Name: Sebastian Schmidl
Type: User
Company: Hasso Plattner Institut, University of Potsdam
Bio: Software engineer and PhD student interested in scalable systems and efficient time series mining.
Location: Berlin Area, Germany
Blog: https://sebastianschmidl.de
Sebastian Schmidl's Projects
An example application for showing the ActiveObject pattern.
Actor Database System Framework using Akka
Paper about https://github.com/CodeLionX/actordb, published in the BTW 2019 – Workshopband
A toolkit for conducting machine learning tasks with time series data
Akka cluster setup that performs distributed calculations
Code for the paper Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles
A crawler and search engine for comments on Washington Post articles
DISTOD algorithm: Distributed discovery of bidirectional order dependencies
Project for "Competitive Problem Solving with Deep Learning" at the Hasso-Plattner Institute.
distributed Order-dependency Detection optimization - A HPI seminar work at the chair of Information Systems
Distributed Order Dependency Discovery Paper
A Java RTP stack with RTCP support and a clean API.
A Flink demo project using Scala and SBT that analyzes HTTP log data from NASA.
Unsupervised Distance-Based Weighted Rank Aggregation with List Pruning
Submissions for the assignments of the HPI lecture Fundamentals of Software Analytics (https://hpi.de/en/studies/courses/it-systems-engineering-ma/course/course/0/sommersemester-2019-fundamentals-of-software-analytics.html)
:symbols: A collection of GitHub issue and pull request templates
Summary Paper about the HoloClean Framework written for the Seminar Horrible Data at HPI.
Generic template for midsize and larger documents based on KOMA script classes.
Lightning-fast Extensible Neural-network Guarding The HPI
The source repository of the Metanome tool
A minimal container-like process isolation tool for linux
Detect dominant periodicity in equidistant time series
Small web application to manage my private cloud on GCP
Unofficial Python implementation of "Precision and Recall for Time Series".