Data lakes are becoming increasingly prevalent for Big Data management and data analytics. In contrast to traditional 'schema-on-write' approaches such as data warehouses, data lakes are repositories storing raw data in its original formats and providing a common access interface. Despite the strong interest raised from both academia and industry, there is a large body of ambiguity regarding the definition, functions and available technologies for data lakes. A complete, coherent picture of data lake challenges and solutions is still missing. This survey reviews the development, architectures, and systems of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing approaches and systems based on their provided functions for data lakes, which makes this survey a useful technical reference for designing, implementing and deploying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey will motivate the future development of data lake research and practice.

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This chapter introduces the most important features of data lake systems, and from there it outlines an architecture for these systems. The vision for a data lake system is based on a generic and extensible architecture with a unified data model, facilitating the ingestion, storage and metadata management over heterogeneous data sources. The chapter also introduces a real-life data lake system called Constance that can deal with sophisticated metadata management over raw data extracted from heterogeneous data sources. With embedded query rewriting engines that support structured data and semi-structured data, Constance provides users with a unified interface for query processing and data exploration. Big Data has undoubtedly become one of the most important challenges in database research. A MetaData Management System for data lakes should provide means to handle metadata in different data models (relational, XML, JSON, RDF), and should be able to represent mappings between the metadata entries.@en

Schema mappings express the relationships between sources in data interoperability scenarios and can be expressed in various formalisms. Source-to-target tuple-generating dependencies (s-t tgds) can be easily used for data transformation or query rewriting tasks. Second-order tgds (SO tgds) are more expressive as they can also represent the composition and inversion of s-t tgds. Yet, the expressive power of SO tgds comes with the problem of undecidability for some reasoning tasks. Nested tgds and plain SO tgds are mapping languages that are between s-t tgds and SO tgds in terms of expressivity, and their properties have been studied in the recent years. Nested tgds are less expressive than plain SO tgds, but the logical equivalence problem for nested tgds is decidable. However, a detailed characterization of plain SO tgds that have an equivalent nested tgd is missing. In this paper, we present an algorithmic solution for translating plain SO tgds into nested tgds. The algorithm computes one or more nested tgds, if a given plain SO tgd is rewritable. Furthermore, we are able to give a detailed characterization of those plain SO tgds for which an equivalent nested tgd exists, based on the structural properties of the source predicates and Skolem functions in the plain SO tgd. In the evaluation, we show that our algorithm covers a larger subset of plain SO tgds than previous approaches and that a rewriting can be computed efficiently although the algorithm has the exponential complexity.@en

Functional dependencies are important for the definition of constraints and relationships that have to be satisfied by every database instance. Relaxed functional dependencies (RFDs) can be used for data exploration and profiling in datasets with lower data quality. In this work, we present an approach for RFD discovery in heterogeneous data lakes. More specifically, the goal of this work is to find RFDs from structured, semi-structured, and graph data. Our solution brings novelty to this problem in the following aspects: (1) We introduce a generic metamodel to the problem of RFD discovery, which allows us to define and detect RFDs for data stored in heterogeneous sources in an integrated manner. (2) We apply clustering techniques during RFD discovery for partitioning and pruning. (3) We performed an intensive evaluation with nine datasets, which shows that our approach is effective for discovering meaningful RFDs, reducing redundancy, and detecting inconsistent data.@en

In this work, we present a Metadata Framework in the direction of extending intelligence mechanisms from the Cloud to the Edge. To this end, we build on our previously introduced notion of Data Lagoons—the analogous to Data Lakes at the network edge—and we introduce a novel architecture and Metadata model for the efficient interaction between Data Lagoons and Data Lakes. We identify the service and data planes of our architecture and we illustrate the application of our framework on a use case from the TPCx-IoT benchmark. To our knowledge, our approach is the first one to examine the integration of Data Lakes with Edge components, taking under consideration data and infrastructure resources of Edge Nodes.@en

New levels of cross-domain collaboration between manufacturing companies throughout the supply chain are anticipated to bring benefits to both suppliers and consumers of products. Enabling a fine-grained sharing and analysis of data among different stakeholders in an automated manner, such a vision of an Internet of Production (IoP) introduces demanding challenges to the communication, storage, and computation infrastructure in production environments. In this work, we present three example cases that would benefit from an IoP (a fine blanking line, a high pressure die casting process, and a connected job shop) and derive requirements that cannot be met by today's infrastructure. In particular, we identify three orthogonal research objectives: (i) real-time control of tightly integrated production processes to offer seamless low-latency analysis and execution, (ii) storing and processing heterogeneous production data to support scalable data stream processing and storage, and (iii) secure privacy-aware collaboration in production to provide a basis for secure industrial collaboration. Based on a discussion of state-of-the-art approaches for these three objectives, we create a blueprint for an infrastructure acting as an enabler for an IoP.@en

JSON has become one of the most popular data formats. Yet studies on JSON data integration (DI) are scarce. In this work, we study one of the key DI tasks, nested mapping generation in the context of integrating heterogeneous JSON based data sources. We propose a novel mapping representation, namely bucket forest mappings that models the nested mappings in an efficient and native manner. We show experimentally the practicality of our approach over six real world data sets. Moreover, via intensive experiments over synthetic scenarios we demonstrate that our approach scales well to the increasing metadata complexity of DI scenarios.@en

The increasing popularity of NoSQL systems has lead to the model of polyglot persistence, in which several data management systems with different data models are used. Data lakes realize the polyglot persistence model by collecting data from various sources, by storing the data in its original structure, and by providing the datasets for querying and analysis. Thus, one of the key tasks of data lakes is to provide a unified querying interface, which is able to rewrite queries expressed in a general data model into a union of queries for data sources spanning heterogeneous data stores. To address this challenge, we propose a novel framework for query rewriting that combines logical methods for data integration based on declarative mappings with a scalable big data query processing system (i.e., Apache Spark) to efficiently execute the rewritten queries and to reconcile the query results into an integrated dataset. Because of the diversity of NoSQL systems, our approach is based on a flexible and extensible architecture that currently supports the major data structures such as relational data, semi-structured data (e.g., JSON, XML), and graphs. We show the applicability of our query rewriting engine with six real world datasets and demonstrate its scalability using an artificial data integration scenario with multiple storage systems.@en

Interdisciplinary research and development projects in medical engineering bene_t from well selected collaboration partners. The process of _nding such partners from often unfamiliar _elds is di_cult, but can be supported by an expert pro_le that is based on patent analysis and classifying the patents to competence _elds in medical engineering. Patent analysis and categorization are di_cult and require the analysis of the semantic content. Hence, we propose a twofold approach using a large controlled vocabulary, a smaller competence _eld ontology, and an alignment between them to assign patents to a certain competence _eld. The approach has two parts: a Topic Map approach and a Publi- cation approach. We evaluate these approaches and its components in several ways. Furthermore, we compare four di_erent ways to assign a patent to a competence _eld and show that the semantic wealth of a large biomedical ontology is bene_cial to the classi_cation task @en

Medical engineering (ME) is an interdisciplinary domain with short innovation cycles. Usually, researchers from several fields cooperate in ME research projects. To support the identification of suitable partners for a project, we present an integrated approach for patent classification combining ideas from topic modeling, ontology modeling & matching, bibliometric analysis, and data integration. First evaluation results show that the use of semantic technologies in patent classification can indeed increase the quality of the results.@en

The heterogeneity of sources in Big Data systems requires new integration approaches which can handle the large volume of the data as well as its variety. Data lakes have been proposed to reduce the upfront integration costs and to provide more _exibility in integrating and analyzing information. In data lakes, data from the sources is copied in its original structure to a repository; only a syntactic integration is done as data is stored in a common semi-structured format. Metadata plays an important role, as the source data is not loaded into an integrated repository with a uni_ed schema; the data has to come with its own metadata. This paper presents GEMMS, a Generic and Extensible Metadata Management System for data lakes which extracts metadata from the sources and manages the structural and semantical information in an extensible metamodel. The system has been developed with a focus on scienti_c data management in the life sciences which is often only _le-based with limited query functionality. The evaluation shows the usefulness in this domain, but also the _exibility and extensibility of our approach which makes GEMMS also applicable to other domains. @en

As the challenge of our time, Big Data still has many research hassles, especially the variety of data. The high diversity of data sources often results in information silos, a collection of non-integrated data management systems with heterogeneous schemas, query languages, and APIs. Data Lake systems have been proposed as a solution to this problem, by providing a schema-less repository for raw data with a common access interface. However, just dumping all data into a data lake without any metadata management, would only lead to a 'data swamp'. To avoid this, we propose Constance, a Data Lake system with sophisticated metadata management over raw data extracted from heterogeneous data sources. Constance discovers, extracts, and summarizes the structural metadata from the data sources, and annotates data and metadata with semantic information to avoid ambiguities. With embedded query rewriting engines supporting structured data and semi-structured data, Constance provides users a unified interface for query processing and data exploration. During the demo, we will walk through each functional component of Constance. Constance will be applied to two real-life use cases in order to show attendees the importance and usefulness of our generic and extensible data lake system.@en

Successful research and development projects start with finding the right partners for the venture. Especially for interdisciplinary projects, this is a difficult and tedious task as experts from foreign domains are not known. Fur thermore, the transfer of knowledge from research into practice is becoming more important in research projects to enable the quick application of research results. This is in particular relevant for projects in medical engineering. Patents and publications contain technical knowledge which can be exploited to find suitable experts. Patents are usually more product-oriented as the inventors have to describe an application area and products might be protected by patents. On the other hand, scientific publications represent the state-of-the-art in research. The challenge is finding the right mixture of research- or application-oriented experts from different domains. Hence, we propose a recommender system for finding experts for a certain topic based on patent topic clustering, ontologies, and ontology matching, which maps patents to corresponding innovation fields. The medical engineering domain serves as a first test bed, since projects in this area are highly interdisciplinary.@en

In addition to volume and velocity, Big data is also characterized by its variety. Variety in structure and semantics requires new integration approaches which can resolve the integration challenges also for large volumes of data. Data lakes should reduce the upfront integration costs and provide a more flexible way for data integration and analysis, as source data is loaded in its original structure to the data lake repository. Some syntactic transformation might be applied to enable access to the data in one common repository; however, a deep semantic integration is done only after the initial loading of the data into the data lake. Thereby, data is easily made available and can be restructured, aggregated, and transformed as required by later applications. Metadata management is a crucial component in a data lake, as the source data needs to be described by metadata to capture its semantics. We developed a Generic and Extensible Metadata Management System for data lakes (called GEMMS) that aims at the automatic extraction of metadata from a wide variety of data sources. Furthermore, the metadata is managed in an extensible metamodel that distinguishes structural and semantical metadata. The use case applied for evaluation is from the life science domain where the data is often stored only in files which hinders data access and efficient querying. The GEMMS framework has been proven to be useful in this domain. Especially, the extensibility and flexibility of the framework are important, as data and metadata structures in scientific experiments cannot be defined a priori.@en

Successful research and development projects start with finding the right partners for the venture. Especially for interdisciplinary projects, this is a difficult task as experts from foreign domains are not known. Furthermore, the transfer of knowledge from research into practice is becoming more important in research projects to enable the quick application of research results. This is in particular relevant for projects in medical engineering. Patents and publications contain technical knowledge which can be exploited to find suitable experts. Patents are usually more product-oriented as the inventors have to describe an application area and products might be protected by patents. On the other hand, scientific publications represent the state-of-the-art in research. The challenge is finding the right mixture of research- or application-oriented experts from different domains. Hence, we propose a recommender system for experts for a certain topic based on patent topic clustering, ontologies, and ontology matching, which maps patents to corresponding innovation fields. The medical engineering domain serves as a first test bed, since projects in this area are highly interdisciplinary. @en