Digital Library of Gesellschaft für Informatik e.V.
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No full textDie Informatik ist eine wahre Gestaltenwandlerin. Man kann sie mit Bindestrich studieren oder klassisch, kann sie als Technik- oder Sozialwissenschaft verstehen, sie zum Selbstzweck oder als Problemlöserin erforschen. Claudia Müller und Barbara Hammer sprechen darüber, wie „richtige“ Informatik heute überhaupt aussieht
45. GIL-Jahrestagung, Digitale Infrastrukturen für eine nachhaltige Land-, Forst- und Ernährungswirtschaft - Komplettband
No full textEnhancing In-Memory Spatial Indexing with Learned Search
No full textSpatial data is generated daily from numerous sources such as GPS-enabled devices, consumer applications (e.g., Uber, Strava), and social media (e.g., location-tagged posts). This exponential growth in spatial data is driving the development of efficient spatial data processing systems. In this study, we enhance spatial indexing with a machine-learned search technique developed for single-dimensional sorted data. Specifically, we partition spatial data using six traditional spatial partitioning techniques and employ machine-learned search within each partition to support point, range, distance, and spatial join queries. By instance-optimizing each partitioning technique, we demonstrate that: (i) grid-based index structures outperform tree-based ones (from 1.23x to 2.47x), (ii) learning-enhanced spatial index structures are faster than their original counterparts (from 1.44x to 53.34x), (iii) machine-learned search within a partition is 11.79% -39.51% faster than binary search when filtering on one dimension, (iv) the benefit of machine-learned search decreases in the presence of other compute-intensive operations (e.g. scan costs in higher selectivity queries, Haversine distance computation, and point-in-polygon tests), and (v) index lookup is the bottleneck for tree-based structures, which could be mitigated by linearizing the indexed partitions
GuiPy - eine Python-IDE für die Schule
No full textIm Informatikunterricht der Sekundarstufe II ist die objektorientierte Modellierung bundesweit verankert. Zur Unterstützung erfolgreicher Lehr- und Lernprozesse stellt die Python-IDE GuiPy hilfreiche Modellierungswerkzeuge bereit, mit denen grundlegende objektorientierte Konzepte veranschaulicht und erarbeitet werden können. Mit dem Struktogrammeditor können Schülerinnen und Schüler Algorithmen modellieren. Der GUI-Designer gibt ihnen eine leicht nutzbare Möglichkeit, ihre eigenen Projekte mit einer modernen und vorzeigbaren grafischen Benutzeroberfläche zu versehen. Im Artikel werden neben grundsätzlichen Anmerkungen zu Python die Modellierungswerkzeuge von GuiPy vorgestellt
Explainable and trustworthy AI compliance for farms
No full textIntegrating artificial intelligence (AI) in agriculture presents both innovative opportunities and complex challenges, particularly regarding trustworthiness and regulatory compliance. As AI technologies reach market maturity, societal apprehensions surrounding their implementation demand an urgent response. This paper proposes a platform-based approach to harmonize the scientific principles of explainable AI (XAI) with the evolving legal regulations, notably the AI Act. By providing a structured framework, the proposed platform assists developers in aligning technical parameters with compliance requirements, fostering trust in AI systems. Beyond mere regulatory adherence, the platform emphasizes the importance of addressing diverse stakeholder expectations regarding trustworthiness. It incorporates ex-ante, ex-nunc, and ex-post assessment methods to ensure that AI systems are interpretable and accountable. It concludes by outlining an innovative way forward that meets regulatory standards and builds public trust, paving the way for broader adoption and successful deployment of AI technologies on farms and underscoring the critical interplay between technology, law, and social perception in agriculture
InKalkTier – enabling livestock farming experts to model and manage their data
No full textTo enable livestock farming experts to model and manage their data independently of the availability of software developers, InKalkTier was created using a different approach than the one used until now at the KTBL. By utilizing off-the-shelf software in combination with customized data formats and creating a small domain-specific language, we reached the goal of reducing the dependence on software developers to develop, release and maintain the agricultural-specific content of a modern web application
Requirements Classification for Requirements Reuse
No full textIn various domains, standards are used to ensure a high level of product quality. During standard tailoring, requirements from the applicable standards are specialized and integrated into the project. The requirement type influences the way the standard requirement interacts with project requirements. Yet, manual classification of large existing standards is time-consuming. This thesis presents a machine learning pipeline to compare four algorithms for this task: k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Logistic Regression (LR), Multinomial Naive Bayes (MNB), as well as an ensemble model combining all four. The models are trained and tested with 466 requirements from the European Cooperation for Space Standardization (ECSS). SVM and LR achieve the best results with F1 scores around 0.85. The integration of term contexts could potentially further increase the prediction accuracy. Yet, the improvement for our dataset is insignificant
Modern Data Analytics in the Cloud Era
No full textCloud computing has been the groundbreaking technology of the last decade. The ease-of-use of the managed environment in combination with nearly infinite amount of resources and a pay-per-use price model enables fast and cost-efficient project realization for a broad range of users. Cloud computing also changes the way software is designed, deployed and used. This thesis focuses on database systems deployed in the cloud environment. We identify three major interaction points of the database engine with the environment that show changed requirements compared to traditional on-premise data warehouse solutions. First, software is deployed on elastic resources. Consequently, systems should support elasticity in order to match workload requirements and be cost-effective. We present an elastic scaling mechanism for distributed database engines, combined with a partition manager that provides load balancing while minimizing partition reassignments in the case of elastic scaling. Furthermore we introduce a buffer pre-heating strategy that allows to mitigate a cold start after scaling and leads to an immediate performance benefit using scaling. Second, cloud based systems are accessible and available from nearly everywhere. Consequently, data is frequently ingested from numerous endpoints, which differs from bulk loads or ETL pipelines in a traditional data warehouse solution. Many users do not define database constraints in order to avoid transaction aborts due to conflicts or to speed up data ingestion. To mitigate this issue we introduce the concept of PatchIndexes, which allow the definition of approximate constraints. PatchIndexes maintain exceptions to constraints, make them usable in query optimization and execution and offer efficient update support. The concept can be applied to arbitrary constraints and we provide examples of approximate uniqueness and approximate sorting constraints. Moreover, we show how PatchIndexes can be exploited to define advanced constraints like an approximate multi-key partitioning, which offers robust query performance over workloads with different partition key requirements. Third, data-centric workloads changed over the last decade. Besides traditional SQL workloads for business intelligence, data science workloads are of significant importance nowadays. For these cases the database system might only act as data delivery, while the computational effort takes place in data science or machine learning (ML) environments. As this workflow has several drawbacks, we follow the goal of pushing advanced analytics towards the database engine and introduce the Grizzly framework as a DataFrame-to-SQL transpiler. Based on this we identify user-defined functions (UDFs) and machine learning inference as important tasks that would benefit from a deeper engine integration and investigate approaches to push these operations towards the database engine
Extending K-Means Clustering with Ptolemy’s Inequality
No full textClustering is a fundamental data analytics operation in the field of unsupervised learning. Given a database of unknown structure, clustering aims to discover the inherent structure of the data objects according to similarity, such that similar data objects are grouped together, while dissimilar ones are separated in different groups or clusters. In this study, we focus on partitioning methods, specifically the k-means clustering approach, which minimizes the intra-cluster variance. As the standard algorithmic approach to k-means clustering, namely the Lloyd algorithm, is neither efficient nor scalable, various adaptations and modifications have been developed, resulting in the family of fast k-means clustering algorithms. In this short paper, we extend the clustering algorithm proposed by Elkan with Ptolemy's inequality to prune superfluous distance calculations. It is not our intention with this paper to compete with the state-of-the-art algorithmic solutions for the k-means clustering problem; rather, we seek to investigate the potential of Ptolemy’s inequality to further enhancements in the standard algorithm’s performance, particularly in terms of reducing computations associated with distance evaluations
Towards GilGPT: an anthropomorphic conversational agent for accessible communication of agricultural research to farmers
No full textThe use of generative AI-based conversational agents is becoming increasingly prevalent in the dissemination of knowledge. In the agricultural sector, there is a substantial corpus of scientific literature, yet there is frequently a deficit of accessible syntheses that can effectively disseminate this knowledge. To enhance the accessibility of scientific findings for farmers and researchers, we introduce our anthropomorphically designed Large Language Model based conversational agent, “GilGPT”. Developed as a design science research project, it uses Retrieval Augmented Generation to access scientific literature from a knowledge base. This approach enables a natural language dialogue between scientific publications and farmers. The implementation of an anthropomorphic design is achieved through the utilization of avatars and adaptive language among other techniques. This approach is employed with the objective of enhancing the accessibility of complex scientific content and facilitating the dialogue between research and practice