Backward Compatibility Research Articles

Optimizing Cloud Data Storage: Evaluating File Formats for Efficient Data Warehousing

This paper presents a detailed analysis of three widely-used data storage formats—Parquet, Avro, and ORC— evaluating their performance across key metrics such as query execution, compression efficiency, data skipping, schema evolution, and throughput. Each format offers distinct advantages depending on the nature of the workload. Parquet is optimized for read-heavy analytical queries, providing excellent compression and efficient query performance through its columnar structure. Avro excels in write-heavy, real-time data streaming scenarios, where schema flexibility and backward compatibility are crucial. ORC balances the two, offering strong support for analytical and transactional workloads, especially in handling complex queries and nested data structures. This comparative study highlights the contexts in which each format performs best, providing valuable insights into the trade-offs associated with their use in cloud data warehouses and large-scale data processing environments.

Rating pome fruit quality traits using deep learning and image processing.

Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit-quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individual raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse-grained and lack consistent objective criteria. Granny, is a tool designed for rating fruit using machine-learning and image-processing to address rater bias and improve resolution. Additionally, Granny supports backward compatibility by providing ratings compatible with long-established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Granny's improved resolution and reduced bias, such as linking fruit outcomes to global scale -omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open-source and freely available.

Development of backward compatible physics-informed neural networks to reduce error accumulation based on a nested framework

Physical information neural network (PINN) provides an effective method for solving partial differential equations, and many variants have been derived, the most representative of which is backward compatible physical information neural network (BC-PINN). The core of BC-PINN is to use the prediction of the previous time period as the label data of the current time period, which leads to error accumulation in the process of backward compatibility. To solve this problem, a nested backward compatible physical information neural network (NBC-PINN) is proposed in this paper. NBC-PINN has an overlap region between the computation domain of the previous time period and the computation domain of the current time period, which is trained twice in total. Numerical experiments on four representative time-varying partial differential equations show that NBC-PINN can effectively reduce error accumulation, improve computational efficiency and accuracy, and improve the L2 relative error of the numerical solution with fewer residual allocation points. The development of NBC-PINN provides a theoretical basis for the scientific calculation of partial differential equations, and promotes the progress of PINN to a certain extent.

Architectural Approaches to Migrating Key Features in Android Apps

The rapid evolution of Android platforms and user expectations necessitates continual enhancement and migration of key features within Android applications. This paper explores various architectural approaches to effectively migrate essential functionalities in Android apps, ensuring scalability, maintainability, and optimal user experience. Initially, the study delineates the challenges inherent in feature migration, including compatibility issues, performance constraints, and the preservation of existing user interfaces. It then examines prominent architectural paradigms such as Model-View-View Model (MVVM), Clean Architecture, and modular architectures, evaluating their suitability for facilitating seamless feature transitions. The research further investigates the role of microservices and component-based designs in enabling incremental migration, allowing developers to update or replace specific modules without overhauling the entire application. Additionally, the paper discusses best practices for managing dependencies, ensuring backward compatibility, and employing automated testing to mitigate risks during the migration process. Case studies of successful Android app migrations are presented to illustrate the practical application of these architectural strategies. The findings highlight that adopting a modular and flexible architectural framework significantly enhances the ability to migrate key features efficiently while minimizing disruptions. Moreover, the integration of robust architectural patterns fosters better code maintainability and accelerates the development cycle for future updates. In conclusion, this study provides a comprehensive guide for Android developers aiming to navigate the complexities of feature migration, advocating for architectural approaches that balance innovation with stability. The insights garnered offer valuable implications for the design and evolution of resilient Android applications in a dynamic technological landscape.

Reformatted Crosswalk-Tables Between Annual ICD-10-Versions for Cross Version Data Analysis.

The International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) is internationally used for coding diagnoses, with the ICD-10 German Modification (GM) being prescribed for morbidity coding in Germany. ICD-10-GM is subject to annual revisions. This can lead to backward compatibility issues leading to undesirable consequences for cross-version data analysis. A study of annual crosswalk-tables concerning 21 ICD-10-GM versions showed that the ratio of difficult transitions from an older to a newer version (0.89 %) and vice versa (0.48 %) is not particularly significant but should nevertheless not be neglected. In this paper we present two solutions (Neo4J database and FHIR ConceptMaps) for the automated handling of different ICD-10-GM versions.

FILO: Automated FIx-LOcus Identification for Android Framework Compatibility Issues

Keeping up with the fast evolution of mobile operating systems is challenging for developers, who have to frequently adapt their apps to the upgrades and behavioral changes of the underlying API framework. Those changes often break backward compatibility. The consequence is that apps, if not updated, may misbehave and suffer unexpected crashes if executed within an evolved environment. Being able to quickly identify the portion of the app that should be modified to provide compatibility with new API versions can be challenging. To facilitate the debugging activities of problems caused by backward incompatible upgrades of the operating system, this paper presents FILO, a technique that is able to recommend the method that should be modified to implement the fix by analyzing a single failing execution. FILO can also provide additional information and key symptomatic anomalous events that can help developers understand the reason for the failure, therefore facilitating the implementation of the fix. We evaluated FILO against 18 real compatibility problems related to Android upgrades and compared it with Spectrum-Based Localization approaches. Results show that FILO is able to efficiently and effectively identify the fix-locus in the apps.

Investigating Documented Privacy Changes in Android OS

Android has empowered third-party apps to access data and services on mobile devices since its genesis.This involves a wide spectrum of user privacy-sensitive data, such as the device ID and location. In recent years, Android has taken proactive measures to adapt its access control policies for such data, in response to the increasingly strict privacy protection regulations around the world. When each new Android version is released, its privacy changes induced by the version evolution are transparently disclosed, and we refer to them as documented privacy changes (DPCs). Implementing DPCs in Android OS is a non-trivial task, due to not only the dispersed nature of those access control points within the OS, but also the challenges posed by backward compatibility. As a result, whether the actual access control enforcement in the OS implementations aligns with the disclosed DPCs becomes a critical concern. In this work, we conduct the first systematic study on the consistency between the operational behaviors of the OS at runtime and the officially disclosed DPCs. We propose DopCheck, an automatic DPC-driven testing framework equipped with a large language model (LLM) pipeline. It features a serial of analysis to extract the ontology from the privacy change documents written in natural language, and then harnesses the few-shot capability of LLMs to construct test cases for the detection of DPC-compliance issues in OS implementations. We apply DopCheck with the latest versions (10 to 13) of Android Open Source Project (AOSP). Our evaluation involving 79 privacy-sensitive APIs demonstrates that DopCheck can effectively recognize DPCs from Android documentation and generate rigorous test cases. Our study reveals that the status quo of the DPC-compliance issues is concerning, evidenced by 19 bugs identified by DopCheck. Notably, 12 of them are discovered in Android 13 and 6 in Android 10 for the first time, posing more than 35% Android users to the risk of privacy leakage. Our findings should raise an alert to Android users and app developers on the DPC compliance issues when using or developing an app, and would also underscore the necessity for Google to comprehensively validate the actual implementation against its privacy documentation prior to the OS release.

On the Determination of Efficiency of a Gas Compressor

For a gas undergoing a compression process, it is more appropriate to think of either isentropic or polytropic efficiency as process-defining parameters indicating that a given end state of compression has been achieved, rather than a measure of effectiveness of conversion of one form of energy into another. The polytropic efficiency, as defined in ASME PTC-10 standard for compressor field trials and acceptance tests, actually involves the comparison of two distinct compression processes, neither of which are actually connected to the performance of the compressors producing them. Consequently, it is not rational to compare the ASME PTC-10 polytropic efficiency of a compressor designed to compress a gas predominantly adiabatically with that for a compressor designed to compress a gas predominantly isothermally. A framework correcting this situation is set out and is illustrated with several numerical examples. Suggestions for maintaining backward compatibility with ASME PTC-10 are also put forward.

How can we consider multi‐core fibre standard?

AbstractThe authors provide an example roadmap for realising the space division multiplexing (SDM)‐based ecosystem. The authors investigate whether the new standard for multi‐core fibre can be specified by considering a 125 μm standard cladding diameter and backward compatibility with the existing single‐core single‐mode fibre. The authors propose that well‐planned standardisation activities need to be initiated considering mandatory technologies for realising the terrestrial SDM ecosystem in the near future.

Going beyond templates: composition and evolution in nested OSTRICH

AbstractLow-code frameworks strive to simplify and speed up application development. An essential mechanism to achieve these goals is to have native support for the safe reuse and usage of parameterized coarse-grain components, providing developers with strong guardrails and a rich software-building experience. —a rich template language for the OutSystems platform—was designed to simplify the use and creation of such components. Thus, the application developer can quickly reuse and assemble sophisticated and thoroughly tested application blocks. However, without a built-in composition and evolution mechanism, templates are still hard to create and maintain. This sometimes requires the repetition of code across different templates and creates a conflict between the customizations of the instantiated application models, and the update and reapplication of a template definition. This paper presents a principled mechanism for using abstraction in the creation of templates and simultaneously supporting the evolution of templates in applications after use. First, we introduce a template composition mechanism, its typing discipline, and its instantiation algorithm for model-driven low-code development environments. We start by extending to support nested templates and allow the instantiation (hatching) of templates in the definition of other templates. Nesting promotes a significant increase in code reuse potential, leading to a safer evolution of applications. We then introduce the support for customizable template instances, which allows one to evolve templates’ code and then update a template instance without losing customizations performed in the generated code. The present definition seamlessly extends the existing OutSystems metamodel with template constructs expressed by model annotations that maintain backward compatibility with the existing language toolchain. We present the metamodel, a set of annotations to support the extensions, and the corresponding validation and instantiation algorithms. In particular, we introduce a type-based validation procedure for abstractions that ensures that using templates always produces valid models. This work also extends prior developments on Nested OSTRICH with the support for safe customizations of instantiated code. We validate Nested OSTRICH using the benchmark by identifying the degree of reusability that can be reached in the existing sample of real templates and template uses. Our prototype is an extension of the OutSystems IDE that allows the annotation of models and their use to produce new models. We also analyze which existing OutSystems sample screen templates can be improved by using and sharing nested templates .

Delivering on a promise: futureproofing automated insect monitoring methods.

Due to rapid technological innovations, the automated monitoring of insect assemblages comes within reach. However, this continuous innovation endangers the methodological continuity needed for calculating reliable biodiversity trends in the future. Maintaining methodological continuity over prolonged periods of time is not trivial, since technology improves, reference libraries grow and both the hard- and software used now may no longer be available in the future. Moreover, because data on many species are collected at the same time, there will be no simple way of calibrating the outputs of old and new devices. To ensure that reliable long-term biodiversity trends can be calculated using the collected data, I make four recommendations: (1) Construct devices to last for decades, and have a five-year overlap period when devices are replaced. (2) Construct new devices to resemble the old ones, especially when some kind of attractant (e.g. light) is used. Keep extremely detailed metadata on collection, detection and identification methods, including attractants, to enable this. (3) Store the raw data (sounds, images, DNA extracts, radar/lidar detections) for future reprocessing with updated classification systems. (4) Enable forward and backward compatibility of the processed data, for example by in-silico data 'degradation' to match the older data quality. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

From PID to ADRC and back: Expressing error‐based active disturbance rejection control schemes as standard industrial 1DOF and 2DOF controllers

AbstractIn this paper, we uncover a new connection between standard proportional‐integral (PI), proportional‐integral‐derivative (PID) controllers and active disturbance rejection control (ADRC), from which we establish formal conditions of equivalence between the two control schemes. Using the equivalence, we devise a step‐by‐step procedure of transitioning from PI/PID to error‐based ADRC. We also show how to go from one degree‐of‐freedom (1DOF) to two degree‐of‐freedom (2DOF) ADRC while retaining a standard 2DOF PI/PID structure. Both procedures facilitate expressing error‐based ADRC schemes as standard industrial 1DOF and 2DOF controllers. This allows the designed controller to have the desired characteristic of ADRC (i.e., strong robustness against internal and external uncertainties) while still being expressed in a form that is familiar to industrial practitioners, where PI/PID structures are still the workhorse of modern control systems. The paper's results ensure backward compatibility of future ADRC‐based solutions and foster the adoption of active disturbance rejection‐based methods in industrial practice as a viable alternative to standard controllers. To further support the findings, a set of tests is conducted in time and frequency domain.

Evolving Parameterized Prompt Memory for Continual Learning

Recent studies have demonstrated the potency of leveraging prompts in Transformers for continual learning (CL). Nevertheless, employing a discrete key-prompt bottleneck can lead to selection mismatches and inappropriate prompt associations during testing. Furthermore, this approach hinders adaptive prompting due to the lack of shareability among nearly identical instances at more granular level. To address these challenges, we introduce the Evolving Parameterized Prompt Memory (EvoPrompt), a novel method involving adaptive and continuous prompting attached to pre-trained Vision Transformer (ViT), conditioned on specific instance. We formulate a continuous prompt function as a neural bottleneck and encode the collection of prompts on network weights. We establish a paired prompt memory system consisting of a stable reference and a flexible working prompt memory. Inspired by linear mode connectivity, we progressively fuse the working prompt memory and reference prompt memory during inter-task periods, resulting in continually evolved prompt memory. This fusion involves aligning functionally equivalent prompts using optimal transport and aggregating them in parameter space with an adjustable bias based on prompt node attribution. Additionally, to enhance backward compatibility, we propose compositional classifier initialization, which leverages prior prototypes from pre-trained models to guide the initialization of new classifiers in a subspace-aware manner. Comprehensive experiments validate that our approach achieves state-of-the-art performance in both class and domain incremental learning scenarios.

Implementing Data Workflows and Data Model Extensions with RDF-star

PurposeThe purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.Design/methodology/approachIn this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.FindingsThe authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.Practical implicationsThrough the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.Originality/valueWith RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

The improved backward compatible physics-informed neural networks for reducing error accumulation and applications in data-driven higher-order rogue waves.

Due to the dynamic characteristics of instantaneity and steepness, employing domain decomposition techniques for simulating rogue wave solutions is highly appropriate. Wherein, the backward compatible physics-informed neural network (bc-PINN) is a temporally sequential scheme to solve PDEs over successive time segments while satisfying all previously obtained solutions. In this work, we propose improvements to the original bc-PINN algorithm in two aspects based on the characteristics of error propagation. One is to modify the loss term for ensuring backward compatibility by selecting the earliest learned solution for each sub-domain as pseudo-reference solution. The other is to adopt the concatenation of solutions obtained from individual subnetworks as the final form of the predicted solution. The improved backward compatible PINN (Ibc-PINN) is applied to study data-driven higher-order rogue waves for the nonlinear Schrödinger (NLS) equation and the AB system to demonstrate the effectiveness and advantages. Transfer learning and initial condition guided learning (ICGL) techniques are also utilized to accelerate the training. Moreover, the error analysis is conducted on each sub-domain, and it turns out that the slowdown of Ibc-PINN in error accumulation speed can yield greater advantages in accuracy. In short, numerical results fully indicate that Ibc-PINN significantly outperforms bc-PINN in terms of accuracy and stability without sacrificing efficiency.

LiDAR-Based Morphometry of Dolines in Aggtelek Karst (Hungary) and Slovak Karst (Slovakia)

LiDAR-based digital terrain models (DTMs) represent an advance in the investigation of small-scale geomorphological features, including dolines of karst terrains. Important issues in doline morphometry are (i) which statistical distributions best model the size distribution of doline morphometric parameters and (ii) how to characterize the volume of dolines based on high-resolution DTMs. For backward compatibility, how previous datasets obtained predominantly from topographic maps relate to doline data derived from LiDAR is also examined. Our study area includes the karst plateaus of Aggtelek Karst and Slovak Karst national parks, whose caves are part of the UNESCO World Heritage. To characterize the study area, the relationships between doline parameters and topography were studied, as well as their geological characteristics. Our analysis revealed that the LiDAR-based doline density is 25% higher than the value calculated from topographic maps. Furthermore, LiDAR-based doline delineations are slightly larger and less rounded than in the case of topographic maps. The plateaus of the study area are characterized by low (5–10 km−2), moderate (10–30 km−2), and medium (30–35 km−2) doline densities. In terms of topography, the slope trend is decisive since the doline density is negligible in areas where the general slope is steeper than 12°. As for the lithology, 75% of the dolines can be linked to Wetterstein Limestone. The statistical distribution of the doline area can be well modeled by the lognormal distribution. To describe the DTM-based volume of dolines, a new parameter (k) is introduced to characterize their 3D shape: it is equal to the product of the area and the depth divided by the volume. This parameter indicates whether the idealized shape of the doline is closer to a cylinder, a bowl (calotte), a cone, or a funnel shape. The results show that most sinkholes in the study area have a transitional shape between a bowl (calotte) and a cone.

A dual-port, single-fed, integrated microwave and mm-wave MIMO antenna system with parasitic decoupling mechanism for 5G-enabled IoT applications

The integration of microwave and mm-wave antenna is recognized as a promising solution to ensure backward compatibility while effectively utilizing available space for 5G-enabled modern IoT devices. This paper proposed a very compact size multi-band dual-port MIMO antenna module, aiming to provide complete coverage in microwave frequency bands, including 2.4 GHz, 3.5 GHz, 5.3 GHz, and 7.5 GHz, along with 5G mm-wave bands such as 28 GHz. This is achieved through a common feedline by incorporating a semi-circular slotted monopole with strategically positioned quarter-wavelength metallic stubs on a partial ground plane. The proposed antenna exhibited -10 dB impedance bandwidths of 22.6% (2.16–2.71 GHz), 17.9% (3.36–4.02 GHz), 9.4% (5.18–5.69 GHz), 15.9% (7.29–8.55 GHz), and 15.5% (26.02–30.39 GHz) across diverse frequency bands. To attain compactness and high isolation capabilities, a composite three-dimensional parasitic element is employed as a decoupling structure to alleviate significant interference among closely positioned radiating elements. The proposed antenna provides radiation efficiency of > 85%, achieving necessary peak gains of 2.32 dBi, 3.46 dBi, 4.15 dBi, and 5.32 dBi at respective microwave frequencies. While in mm-wave frequency bands, radiation efficiency exceeds 95% having a measured realized gain of 6.2 dBi at 28 GHz. The proposed design successfully maintains an average isolation of > 28 dB in all frequency bands. The suggested MIMO configuration demonstrates outstanding diversity performance, which is evidenced by ECC, DG, MEG, CCL, and TARC.

Indexed Types for a Statically Safe WebAssembly

We present Wasm-prechk, a superset of WebAssembly (Wasm) that uses indexed types to express and check simple constraints over program values. This additional static reasoning enables safely removing dynamic safety checks from Wasm, such as memory bounds checks. We implement Wasm-prechk as an extension of the Wasmtime compiler and runtime, evaluate the run-time and compile-time performance of Wasm-prechk vs WebAssembly configurations with explicit dynamic checks, and find an average run-time performance gain of 1.71x faster in the widely used PolyBenchC benchmark suite, for a small overhead in binary size (7.18% larger) and type-checking time (1.4% slower). We also prove type and memory safety of Wasm-prechk, prove Wasm safely embeds into Wasm-prechk ensuring backwards compatibility, prove Wasm-prechk type-erases to Wasm, and discuss design and implementation trade-offs.

DeepStream: Video Streaming Enhancements using Compressed Deep Neural Networks

InIn HTTP Adaptive Streaming (HAS), each video is divided into smaller segments, and each segment is encoded at multiple pre-defined bitrates to construct a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bitrate ladder</i> . To optimize bitrate ladders, per-title encoding approaches encode each segment at various bitrates and resolutions to determine the convex hull. From the convex hull, an optimized bitrate ladder is constructed, resulting in an increased Quality of Experience (QoE) for end-users. With the ever-increasing efficiency of deep learning-based video enhancement approaches, they are more and more employed at the client-side to increase the QoE, specifically when GPU capabilities are available. Therefore, scalable approaches are needed to support end-user devices with both CPU and GPU capabilities (denoted as CPU-only and GPU-available end-users, respectively) as a new dimension of a bitrate ladder. To address this need, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepStream</i> , a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scalable content-aware</i> per-title encoding approach to support both CPU-only and GPU-available end-users. ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) To support <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">backward compatibility</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepStream</i> constructs a bitrate ladder based on any existing per-title encoding approach. Therefore, the video content will be provided for legacy end-user devices with CPU-only capabilities as a base layer (BL). ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) For high-end end-user devices with GPU capabilities, an enhancement layer (EL) is added on top of the base layer comprising lightweight video super-resolution deep neural networks (DNNs) for each bitrate-resolution pair of the bitrate ladder. A content-aware video super-resolution approach leads to higher video quality, however, at the cost of bitrate overhead. To reduce the bitrate overhead for streaming content-aware video super-resolution DNNs, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepCABAC</i> , context-adaptive binary arithmetic coding for DNN compression, is used. Furthermore, the similarity among ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) segments within a scene and ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) frames within a segment are used to reduce the training costs of DNNs. Experimental results show bitrate savings of 34% and 36% to maintain the same PSNR and VMAF, respectively, for GPU-available end-users, while the CPU-only users get the desired video content as usual.

Towards podio v1.0 - A first stable release of the EDM toolkit

A performant and easy-to-use event data model (EDM) is a key component of any HEP software stack. The podio EDM toolkit provides a user friendly way of generating such a performant implementation in C++ from a high level description in yaml format. Finalizing a few important developments, we are in the final stretches for release v1.0 of podio, a stable release with backward compatibility for datafiles written with podio from then on. We present an overview of the podio basics, and go into slighty more technical detail on the most important topics and developments. These include: schema evolution for generated EDMs, multithreading with podio generated EDMs, the implementation of them as well as the basics of I/O. Using EDM4hep, the common and shared EDM of the Key4hep project, we highlight a few of the smaller features in action as well as some lessons learned during the development of EDM4hep and podio. Finally, we show how podio has been integrated into the Gaudi based event processing framework that is used by Key4hep, before we conclude with a brief outlook on potential developments after v1.0.