About Machine learning and system architecture for data streams (time series data)
This world is full of dynamic data, not static data. For example, a huge amount of dynamic data is formed in factories, plants, transportation, economy, social networks, and so on. In the case of factories and plants, a typical sensor on an oil production platform makes 10,000 observations per minute, peaking at 100,000 o/m. In the case of mobile data, a mobile user in Milan makes 20,000 calls/SMS/data connections per minute, and 20,000 connections per minute. In the case of mobile data, mobile users in Milan make 20,000 calls/sms/data connections per minute, reaching 20,000 connections per minute and 80,000 connections at peak times, and in the case of social networks, Facebook, for example, observed 3 million likes per minute as of May 2013. as of May 2013.
Use cases where these data appear include “What is the expected timing of failure when the turbine barring starts to vibrate in the last 10 minutes? What is the expected failure time when the turbine barring starts to vibrate, as detected in the last 10 minutes?” or “Is there public transportation where people are?” or “Who is discussing the top ten topics? and so on, and solutions to these issues are required.
By handling data on such a time axis, it is possible to handle various DX and artificial intelligence applications. In addition, there are various machine learning techniques for processing such data, such as time series data analysis techniques. There are various approaches to system architecture, including IOT technology, databases, and search technology.
In this blog, I will discuss them in the following sections.
Implementation
WoT (Web of Things) will be a standardized architecture and protocol for interconnecting various devices on the Internet and enabling communication and interaction between devices. The WoT is intended to extend the Internet of Things (IoT), simplify interactions with devices, and increase interoperability.
This article describes general implementation procedures, libraries, platforms, and concrete examples of WoT implementations in python and C.
A distributed Internet of Things (IOT) system refers to a system in which different devices and sensors communicate with each other, share information, and work together. In this article, we will provide an overview and implementation examples of inter-device communication technology in this distributed IOT system.
Online learning is a method of learning by sequentially updating a model in a situation where data arrives sequentially. Unlike batch learning in ordinary machine learning, this algorithm is characterized by the fact that the model is updated each time new data arrives. This section describes various algorithms and examples of applications of on-run learning, as well as examples of implementations in python.
Online Prediction (Online Prediction) is a technique that uses models to make predictions in real time under conditions where data arrive sequentially.” Online learning, as described in “Overview of Online Learning, Various Algorithms, Application Examples, and Specific Implementations,” is characterized by the fact that models are learned sequentially but the immediacy of model application is not clearly defined, whereas online prediction is characterized by the fact that predictions are made immediately upon the arrival of new data and the results are used. characteristic.
This section discusses various applications and specific implementation examples for this online forecasting.
Bayesian Structural Time Series Model (BSTS) is a type of statistical model that models phenomena that change over time and is used for forecasting and causal inference. This section provides an overview of BSTS and its various applications and implementations.
Technical Topics
Apache Spark is an open source parallel and distributed processing infrastructure. Based on Spark Core, the engine of parallel and distributed processing, Spark consists of a set of application-specific libraries, including Spark SQL for SQL processing, Spark Streaming for stream processing, MLlib for machine learning processing, and GraphX for graph processing.
Spark Core can accept HDFS (Hadoop Distributed File System) as well as HIve, HBase, PostgreSQL, MySQL, CSV files, and other inputs as data sources.
Spark provides fast parallel distributed processing for large amounts of data, and after reading data from the data source, Spark processes it with minimal I/O to storage and network I/O. Therefore, Spark is suitable for cases where the same data is transformed in succession, or where the result set is iterated over multiple times, such as in machine learning. Spark’s features are described below in terms of “machine learning processing,” “business processing,” and “stream processing,” respectively.
Apache Spark’s data processing uses a data structure called a “Resillient Distributed Dataset (RDD),” and its programming model is to “process the RDD, generate a new RDD, and repeat the process to obtain the desired result. This is the programming model.
In this section, we will take a closer look at how this works and describe the structure and characteristics of RDDs and how the distributed structure around RDDs is implemented on clusters. First, we describe the structure and characteristics of RDDs and the processing that can be applied to RDDs.
In this article, we will discuss how to set up an environment for running Spark, which is designed to run on a cluster. However, if you want to check the behavior of an application you are creating or perform functional tests, it is possible to run Spark on a single machine, as building a cluster environment is a large and cumbersome task. In this article, we first describe how to build an environment on a single machine, and then describe the procedure for building a cluster environment.
In this article, we will discuss how to build a development environment for Apache Spark and how to build and run an application. Spark provides APIs for a variety of programming languages. In this article, we will discuss how to build an application using Scala, the language in which Apache Spark is written, under the assumption that Spark-clint is used. First, a Spark application needs to have its source code compiled and packaged into a JAR file. For this purpose, we will use sbt, a tool to manage the build process of an application development project, including compilation of source code written in Scala and Java, management of library dependencies, and packaging.
The Spark project is a cluster computing framework that emphasizes low-latency job execution and is a relatively new project that emerged from UC Berkley’s AMP Lab in 2009. Distributed File System (HDFS)), it aims to significantly accelerate job execution time by keeping much of the computation in memory.
In this article, we will discuss the key concepts required to run Spark jobs using Sparkling, a Clojure library.
GraphX will be a distributed graph processing library designed to work in conjunction with Spark. Like the MLlib library described in “Large-scale Machine Learning with Apache Spark and MLlib,” GraphX provides a set of abstractions built on top of Spark’s RDDs. By representing graph vertices and edges as RDDs, GraphX can handle very large graphs in a scalable manner.
By restricting the types of computations that can be represented and introducing techniques for partitioning and distributing graphs, graph parallel systems can execute sophisticated graph algorithms several orders of magnitude faster and more efficiently than typical data parallel systems.
- Analysis of Time Series Data (1) Introduction
- Analysis of Time Series Data (2) Basics of statistical analysis
- Analysis of Time Series Data (3) Analysis of Time Dependent Data and AR Model
- Analysis of Time Series Data(4) ARCH Model and GARCH Model
- Data Stream System Architecture Overview
Solving the data stream challenge requires a variety of requirements. The following are the characteristics required of those systems. (1) handle huge data, (2) handle stream data, (3) coordinate heterogeneous data sets, (4) handle incomplete data, (5) handle noisy data, (6) provide highly responsive (speed) answers, (7) access granular information, and (8) integrate complex domain model integration
There are two existing systems that can potentially address these issues: (1) Data Stream Management System (DSMS) and (2) Complex Event Processing (CEP) system. (2) Complex Event Processing System (CEP(Complex Event Procesing)system).
A Data Stream Management System (DSMS) is a computer program that manages the continuous flow of data. A DSMS is similar to a database management system (DBMS), but instead of executing a query once, it executes it continuously and permanently for as long as it is installed. Since most DSMSs are data-driven, a continuous query will continue to produce new results as long as data is ingested into the system.
The major challenge in a DSMS is to process a potentially infinite amount of data streams without a fixed amount of memory and random access to the data. One is compression techniques that attempt to summarize the data, and the other is windowing techniques that attempt to divide the data into (finite) portions.
Event processing is a method of tracking and analyzing (processing) a stream of information (data) about an event (event) that has occurred to reach some conclusion. [1] Complex Event Processing (CEP: Complex Event Procesing) is event processing that combines data from multiple sources [2] to infer events or patterns that suggest more complex situations. The goal of complex event processing is to reveal meaningful events (e.g., some opportunity or threat) and [3] address them as quickly as possible.
These events occur at various levels of the organization, such as sales inquiries, orders, customer inquiry calls, and so on. They may also be news articles[4], text messages, social networking postings, stock market conditions, traffic reports, weather forecasts, or other types of data. An event can be defined as a “change in state” where a measured value exceeds a predefined threshold, such as time of day, temperature, etc. CEP gives organizations a new way to analyze patterns in real time and allows the business side to better communicate with IT and service departments, analysts suggest. Analysts advocate.
Stream Resoning technology is a real-time system that uses multiple, heterogeneous, massive, and necessarily noisy streams of data to make sense of the data in order to support the decision-making processes of a very large number of simultaneous users.
The distributed platform for stream processing provided by Apche is summarized.
Apache Spark is an open source parallel and distributed processing platform. Based on Spark Core, the engine of parallel and distributed processing, Spark consists of a set of application-specific libraries: Spark SQL for SQL processing, Spark Streaming for stream processing, MLlib for machine learning processing, and GraphX for graph processing.
Spark Core can accept HDFS (Hadoop Distributed File System) as well as HIve, HBase, PostgreSQL, MySQL, CSV files, and other inputs as data sources.
Spark provides fast parallel distributed processing of large amounts of data, and after reading data from the data source, Spark processes it with minimal I/O to storage and network I/O. This allows Spark to process the same data in the same way, with the same amount of I/O to storage and network I/O. This makes Spark suitable for cases where the same data needs to be transformed in succession, or where the result set needs to be iterated over multiple times, such as in machine learning. Spark’s features are described below in terms of “machine learning processing,” “business processing,” and “stream processing,” respectively.
The topic of Daniel Metz’s dissertation at the Business & Information Systems Engineering (BISE) Institute at Siegen University is an analysis of the Real Time Enterprise (RTE) concept and supporting technologies over the past decade. Its main objective is to identify shortcomings. Subsequently, leveraging the Event Driven Architecture (EDA) and Complex Event Processing (CEP) paradigms, a reference architecture was developed that overcomes the gaps in temporal and semantic vertical integration across different enterprise levels that are essential to realize the RTE concept. The developed reference architecture has been implemented and validated in a foundry with typical characteristics of SMEs
In this article, we describe an implementation of the Kalman filter, one of the applications of the state-space model, in Clojure. The Kalman filter is an infinite impulse response filter used to estimate time-varying quantities (e.g., position and velocity of an object) from discrete observations with errors, and is used in a wide range of engineering fields such as radar and computer vision due to its ease of use. Specific examples of its use include integrating information with errors from device built-in accelerometers and GPS to estimate the ever-changing position of vehicles, as well as in satellite and rocket control.
The Kalman filter is a state-space model with hidden states and observation data generated from them, similar to the hidden Markov model described previously, in which the states are continuous and changes in state variables are statistically described using noise following a Gaussian distribution.
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