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 - TY - CHAP A1 - Cerqueus, Thomas A1 - de Almeida, Eduardo Cunha A1 - Scherzinger, Stefanie ED - Gangemi, Aldo ED - Leonardi, Stefano ED - Panconesi, Alessandro T1 - ControVol: Let Yesterday's Data Catch Up with Today's Application Code T2 - Proceedings of the 24th International Conference on World Wide Web (WWW '15) ; Florence Italy, 18.05.2015 - 22.0.2015 N2 - In building software-as-a-service applications, a flexible development environment is key to shipping early and often. Therefore, schema-flexible data stores are becoming more and more popular. They can store data with heterogeneous structure, allowing for new releases to be pushed frequently, without having to migrate legacy data first. However, the current application code must continue to work with any legacy data that has already been persisted in production. To let legacy data structurally "catch up" with the latest application code, developers commonly employ object mapper libraries with life-cycle annotations. Yet when used without caution, they can cause runtime errors and even data loss. We present ControVol, an IDE plugin that detects evolutionary changes to the application code that are incompatible with legacy data. ControVol warns developers already at development time, and even suggests automatic fixes for lazily migrating legacy data when it is loaded into the application. Thus, ControVol ensures that the structure of legacy data can catch up with the structure expected by the latest software release. Y1 - 2015 SN - 9781450334730 U6 - https://doi.org/10.1145/2740908.2742719 SP - 15 EP - 16 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Cerqueus, Thomas A1 - Cunha de Almeida, Eduardo A1 - Scherzinger, Stefanie T1 - Safely Managing Data Variety in Big Data Software Development T2 - 2015 IEEE/ACM 1st International Workshop on Big Data Software Engineering, 23-23 May 2015, Florence, Italy N2 - We consider the task of building Big Data software systems, offered as software-as-a-service. These applications are commonly backed by NoSQL data stores that address the proverbial Vs of Big Data processing: NoSQL data stores can handle large volumes of data and many systems do not enforce a global schema, to account for structural variety in data. Thus, software engineers can design the data model on the go, a flexibility that is particularly crucial in agile software development. However, NoSQL data stores commonly do not yet account for the veracity of changes when it comes to changes in the structure of persisted data. Yet this is an inevitable consequence of agile software development. In most NoSQL-based application stacks, schema evolution is completely handled within the application code, usually involving object mapper libraries. Yet simple code refactorings, such as renaming a class attribute at the source code level, can cause data loss or runtime errors once the application has been deployed to production. We address this pain point by contributing type checking rules that we have implemented within an IDE plug in. Our plug in ControVol statically type checks the object mapper class declarations against the code release history. ControVol is thus capable of detecting common yet risky cases of mismatched data and schema, and can even suggest automatic fixes. KW - Big data KW - history KW - Java KW - Loading KW - NoSQL data stores KW - object mapping KW - Production KW - Runtime KW - schema evolution KW - Software KW - type checking Y1 - 2015 U6 - https://doi.org/10.1109/BIGDSE.2015.9 SP - 4 EP - 10 PB - IEEE ER - TY - CHAP A1 - Heckner, Markus A1 - Bazo, Alexander A1 - Wolff, Christian A1 - Scherzinger, Stefanie T1 - Karel relearns c: teaching good software engineering practices in cs1 with karel the robot T2 - 2018 IEEE Global Engineering Education Conference (EDUCON), 17-20 April 2018, Santa Cruz de Tenerife, Spain N2 - This paper describes our implementation, teaching philosophy, and experiences with our C-based version of the widely known Karel the Robot introductory programming micro-language. Karel enables students to programmatically solve problems, using the C language, in a graphical two-dimensional world by moving the robot around while checking and manipulating its surroundings. We use Karel to solve the dilemma of either demanding too much or not enough from students during the first weeks of an introductory CS course, as interesting problems can be solved with limited input from lectures. Karel enables problem solving from day one of CS1, and encourages good software engineering practices such as top-down design from the beginning. We outline typical problems in the first weeks of CS1. We present a short overview of existing Karel implementations in various programming languages and our rationale for re-implementing Karel. We present our teaching philosophy and use of Karel in the classroom. We demonstrate how Karel is being used from a student perspective, along with a typical programming task. We discuss preliminary results of a survey and interviews with students from a first course in which Karel was used. KW - Computer languages KW - Education KW - Problem-solving KW - Programming profession KW - Robots KW - Writing Y1 - 2018 U6 - https://doi.org/10.1109/EDUCON.2018.8363402 SP - 1447 EP - 1454 PB - IEEE ER - TY - CHAP A1 - Scherzinger, Stefanie A1 - Cerqueus, Thomas A1 - Cunha de Almeida, Eduardo T1 - ControVol: A framework for controlled schema evolution in NoSQL application development T2 - 2015 IEEE 31st International Conference on Data Engineering, 13-17 April 2015, Seoul, Korea (South) N2 - Building scalable web applications on top of NoSQL data stores is becoming common practice. Many of these data stores can easily be accessed programmatically, and do not enforce a schema. Software engineers can design the data model on the go, a flexibility that is crucial in agile software development. The typical tasks of database schema management are now handled within the application code, usually involving object mapper libraries. However, today’s Integrated Development Environments (IDEs) lack the proper tool support when it comes to managing the combined evolution of the application code and of the schema. Yet simple refactorings such as renaming an attribute at the source code level can cause irretrievable data loss or runtime errors once the application is serving in production. In this demo, we present ControVol, a framework for controlled schema evolution in application development against NoSQL data stores. ControVol is integrated into the IDE and statically type checks object mapper class declarations against the schema evolution history, as recorded by the code repository. ControVol is capable of warning of common yet risky cases of mismatched data and schema. ControVol is further able to suggest quick fixes by which developers can have these issues automatically resolved. KW - Databases KW - Google KW - history KW - Java KW - Production KW - Runtime KW - Software Y1 - 2015 SN - 978-1-4799-7964-6 U6 - https://doi.org/10.1109/ICDE.2015.7113402 SP - 1464 EP - 1467 PB - IEEE ER - TY - CHAP A1 - Seifert, Christin A1 - Scherzinger, Stefanie A1 - Wiese, Lena T1 - Towards Generating Consumer Labels for Machine Learning Models T2 - 2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI), 12-14 Dec. 2019, Dallas, TX, USA N2 - Machine learning (ML) based decision making is becoming commonplace. For persons affected by ML-based decisions, a certain level of transparency regarding the properties of the underlying ML model can be fundamental. In this vision paper, we propose to issue consumer labels for trained and published ML models. These labels primarily target machine learning lay persons, such as the operators of an ML system, the executors of decisions, and the decision subjects themselves. Provided that consumer labels comprehensively capture the characteristics of the trained ML model, consumers are enabled to recognize when human intelligence should supersede artificial intelligence. In the long run, we envision a service that generates these consumer labels (semi-)automatically. In this paper, we survey the requirements that an ML system should meet, and correspondingly, the properties that an ML consumer label could capture. We further discuss the feasibility of operationalizing and benchmarking these requirements in the automated generation of ML consumer labels. KW - Artificial-intelligence-machine-learning-consumer-labels-transparency-x-AI KW - Biological system modeling KW - Data models KW - Machine learning KW - Measurement KW - Predictive models KW - Robustness KW - Training Y1 - 2019 U6 - https://doi.org/10.1109/CogMI48466.2019.00033 SP - 173 EP - 179 PB - IEEE ER - TY - CHAP A1 - Holubová, Irena A1 - Scherzinger, Stefanie ED - Groppe, Sven ED - Gruenwald, Le T1 - Unlocking the potential of nextGen multi-model databases for semantic big data projects T2 - Proceedings of the International Workshop on Semantic Big Data - (SBD '19) 05.07.2019 - 05.07.2019, Amsterdam, Netherlands N2 - A new vision in semantic big data processing is to create enterprise data hubs, with a 360° view on all data that matters to a corporation. As we discuss in this paper, a new generation of multi-model database systems seems a promising architectural choice for building such scalable, non-native triple stores. In this paper, we first characterize this new generation of multi-model databases. Then, discussing an example scenario, we show how they allow for agile and flexible schema management, spanning a large design space for creative and incremental data modelling. We identify the challenge of generating sound triple-views from data stored in several, interlinked models, for SPARQL querying. We regard this as one of several appealing research challenges where the semantic big data and the database architecture community may join forces. Y1 - 2019 SN - 9781450367660 U6 - https://doi.org/10.1145/3323878.3325807 SP - 1 EP - 6 PB - ACM Press CY - New York ER - TY - CHAP A1 - Pilven, Matthieu A1 - Scherzinger, Stefanie A1 - d’Orazio, Laurent ED - Guizzardi, Giancarlo ED - Gailly, Frederik ED - Suzana Pitangueira Maciel, Rita T1 - On Complex Value Relations in Hive T2 - Advances in Conceptual Modeling, ER 2020 workshops CMAI, CMLS, CMOMM4FAIR, CoMoNoS, EmpER, Vienna, Austria, November 3-6, 2020, Proceedings N2 - In this paper, we raise the question how data architects model their data for processing in Apache Hive. This well-known SQL-on-Hadoop engine supports complex value relations, where attribute types need not be atomic. In fact, this feature seems to be one of the prominent selling points, e.g., in Hive reference books. In an empirical study, we analyze Hive schemas in open source repositories. We examine to which extent practitioners make use of complex value relations and accordingly, whether they write queries over complex types. Understanding which features are actively used will help make the right decisions in setting up benchmarks for SQL-on-Hadoop engines, as well as in choosing which query operators to optimize for. KW - Complex value relations KW - Empirical study KW - Hive Y1 - 2019 SN - 978-3-030-34145-9 U6 - https://doi.org/10.1007/978-3-030-34146-6_13 SP - 146 EP - 156 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Maiwald, Benjamin A1 - Riedle, Benjamin A1 - Scherzinger, Stefanie ED - Guizzardi, Giancarlo ED - Gailly, Frederik ED - Suzana Pitangueira Maciel, Rita T1 - What Are Real JSON Schemas Like? T2 - Advances in Conceptual Modeling N2 - Recently, the semantics of the JSON Schema format, a de-facto standard for JSON schema declarations, has been formalized. It turns out that JSON Schema is a surprisingly complex schema language based on an open document semantics. In this paper, we present a first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing. Y1 - 2019 SN - 978-3-030-34145-9 U6 - https://doi.org/10.1007/978-3-030-34146-6_9 VL - 11787 SP - 95 EP - 105 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Filho, Edson Ramiro Lucas A1 - de Almeida, Eduardo Cunha A1 - Scherzinger, Stefanie T1 - Don’t Tune Twice: Reusing Tuning Setups for SQL-on-Hadoop Queries T2 - Conceptual Modeling : 38th International Conference, ER 2019, Salvador, Brazil, November 4-7, 2019, Proceedings N2 - SQL-on-Hadoop processing engines have become state-of-the-art in data lake analysis. However, the skills required to tune such systems are rare. This has inspired automated tuning advisors which profile the query workload and produce tuning setups for the low-level MapReduce jobs. Yet with highly dynamic query workloads, repeated re-tuning costs time and money in IaaS environments. In this paper, we focus on reducing the costs for up-front tuning. At the heart of our approach is the observation that a SQL query is compiled into a query plan of MapReduce jobs. While the plans differ from query to query, single jobs tend to be similar between queries. We introduce the notion of the code signature of a MapReduce job and, based on this, our concept of job similarity. We show that we can effectively recycle tuning setups from similar MapReduce jobs already profiled. In doing so, we can leverage any third-party tuning adviser for MapReduce engines. We are able to show that by recycling tuning setups, we can reduce the time spent on profiling by 50% in the TPC-H benchmark. Y1 - 2019 U6 - https://doi.org/10.1007/978-3-030-33223-5_9 SP - 93 EP - 107 PB - Springer CY - Cham ER - TY - CHAP A1 - Scherzinger, Stefanie A1 - de Almeida, Eduardo Cunha A1 - Ickert, Felipe A1 - Del Fabro, Marcos Didonet T1 - On the necessity of model checking NoSQL database schemas when building SaaS applications T2 - Proceedings of the 2013 International Workshop on Testing the Cloud - TTC 2013 N2 - The design of the NoSQL schema has a direct impact on the scalability of web applications. Especially for developers with little experience in NoSQL stores, the risks inherent in poor schema design can be incalculable. Worse yet, the issues will only manifest once the application has been deployed, and the growing user base causes highly concurrent writes. In this paper, we present a model checking approach to reveal scalability bottlenecks in NoSQL schemas. Our approach draws on formal methods from tree automata theory to perform a conservative static analysis on both the schema and the expected write-behavior of users. We demonstrate the impact of schema-inherent bottlenecks for a popular NoSQL store, and show how concurrent writes can ultimately lead to a considerable share of failed transactions. Y1 - 2013 U6 - https://doi.org/10.1145/2489295.2489297 SP - 1 EP - 6 PB - ACM CY - New York, NY ER -