Large-scale data analytics using statistical machine learning (ML), popularly called
advanced analytics, underpins many modern data-driven applications. The data …

Indexes are models: a\btree-Index can be seen as a model to map a key to the position of a
record within a sorted array, a Hash-Index as a model to map a key to a position of a record …

This paper provides a survey of the state-of-the-art and future directions of one of the most
important emerging technologies within business analytics (BA), namely prescriptive …

The rising need for custom machine learning (ML) algorithms and the growing data sizes
that require the exploitation of distributed, data-parallel frameworks such as MapReduce or …

Next generation high-performance RDMA-capable networks will require a fundamental
rethinking of the design and architecture of modern distributed DBMSs. These systems are …

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Modern analytics applications combine multiple functions from different libraries and …

In-memory database management systems (DBMSs) are a key component of modern on-
line analytic processing (OLAP) applications, since they provide low-latency access to large …

Machine Learning models are often composed of pipelines of transformations. While this
design allows to efficiently execute single model components at training time, prediction …

© 2018 VLDB Endowment. In order to democratize data science, we need to fundamentally
rethink the current analytics stack, from the user interface to the “guts.“Most importantly …

The query engines of most modern database systems are either based on vectorization or
data-centric code generation. These two state-of-the-art query processing paradigms are …