TY - JOUR A1 - Bonifati, Angela A1 - Mior, Michael J. A1 - Naumann, Felix A1 - Noack, Nele Sina T1 - How inclusive are we? BT - an analysis of gender diversity in database venues JF - SIGMOD record / Association for Computing Machinery, Special Interest Group on Management of Data N2 - ACM SIGMOD, VLDB and other database organizations have committed to fostering an inclusive and diverse community, as do many other scientific organizations. Recently, different measures have been taken to advance these goals, especially for underrepresented groups. One possible measure is double-blind reviewing, which aims to hide gender, ethnicity, and other properties of the authors.
We report the preliminary results of a gender diversity analysis of publications of the database community across several peer-reviewed venues, and also compare women's authorship percentages in both single-blind and double-blind venues along the years. We also obtained a cross comparison of the obtained results in data management with other relevant areas in Computer Science. Y1 - 2022 U6 - https://doi.org/10.1145/3516431.3516438 SN - 0163-5808 SN - 1943-5835 VL - 50 IS - 4 SP - 30 EP - 35 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Caruccio, Loredana A1 - Deufemia, Vincenzo A1 - Naumann, Felix A1 - Polese, Giuseppe T1 - Discovering relaxed functional dependencies based on multi-attribute dominance JF - IEEE transactions on knowledge and data engineering N2 - With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (fds). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through fds, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed fds (rfds) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose Domino, a new discovery algorithm for rfds that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring rfds. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm. KW - Complexity theory KW - Approximation algorithms KW - Big Data KW - Distributed KW - databases KW - Semantics KW - Lakes KW - Functional dependencies KW - data profiling KW - data cleansing Y1 - 2020 U6 - https://doi.org/10.1109/TKDE.2020.2967722 SN - 1041-4347 SN - 1558-2191 VL - 33 IS - 9 SP - 3212 EP - 3228 PB - Institute of Electrical and Electronics Engineers CY - New York, NY ER - TY - JOUR A1 - Koßmann, Jan A1 - Papenbrock, Thorsten A1 - Naumann, Felix T1 - Data dependencies for query optimization BT - a survey JF - The VLDB journal : the international journal on very large data bases / publ. on behalf of the VLDB Endowment N2 - Effective query optimization is a core feature of any database management system. While most query optimization techniques make use of simple metadata, such as cardinalities and other basic statistics, other optimization techniques are based on more advanced metadata including data dependencies, such as functional, uniqueness, order, or inclusion dependencies. This survey provides an overview, intuitive descriptions, and classifications of query optimization and execution strategies that are enabled by data dependencies. We consider the most popular types of data dependencies and focus on optimization strategies that target the optimization of relational database queries. The survey supports database vendors to identify optimization opportunities as well as DBMS researchers to find related work and open research questions. KW - Query optimization KW - Query execution KW - Data dependencies KW - Data profiling KW - Unique column combinations KW - Functional dependencies KW - Order dependencies KW - Inclusion dependencies KW - Relational data KW - SQL Y1 - 2021 U6 - https://doi.org/10.1007/s00778-021-00676-3 SN - 1066-8888 SN - 0949-877X VL - 31 IS - 1 SP - 1 EP - 22 PB - Springer CY - Berlin ; Heidelberg ; New York ER - TY - JOUR A1 - Loster, Michael A1 - Koumarelas, Ioannis A1 - Naumann, Felix T1 - Knowledge transfer for entity resolution with siamese neural networks JF - ACM journal of data and information quality N2 - The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity-duplicates-into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise.
We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent. KW - Entity resolution KW - duplicate detection KW - transfer learning KW - neural KW - networks KW - metric learning KW - similarity learning KW - data quality Y1 - 2021 U6 - https://doi.org/10.1145/3410157 SN - 1936-1955 SN - 1936-1963 VL - 13 IS - 1 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Vitagliano, Gerardo A1 - Jiang, Lan A1 - Naumann, Felix T1 - Detecting layout templates in complex multiregion files JF - Proceedings of the VLDB Endowment N2 - Spreadsheets are among the most commonly used file formats for data management, distribution, and analysis. Their widespread employment makes it easy to gather large collections of data, but their flexible canvas-based structure makes automated analysis difficult without heavy preparation. One of the common problems that practitioners face is the presence of multiple, independent regions in a single spreadsheet, possibly separated by repeated empty cells. We define such files as "multiregion" files. In collections of various spreadsheets, we can observe that some share the same layout. We present the Mondrian approach to automatically identify layout templates across multiple files and systematically extract the corresponding regions. Our approach is composed of three phases: first, each file is rendered as an image and inspected for elements that could form regions; then, using a clustering algorithm, the identified elements are grouped to form regions; finally, every file layout is represented as a graph and compared with others to find layout templates. We compare our method to state-of-the-art table recognition algorithms on two corpora of real-world enterprise spreadsheets. Our approach shows the best performances in detecting reliable region boundaries within each file and can correctly identify recurring layouts across files. Y1 - 2022 U6 - https://doi.org/10.14778/3494124.3494145 SN - 2150-8097 VL - 15 IS - 3 SP - 646 EP - 658 PB - Association for Computing Machinery CY - New York ER - TY - BOOK A1 - Meinel, Christoph A1 - Döllner, Jürgen Roland Friedrich A1 - Weske, Mathias A1 - Polze, Andreas A1 - Hirschfeld, Robert A1 - Naumann, Felix A1 - Giese, Holger A1 - Baudisch, Patrick A1 - Friedrich, Tobias A1 - Böttinger, Erwin A1 - Lippert, Christoph A1 - Dörr, Christian A1 - Lehmann, Anja A1 - Renard, Bernhard A1 - Rabl, Tilmann A1 - Uebernickel, Falk A1 - Arnrich, Bert A1 - Hölzle, Katharina T1 - Proceedings of the HPI Research School on Service-oriented Systems Engineering 2020 Fall Retreat N2 - Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application. Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns. The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the research school, this technical report covers a wide range of topics. These include but are not limited to: Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; and Services Specification, Composition, and Enactment. N2 - Der Entwurf und die Realisierung dienstbasierender Architekturen wirft eine Vielzahl von Forschungsfragestellungen aus den Gebieten der Softwaretechnik, der Systemmodellierung und -analyse, sowie der Adaptierbarkeit und Integration von Applikationen auf. Komponentenorientierung und WebServices sind zwei Ansätze für den effizienten Entwurf und die Realisierung komplexer Web-basierender Systeme. Sie ermöglichen die Reaktion auf wechselnde Anforderungen ebenso, wie die Integration großer komplexer Softwaresysteme. "Service-Oriented Systems Engineering" repräsentiert die Symbiose bewährter Praktiken aus den Gebieten der Objektorientierung, der Komponentenprogrammierung, des verteilten Rechnen sowie der Geschäftsprozesse und berücksichtigt auch die Integration von Geschäftsanliegen und Informationstechnologien. Die Klausurtagung des Forschungskollegs "Service-oriented Systems Engineering" findet einmal jährlich statt und bietet allen Kollegiaten die Möglichkeit den Stand ihrer aktuellen Forschung darzulegen. Bedingt durch die Querschnittstruktur des Kollegs deckt dieser Bericht ein weites Spektrum aktueller Forschungsthemen ab. Dazu zählen unter anderem Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; sowie Services Specification, Composition, and Enactment. T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 138 KW - Hasso Plattner Institute KW - research school KW - Ph.D. retreat KW - service-oriented systems engineering KW - Hasso-Plattner-Institut KW - Forschungskolleg KW - Klausurtagung KW - Service-oriented Systems Engineering Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-504132 SN - 978-3-86956-513-2 SN - 1613-5652 SN - 2191-1665 IS - 138 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - JOUR A1 - Bonnet, Philippe A1 - Dong, Xin Luna A1 - Naumann, Felix A1 - Tözün, Pınar T1 - VLDB 2021 BT - Designing a hybrid conference JF - SIGMOD record N2 - The 47th International Conference on Very Large Databases (VLDB'21) was held on August 16-20, 2021 as a hybrid conference. It attracted 180 in-person attendees in Copenhagen and 840 remote attendees. In this paper, we describe our key decisions as general chairs and program committee chairs and share the lessons we learned. Y1 - 2021 SN - 0163-5808 SN - 1943-5835 VL - 50 IS - 4 SP - 50 EP - 53 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Koumarelas, Ioannis A1 - Papenbrock, Thorsten A1 - Naumann, Felix T1 - MDedup BT - duplicate detection with matching dependencies JF - Proceedings of the VLDB Endowment N2 - Duplicate detection is an integral part of data cleaning and serves to identify multiple representations of same real-world entities in (relational) datasets. Existing duplicate detection approaches are effective, but they are also hard to parameterize or require a lot of pre-labeled training data. Both parameterization and pre-labeling are at least domain-specific if not dataset-specific, which is a problem if a new dataset needs to be cleaned. For this reason, we propose a novel, rule-based and fully automatic duplicate detection approach that is based on matching dependencies (MDs). Our system uses automatically discovered MDs, various dataset features, and known gold standards to train a model that selects MDs as duplicate detection rules. Once trained, the model can select useful MDs for duplicate detection on any new dataset. To increase the generally low recall of MD-based data cleaning approaches, we propose an additional boosting step. Our experiments show that this approach reaches up to 94% F-measure and 100% precision on our evaluation datasets, which are good numbers considering that the system does not require domain or target data-specific configuration. Y1 - 2020 U6 - https://doi.org/10.14778/3377369.3377379 SN - 2150-8097 VL - 13 IS - 5 SP - 712 EP - 725 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Koumarelas, Ioannis A1 - Jiang, Lan A1 - Naumann, Felix T1 - Data preparation for duplicate detection JF - Journal of data and information quality : (JDIQ) N2 - Data errors represent a major issue in most application workflows. Before any important task can take place, a certain data quality has to be guaranteed by eliminating a number of different errors that may appear in data. Typically, most of these errors are fixed with data preparation methods, such as whitespace removal. However, the particular error of duplicate records, where multiple records refer to the same entity, is usually eliminated independently with specialized techniques. Our work is the first to bring these two areas together by applying data preparation operations under a systematic approach prior to performing duplicate detection.
Our process workflow can be summarized as follows: It begins with the user providing as input a sample of the gold standard, the actual dataset, and optionally some constraints to domain-specific data preparations, such as address normalization. The preparation selection operates in two consecutive phases. First, to vastly reduce the search space of ineffective data preparations, decisions are made based on the improvement or worsening of pair similarities. Second, using the remaining data preparations an iterative leave-one-out classification process removes preparations one by one and determines the redundant preparations based on the achieved area under the precision-recall curve (AUC-PR). Using this workflow, we manage to improve the results of duplicate detection up to 19% in AUC-PR. KW - data preparation KW - data wrangling KW - record linkage KW - duplicate detection KW - similarity measures Y1 - 2020 U6 - https://doi.org/10.1145/3377878 SN - 1936-1955 SN - 1936-1963 VL - 12 IS - 3 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Hameed, Mazhar A1 - Naumann, Felix T1 - Data Preparation BT - a survey of commercial tools JF - SIGMOD record N2 - Raw data are often messy: they follow different encodings, records are not well structured, values do not adhere to patterns, etc. Such data are in general not fit to be ingested by downstream applications, such as data analytics tools, or even by data management systems. The act of obtaining information from raw data relies on some data preparation process. Data preparation is integral to advanced data analysis and data management, not only for data science but for any data-driven applications. Existing data preparation tools are operational and useful, but there is still room for improvement and optimization. With increasing data volume and its messy nature, the demand for prepared data increases day by day.
To cater to this demand, companies and researchers are developing techniques and tools for data preparation. To better understand the available data preparation systems, we have conducted a survey to investigate (1) prominent data preparation tools, (2) distinctive tool features, (3) the need for preliminary data processing even for these tools and, (4) features and abilities that are still lacking. We conclude with an argument in support of automatic and intelligent data preparation beyond traditional and simplistic techniques. KW - data quality KW - data cleaning KW - data wrangling Y1 - 2020 U6 - https://doi.org/10.1145/3444831.3444835 SN - 0163-5808 SN - 1943-5835 VL - 49 IS - 3 SP - 18 EP - 29 PB - Association for Computing Machinery CY - New York ER -