TY - CHAP A1 - Klettke, Meike A1 - Störl, Uta A1 - Shenavai, Manuel A1 - Scherzinger, Stefanie A1 - Storl, Uta T1 - NoSQL schema evolution and big data migration at scale T2 - 2016 IEEE International Conference on Big Data (Big Data), 5-8 Dec. 2016, Washington, DC N2 - This paper explores scalable implementation strategies for carrying out lazy schema evolution in NoSQL data stores. For decades, schema evolution has been an evergreen in database research. Yet new challenges arise in the context of cloud-hosted data backends: With all database reads and writes charged by the provider, migrating the entire data instance eagerly into a new schema can be prohibitively expensive. Thus, lazy migration may be more cost-efficient, as legacy entities are only migrated in case they are actually accessed by the application. Related work has shown that the overhead of migrating data lazily is affordable when a single evolutionary change is carried out, such as adding a new property. In this paper, we focus on long-term schema evolution, where chains of pending schema evolution operations may have to be applied. Chains occur when legacy entities written several application releases back are finally accessed by the application. We discuss strategies for dealing with chains of evolution operations, in particular, the composition into a single, equivalent composite migration that performs the required version jump. Our experiments with MongoDB focus on scalable implementation strategies. Our lineup further compares the number of write operations, and thus, the operational costs of different data migration strategies. KW - Big data KW - Context KW - Data Migration Strategies KW - Data models KW - Databases KW - Incremental Migration KW - Lazy Composite Migration KW - Lazy Migration KW - NoSQL databases KW - Predictive Migration KW - Production KW - Runtime KW - schema evolution KW - Software Y1 - 2016 U6 - https://doi.org/10.1109/BigData.2016.7840924 SP - 2764 EP - 2774 PB - IEEE ER - TY - CHAP A1 - Möller, Mark Lukas A1 - Berton, Nicolas A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Störl, Uta T1 - jHound: Large-Scale Profiling of Open JSON Data T2 - Datenbanksysteme für Business, Technologie und Web (BTW 2019), 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS) : 4.-8. März 2019 in Rostock N2 - We present jHound, a tool for profiling large collections of JSON data, and apply it to thousands of data sets holding open government data. jHound reports key characteristics of JSON documents, such as their nesting depth. As we show, jHound can help detect structural outliers, and most importantly, badly encoded documents: jHound can pinpoint certain cases of documents that use string-typed values where other native JSON datatypes would have been a better match. Moreover, we can detect certain cases of maladaptively structured JSON documents, which obviously do not comply with good data modeling practices. By interactively exploring particular example documents, we hope to inspire discussions in the community about what makes a good JSON encoding. KW - Datenerhebung KW - Datenformat KW - Datenmodell KW - Eigenschaftskennwert KW - Hauptspeicher KW - Histogramm KW - Zeitüberwachung Y1 - 2018 UR - https://btw.informatik.uni-rostock.de/index.php/de/tagungsbaende/send/3-tagungsbaende/tagungsband.pdf SN - 978-3-88579-683-1 VL - 289 SP - 557 EP - 560 PB - GI - Gesellschaft für Informatik CY - Bonn ER - TY - CHAP A1 - Hillenbrand, Andrea A1 - Levchenko, Maksym A1 - Störl, Uta A1 - Scherzinger, Stefanie A1 - Klettke, Meike ED - Boncz, Peter ED - Manegold, Stefan ED - Ailamaki, Anastasia ED - Deshpande, Amol ED - Kraska, Tim T1 - MigCast : Putting a Price Tag on Data Model Evolution in NoSQL Data Stores T2 - Proceedings of the 2019 International Conference on Management of Data (SIGMOD/PODS '19) June 2019, Amsterdam, Netherlands N2 - We demonstrate MigCast, a tool-based advisor for exploring data migration strategies in the context of developing NoSQL-backed applications. Users of MigCast can consider their options for evolving their data model along with legacy data already persisted in the cloud-hosted production data-base. They can explore alternative actions as the financial costs are predicted respective to the cloud provider chosen. Thereby they are better equipped to assess potential consequences of imminent data migration decisions. To this end, MigCast maintains an internal cost model, taking into account characteristics of the data instance, expected work-load, data model changes, and cloud provider pricing models. Hence, MigCast enables software project stakeholders to remain in control of the operative costs and to make informed decisions evolving their applications. KW - Data Migration Strategies KW - latency KW - migration costs KW - NoSQL databases KW - Predictive Migration KW - schema evolution Y1 - 2019 SN - 9781450356435 U6 - https://doi.org/10.1145/3299869.3320223 SP - 1925 EP - 1928 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Störl, Uta A1 - Müller, Daniel A1 - Klettke, Meike A1 - Scherzinger, Stefanie ED - Mitschang, Bernhard T1 - Enabling Efficient Agile Software Development of NoSQL-backed Applications T2 - Datenbanksysteme für Business, Technologie und Web (BTW 2017) : 17. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssyteme" (DBIS) : 06.-10.03.2017 in Stuttgart Deutschland N2 - NoSQL databases are popular in agile software development, where a frequently changing database schema imposes challenges for the production database. In this demo, we present Darwin, a middleware for systematic, tool-based support specifically designed for NoSQL database systems. Darwin carries out schema evolution and data migration tasks. To the best of our knowledge, Darwin is the first tool of its kind that supports both eager and lazy NoSQL data migration. Y1 - 2017 SN - 978-3-88579-659-6 PB - Gesellschaft für Informatik e.V. (GI) CY - Bonn ER - TY - CHAP A1 - Scherzinger, Stefanie A1 - Sombach, Stephanie A1 - Wiech, Katharina A1 - Klettke, Meike A1 - Störl, Uta T1 - Datalution: a tool for continuous schema evolution in NoSQL-backed web applications T2 - QUDOS 2016: Proceedings of the 2nd International Workshop on Quality-Aware DevOps N2 - When an incremental release of a web application is deployed, the structure of data already persisted in the production database may no longer match what the application code expects. Traditionally, eager schema migration is called for, where all legacy data is migrated in one go. With the growing popularity of schema-flexible NoSQL data stores, lazy forms of data migration have emerged: Legacy entities are migrated on-the-fly, one at-a-time, when they are loaded by the application. In this demo, we present Datalution, a tool demonstrating the merits of lazy data migration. Datalution can apply chains of pending schema changes, due to its Datalog-based internal representation. The Datalution approach thus ensures that schema evolution, as part of continous deployment, is carried out correctly. Y1 - 2016 U6 - https://doi.org/10.1145/2945408.2945416 SP - 38 EP - 39 PB - ACM ER - TY - JOUR A1 - Klettke, Meike A1 - Scherzinger, Stefanie A1 - Störl, Uta T1 - Datenbanken ohne Schema? JF - Datenbank-Spektrum N2 - In der Entwicklung von interaktiven Web-Anwendungen sind NoSQL-Datenbanksysteme zunehmend beliebt, nicht zuletzt, weil sie flexible Datenmodelle erlauben. Das erleichtert insbesondere ein agiles Projektmanagement, das sich durch häufige Releases und entsprechend häufige Änderungen am Datenmodell auszeichnet. In diesem Artikel geben wir einen Überblick über die besonderen Herausforderungen der agilen Anwendungsentwicklung gegen schemalose NoSQL-Datenbanksysteme. Wir stellen Strategien für die Schema-Evolution aus der Praxis vor, und postulieren unsere Vision einer eigenen Schema-Management-Komponente für NoSQL-Datenbanksysteme, die für eine kontinuierliche und systematische Schema-Evolution ausgelegt ist. KW - NoSQL-Datenbanksysteme KW - Schema-Evolution KW - Schema-Informationen Y1 - 2014 U6 - https://doi.org/10.1007/s13222-014-0156-z VL - 14 IS - 2 SP - 119 EP - 129 PB - Springer ER - TY - JOUR A1 - Störl, Uta A1 - Klettke, Meike A1 - Scherzinger, Stefanie T1 - Kurz erklärt: Objekt-NoSQL-Mapping JF - Datenbank-Spektrum Y1 - 2016 U6 - https://doi.org/10.1007/s13222-016-0212-y VL - 16 IS - 1 SP - 83 EP - 87 PB - Springer ER - TY - CHAP A1 - Mauerer, Wolfgang A1 - Scherzinger, Stefanie ED - Krusche, Stephan ED - Wagner, Stefan T1 - Educating Future Software Architects in the Art and Science of Analysing Software Data. T2 - SEUH 2020: Software Engineering im Unterricht der Hochschulen, Tagungsband des 17. Workshops "Software Engineering im Unterricht der Hochschulen", Innsbruck, Österreich, 26. - 27.02.2020 N2 - We report the design and teaching experience of a Master-level seminar course on quantitative and empirical software engineering. The course combines elements of traditional literature seminars with active learning by scientific project work, in particular quantitative mixed-method analyses of open source systems. It also provides short introductions and refreshers to data mining and statistical analysis, and discusses the nature and practice of scientific knowledge inference. Student presentations of published research, augmented by summary reports, bridge to standard seminars. We discuss our educational goals and the course structure derived from them. We review research questions addressed by students in mini research reports, and analyse them as tokens on how junior-level software engineers perceive the potential of empirical software engineering research. We assess challenges faced, and discuss possible solutions. Y1 - 2020 UR - http://ceur-ws.org/Vol-2531/paper10.pdf SP - 56 EP - 60 PB - RWTH Aachen ER - TY - JOUR A1 - Scherzinger, Stefanie A1 - Thor, Andreas T1 - Cloud-Technologien in der Hochschullehre – Pflicht oder Kür? JF - Datenbank-Spektrum N2 - Ein eigenes Themenheft zum Datenmanagement in der Cloud dient uns als Anlass, die Präsenz von Cloud-Themen in der akademischen Datenbanklehre zu erfassen. In diesem Artikel geben wir die Ergebnisse einer Umfrage innerhalb der Fachgruppe Datenbanksysteme durch den Arbeitskreis Datenmanagement in der Cloud wieder. Dozentinnen und Dozenten von über zwanzig Hochschulen nahmen an der Umfrage teil. Es zeigt sich deutlich, dass sich das Thema „Cloud“ in der Hochschullehre zunehmend etabliert, jedoch überwiegend als ergänzendes Angebot, und seltener in der grundständigen Lehre verankert. Wir fassen die Ergebnisse unserer Umfrage zusammen und wagen Deutungsversuche. KW - Cloud Computing KW - Hochschullehre Y1 - 2014 U6 - https://doi.org/10.1007/s13222-014-0161-2 VL - 14 IS - 2 SP - 131 EP - 134 PB - Springer Nature ER - TY - JOUR A1 - Scherzinger, Stefanie T1 - Build your own SQL-on-Hadoop Query Engine A Report on a Term Project in a Master-level Database Course JF - ACM SIGMOD Record N2 - This is a report on a course taught at OTH Regensburg in the summer term of 2018. The students in this course built their own SQL-on-Hadoop engine as a term project in just 8 weeks. miniHive is written in Python and compiles SQL queries into MapReduce workflows. These are then executed on Hadoop. miniHive performs generic query optimizations (selection and projection pushdown, or cost-based join reordering), as well as MapReduce-specific optimizations. The course was taught in English, using a flipped classroom model. The course material was mainly compiled from third-party teaching videos. This report describes the course setup, the miniHive milestones, and gives a short review of the most successful student projects. Y1 - 2019 U6 - https://doi.org/10.1145/3377330.3377336 VL - 48 IS - 2 SP - 33 EP - 38 PB - ACM ER - TY - CHAP A1 - Scherzinger, Stefanie A1 - Seifert, Christin A1 - Wiese, Lena T1 - The Best of Both Worlds: Challenges in Linking Provenance and Explainability in Distributed Machine Learning T2 - 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 7-10 July 2019, Dallas, TX, USA N2 - Machine learning experts prefer to think of their input as a single, homogeneous, and consistent data set. However, when analyzing large volumes of data, the entire data set may not be manageable on a single server, but must be stored on a distributed file system instead. Moreover, with the pressing demand to deliver explainable models, the experts may no longer focus on the machine learning algorithms in isolation, but must take into account the distributed nature of the data stored, as well as the impact of any data pre-processing steps upstream in their data analysis pipeline. In this paper, we make the point that even basic transformations during data preparation can impact the model learned, and that this is exacerbated in a distributed setting. We then sketch our vision of end-to-end explainability of the model learned, taking the pre-processing into account. In particular, we point out the potentials of linking the contributions of research on data provenance with the efforts on explainability in machine learning. In doing so, we highlight pitfalls we may experience in a distributed system on the way to generating more holistic explanations for our machine learning models. KW - Computational modeling KW - Data models KW - Decision trees KW - distributed computing KW - Distributed databases KW - explainable machine learning KW - Machine learning Y1 - 2019 U6 - https://doi.org/10.1109/ICDCS.2019.00161 SP - 1620 EP - 1629 PB - IEEE ER - TY - CHAP A1 - Scherzinger, Stefanie A1 - Mauerer, Wolfgang A1 - Kondylakis, Haridimos ED - Ailamaki, Anastasia T1 - DeBinelle: Semantic Patches for Coupled Database-Application Evolution T2 - 2021 IEEE 37th International Conference on Data Engineering (ICDE 2021): 19-22 April 2021, Chania, Greece N2 - Databases are at the core of virtually any software product. Changes to database schemas cannot be made in isolation, as they are intricately coupled with application code. Such couplings enforce collateral evolution, which is a recognised, important research problem. In this demonstration, we show a new dimension to this problem, in software that supports alternative database backends: vendor-specific SQL dialects necessitate a simultaneous evolution of both, database schema and program code, for all supported DB variants. These near-same changes impose substantial manual effort for software developers. We introduce DeBinelle, a novel framework and domain-specific language for semantic patches that abstracts DB-variant schema changes and coupled program code into a single, unified representation. DeBinelle further offers a novel alternative to manually evolving coupled schemas and code. DeBinelle considerably extends established, seminal results in software engineering research, supporting several programming languages, and the many dialects of SQL. It effectively eliminates the need to perform vendor-specific changes, replacing them with intuitive semantic patches. Our demo of DeBinelle is based on real-world use cases from reference systems for schema evolution. KW - database management systems KW - databases KW - evolution KW - programming language semantics KW - semantic patches KW - software product lines KW - specification languages KW - SQL Y1 - 2021 SN - 978-1-7281-9184-3 U6 - https://doi.org/10.1109/ICDE51399.2021.00307 SP - 2697 EP - 2700 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Fruth, Michael A1 - Scherzinger, Stefanie A1 - Mauerer, Wolfgang A1 - Ramsauer, Ralf ED - Nambiar, Raghunath ED - Poess, Meikel T1 - Tell-Tale Tail Latencies: Pitfalls and Perils in Database Benchmarking T2 - Performance evaluation and benchmarking, 13th TPC Technology Conference (TPCTC 2021): Copenhagen, Denmark, August 20, 2021, Revised Selected Papers N2 - The performance of database systems is usually characterised by their average-case (i.e., throughput) behaviour in standardised or de-facto standard benchmarks like TPC-X or YCSB. While tails of the latency (i.e., response time) distribution receive considerably less attention, they have been identified as a threat to the overall system performance: In large-scale systems, even a fraction of requests delayed can build up into delays perceivable by end users. To eradicate large tail latencies from database systems, the ability to faithfully record them, and likewise pinpoint them to the root causes, is imminently required. In this paper, we address the challenge of measuring tail latencies using standard benchmarks, and identify subtle perils and pitfalls. In particular, we demonstrate how Java-based benchmarking approaches can substantially distort tail latency observations, and discuss how the discovery of such problems is inhibited by the common focus on throughput performance. We make a case for purposefully re-designing database benchmarking harnesses based on these observations to arrive at faithful characterisations of database performance from multiple important angles. KW - Benchmark harness KW - Database benchmarks KW - Tail latencies Y1 - 2022 SN - 9783030944377 U6 - https://doi.org/10.1007/978-3-030-94437-7_8 SP - 119 EP - 134 PB - Springer CY - Cham, Switzerland ER - TY - CHAP A1 - Mauerer, Wolfgang A1 - Scherzinger, Stefanie T1 - 1-2-3 Reproducibility for Quantum Software Experiments T2 - 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Honolulu, HI, USA, 15-18 March 2022 N2 - Various fields of science face a reproducibility crisis. For quantum software engineering as an emerging field, it is therefore imminent to focus on proper reproducibility engineering from the start. Yet the provision of reproduction packages is almost universally lacking. Actionable advice on how to build such packages is rare, particularly unfortunate in a field with many contributions from researchers with backgrounds outside computer science. In this article, we argue how to rectify this deficiency by proposing a 1-2-3~approach to reproducibility engineering for quantum software experiments: Using a meta-generation mechanism, we generate DOI-safe, long-term functioning and dependency-free reproduction packages. They are designed to satisfy the requirements of professional and learned societies solely on the basis of project-specific research artefacts (source code, measurement and configuration data), and require little temporal investment by researchers. Our scheme ascertains long-term traceability even when the quantum processor itself is no longer accessible. By drastically lowering the technical bar, we foster the proliferation of reproduction packages in quantum software experiments and ease the inclusion of non-CS researchers entering the field. KW - Computer Science KW - Quantum Physics KW - Software Engineering Y1 - 2022 U6 - https://doi.org/10.1109/SANER53432.2022.00148 SP - 1247 EP - 1248 PB - IEEE ER - TY - CHAP A1 - Braininger, Dimitri A1 - Mauerer, Wolfgang A1 - Scherzinger, Stefanie ED - Grossmann, Georg ED - Ram, Sudha T1 - Replicability and Reproducibility of a Schema Evolution Study in Embedded Databases T2 - Advances in conceptual modeling: ER 2020 Workshops CMAI, CMLS, CMOMM4FAIR, CoMoNoS, EmpER, Vienna, Austria, November 3-6, 2020, Proceedings N2 - Ascertaining the feasibility of independent falsification or repetition of published results is vital to the scientific process, and replication or reproduction experiments are routinely performed in many disciplines. Unfortunately, such studies are only scarcely available in database research, with few papers dedicated to re-evaluating published results. In this paper, we conduct a case study on replicating and reproducing a study on schema evolution in embedded databases. We can exactly repeat the outcome for one out of four database applications studied, and come close in two further cases. By reporting results, efforts, and obstacles encountered, we hope to increase appreciation for the substantial efforts required to ensure reproducibility. By discussing minutiae details required to ascertain reproducible work, we argue that such important, but often ignored aspects of scientific work should receive more credit in the evaluation of future research. KW - replicability KW - reproducibility KW - schema evolution Y1 - 2020 SN - 978-3-030-65846-5 U6 - https://doi.org/10.1007/978-3-030-65847-2_19 VL - 12584 SP - 210 EP - 219 PB - Springer CY - Cham ER - TY - JOUR A1 - Mauerer, Wolfgang A1 - Scherzinger, Stefanie T1 - Digitale Forschungswerkzeuge : Nachhaltigkeit für Software und Daten JF - Forschung & Lehre (Forschung und Lehre) N2 - Die wissenschaftliche Reproduktionskrise hat den Blick auf digitale Forschungswerkzeuge intensiviert. Auch wenn der Mehraufwand für Reproduzierbarkeit und Zugänglichkeit zunehmend anerkannt wird, existieren noch Defizite in der Umsetzung, wenn es darum geht, die Datenbasis und Forschungswerkzeuge verfügbar zu machen Y1 - 2021 UR - https://www.wissenschaftsmanagement-online.de/system/files/downloads-wimoarticle/2021-10_WIMO_Digitale_Forschungswerkzeuge_Mauerer_Scherzinger.pdf VL - 28 IS - 10 SP - 816 EP - 817 PB - Zentrum für Wissenschaftsmanagement e.V. (ZWM)
 ER - TY - CHAP A1 - Ringlstetter, Andreas A1 - Scherzinger, Stefanie A1 - Bissyandé, Tegawendé F. T1 - Data Model Evolution Using Object-NoSQL Mappers: Folklore or State-of-the-Art? T2 - 2016 IEEE/ACM 2nd International Workshop on Big Data Software Engineering (BIGDSE), 16 May 2016, Austin, TX, USA N2 - In big data software engineering, the schema flexibility of NoSQL document stores is a major selling point: When the document store itself does not actively manage a schema, the data model is maintained within the application. Just like object-relational mappers for relational databases, object-NoSQL mappers are part of professional software development with NoSQL document stores. Some mappers go beyond merely loading and storing Java objects: Using dedicated evolution annotations, developers may conveniently add, remove, or rename attributes from stored objects, and also conduct more complex transformations. In this paper, we analyze the dissemination of this technology in Java open source projects. While we find evidence on GitHub that evolution annotations are indeed being used, developers do not employ them so much for evolving the data model, but to solve different tasks instead. Our observations trigger interesting questions for further research. KW - Big data KW - data model evolution KW - Data models KW - Java KW - Loading KW - Object-NoSQL mappers KW - Software KW - Software engineering KW - Transient analysis Y1 - 2016 U6 - https://doi.org/10.1145/2896825.2896827 SP - 33 EP - 36 PB - ACM ER -