Infrastructure construction, often dubbed an "industry of industries," is closely linked with government spending and public procurement, offering significant opportunities for improved efficiency and productivity through better transparency and information access. By leveraging these opportunities, we can achieve notable gains in productivity, cost savings, and broader economic benefits. Our approach introduces an integrated software ecosystem utilizing Data Mesh and Service Mesh architectures. This system includes the largest training dataset for infrastructure and procurement, encompassing over 100 billion tokens, scientific publications, activities, and risk data, all structured by a systematic AI framework. Supported by a Knowledge Graph linked to domain-specific multi-agent tasks and Q&A capabilities, our platform standardizes and ingests diverse data sources, transforming them into structured knowledge. Leveraging large language models (LLMs) and automation, our system revolutionizes data structuring and knowledge creation, aiding decision-making in early-stage project planning, detailed research, market trend analysis, and qualitative assessments. Its web-scalable architecture delivers domain-curated information, enabling AI agents to facilitate reasoning and manage uncertainties, while preparing for future expansions with specialized agents targeting particular challenges. This integration of AI with domain expertise not only boosts efficiency and decision-making in construction and infrastructure but also establishes a framework for enhancing government efficiency and accelerating the transition of traditional industries to digital workflows. This work is poised to significantly influence AI-driven initiatives in this sector and guide best practices in AI Operations.

Entities handling extensive and complex data environments increasingly adopt the data mesh paradigm across all sectors. Data mesh is an architectural and organizational governance approach that treats data as a product, promoting domain-specific ownership and self-serve infrastructure.11 It encourages domain teams to manage their data, with standardized metadata, governance, and a services layer for accessibility, which reduces centralization bottlenecks and improves data scalability and usability across complex organizations. In the commercial sector, multinational technology companies value data mesh for domain-oriented governance and decentralized structure. Often managing large, heterogeneous data, they benefit from enhanced scalability and domain-specific data management.

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. Data Mesh treats domains as a first-class concern by distributing the data ownership from the central team to each data domain, while keeping the federated governance to monitor domains and their data products. Many multi-million dollar organizations like Paypal, Netflix, and Zalando have already transformed their data analysis pipelines based on this new architecture. In this decentralized architecture where data is locally preserved by each domain team, traditional centralized machine learning is incapable of conducting 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. To the best of our knowledge, this is the first open-source applied work that represents a critical advancement toward the integration of federated learning methods into the Data Mesh paradigm, underscoring the promising prospects for privacy-preserving and decentralized data analysis strategies within Data Mesh architecture.

With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised value associated with them. Data mesh is a socio-technical, decentralized, distributed concept for enterprise data management. As the concept of data mesh is still novel, it lacks empirical insights from the field. Specifically, an understanding of the motivational factors for introducing data mesh, the associated challenges, implementation strategies, its business impact, and potential archetypes is missing. To address this gap, we conduct 15 semi-structured interviews with industry experts. Our results show, among other insights, that organizations have difficulties with the transition toward federated governance associated with the data mesh concept, the shift of responsibility for the development, provision, and maintenance of data products, and the comprehension of the overall concept. In our work, we derive multiple implementation strategies and suggest organizations introduce a cross-domain steering unit, observe the data product usage, create quick wins in the early phases, and favor small dedicated teams that prioritize data products. While we acknowledge that organizations need to apply implementation strategies according to their individual needs, we also deduct two archetypes that provide suggestions in more detail. Our findings synthesize insights from industry experts and provide researchers and professionals with preliminary guidelines for the successful adoption of data mesh.

Data mesh is a socio-technical approach to decentralized analytics data management. To manage this decentralization efficiently, data mesh relies on automation provided by a self-service data infrastructure platform. A key aspect of this platform is to enable decentralized data governance. Because data mesh is a young approach, there is a lack of coherence in how data mesh concepts are interpreted in the industry, and almost no work on how a data mesh platform facilitates governance. This paper presents a conceptual model of key data mesh concepts and discusses different approaches to drive governance through platform means. The insights presented are drawn from concrete experiences of implementing a fully-functional data mesh platform that can be used as a reference on how to approach data mesh platform development.

We present an unsupervised algorithm for registering 3D surface scans of an object undergoing significant deformations. Our algorithm does not need markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or scan alignment. This model enforces preservation of local mesh geometry, as well as more global constraints that capture the preservation of geodesic distance between corresponding point pairs. The algorithm applies even when one of the meshes is an incomplete range scan; thus, it can be used to automatically fill in the remaining sur- faces for this partial scan, even if those surfaces were previously only seen in a different configuration. We evaluate the algorithm on several real-world datasets, where we demonstrate good results in the presence of significant movement of articulated parts and non-rigid surface defor- mation. Finally, we show that the output of the algorithm can be used for compelling computer graphics tasks such as interpolation between two scans of a non-rigid object and automatic recovery of articulated object models. The construction of 3D object models is a key task for many graphics applications.

As we look back on 2022, it's been exciting to see the rapid advancements in data, analytics and AI that have helped shape the way organizations operate. It was a turning point for many businesses as they began to realize the true potential of data-driven insights and the power of AI to drive innovation. As we head into 2023, we can expect even more breakthroughs and developments that will change the way companies leverage data and analytics to gain a competitive edge. Let's take a closer look at my predictions for the coming year and explore how organizations can prepare for the future of data, analytics and AI. Expect to see more M&A activity in 2023 as vendors look to tell more unified, end-to-end and comprehensive stories.

In web 3.0, the dynamics of not just the internet but the data streams are undergoing a decentralized transformation. As a first step, thanks to distributed data governance every domain can now manage and govern its data products but at the same time, it also relies on central control of security policies, data modelling, and compliance. Data mesh distributes data across physical and virtual networks in a decentralized manner. Unlike conventional data integration tools that call for a highly centralized infrastructure, a data mesh instead works across on-premise, multi and single-cloud, edge environments. As per the findings from MIT, only 13% of surveyed organizations could successfully deliver as per their data strategy.

Vendors, consultants, and their clients have been talking in data fabric terms for close to a decade now, if not longer. If "big data" was the problem to solve, then a data fabric suggested a ready solution. John Mashey, then chief scientist at Silicon Graphics, used the term "big data" to describe the wave of large, less structured datasets and its impact on infrastructure in a slide deck in 1998. Apache Hadoop gained popularity after an engineer at the New York Times wrote a blog post in 2009 about automating a PDF integration task using Hadoop. The term "data lake" came into vogue in the early 2010s to describe an informal means of making data of various kinds accessible to analyst teams.

Data fabric is expected to be a key focus area for organisations this year, as they look to optimise the value of their data. This is a logical progression, as organisations spread across regional boundaries and increasingly need to integrate external data into their planning and forecasting, and seek to automate ingestion, integration and exploration, embed governance, and enable self-service across the enterprise. Data fabrics add a semantic layer to data lakes, making the vast volumes of data spread across a complex ecosystem of devices, applications and data infrastructure more readily available for consumption and reducing time to delivery. Unlike data mesh, which connects data on the fly and plugs in various functionalities, a data fabric is architecturally in place – with data interlinked, partitioned and served up off a platform. Data warehouses and data lakes are becoming limited in terms of functionality – they have become too large and may not have all the data the organisation needs, including data from external sources such as weather patterns or social media behaviour.