ACID vs BASE Concepts
Understanding databases for storing, updating and analyzing data requires the understanding of two concepts: ACID and BASE.…
Data Warehousing Basiscs
Data Warehousing is applied Big Data Management and a key success factor in almost every company. Without a data warehouse,…
Why Do Companies Use Data Lakes?
Modern enterprise-level computing operations have to capture a truly monumental amount of information every single hour.…
Understanding the "simplicity" of reinforcement learning: comprehensive tips to take the trouble out of RL
This is the first article of my article series "My elaborate study notes on reinforcement learning."
*I adjusted mathematical…
Process Mining mit Fluxicon Disco – Artikelserie
Dieser Artikel der Artikelserie Process Mining Tools beschäftigt sich mit dem Anbieter Fluxicon. Das im Jahr 2010 gegründete…
Why Your Data Science Team Needs Separate Testing, Validation & Training Sets
Automated testing of machine learning models can help to dramatically reduce the amount of time and effort that your team…
My elaborate study notes on reinforcement learning
I will not tell you why, but all of a sudden I was in need of writing an article series on Reinforcement Learning. Though…
AI Platforms - A Comprehensive Guide
A comprehensive guide compiled to introduce readers to AI platforms, their types, and benefits. A concluding section to discuss…
Predictive Maintenance – Konzept und Chancen
(Maschinen)Zeit ist kostbar. Das trifft besonders auf produzierende Unternehmen zu. Denn hier gilt, jeder Stillstand einer…
ACID vs BASE Concepts
/0 Comments/in Big Data, Business Intelligence, Data Engineering, Data Warehousing, Database, Main Category /by Benjamin AunkoferUnderstanding databases for storing, updating and analyzing data requires the understanding of two concepts: ACID and BASE. This is the first article of the article series Data Warehousing Basics. The properties of ACID are being applied for databases in order to fulfill enterprise requirements of reliability and consistency. ACID is an acronym, and stands for: […]
Data Warehousing Basiscs
/0 Comments/in Big Data, Business Intelligence, Data Engineering, Data Migration, Data Warehousing, Database, ETL, Graph Database, InMemory, Main Category /by Benjamin AunkoferData Warehousing is applied Big Data Management and a key success factor in almost every company. Without a data warehouse, no company today can control its processes and make the right decisions on a strategic level as there would be a lack of data transparency for all decision makers. Bigger comanies even have multiple data […]
Why Do Companies Use Data Lakes?
/0 Comments/in Data Engineering, Data Warehousing /by Philip PileticModern enterprise-level computing operations have to capture a truly monumental amount of information every single hour. As the scale of data has grown almost exponentially over the years, so has the scale and complexity of data stores. Traditional databases couldn’t possibly keep up with the massive numbers of records that have to be created today, […]
Understanding the “simplicity” of reinforcement learning: comprehensive tips to take the trouble out of RL
/0 Comments/in Uncategorized /by Yasuto TamuraThis is the first article of my article series “My elaborate study notes on reinforcement learning.” *I adjusted mathematical notations in this article as close as possible to “Reinforcement Learning:An Introduction.” This book by Sutton and Barto is said to be almost mandatory for those who studying reinforcement learning. Also I tried to avoid as […]
The Basics of Logistic Regression in Data Science
/0 Comments/in Data Science /by Shannon FlynnData science is a field that is growing by leaps and bounds. A couple decades ago, using a machine learning program to make predictions about datasets was the purview of science fiction. Today, all you need is a computer, some solid programming, and a bit of patience, and you, too, can have your own digital […]
Process Mining mit Fluxicon Disco – Artikelserie
/0 Comments/in Business Analytics, Business Intelligence, Main Category, Process Mining, Tool Introduction, Tools /by Benjamin AunkoferDieser Artikel der Artikelserie Process Mining Tools beschäftigt sich mit dem Anbieter Fluxicon. Das im Jahr 2010 gegründete Unternehmen, bis heute geführt von den zwei Gründern Dr. Anne Rozinat und Dr. Christian W. Günther, die beide bei Prof. Wil van der Aalst in Eindhoven promovierten, sowie einem weiteren Mitarbeiter, ist eines der ersten Tool-Anbieter für Process […]
Why Your Data Science Team Needs Separate Testing, Validation & Training Sets
/0 Comments/in Data Science /by Philip PileticAutomated testing of machine learning models can help to dramatically reduce the amount of time and effort that your team has to dedicate to debugging them. Not applying these techniques properly, however, could potentially do a great deal of harm if the process is completely unmonitored and doesn’t adhere to a list of best practices. […]
My elaborate study notes on reinforcement learning
/0 Comments/in Artificial Intelligence, Data Science, Data Science Hack, Deep Learning, Machine Learning, Main Category, Use Cases /by Yasuto TamuraI will not tell you why, but all of a sudden I was in need of writing an article series on Reinforcement Learning. Though I am also a beginner in reinforcement learning field. Everything I knew was what I learned from one online lecture conducted in a lazy tone in my college. However in the […]
AI Platforms – A Comprehensive Guide
/0 Comments/in Tool Introduction /by Jaimin DaveA comprehensive guide compiled to introduce readers to AI platforms, their types, and benefits. A concluding section to discuss AI platform selection strategy with Attri’s Best of Breed approach to build AI platforms. Don’t you think that this century is really fortunate? In my opinion, the answer is yes; we witnessed technological transformations and their […]
Predictive Maintenance – Konzept und Chancen
/0 Comments/in Uncategorized /by Georg Ungerböck(Maschinen)Zeit ist kostbar. Das trifft besonders auf produzierende Unternehmen zu. Denn hier gilt, jeder Stillstand einer Anlage kostet wertvolle Produktionskapazität. Stillstände einer Maschine lassen sich nicht 100%ig vermeiden, nur können sie mit dem passenden Predictive Maintenance Konzept (im weiteren Text als PdM abgekürzt) reduziert und besser planbar gemacht werden. Mit intelligenten Add-ons wie IoT Anbindung […]