TY  - BOOK
A1  - Albrecht, Alexander
A1  - Naumann, Felix
T1  - Understanding cryptic schemata in large extract-transform-load systems
N2  - Extract-Transform-Load (ETL) tools are used for the creation, maintenance, and evolution of data warehouses, data marts, and operational data stores. ETL workflows populate those systems with data from various data sources by specifying and executing a DAG of transformations. Over time, hundreds of individual workflows evolve as new sources and new requirements are integrated into the system. The maintenance and evolution of large-scale ETL systems requires much time and manual effort. A key problem is to understand the meaning of unfamiliar attribute labels in source and target databases and ETL transformations. Hard-to-understand attribute labels lead to frustration and time spent to develop and understand ETL workflows. We present a schema decryption technique to support ETL developers in understanding cryptic schemata of sources, targets, and ETL transformations. For a given ETL system, our recommender-like approach leverages the large number of mapped attribute labels in existing ETL workflows to produce good and meaningful decryptions. In this way we are able to decrypt attribute labels consisting of a number of unfamiliar few-letter abbreviations, such as UNP_PEN_INT, which we can decrypt to UNPAID_PENALTY_INTEREST. We evaluate our schema decryption approach on three real-world repositories of ETL workflows and show that our approach is able to suggest high-quality decryptions for cryptic attribute labels in a given schema.
N2  - Extract-Transform-Load (ETL) Tools werden häufig beim Erstellen, der Wartung und der Weiterentwicklung von Data Warehouses, Data Marts und operationalen Datenbanken verwendet. ETL Workflows befüllen diese Systeme mit Daten aus vielen unterschiedlichen Quellsystemen. Ein ETL Workflow besteht aus mehreren Transformationsschritten, die einen DAG-strukturierter Graphen bilden. Mit der Zeit entstehen hunderte individueller ETL Workflows, da neue Datenquellen integriert oder neue Anforderungen umgesetzt werden müssen. Die Wartung und Weiterentwicklung von großen ETL Systemen benötigt viel Zeit und manuelle Arbeit. Ein zentrales Problem ist dabei das Verständnis unbekannter Attributnamen in Quell- und Zieldatenbanken und ETL Transformationen. Schwer verständliche Attributnamen führen zu Frustration und hohen Zeitaufwänden bei der Entwicklung und dem Verständnis von ETL Workflows. Wir präsentieren eine Schema Decryption Technik, die ETL Entwicklern das Verständnis kryptischer Schemata in Quell- und Zieldatenbanken und ETL Transformationen erleichtert. Unser Ansatz berücksichtigt für ein gegebenes ETL System die Vielzahl verknüpfter Attributnamen in den existierenden ETL Workflows. So werden gute und aussagekräftige "Decryptions" gefunden und wir sind in der Lage Attributnamen, die aus unbekannten Abkürzungen bestehen, zu "decrypten". So wird z.B. für den Attributenamen UNP_PEN_INT als Decryption UNPAIN_PENALTY_INTEREST vorgeschlagen. Unser Schema Decryption Ansatz wurde für drei ETL-Repositories evaluiert und es zeigte sich, dass unser Ansatz qualitativ hochwertige Decryptions für kryptische Attributnamen vorschlägt.
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 60 
KW  - Extract-Transform-Load (ETL)
KW  - Data Warehouse
KW  - Datenintegration
KW  - Extract-Transform-Load (ETL)
KW  - Data Warehouse
KW  - Data Integration
Y1  - 2012
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-61257
SN  - 978-3-86956-201-8
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  - 
TY  - JOUR
A1  - Draisbach, Uwe
A1  - Christen, Peter
A1  - Naumann, Felix
T1  - Transforming pairwise duplicates to entity clusters for high-quality duplicate detection
JF  - ACM Journal of Data and Information Quality
N2  - Duplicate detection algorithms produce clusters of database records, each cluster representing a single real-world entity. As most of these algorithms use pairwise comparisons, the resulting (transitive) clusters can be inconsistent: Not all records within a cluster are sufficiently similar to be classified as duplicate. Thus, one of many subsequent clustering algorithms can further improve the result. <br /> We explain in detail, compare, and evaluate many of these algorithms and introduce three new clustering algorithms in the specific context of duplicate detection. Two of our three new algorithms use the structure of the input graph to create consistent clusters. Our third algorithm, and many other clustering algorithms, focus on the edge weights, instead. For evaluation, in contrast to related work, we experiment on true real-world datasets, and in addition examine in great detail various pair-selection strategies used in practice. While no overall winner emerges, we are able to identify best approaches for different situations. In scenarios with larger clusters, our proposed algorithm, Extended Maximum Clique Clustering (EMCC), and Markov Clustering show the best results. EMCC especially outperforms Markov Clustering regarding the precision of the results and additionally has the advantage that it can also be used in scenarios where edge weights are not available.
KW  - Record linkage
KW  - data matching
KW  - entity resolution
KW  - deduplication
KW  - clustering
Y1  - 2019
U6  - https://doi.org/10.1145/3352591
SN  - 1936-1955
SN  - 1936-1963
VL  - 12
IS  - 1
SP  - 1
EP  - 30
PB  - Association for Computing Machinery
CY  - New York
ER  - 
TY  - JOUR
A1  - Momtazi, Saeedeh
A1  - Naumann, Felix
T1  - Topic modeling for expert finding using latent Dirichlet allocation
JF  - Wiley interdisciplinary reviews : Data mining and knowledge discovery
N2  - The task of expert finding is to rank the experts in the search space given a field of expertise as an input query. In this paper, we propose a topic modeling approach for this task. The proposed model uses latent Dirichlet allocation (LDA) to induce probabilistic topics. In the first step of our algorithm, the main topics of a document collection are extracted using LDA. The extracted topics present the connection between expert candidates and user queries. In the second step, the topics are used as a bridge to find the probability of selecting each candidate for a given query. The candidates are then ranked based on these probabilities. The experimental results on the Text REtrieval Conference (TREC) Enterprise track for 2005 and 2006 show that the proposed topic-based approach outperforms the state-of-the-art profile- and document-based models, which use information retrieval methods to rank experts. Moreover, we present the superiority of the proposed topic-based approach to the improved document-based expert finding systems, which consider additional information such as local context, candidate prior, and query expansion.
Y1  - 2013
U6  - https://doi.org/10.1002/widm.1102
SN  - 1942-4787
VL  - 3
IS  - 5
SP  - 346
EP  - 353
PB  - Wiley
CY  - San Fransisco
ER  - 
TY  - BOOK
A1  - Herschel, Melanie
A1  - Naumann, Felix
T1  - Space and time scalability of duplicate detection in graph data
N2  - Duplicate detection consists in determining different representations of real-world objects in a database. Recent research has considered the use of relationships among object representations to improve duplicate detection. In the general case where relationships form a graph, research has mainly focused on duplicate detection quality/effectiveness. Scalability has been neglected so far, even though it is crucial for large real-world duplicate detection tasks. In this paper we scale up duplicate detection in graph data (DDG) to large amounts of data and pairwise comparisons, using the support of a relational database system. To this end, we first generalize the process of DDG. We then present how to scale algorithms for DDG in space (amount of data processed with limited main memory) and in time. Finally, we explore how complex similarity computation can be performed efficiently. Experiments on data an order of magnitude larger than data considered so far in DDG clearly show that our methods scale to large amounts of data not residing in main memory.
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 25 
Y1  - 2008
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-32851
SN  - 978-3-940793-46-1
ER  - 
TY  - BOOK
A1  - Meinel, Christoph
A1  - Plattner, Hasso
A1  - Döllner, Jürgen Roland Friedrich
A1  - Weske, Mathias
A1  - Polze, Andreas
A1  - Hirschfeld, Robert
A1  - Naumann, Felix
A1  - Giese, Holger
A1  - Baudisch, Patrick
T1  - Proceedings of the 7th Ph.D. Retreat of the HPI Research School on Service-oriented Systems Engineering
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. Commonly used technologies, such as J2EE and .NET, form de facto standards for the realization of complex distributed systems. Evolution of component systems has lead to web services and service-based architectures. This has been manifested in a multitude of industry standards and initiatives such as XML, WSDL UDDI, SOAP, etc. All these achievements lead to a new and promising paradigm in IT systems engineering which proposes to design complex software solutions as collaboration of contractually defined software services. 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 Scholl, this technical report covers a wide range of research topics. These include but are not limited to: Self-Adaptive Service-Oriented Systems, Operating System Support for Service-Oriented Systems, Architecture and Modeling of Service-Oriented Systems, Adaptive Process Management, Services Composition and Workflow Planning, Security Engineering of Service-Based IT Systems, Quantitative Analysis and Optimization of Service-Oriented Systems, Service-Oriented Systems in 3D Computer Graphics sowie Service-Oriented Geoinformatics.
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. Heute übliche Technologien, wie J2EE und .NET, sind de facto Standards für die Entwicklung großer verteilter Systeme. Die Evolution solcher Komponentensysteme führt über WebServices zu dienstbasierenden Architekturen. Dies manifestiert sich in einer Vielzahl von Industriestandards und Initiativen wie XML, WSDL, UDDI, SOAP. All diese Schritte führen letztlich zu einem neuen, vielversprechenden Paradigma für IT Systeme, nach dem komplexe Softwarelösungen durch die Integration vertraglich vereinbarter Software-Dienste aufgebaut werden sollen. "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 große Bandbreite aktueller Forschungsthemen ab. Dazu zählen unter anderem Self-Adaptive Service-Oriented Systems, Operating System Support for Service-Oriented Systems, Architecture and Modeling of Service-Oriented Systems, Adaptive Process Management, Services Composition and Workflow Planning, Security Engineering of Service-Based IT Systems, Quantitative Analysis and Optimization of Service-Oriented Systems, Service-Oriented Systems in 3D Computer Graphics sowie Service-Oriented Geoinformatics.
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 83 
KW  - Hasso-Plattner-Institut
KW  - Forschungskolleg
KW  - Klausurtagung
KW  - Service-oriented Systems Engineering
KW  - Hasso Plattner Institute
KW  - Research School
KW  - Ph.D. Retreat
KW  - Service-oriented Systems Engineering
Y1  - 2014
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-63490
SN  - 978-3-86956-273-5
PB  - Universitätsverlag Potsdam
CY  - Potsdam
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  - 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. <br /> 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  - 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. <br /> 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  - Birnick, Johann
A1  - Bläsius, Thomas
A1  - Friedrich, Tobias
A1  - Naumann, Felix
A1  - Papenbrock, Thorsten
A1  - Schirneck, Friedrich Martin
T1  - Hitting set enumeration with partial information for unique column combination discovery
JF  - Proceedings of the VLDB Endowment
N2  - Unique column combinations (UCCs) are a fundamental concept in relational databases. They identify entities in the data and support various data management activities. Still, UCCs are usually not explicitly defined and need to be discovered. State-of-the-art data profiling algorithms are able to efficiently discover UCCs in moderately sized datasets, but they tend to fail on large and, in particular, on wide datasets due to run time and memory limitations. <br /> In this paper, we introduce HPIValid, a novel UCC discovery algorithm that implements a faster and more resource-saving search strategy. HPIValid models the metadata discovery as a hitting set enumeration problem in hypergraphs. In this way, it combines efficient discovery techniques from data profiling research with the most recent theoretical insights into enumeration algorithms. Our evaluation shows that HPIValid is not only orders of magnitude faster than related work, it also has a much smaller memory footprint.
Y1  - 2020
U6  - https://doi.org/10.14778/3407790.3407824
SN  - 2150-8097
VL  - 13
IS  - 11
SP  - 2270
EP  - 2283
PB  - Association for Computing Machinery
CY  - [New York, NY]
ER  - 
TY  - BOOK
A1  - Lange, Dustin
A1  - Böhm, Christoph
A1  - Naumann, Felix
T1  - Extracting structured information from Wikipedia articles to populate infoboxes
N2  - Roughly every third Wikipedia article contains an infobox - a table that displays important facts about the subject in attribute-value form. The schema of an infobox, i.e., the attributes that can be expressed for a concept, is defined by an infobox template. Often, authors do not specify all template attributes, resulting in incomplete infoboxes. With iPopulator, we introduce a system that automatically populates infoboxes of Wikipedia articles by extracting attribute values from the article's text. In contrast to prior work, iPopulator detects and exploits the structure of attribute values for independently extracting value parts. We have tested iPopulator on the entire set of infobox templates and provide a detailed analysis of its effectiveness. For instance, we achieve an average extraction precision of 91% for 1,727 distinct infobox template attributes.
N2  - Ungefähr jeder dritte Wikipedia-Artikel enthält eine Infobox - eine Tabelle, die wichtige Fakten über das beschriebene Thema in Attribut-Wert-Form darstellt. Das Schema einer Infobox, d.h. die Attribute, die für ein Konzept verwendet werden können, wird durch ein Infobox-Template definiert. Häufig geben Autoren nicht für alle Template-Attribute Werte an, wodurch unvollständige Infoboxen entstehen. Mit iPopulator stellen wir ein System vor, welches automatisch Infoboxen von Wikipedia-Artikeln durch Extrahieren von Attributwerten aus dem Artikeltext befüllt. Im Unterschied zu früheren Arbeiten erkennt iPopulator die Struktur von Attributwerten und nutzt diese aus, um die einzelnen Bestandteile von Attributwerten unabhängig voneinander zu extrahieren. Wir haben iPopulator auf der gesamten Menge der Infobox-Templates getestet und analysieren detailliert die Effektivität. Wir erreichen beispielsweise für die Extraktion einen durchschnittlichen Precision-Wert von 91% für 1.727 verschiedene Infobox-Template-Attribute.
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 38 
KW  - Informationsextraktion
KW  - Wikipedia
KW  - Linked Data
KW  - Information Extraction
KW  - Wikipedia
KW  - Linked Data
Y1  - 2010
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-45714
SN  - 978-3-86956-081-6
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  -