Overview of Vector Database
Overview of Vector Database
A vector database is a type of database that will primarily store vector data a...
Stream Data Processing
Overview of parallel distributed processing in machine learning and examples of on-prem/cloud implementations
Parallel and Distributed Processing in Machine Learning
Parallel and distributed processing in machine learni...
Overview of communication functions in distributed IOT systems and examples of implementation
Distributed IOT Systems
A distributed Internet of Things (IoT) system is one in which different devices an...
Examples of Wireless IOT Control Implementations in Various Languages
Wireless IOT Control
Typically, IoT devices are small devices with sensors and actuators, and use wi...
Overview of online forecasting technology and various applications and implementations
About Online Forecasting
Online Prediction (Online Prediction) is a method that uses models to make predictio...
Overview of online learning and various algorithms, application examples and specific implementations
Online Learning
Online Learning is a method of learning by sequentially updating a model under conditions whe...
Specific examples of WoT implementations
introduction
In "About WoT (Web of Things) Technology" I gave an overview of the WoT. This time, I would l...
Overview of Petri-net technology and its combination with artificial intelligence technology and various implementations
Petri Net Overview
Petri nets are a descriptive model of discrete event systems proposed by Petri in 1962...
Overview of RFID and its applications and integration with AI technology
Overview of RFID and its applications in digital transformation, artificial intelligence and machine learning tasks and its integration with AI technologies cost reduction, IMPINJ Inc, M700, privacy, radio interference, distance limitation, standardization, smart cities, logistics, Supply chain management, asset management, Web 3.0, WoT, IoT, manufacturing, healthcare, agriculture, logistics, RFID tags, RFID readers, databases
Protected: Leveraging Apache Spark for Distributed Data Processing – Developing and Executing Applications
Leveraging Apache Spark to enable distributed data processing for digital transformation, artificial intelligence, and machine learning tasks -Application development and execution (forced termination, yarn-client mode, yarn-cluster mode, YARN, and YARN) management commands, cluster, python, Clojure, Shell, AWS, Glue, sparkplug, spark-shell, spark-submit, Nodemanager, HDFS, Spark applications, Scala, sbt, plugin.sbt, build.sbt build.sbt, build, sbt-assembly plugin, JAR file)