Learned Cardinalities: Estimating Correlated Joins with Deep Learning

We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning significantly enhances the quality of cardinality estimation, which is the core problem in query optimization.Comment: CIDR 2019. https://github.com/andreaskipf/learnedcardinalitie

Estimating Cardinalities with Deep Sketches

We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approach to cardinality estimation that can capture correlations between columns, even across tables. Our demonstration allows users to define such sketches on the TPC-H and IMDb datasets, monitor the training process, and run ad-hoc queries against trained sketches. We also estimate query cardinalities with HyPer and PostgreSQL to visualize the gains over traditional cardinality estimators.Comment: To appear in SIGMOD'1

Enigmatic Intraplate Volcanism: A geochronological and geochemical approach for the Marie Byrd Seamounts (Antarctica) and the Christmas Island Seamount Province (Indian Ocean)

The dissertation at hand comprises three independent chapters The first chapter contains for the first time a comprehensive data set from the enigmatic Marie Byrd Seamount province including morphological, geochronological, and geochemical data, combined with additional data for the De Gerlache and Peter I Island volcanic complexes. The centerpiece of this study is the 40Ar/39Ar dating of 9 samples including the Marie Byrd Seamounts and the submarine base of Peter I Island. The second chapter is focused on a geochronological and geochemical study presenting new 40Ar/39Ar age and XRF, ICPMS element concentration data, from recovered plutonic and dyke rocks of the Pine Island Bay region, Marie Byrd Land. The third chapter encompasses a major and trace element study on diffuse intraplate volcanism of the Christmas Island Seamount Province (CHRISP), situated in the northeastern Indian Ocean

Graph Convolutional Matrix Completion

We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. Our model shows competitive performance on standard collaborative filtering benchmarks. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods.Comment: 9 pages, 3 figures, updated with additional experimental evaluatio

Magnetic Minerals of the Lower Poleslide Member of the Brule Formation, Badlands National Park

The White River Group in Badlands National Park is significant because it contains abundant mammal fossils. Many of these fossils are unique to the Great Plains and one of the major challenges has been to learn where these fossils fit in the geologic global time scale. Paleomagnetic studies have been instrumental in correlating and dating these faunas. The purpose of this study is to look at the specific magnetic mineralogy for a complete stratigraphic column of the lower Poleslide Member of the Brule Formation at Cedar Pass. This study attempts to determine what minerals provide the paleomagnetic remanence in these rocks. This study used three techniques to determine the quantities and kinds of magnetic minerals in the samples and determine if there are any significant patterns in the amounts of magnetic minerals or in the types of magnetic minerals. The magnetic minerals were removed from crushed rock samples with a strong bar magnet. Portions of the separated magnetic minerals were treated with hydrochloric acid (HCl), to separate the easily dissolved magnetite grains from other resistant magnetic materials. Four samples were analyzed with a scanning electron microscope to determine the composition of the magnetic minerals. These techniques were used to determine if there are significant patterns in the amounts and kinds of magnetic materials through the stratigraphic section. The overall quantities of magnetic minerals in the rock samples ranted from 0.05% to 0.63% of the total rock mass. The most significant results show that the quantities of HCl resistant minerals changes at 31.5 meters above the base of the stratigraphic section. Below this level, acid-resistant magnetic minerals occur in low concentrations in many of the treated samples. Above the 31.5 m level, these acidresistant minerals are essentially absent, occurring in only trace amounts ( total rock mass). The SEM analysis is not conclusive in confidently identifying the specific minerals. However, the evidence suggests the magnetic minerals contain magnetite, titanomagnitite or hemo-ilmenite and perhaps ulv ӧspinel