@misc{MintelRamsauerLohmannetal., author = {Mintel, Mario and Ramsauer, Ralf and Lohmann, Daniel and Scherzinger, Stefanie and Mauerer, Wolfgang}, title = {Fork {\`a} la carte f{\"u}r In-Memory-Datenbanken}, series = {Fr{\"u}hjahrstreffen der Fachgruppen Betriebssysteme, Hamburg, 17. M{\"a}rz 2022}, journal = {Fr{\"u}hjahrstreffen der Fachgruppen Betriebssysteme, Hamburg, 17. M{\"a}rz 2022}, language = {de} } @article{SchoenbergerScherzingerMauerer, author = {Sch{\"o}nberger, Manuel and Scherzinger, Stefanie and Mauerer, Wolfgang}, title = {Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware}, series = {Proceedings of the ACM on Management of Data, PACMMOD}, volume = {1}, journal = {Proceedings of the ACM on Management of Data, PACMMOD}, number = {1}, publisher = {ACM}, address = {New York, NY,}, doi = {10.1145/3588946}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-56634}, pages = {1 -- 27}, abstract = {The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for the known quantum algorithms. We present the first quantum implementation of join ordering, which is one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes that can be processed with current quantum annealers. However, we also confirm that limits of early-stage technology are quickly reached. Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future quantum computers, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and possible problem size. In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.}, language = {en} } @article{ThorScherzingerSpecht, author = {Thor, Andreas and Scherzinger, Stefanie and Specht, G{\"u}nther}, title = {Editorial}, series = {Datenbank-Spektrum}, volume = {14}, journal = {Datenbank-Spektrum}, number = {2}, publisher = {Springer}, doi = {10.1007/s13222-014-0162-1}, pages = {81 -- 84}, language = {de} } @inproceedings{StoerlMuellerTekleabetal., author = {St{\"o}rl, Uta and M{\"u}ller, Daniel and Tekleab, Alexander and Tolale, Stephane and Stenzel, Julian and Klettke, Meike and Scherzinger, Stefanie and Storl, Uta and Muller, Daniel}, title = {Curating Variational Data in Application Development}, series = {2018 IEEE 34th International Conference on Data Engineering, 16-19 April 2018, Paris, France}, booktitle = {2018 IEEE 34th International Conference on Data Engineering, 16-19 April 2018, Paris, France}, publisher = {IEEE}, doi = {10.1109/ICDE.2018.00187}, pages = {1605 -- 1608}, abstract = {Building applications for processing data lakes is a software engineering challenge. We present Darwin, a middleware for applications that operate on variational data. This concerns data with heterogeneous structure, usually stored within a schema-flexible NoSQL database. Darwin assists application developers in essential data and schema curation tasks: Upon request, Darwin extracts a schema description, discovers the history of schema versions, and proposes mappings between these versions. Users of Darwin may interactively choose which mappings are most realistic. Darwin is further capable of rewriting queries at runtime, to ensure that queries also comply with legacy data. Alternatively, Darwin can migrate legacy data to reduce the structural heterogeneity. Using Darwin, developers may thus evolve their data in sync with their code. In our hands-on demo, we curate synthetic as well as real-life datasets.}, language = {en} } @inproceedings{MauererScherzinger, author = {Mauerer, Wolfgang and Scherzinger, Stefanie}, title = {Nullius in Verba: Reproducibility for Database Systems Research, Revisited}, series = {2021 IEEE 37th International Conference on Data Engineering (ICDE 2021): 19-22 April 2021, Chania, Greece}, booktitle = {2021 IEEE 37th International Conference on Data Engineering (ICDE 2021): 19-22 April 2021, Chania, Greece}, editor = {Ailamaki, Anastasia}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-7281-9184-3}, doi = {10.1109/ICDE51399.2021.00270}, pages = {2377 -- 2380}, abstract = {Over the last decade, reproducibility of experimental results has been a prime focus in database systems research, and many high-profile conferences award results that can be independently verified. Since database systems research involves complex software stacks that non-trivially interact with hardware, sharing experimental setups is anything but trivial: Building a working reproduction package goes far beyond providing a DOI to some repository hosting data, code, and setup instructions.This tutorial revisits reproducible engineering in the face of state-of-the-art technology, and best practices gained in other computer science research communities. In particular, in the hands-on part, we demonstrate how to package entire system software stacks for dissemination. To ascertain long-term reproducibility over decades (or ideally, forever), we discuss why relying on open source technologies massively employed in industry has essential advantages over approaches crafted specifically for research. Supplementary material shows how version control systems that allow for non-linearly rewriting recorded history can document the structured genesis behind experimental setups in a way that is substantially easier to understand, without involvement of the original authors, compared to detour-ridden, strictly historic evolution.}, language = {en} } @inproceedings{KlettkeAwolinStoerletal., author = {Klettke, Meike and Awolin, Hannes and St{\"o}rl, Uta and M{\"u}ller, Daniel and Scherzinger, Stefanie and Storl, Uta and Muller, Daniel}, title = {Uncovering the evolution history of data lakes}, series = {2017 IEEE International Conference on Big Data (Big Data),,11-14 Dec. 2017, Boston, MA, USA}, booktitle = {2017 IEEE International Conference on Big Data (Big Data),,11-14 Dec. 2017, Boston, MA, USA}, publisher = {IEEE}, doi = {10.1109/BigData.2017.8258204}, pages = {2462 -- 2471}, abstract = {Data accumulating in data lakes can become inaccessible in the long run when its semantics are not available. The heterogeneity of data formats and the sheer volumes of data collections prohibit cleaning and unifying the data manually. Thus, tools for automated data lake analysis are of great interest. In this paper, we target the particular problem of reconstructing the schema evolution history from data lakes. Knowing how the data is structured, and how this structure has evolved over time, enables programmatic access to the lake. By deriving a sequence of schema versions, rather than a single schema, we take into account structural changes over time. Moreover, we address the challenge of detecting inclusion dependencies. This is a prerequisite for mapping between succeeding schema versions, and in particular, detecting nontrivial changes such as a property having been moved or copied. We evaluate our approach for detecting inclusion dependencies using the MovieLens dataset, as well an adaption of a dataset containing botanical descriptions, to cover specific edge cases.}, language = {en} } @inproceedings{MauererRamsauerLucasetal., author = {Mauerer, Wolfgang and Ramsauer, Ralf and Lucas, Edson R. F. and Scherzinger, Stefanie}, title = {Silentium! Run-Analyse-Eradicate the Noise out of the DB/OS Stack}, series = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2021): 13.-17. September 2021, Dresden, Deutschland}, booktitle = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2021): 13.-17. September 2021, Dresden, Deutschland}, publisher = {Gesellschaft f{\"u}r Informatik}, doi = {10.18420/btw2021-21}, pages = {397 -- 421}, abstract = {When multiple tenants compete for resources, database performance tends to suffer. Yet there are scenarios where guaranteed sub-millisecond latencies are crucial, such as in real-time data processing, IoT devices, or when operating in safety-critical environments. In this paper, we study how to make query latencies deterministic in the face of noise (whether caused by other tenants or unrelated operating system tasks). We perform controlled experiments with an in-memory database engine in a multi-tenant setting, where we successively eradicate noisy interference from within the system software stack, to the point where the engine runs close to bare-metal on the underlying hardware. We show that we can achieve query latencies comparable to the database engine running as the sole tenant, but without noticeably impacting the workload of competing tenants. We discuss these results in the context of ongoing efforts to build custom operating systems for database workloads, and point out that for certain use cases, the margin for improvement is rather narrow. In fact, for scenarios like ours, existing operating systems might just be good enough, provided that they are expertly configured. We then critically discuss these findings in the light of a broader family of database systems (e.g., including disk-based), and how to extend the approach of this paper accordingly. Low-latency databases; tail latency; real-time databases; bounded-time query processing; DB-OS co-engineering}, language = {de} } @inproceedings{ScherzingerStoerlKlettke, author = {Scherzinger, Stefanie and St{\"o}rl, Uta and Klettke, Meike}, title = {A Datalog-based protocol for lazy data migration in agile NoSQL Application development}, series = {Proceedings of the 15th Symposium on Database Programming Languages : SPLASH '15: Conference on Systems, Programming, Languages, and Applications: Software for Humanity, Pittsburgh PA USA, 27.10.2015 - 27.10.2015}, booktitle = {Proceedings of the 15th Symposium on Database Programming Languages : SPLASH '15: Conference on Systems, Programming, Languages, and Applications: Software for Humanity, Pittsburgh PA USA, 27.10.2015 - 27.10.2015}, editor = {Cheney, James and Neumann, Thomas}, publisher = {ACM}, address = {New York, NY, USA}, isbn = {9781450339025}, doi = {10.1145/2815072.2815078}, pages = {41 -- 44}, abstract = {We address a practical challenge in agile web development against NoSQL data stores: Upon a new release of the web application, entities already persisted in production no longer match the application code. Rather than migrating all legacy entities eagerly (prior to the release) and at the cost of application downtime, lazy data migration is a popular alternative: When a legacy entity is loaded by the application, all pending structural changes are applied. Yet correctly migrating legacy data from several releases back, involving more than one entity at-a-time, is not trivial. In this paper, we propose a holistic Datalog ¬non-rec model model for reading, writing, and migrating data. In implementing our model, we may blend established Datalog evaluation algorithms, such as an incremental evaluation with certain rules evaluated bottom-up, and certain rules evaluated top-down with sideways information passing. Our systematic approach guarantees that from the viewpoint of the application, it remains transparent whether data is migrated eagerly or lazily.}, language = {en} } @inproceedings{HauboldSchildgenScherzingeretal., author = {Haubold, Florian and Schildgen, Johannes and Scherzinger, Stefanie and Deßloch, Stefan}, title = {ControVol Flex: Flexible Schema Evolution for NoSQL Application Development}, series = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2017) : 17. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssyteme" (DBIS) : 06.-10.03.2017 in Stuttgart Deutschland}, booktitle = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2017) : 17. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssyteme" (DBIS) : 06.-10.03.2017 in Stuttgart Deutschland}, editor = {Mitschang, Bernhard}, publisher = {Gesellschaft f{\"u}r Informatik e.V. (GI)}, address = {Bonn}, abstract = {We demonstrate ControVol Flex, an Eclipse plugin for controlled schema evolution in Java applications backed by NoSQL document stores. The sweet spot of our tool are applications that are deployed continuously against the same production data store: Each new release may bring about schema changes that conflict with legacy data already stored in production. The type system internal to the predecessor tool ControVol is able to detect common schema conflicts, and enables developers to resolve them with the help of object-mapper annotations. Our new tool ControVol Flex lets developers choose their schema-migration strategy, whether all legacy data is to be migrated eagerly by means of NotaQL transformation scripts, or lazily, as declared by object-mapper annotations. Our tool is even capable of carrying out both strategies in combination, eagerly migrating data in the background, while lazily migrating data that is meanwhile accessed by the application. From the viewpoint of the application, it remains transparent how legacy data is migrated: Every read access yields an entity that matches the structure that the current application code expects. Our live demo shows how ControVol Flex gracefully solves a broad range of common schema-evolution tasks.}, language = {en} } @inproceedings{StoerlTekleabKlettkeetal., author = {St{\"o}rl, Uta and Tekleab, Alexander and Klettke, Meike and Scherzinger, Stefanie and Storl, Uta}, title = {In for a Surprise When Migrating NoSQL Data}, series = {2018 IEEE 34th International Conference on Data Engineering (ICDE), 16-19 April 2018, Paris, France}, booktitle = {2018 IEEE 34th International Conference on Data Engineering (ICDE), 16-19 April 2018, Paris, France}, publisher = {IEEE}, doi = {10.1109/ICDE.2018.00202}, pages = {1662}, abstract = {Schema-flexible NoSQL data stores lend themselves nicely for storing versioned data, a product of schema evolution. In this lightning talk, we apply pending schema changes to records that have been persisted several schema versions back. We present first experiments with MongoDB and Cassandra, where we explore the trade-off between applying chains of pending changes stepwise (one after the other), and as composite operations. Contrary to intuition, composite migration is not necessarily faster. The culprit is the computational overhead for deriving the compositions. However, caching composition formulae achieves a speed up: For Cassandra, we can cut the runtime by nearly 80\%. Surprisingly, the relative speedup seems to be system-dependent. Our take away message is that in applying pending schema changes in NoSQL data stores, we need to base our design decisions on experimental evidence rather than on intuition alone.}, language = {en} } @article{KlettkeStoerlScherzinger, author = {Klettke, Meike and St{\"o}rl, Uta and Scherzinger, Stefanie}, title = {Herausforderungen bei der Anwendungsentwicklung mit schema-flexiblen NoSQL-Datenbanken}, series = {HMD Praxis der Wirtschaftsinformatik}, volume = {53}, journal = {HMD Praxis der Wirtschaftsinformatik}, number = {4}, publisher = {Springer}, doi = {10.1365/s40702-016-0234-9}, pages = {428 -- 442}, abstract = {NoSQL-Datenbanksysteme sind in den letzten Jahren sehr popul{\"a}r geworden, gute Gr{\"u}nde sprechen f{\"u}r ihren Einsatz: Eine attraktive Eigenschaft vieler Systeme ist ihre Schema-Flexibilit{\"a}t, die insbesondere in der agilen Anwendungsentwicklung Vorteile bietet. Durch horizontale Skalierbarkeit erm{\"o}glichen NoSQL-Datenbanksysteme eine effiziente Verarbeitung großer Datenmengen. Einige Systeme, die f{\"u}r die Datenhaltung interaktiver Anwendungen konzipiert sind, k{\"o}nnen zudem hochfrequente Nutzeranfragen bedienen. Diesen Vorteilen stehen eine Reihe von Nachteilen gegen{\"u}ber, aus denen sich neue Herausforderungen f{\"u}r die Anwendungsentwicklung ergeben: Fehlende Standards bei den Anfragesprachen erschweren die Entwicklung datenbanksystemunabh{\"a}ngiger Anwendungen. Schema-Flexibilit{\"a}t im Datenbankmanagementsystem f{\"u}hrt dazu, dass die Verantwortung f{\"u}r das Schema-Management in die Anwendung verlagert wird. Im vorliegenden Beitrag werden wesentliche Herausforderungen identifiziert und L{\"o}sungsans{\"a}tze aus Forschung und Praxis vorgestellt. Dabei liegt der Fokus auf schema-flexiblen NoSQL-Datenbanksystemen, mit einem aggregat-orientierten Datenmodell, d. h. Key-Value Datenbanksysteme, dokumentenorientierten Datenbanksystemen und Column-Family Datenbanksystemen. NoSQL data stores have become very popular over the last years, as good reasons are justifying their application: One attractive feature of many systems is their schema flexibility, which may be preferable in agile software development projects. Due to their horizontal scalability, NoSQL data stores make it possible to efficiently process large amounts of data. Some systems, designed as data backends for interactive applications, can also manage highly frequent user requests. Apart from these advantages, there are also downsides to NoSQL data stores that create new challenges for software development: Missing standards in query languages make it difficult to build data store independent applications. Schema flexibility in the data store shifts the responsibility for schema management into the application. This article identifies substantial challenges as well as solution statements from research and practice. The focus of our survey is on schema-flexible NoSQL data management systems with an aggregate-oriented data model, i. e., key-value data management systems, as well as document and column family data management systems.}, language = {de} } @inproceedings{KlettkeStoerlShenavaietal., author = {Klettke, Meike and St{\"o}rl, Uta and Shenavai, Manuel and Scherzinger, Stefanie and Storl, Uta}, title = {NoSQL schema evolution and big data migration at scale}, series = {2016 IEEE International Conference on Big Data (Big Data), 5-8 Dec. 2016, Washington, DC}, booktitle = {2016 IEEE International Conference on Big Data (Big Data), 5-8 Dec. 2016, Washington, DC}, publisher = {IEEE}, doi = {10.1109/BigData.2016.7840924}, pages = {2764 -- 2774}, abstract = {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.}, language = {en} } @inproceedings{MoellerBertonKlettkeetal., author = {M{\"o}ller, Mark Lukas and Berton, Nicolas and Klettke, Meike and Scherzinger, Stefanie and St{\"o}rl, Uta}, title = {jHound: Large-Scale Profiling of Open JSON Data}, series = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2019), 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS) : 4.-8. M{\"a}rz 2019 in Rostock}, volume = {289}, booktitle = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2019), 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS) : 4.-8. M{\"a}rz 2019 in Rostock}, publisher = {GI - Gesellschaft f{\"u}r Informatik}, address = {Bonn}, isbn = {978-3-88579-683-1}, pages = {557 -- 560}, abstract = {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.}, language = {en} } @inproceedings{HillenbrandLevchenkoStoerletal., author = {Hillenbrand, Andrea and Levchenko, Maksym and St{\"o}rl, Uta and Scherzinger, Stefanie and Klettke, Meike}, title = {MigCast : Putting a Price Tag on Data Model Evolution in NoSQL Data Stores}, series = {Proceedings of the 2019 International Conference on Management of Data (SIGMOD/PODS '19) June 2019, Amsterdam, Netherlands}, booktitle = {Proceedings of the 2019 International Conference on Management of Data (SIGMOD/PODS '19) June 2019, Amsterdam, Netherlands}, editor = {Boncz, Peter and Manegold, Stefan and Ailamaki, Anastasia and Deshpande, Amol and Kraska, Tim}, publisher = {ACM}, address = {New York, NY, USA}, isbn = {9781450356435}, doi = {10.1145/3299869.3320223}, pages = {1925 -- 1928}, abstract = {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.}, language = {en} } @incollection{StoerlMuellerKlettkeetal., author = {St{\"o}rl, Uta and M{\"u}ller, Daniel and Klettke, Meike and Scherzinger, Stefanie}, title = {Enabling Efficient Agile Software Development of NoSQL-backed Applications}, series = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2017) : 17. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssyteme" (DBIS) : 06.-10.03.2017 in Stuttgart Deutschland}, booktitle = {Datenbanksysteme f{\"u}r Business, Technologie und Web (BTW 2017) : 17. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssyteme" (DBIS) : 06.-10.03.2017 in Stuttgart Deutschland}, editor = {Mitschang, Bernhard}, publisher = {Gesellschaft f{\"u}r Informatik e.V. (GI)}, address = {Bonn}, isbn = {978-3-88579-659-6}, abstract = {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.}, language = {en} } @inproceedings{ScherzingerSombachWiechetal., author = {Scherzinger, Stefanie and Sombach, Stephanie and Wiech, Katharina and Klettke, Meike and St{\"o}rl, Uta}, title = {Datalution: a tool for continuous schema evolution in NoSQL-backed web applications}, series = {QUDOS 2016: Proceedings of the 2nd International Workshop on Quality-Aware DevOps}, booktitle = {QUDOS 2016: Proceedings of the 2nd International Workshop on Quality-Aware DevOps}, publisher = {ACM}, doi = {10.1145/2945408.2945416}, pages = {38 -- 39}, abstract = {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.}, language = {en} } @article{KlettkeScherzingerStoerl, author = {Klettke, Meike and Scherzinger, Stefanie and St{\"o}rl, Uta}, title = {Datenbanken ohne Schema?}, series = {Datenbank-Spektrum}, volume = {14}, journal = {Datenbank-Spektrum}, number = {2}, publisher = {Springer}, doi = {10.1007/s13222-014-0156-z}, pages = {119 -- 129}, abstract = {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 h{\"a}ufige Releases und entsprechend h{\"a}ufige {\"A}nderungen am Datenmodell auszeichnet. In diesem Artikel geben wir einen {\"U}berblick {\"u}ber die besonderen Herausforderungen der agilen Anwendungsentwicklung gegen schemalose NoSQL-Datenbanksysteme. Wir stellen Strategien f{\"u}r die Schema-Evolution aus der Praxis vor, und postulieren unsere Vision einer eigenen Schema-Management-Komponente f{\"u}r NoSQL-Datenbanksysteme, die f{\"u}r eine kontinuierliche und systematische Schema-Evolution ausgelegt ist.}, language = {de} } @article{StoerlKlettkeScherzinger, author = {St{\"o}rl, Uta and Klettke, Meike and Scherzinger, Stefanie}, title = {Kurz erkl{\"a}rt: Objekt-NoSQL-Mapping}, series = {Datenbank-Spektrum}, volume = {16}, journal = {Datenbank-Spektrum}, number = {1}, publisher = {Springer}, doi = {10.1007/s13222-016-0212-y}, pages = {83 -- 87}, language = {de} } @inproceedings{MauererScherzinger, author = {Mauerer, Wolfgang and Scherzinger, Stefanie}, title = {Educating Future Software Architects in the Art and Science of Analysing Software Data.}, series = {SEUH 2020: Software Engineering im Unterricht der Hochschulen, Tagungsband des 17. Workshops "Software Engineering im Unterricht der Hochschulen", Innsbruck, {\"O}sterreich, 26. - 27.02.2020}, booktitle = {SEUH 2020: Software Engineering im Unterricht der Hochschulen, Tagungsband des 17. Workshops "Software Engineering im Unterricht der Hochschulen", Innsbruck, {\"O}sterreich, 26. - 27.02.2020}, editor = {Krusche, Stephan and Wagner, Stefan}, publisher = {RWTH Aachen}, pages = {56 -- 60}, abstract = {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.}, language = {en} } @article{ScherzingerThor, author = {Scherzinger, Stefanie and Thor, Andreas}, title = {Cloud-Technologien in der Hochschullehre - Pflicht oder K{\"u}r?}, series = {Datenbank-Spektrum}, volume = {14}, journal = {Datenbank-Spektrum}, number = {2}, publisher = {Springer Nature}, doi = {10.1007/s13222-014-0161-2}, pages = {131 -- 134}, abstract = {Ein eigenes Themenheft zum Datenmanagement in der Cloud dient uns als Anlass, die Pr{\"a}senz 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 {\"u}ber zwanzig Hochschulen nahmen an der Umfrage teil. Es zeigt sich deutlich, dass sich das Thema „Cloud" in der Hochschullehre zunehmend etabliert, jedoch {\"u}berwiegend als erg{\"a}nzendes Angebot, und seltener in der grundst{\"a}ndigen Lehre verankert. Wir fassen die Ergebnisse unserer Umfrage zusammen und wagen Deutungsversuche.}, language = {de} } @article{Scherzinger, author = {Scherzinger, Stefanie}, title = {Build your own SQL-on-Hadoop Query Engine A Report on a Term Project in a Master-level Database Course}, series = {ACM SIGMOD Record}, volume = {48}, journal = {ACM SIGMOD Record}, number = {2}, publisher = {ACM}, doi = {10.1145/3377330.3377336}, pages = {33 -- 38}, abstract = {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.}, language = {en} } @inproceedings{ScherzingerSeifertWiese, author = {Scherzinger, Stefanie and Seifert, Christin and Wiese, Lena}, title = {The Best of Both Worlds: Challenges in Linking Provenance and Explainability in Distributed Machine Learning}, series = {2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 7-10 July 2019, Dallas, TX, USA}, booktitle = {2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 7-10 July 2019, Dallas, TX, USA}, publisher = {IEEE}, doi = {10.1109/ICDCS.2019.00161}, pages = {1620 -- 1629}, abstract = {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.}, language = {en} } @inproceedings{ScherzingerMauererKondylakis, author = {Scherzinger, Stefanie and Mauerer, Wolfgang and Kondylakis, Haridimos}, title = {DeBinelle: Semantic Patches for Coupled Database-Application Evolution}, series = {2021 IEEE 37th International Conference on Data Engineering (ICDE 2021): 19-22 April 2021, Chania, Greece}, booktitle = {2021 IEEE 37th International Conference on Data Engineering (ICDE 2021): 19-22 April 2021, Chania, Greece}, editor = {Ailamaki, Anastasia}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-7281-9184-3}, doi = {10.1109/ICDE51399.2021.00307}, pages = {2697 -- 2700}, abstract = {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.}, language = {en} }