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

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Applying extreme value statistics in meteorology and environmental science requires accurate estimators on extreme value indices that can be around zero. Without having prior knowledge on the sign of the extreme value indices, the probability weighted moment (PWM) estimator is a favorable candidate. As most other estimators on the extreme value index, the PWM estimator bears an asymptotic bias. In this paper, we develop a bias correction procedure for the PWM estimator. Moreover, we provide bias-corrected PWM estimators for high quantiles and, when the extreme value index is negative, the endpoint of a distribution. The choice of k, the number of high order statistics used for estimation, is crucial in applications. The asymptotically unbiased PWM estimators allows the choice of higher level k, which results in a lower asymptotic variance. Moreover, since the bias-corrected PWM estimators can be applied for a wider range of k compared to the original PWM estimator, one gets more flexibility in choosing k for finite sample applications. All advantages become apparent in simulations and an environmental application on estimating “once per 10,000 years” still water level at Hoek van Holland, The Netherlands.@en