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On March 25th 2019, the Belle II detector recorded the first collisions delivered by the SuperKEKB accelerator. This marked the beginning of the physics run with vertex detector. The vertex detector was aligned initially with cosmic ray tracks without magnetic field simultaneously with the drift chamber. The alignment method is based on Millepede II and the General Broken Lines track model and includes also the muon system or primary vertex position alignment. To control weak modes, we employ sensitive validation tools and various track samples can be used as alignment input, from straight cosmic tracks to mass-constrained decays. With increasing luminosity and experience, the alignment is approaching the target performance, crucial for the first physics analyses in the era of Super-BFactories. We will present the software framework for the detector calibration and alignment, the results from the first physics run and the prospects in view of the experience with the first data.

The Event Index service of the ATLAS experiment at the LHC keeps references to all real and simulated events. Hadoop Map files and HBase tables are used to store the Event Index data, a subset of data is also stored in the Oracle database. Several user interfaces are currently used to access and search the data, from a simple command line interface, through a programmable API, to sophisticated graphical web services. It provides a dynamic graph-like overview of all available data (and data collections). Data are shown together with their relations, like paternity or overlaps. Each data entity then gives users a set of actions available for the referenced data. Some actions are provided directly by the Event Index system, others are just interfaces to different ATLAS services. In many cases, specialized views are offered for detailed data inspection, such as histograms, Venn diagrams, etc. This paper documents the current status of the service, its features and performance. The future system evolution to the new Event Index architecture based on the Apache Phoenix is also described as well as possible extension to a more general framework for giving a new, more intuitive access to experiment data.

The Belle II experiment features a substantial upgrade of the Belle detector and will operate at the SuperKEKB energy-asymmetric e+e− collider at KEK in Tsukuba, Japan. The accelerator completed its first phase of commissioning in 2016, and the Belle II detector saw its first electron-positron collisions in April 2018. Belle II features a newly designed silicon vertex detector based on double-sided strip layers and DEPFET pixel layers. A subset of the vertex detector was operated in 2018 to determine background conditions (Phase 2 operation). The collaboration completed full detector installation in January 2019, and the experiment started full data taking. This paper will report on the final arrangement of the silicon vertex detector part of Belle II with a focus on online monitoring of detector conditions and data quality, on the design and use of diagnostic and reference plots, and on integration with the software framework of Belle II. Data quality monitoring plots will be discussed with a focus on simulation and acquired cosmic and collision data.

The ATLAS EventIndex was designed in 2012-2013 to provide a global event catalogue and limited event-level metadata for ATLAS analysis groups and users during the LHC Run 2 (2015-2018). It provides a good and reliable service for the initial use cases (mainly event picking) and several additional ones, such as production consistency checks, duplicate event detection and measurements of the overlaps of trigger chains and derivation datasets. The LHC Run 3, starting in 2021, will see increased data-taking and simulation production rates, with which the current infrastructure would still cope but may be stretched to its limits by the end of Run 3. This proceeding describes the implementation of a new core storage service that will be able to provide at least the same functionality as the current one for increased data ingestion and search rates, and with increasing volumes of stored data. It is based on a set of HBase tables, with schemas derived from the current Oracle implementation, coupled to Apache Phoenix for data access; in this way we will add to the advantages of a BigData based storage system the possibility of SQL as well as NoSQL data access, allowing to re-use most of the existing code for metadata integration.

The ATLAS Distributed Computing system uses the Frontier system to access the Conditions, Trigger, and Geometry database data stored in the Oracle Offline Database at CERN by means of the HTTP protocol. All ATLAS computing sites use Squid web proxies to cache the data, greatly reducing the load on the Frontier servers and the databases. One feature of the Frontier client is that in the event of failure, it retries with different services. While this allows transient errors and scheduled maintenance to happen transparently, it does open the system up to cascading failures if the load is high enough. Throughout LHC Run 2 there has been an ever increasing demand on the Frontier service. There have been multiple incidents where parts of the service failed due to high load. A significant improvement in the monitoring of the Frontier service wasrequired. The monitoring was needed to identify both problematic tasks, which could then be killed or throttled, and to identify failing site services as the consequence of a cascading failure is much higher. This presentation describes the implementation and features of the monitoring system.

The ATLAS EventIndex currently runs in production in order to build a complete catalogue of events for experiments with large amounts of data. The current approach is to index all final produced data files at CERN Tier0, and at hundreds of grid sites, with a distributed data collection architecture using Object Stores to temporarily maintain the conveyed information, with references to them sent with a Messaging System. The final backend of all the indexed data is a central Hadoop infrastructure at CERN; an Oracle relational database is used for faster access to a subset of this information. In the future of ATLAS, instead of files, the event should be the atomic information unit for metadata, in order to accommodate future data processing and storage technologies. Files will no longer be static quantities, possibly dynamically aggregating data, and also allowing event-level granularity processing in heavily parallel computing environments. It also simplifies the handling of loss and or extension of data. In this sense the EventIndex may evolve towards a generalized whiteboard, with the ability to build collections and virtual datasets for end users. This proceedings describes the current Distributed Data Collection Architecture of the ATLAS EventIndex project, with details of the Producer, Consumer and Supervisor entities, and the protocol and information temporarily stored in the ObjectStore. It also shows the data flow rates and performance achieved since the new Object Store as temporary store approach was put in production in July 2017. We review the challenges imposed by the expected increasing rates that will reach 35 billion new real events per year in Run 3, and 100 billion new real events per year in Run 4. For simulated events the numbers are even higher, with 100 billion events/year in run 3, and 300 billion events/year in run 4. We also outline the challenges we face in order to accommodate future use cases in the EventIndex.

The ATLAS EventIndex has been in operation since the beginning of LHC Run 2 in 2015. Like all software projects, its components have been constantly evolving and improving in performance. The main data store in Hadoop, based on MapFiles and HBase, can work for the rest of Run 2 but new solutions are explored for the future. Kudu offers an interesting environment, with a mixture of BigData and relational database features, which look promising at the design level. This environment is used to build a prototype to measure the scaling capabilities as functions of data input rates, total data volumes and data query and retrieval rates. In this proceedings we report on the selected data schemas and on the current performance measurements with the Kudu prototype.

The ATLAS Eventlndex is the global catalogue of all ATLAS real and simulated events. During the LHC long shutdown between Run 2 (20152018) and Run 3 (2022-2025) all its components were substantially revised and a new system was deployed for the start of Run 3 in Spring 2022. The new core storage system, based on HBase tables with a SQL interface provided by Phoenix, allows much faster data ingestion rates and scales much better than the old one to the data rates expected for the end of Run 3 and beyond. All user interfaces were also revised and a new command-line interface and web services were also deployed. The new system was initially populated with all existing data relative to Run 1 and Run 2 datasets, and then put online to receive Run 3 data in real time. After extensive testing, the old system, which ran in parallel to the new one for a few months, was finally switched off in October 2022. This paper describes the new system, the move of all existing data from the old to the new storage schemas and the operational experience gathered so far.

The ATLAS EventIndex is the global catalogue of all ATLAS real and simulated events. During the LHC long shutdown between Run 2 (20152018) and Run 3 (2022-2025) its components were substantially revised, and a new system was deployed for the start of Run 3 in Spring 2022. The new core storage system is based on HBase tables with a Phoenix interface. It allows faster data ingestion rates and scales better than the old system. This paper describes the data collection, the technical design of the core storage, and the properties that make it fast and efficient, namely the compact and optimized design of the events table, which already holds more than 400 billion entries, and all the auxiliary tables, and the EventIndex Supervisor, in charge of orchestrating the whole data collection, now simplified thanks to the Loaders, the Spark jobs that load the data into the new core system. The extractors, in charge of preparing the pieces of data that the loaders will put into the final back-end, have been updated too. The data migration from HDFS to HBase and Phoenix is also described.

The ATLAS Spanish Tier-1 and Tier-2s have more than 18 years of experience in the deployment and development of LHC computing components and their successful operation. The sites are actively participating in, and in some cases coordinating, R&D computing activities in the LHC Run 3 and developing the computing models needed in the HL-LHC period. In this contribution, we present details on the integration of some components, such as HPC computing resources to execute ATLAS simulation workflows; the development of new techniques to improve efficiency in a cost-effective way; and improvements in Data Organization, Management and Access through storage consolidations, the use of data caches, and improving experiment data catalogues, through contributions such as Event Index. The design and deployment of novel analysis facilities using GPUs together with CPUs and techniques like Machine Learning are also presented. ATLAS Tier-1 and Tier-2 sites in Spain, are, and will be, contributing to significant R&D in computing and evaluating different models for improving performance of computing and data storage capacity in the LHC High Luminosity era.

In the context of smart manufacturing, the concept of Visual Computing is a key enabling technology for Industry 4.0. Visual Computing and Physically-based simulation enables the implementation of interactive, visual and (in most cases) non-disruptive solutions within the context of (a) the production process (off-line or in-line) the shop floor, (b) the interaction with customers and clients in the whole value chain, and/or (c) the product-service schemas. Technologies such as Industrial Big Data, Human-Robot Collaboration, IIOT & 5G, Cybersecurity, Cyber-Physical Systems, Semantic Technologies, Visual Computing and other related technologies have a strong impact in the new ways smart manufacturing is reconfiguring the production. This article presents a theoretical framework for the incorporation of Visual Computing and Physically-based Simulation technologies to Industry 4.0 and Operator 4.0 scenarios. This framework has been already implemented and supports different smart manufacturing scenarios, already running in many SME. Three practical study case scenarios (already deployed into SMEs) are presented and discussed.