Transfermatrix-DMRG for dynamics of stochastic models and thermodynamics of fermionic models

The present work applies a numerical method, namely the transfer-matrix density-matrix renormalization group (TMRG), to two seemingly different types of models. In a first part the TMRG is used to investigate the thermodynamics of one-dimensional fermionic models. A second part deals with a novel TMRG method for one-dimensional stochastic models, whose development is an integral part of the thesis. First, the traditional TMRG algorithm for quantum systems is outlined in its historical context. Two different variants are presented, following works of Xiang et al. and Sirker and Klümper, respectively. The basic idea of the method is to map the thermodynamics of a one-dimensional quantum model by Trotter-Suzuki decomposition onto a two-dimensional statistical one. The latter is then solved by a transfer-matrix approach combined with the iterative numerical procedure of White's density-matrix renormalization-group (DMRG) algorithm. Thereby precise computations of various thermodynamic properties, such as thermodynamic potentials, susceptibilities, thermal expectation values and correlation functions are possible. As the first part of the thesis deals with fermionic models, we next review some basics about the theory of strongly correlated fermions in one dimension. Thereupon we elucidate the so-called Hirsch model, which recently gained a lot of theoretic interest in respect to high-temperature superconductivity. It extends the well-studied Hubbard model by an off-diagonal bond-charge interaction term. The current state of research is briefly summarized and mainly refers to ground state properties. Showing numerical TMRG results we then investigate and discuss the almost unknown thermodynamics of the Hirsch model. Various phases are identified and characterized in terms of Tomonaga-Luttinger and Luther-Emery liquid properties, in accordance with previous studies of the ground state. As an important result, superconducting singlet-pair correlation lenths are observed to dominate the physics at finite temperatures in a certain spin-gaped phase. Subsequent to our thermodynamic studies, we turn to the second part of the thesis and outline some theoretic basics of stochastic models. Most notably is the important formal analogy of the master equation, that describes the dynamics of the model, to a Schrödinger equation in imaginary time. This analogy is used to construct a stochastic TMRG algorithm almost similar to the quantum case, that facilitates the computation of dynamic properties, e.g. the local density of particles. We intensively focus on interesting mathematical properties of the stochastic transfer-matrix. As an astonishing result it is found, that the temporal evolution of the non-equilibrium process is reflected by a certain causal structure of the stochastic TMRG. But even if this new approach seems to be promising at first glance, severe numerical problems limit significantly its practical use. In order to solve these instabilities we propose a completely new variant of the algorithm, which we call stochastic light-cone corner-transfermatrix DMRG (LCTMRG). As suggested by its name, the LCTMRG makes use of the causal structure mentioned above and combines it with the stochastic TMRG algorithm. Applications of the LCTMRG onto various reaction-diffusion models verify highly precise numerical data and a great improve compared to the old algorithm by several orders of magnitude. Additionally it is stressed, that the newly proposed analysis tool provides some considerable advantages to common simulation techniques

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

Klassismus: Theorie-Missverständnisse als Folge fehlender anti-klassistischer Selbstorganisation? Replik zu Christian Baron: Klasse und Klassismus, PROKLA 175

Christian Baron formuliert in seinem Artikel „Klasse und Klassismus“ (Baron 2014) an bisherigen Klassismus-Analysen eine terminologische, methodische und handlungstheoretische Kritik. Bezugspunkte der Kritik sind jeweils von mir (mit)verfasste Texte. Alle drei Kritiken weisen sowohl Missverständnisse als auch inhaltliche Differenzen auf. In dieser Replik kann aus Platzgründen nur versucht werden, die Missverständnisse deutlich zu machen, eine Fortsetzung der inhaltlichen Diskussion bietet sich für spätere Ausgaben der PROKLA an. Grundlegend ist anzumerken, dass „Klassismus“ zunächst als Empowerment- Begriff in der links-emanzipatorischen Bewegung geprägt wurde und dass eine gesellschaftsanalytisch-theoriebasierte Schärfung des Begriffs nicht die praktische Verwendbarkeit einschränken sollte. Erschwerend kommt zu diesem Doppelcharakter der Funktion des Begriffs (Empowerment, Gesellschaftsanalyse) die doppelte Thematisierung in den unterschiedlichen Feldern von Klassen- und Diskriminierungstheorie hinzu. Zudem fehlt bislang ein eigenständiger Ort der Diskussion, so dass Klassismus in Zeitschriften diskutiert wird, die marxistische (PROKLA 175), antirassistische (ZAG. Antirassistische Zeitung 2014, Nr. 67) oder feministische (an.schläge 2014, Nr. 7), aber keine dezidiert antiklassistische Ausrichtungen haben. Hierdurch sind Missverständnisse wie die folgenden erklärbar

RadixSpline: A Single-Pass Learned Index

Recent research has shown that learned models can outperform state-of-the-art index structures in size and lookup performance. While this is a very promising result, existing learned structures are often cumbersome to implement and are slow to build. In fact, most approaches that we are aware of require multiple training passes over the data. We introduce RadixSpline (RS), a learned index that can be built in a single pass over the data and is competitive with state-of-the-art learned index models, like RMI, in size and lookup performance. We evaluate RS using the SOSD benchmark and show that it achieves competitive results on all datasets, despite the fact that it only has two parameters.Comment: Third International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM 2020

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

Shaping the Next Incarnation of Business Intelligence - Towards a Flexibly Governed Network of Information Integration and Analysis Capabilities

The body of knowledge generated by Business Intelligence (BI) research is constantly extended by a stream of heterogeneous technological and or- ganizational innovations. This paper shows how these can be bundled to a new vision for BI that is aligned with new requirements coming from socio- technical macro trends. The building blocks of the vision come from five research strings that have been ex- tracted from an extensive literature re- view: BI and Business Process Man- agement, BI across enterprise borders, new approaches of dealing with un- structured data, agile and user-driven BI, and new concepts for BI gover- nance. The macro trend of the diffu- sion of cyber-physical systems is used to illustrate the argumentation. The realization of this vision comes with an array of open research ques- tions and requires the coordination of research initiatives from a variety of dis- ciplines. Due to the embedded nature of the addressed topics within gen- eral research areas of the Information Systems (IS) discipline and the linking pins that come with the underlying Dynamic Capabilities Approach such research provides a contribution to IS