential. Placement decisions are a non-trivial task since they should be made according to the characteristics of the dataset, the tier status in a hierarchy, and access patterns. This paper presents an open-source hierarchical storage framework with a dynamic migration policy based on reinforcement learning (RL).

With the rapid development of big data and cloud computing, data management has become increasingly challenging. Over the years, a number of frameworks for data management have become available. Most of them are highly efficient, but ultimately create data silos. It becomes difficult to move and work coherently with data as new requirements emerge. A possible solution is to use an intelligent hierarchical (multi-tier) storage system (HSS). A HSS is a meta solution that consists of different storage frameworks organized as a jointly constructed storage pool. A built-in data migration policy that determines the optimal placement of the datasets in the hierarchy is essential. Placement decisions is a non-trivial task since it should be made according to the characteristics of the dataset, the tier status in a hierarchy, and access patterns. This paper presents an open-source hierarchical storage framework with a dynamic migration policy based on reinforcement learning (RL). We present a mathematical model, a software architecture, and implementations based on both simulations and a live cloud-based environment. We compare the proposed RL-based strategy to a baseline of three rule-based policies, showing that the RL-based policy achieves significantly higher efficiency and optimal data distribution in different scenarios.

Software performance testing requires a set of inputs that exercise different sections of the code to identify performance issues. However, running tests on a large set of inputs can be a very time consuming process. It is even more problematic when test inputs are constantly growing, which is the case with a large-scale scientific organization such as CERN where the process of performing scientific experiment generates plethora of data that is analyzed by physicists leading to new scientific discoveries. Therefore, in this article, we present a test input minimization approach based on a clustering technique to handle the issue of testing on growing data. Furthermore, we use clustering information to propose an automatic approach that recommends the tester to decide when to run the complete test suite for performance testing. To demonstrate the efficacy of our approach, we applied it to two different code updates of a web service which is used at CERN and we found that the recommendation for performance test initiation made by our approach for an update with bottleneck is valid.

Data stream processing frameworks provide reliable and efficient mechanisms for executing complex workflows over large datasets. A common challenge for the majority of currently available streaming frameworks is efficient utilization of resources. Most frameworks use static or semi-static settings for resource utilization that work well for established use cases but lead to marginal improvements for unseen scenarios. Another pressing issue is the efficient processing of large individual objects such as images and matrices typical for scientific datasets. HarmonicIO has proven to be a good solution for streams of relatively large individual objects, as demonstrated in a benchmark comparison with the Apache Spark and Kafka streaming frameworks. We here present an extension of the HarmonicIO framework based on the online bin-packing algorithm. The main focus is to compare different strategies adapted in streaming frameworks for efficient resource utilization. Based on a real world use case from large-scale microscopy pipelines, we compare two different strategies of auto-scaling implemented in the HarmonicIO and Spark Streaming frameworks.