Stream processing is a method of continuously processing data as it is generated in real-time, rather than processing large batches later. It is useful for scenarios where data is generated rapidly from IoT devices or markets. Popular stream processing frameworks include Apache Flink, Apache Kafka, Apache Storm and Apache Spark Streaming. Mining data streams involves extracting patterns from continuous, high-volume data streams in real-time using algorithms that can handle dynamic data and real-time processing. Applications include fraud detection and recommendation systems. Sampling data in a stream selects a subset of data points for analysis using techniques like random sampling and stratified sampling.
This document discusses data streams and stream management systems. It defines data streams as continuous high-volume flows of data that arrive sequentially in real-time, such as social media feeds and sensor data. It describes the characteristics and challenges of managing data streams, including high volume and velocity, real-time processing, and limited resources. The document then defines stream management systems as software that handles processing, analyzing, and storing data streams efficiently in real-time. It lists the key features and architectures of stream management systems and provides examples of their use cases. Finally, it mentions some popular stream management system platforms.
Mining Stream Data using k-Means clustering AlgorithmManishankar Medi
This document discusses using k-means clustering to analyze urban road traffic stream data. Stream data arrives continuously over time and is challenging to process due to its high volume, velocity and volatility. The document proposes using a sliding window technique with k-means clustering to analyze recent urban traffic data and visualize clusters in real-time to provide insights into traffic patterns and congested roads. This analysis could help travelers and authorities respond to traffic issues more quickly.
Stream Meets Batch for Smarter Analytics- Impetus White PaperImpetus Technologies
For Impetus’ White Papers archive, visit- http://www.impetus.com/whitepaper
The paper discusses how the traditional batch and real time paradigm can work together to deliver smarter, quicker and better insights on large volumes of data picking the right strategy and right technology.
About Streaming Data Solutions for HadoopLynn Langit
This document discusses selecting the best approach for fast big data and streaming analytics projects. It describes key considerations for the architectural design phases such as scalable ingestion, real-time ETL, analytics, alerts and actions, and visualization. Component selection factors include the overall architecture, enterprise-grade streaming engine, ease of use and development, and management/DevOps. The document provides definitions of relevant technologies and compares representative solutions to help identify the best fit based on an organization's needs and skills.
This document discusses stream computing and its applications. Stream computing involves processing continuous streams of data in real-time, as opposed to batch processing of large static datasets. It describes key aspects of stream computing like filtering data streams and producing output streams. It also provides examples of applications that can benefit from stream computing, such as efficient traffic management, real-time surveillance, critical care monitoring in hospitals, and intrusion detection systems. The document concludes that stream computing platforms like System S are well-suited for scalable and adaptive real-time data processing.
The document discusses the need for a model management framework to ease the development and deployment of analytical models at scale. It describes how such a framework could capture and template models created by data scientists, enable faster model iteration through a brute force approach, and visually compare models. The framework would reduce complexity for data scientists and allow business analysts to participate in modeling. It is presented as essential for enabling predictive modeling on data from thousands of sensors in an Internet of Things platform.
IRJET- Classification of Pattern Storage System and Analysis of Online Shoppi...IRJET Journal
This document discusses classifying patterns from online shopping data using data mining techniques. It proposes using the Apriori algorithm to mine frequent patterns from transaction data stored in a data warehouse. Patterns mined from the data warehouse using Apriori would then be stored in a pattern warehouse. This would allow users to view product details and related patterns when browsing items online. The system aims to efficiently analyze large amounts of user data to discover useful patterns for improving the online shopping experience.
This document discusses data streams and stream management systems. It defines data streams as continuous high-volume flows of data that arrive sequentially in real-time, such as social media feeds and sensor data. It describes the characteristics and challenges of managing data streams, including high volume and velocity, real-time processing, and limited resources. The document then defines stream management systems as software that handles processing, analyzing, and storing data streams efficiently in real-time. It lists the key features and architectures of stream management systems and provides examples of their use cases. Finally, it mentions some popular stream management system platforms.
Mining Stream Data using k-Means clustering AlgorithmManishankar Medi
This document discusses using k-means clustering to analyze urban road traffic stream data. Stream data arrives continuously over time and is challenging to process due to its high volume, velocity and volatility. The document proposes using a sliding window technique with k-means clustering to analyze recent urban traffic data and visualize clusters in real-time to provide insights into traffic patterns and congested roads. This analysis could help travelers and authorities respond to traffic issues more quickly.
Stream Meets Batch for Smarter Analytics- Impetus White PaperImpetus Technologies
For Impetus’ White Papers archive, visit- http://www.impetus.com/whitepaper
The paper discusses how the traditional batch and real time paradigm can work together to deliver smarter, quicker and better insights on large volumes of data picking the right strategy and right technology.
About Streaming Data Solutions for HadoopLynn Langit
This document discusses selecting the best approach for fast big data and streaming analytics projects. It describes key considerations for the architectural design phases such as scalable ingestion, real-time ETL, analytics, alerts and actions, and visualization. Component selection factors include the overall architecture, enterprise-grade streaming engine, ease of use and development, and management/DevOps. The document provides definitions of relevant technologies and compares representative solutions to help identify the best fit based on an organization's needs and skills.
This document discusses stream computing and its applications. Stream computing involves processing continuous streams of data in real-time, as opposed to batch processing of large static datasets. It describes key aspects of stream computing like filtering data streams and producing output streams. It also provides examples of applications that can benefit from stream computing, such as efficient traffic management, real-time surveillance, critical care monitoring in hospitals, and intrusion detection systems. The document concludes that stream computing platforms like System S are well-suited for scalable and adaptive real-time data processing.
The document discusses the need for a model management framework to ease the development and deployment of analytical models at scale. It describes how such a framework could capture and template models created by data scientists, enable faster model iteration through a brute force approach, and visually compare models. The framework would reduce complexity for data scientists and allow business analysts to participate in modeling. It is presented as essential for enabling predictive modeling on data from thousands of sensors in an Internet of Things platform.
IRJET- Classification of Pattern Storage System and Analysis of Online Shoppi...IRJET Journal
This document discusses classifying patterns from online shopping data using data mining techniques. It proposes using the Apriori algorithm to mine frequent patterns from transaction data stored in a data warehouse. Patterns mined from the data warehouse using Apriori would then be stored in a pattern warehouse. This would allow users to view product details and related patterns when browsing items online. The system aims to efficiently analyze large amounts of user data to discover useful patterns for improving the online shopping experience.
The document discusses stream processing models. It describes the key components as data sources, stream processing pipelines, and data sinks. Data sources refer to the inputs of streaming data, pipelines are the processing applied to the streaming data, and sinks are the outputs where the results are stored or sent. Stateful stream processing requires ensuring state is preserved over time and data consistency even during failures. Frameworks like Apache Spark use sources and sinks to connect to streaming data sources like Kafka and send results to other systems, acting as pipelines between different distributed systems.
1. The document discusses stream data mining and compares classification algorithms. It defines stream data and challenges in mining stream data.
2. It describes sampling techniques and classification algorithms for stream data mining including Naive Bayesian, Hoeffding Tree, VFDT, and CVFDT.
3. The algorithms are experimentally compared in terms of time, memory usage, accuracy, and ability to handle concept drift. VFDT and CVFDT are found to have advantages over Hoeffding Tree in accuracy while maintaining speed, but CVFDT can additionally detect and respond to concept drift.
A New Data Stream Mining Algorithm for Interestingness-rich Association RulesVenu Madhav
Frequent itemset mining and association rule generation is
a challenging task in data stream. Even though, various algorithms
have been proposed to solve the issue, it has been found
out that only frequency does not decides the significance
interestingness of the mined itemset and hence the association
rules. This accelerates the algorithms to mine the association
rules based on utility i.e. proficiency of the mined rules. However,
fewer algorithms exist in the literature to deal with the utility
as most of them deals with reducing the complexity in frequent
itemset/association rules mining algorithm. Also, those few
algorithms consider only the overall utility of the association
rules and not the consistency of the rules throughout a defined
number of periods. To solve this issue, in this paper, an enhanced
association rule mining algorithm is proposed. The algorithm
introduces new weightage validation in the conventional
association rule mining algorithms to validate the utility and
its consistency in the mined association rules. The utility is
validated by the integrated calculation of the cost/price efficiency
of the itemsets and its frequency. The consistency validation
is performed at every defined number of windows using the
probability distribution function, assuming that the weights are
normally distributed. Hence, validated and the obtained rules
are frequent and utility efficient and their interestingness are
distributed throughout the entire time period. The algorithm is
implemented and the resultant rules are compared against the
rules that can be obtained from conventional mining algorithms
Apache Flink: Real-World Use Cases for Streaming AnalyticsSlim Baltagi
This face to face talk about Apache Flink in Sao Paulo, Brazil is the first event of its kind in Latin America! It explains how Apache Flink 1.0 announced on March 8th, 2016 by the Apache Software Foundation (link), marks a new era of Big Data analytics and in particular Real-Time streaming analytics. The talk maps Flink's capabilities to real-world use cases that span multiples verticals such as: Financial Services, Healthcare, Advertisement, Oil and Gas, Retail and Telecommunications.
In this talk, you learn more about:
1. What is Apache Flink Stack?
2. Batch vs. Streaming Analytics
3. Key Differentiators of Apache Flink for Streaming Analytics
4. Real-World Use Cases with Flink for Streaming Analytics
5. Who is using Flink?
6. Where do you go from here?
IRJET- AC Duct Monitoring and Cleaning Vehicle for Train CoachesIRJET Journal
This document summarizes research on techniques for handling concept drift in data stream mining. It begins with an introduction to the challenges of concept drift in data streams and the two main approaches for handling concept drift using ensembles: online and block-based. It then reviews several existing studies on concept drift detection and handling in data streams. Finally, it proposes an adaptive online ensemble approach that uses an internal change detector to dynamically determine block sizes and capture concept drifts in a timely manner. Experimental results show this approach outperforms other ensemble techniques, especially on datasets with sudden concept changes.
IRJET- A Data Stream Mining Technique Dynamically Updating a Model with Dynam...IRJET Journal
This document summarizes several techniques for handling concept drift in data stream mining. It discusses how ensemble methods are commonly used to deal with concept drift and categorizes ensemble approaches into online and block-based. It also reviews several existing studies on handling concept drift, including methods that use adaptive windowing and online learning as well as techniques for detecting concept drift and efficiently updating models. The document concludes by discussing the need for approaches that can adapt to different types of concept drift and changes in non-stationary data streams.
Odam an optimized distributed association rule mining algorithm (synopsis)Mumbai Academisc
This document proposes ODAM, an optimized distributed association rule mining algorithm. It aims to discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. Modern organizations have geographically distributed data stored locally at each site, making centralized data mining infeasible due to high communication costs. Distributed data mining emerged to address this challenge. ODAM reduces communication costs compared to previous distributed ARM algorithms by mining patterns across distributed databases without requiring data consolidation.
Streaming data involves the continuous analysis of data as it is generated in real-time. It allows for data to be processed and transformed in memory before being stored. Popular streaming technologies include Apache Storm, Apache Flink, and Apache Spark Streaming, which allow for processing streams of data across clusters. Each technology has its own approach such as micro-batching but all aim to enable real-time analysis of high-velocity data streams.
This document provides an overview of data stream processing. It discusses what streaming data is and examples like IoT sensors, social media, and website monitoring. It also outlines the typical components of a streaming data pipeline including collecting and ingesting data from various sources, processing the data in real-time, storing the processed data, and serving it for analytics, search, and dashboards. Key streaming technologies mentioned include Apache Kafka, Apache NiFi, and various stream processing frameworks. It also introduces Stishovite as a console for managing an entire streaming data platform built from open source components.
Adaptive Real Time Data Mining Methodology for Wireless Body Area Network Bas...acijjournal
This document discusses adaptive real-time data mining techniques for wireless body area networks used in healthcare applications. It presents an innovative framework called Wireless Mobile Real-time Health care Monitoring (WMRHM) that applies data mining to physiological signals acquired through wireless sensors to predict a patient's health risk. Key challenges addressed include the continuous and changing nature of real-time data streams, which require efficient concept-adapting algorithms to handle concept drift. The paper reviews state-of-the-art approaches and introduces five algorithms for tasks like ensemble classification, concept drift detection and adaptation that are suitable for mining real-time physiological signals to support healthcare predictions and decisions.
The document discusses data stream mining and summarizes some key challenges and techniques. It describes how traditional data mining cannot be directly applied to data streams due to their continuous, rapid arrival. It then outlines several techniques used for summarizing and extracting knowledge from data streams, including sampling, sketching, load shedding, synopsis data structures, and algorithms modified from basic data mining to handle streams.
Grid computing is the sharing of computer resources from multiple administrative domains to achieve common goals. It allows for independent, inexpensive access to high-end computational capabilities. Grid computing federates resources like computers, data, software and other devices. It provides a single login for users to access distributed resources for tasks like drug discovery, climate modeling and other data-intensive applications. Current grids are used for distributed supercomputing, high-throughput computing, on-demand computing and other methods. Grids benefit scientists, engineers and other users who need to solve large problems or collaborate globally.
The document discusses essential elements to look for in a high-performance real-time streaming analytics platform. It identifies 7 key features: open source architecture, low latency, data integration using Lambda architecture, rapid application development through visual tools and pre-built operators, elastic scaling, future-proof design, and data visualization. The document argues that an ideal platform would incorporate these features to handle massive streaming data volumes with low latency and flexibility.
This document presents an analytical framework for classifying data stream mining techniques based on their approaches to challenges. It discusses how data streams pose computational challenges due to their continuous, massive, and potentially infinite nature. It classifies data stream mining challenges and techniques for addressing them, such as approaches that modify existing data mining algorithms or develop new ones. The document proposes an analytical framework to evaluate how data mining applications can help develop novel data stream mining algorithms to handle different tasks.
RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics.
The server will record large web log files when user visits the website. Extracting knowledge from such
huge data demands for new methods. In this paper, we propose a web usage mining method with
RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which
has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by
processing and analyzing log file, and mines unusual rules, and provides the reference for the policy
decision and construction of website. Experimental result analysis show that, applying RapidMiner in web
usage mining, will obtain frequent model which user visits the website, manage to optimize the website
structure and recommends for users.
RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics. The server will record large web log files when user visits the website. Extracting knowledge from such huge data demands for new methods. In this paper, we propose a web usage mining method with RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which
has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by processing and analyzing log file, and mines unusual rules, and provides the reference for the policy decision and construction of website. Experimental result analysis show that, applying RapidMiner in web usage mining, will obtain frequent model which user visits the website, manage to optimize the website structure and recommends for users.
RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics.
The server will record large web log files when user visits the website. Extracting knowledge from such
huge data demands for new methods. In this paper, we propose a web usage mining method with
RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which
has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by
processing and analyzing log file, and mines unusual rules, and provides the reference for the policy
decision and construction of website. Experimental result analysis show that, applying RapidMiner in web
usage mining, will obtain frequent model which user visits the website, manage to optimize the website
structure and recommends for users.
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, OathYahoo Developer Network
Offline and stream processing of big data sets can be done with tools such as Hadoop, Spark, and Storm, but what if you need to process big data at the time a user is making a request? Vespa (http://www.vespa.ai) allows you to search, organize and evaluate machine-learned models from e.g TensorFlow over large, evolving data sets with latencies in the tens of milliseconds. Vespa is behind the recommendation, ad targeting, and search at Yahoo where it handles billions of daily queries over billions of documents.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
The document discusses stream processing models. It describes the key components as data sources, stream processing pipelines, and data sinks. Data sources refer to the inputs of streaming data, pipelines are the processing applied to the streaming data, and sinks are the outputs where the results are stored or sent. Stateful stream processing requires ensuring state is preserved over time and data consistency even during failures. Frameworks like Apache Spark use sources and sinks to connect to streaming data sources like Kafka and send results to other systems, acting as pipelines between different distributed systems.
1. The document discusses stream data mining and compares classification algorithms. It defines stream data and challenges in mining stream data.
2. It describes sampling techniques and classification algorithms for stream data mining including Naive Bayesian, Hoeffding Tree, VFDT, and CVFDT.
3. The algorithms are experimentally compared in terms of time, memory usage, accuracy, and ability to handle concept drift. VFDT and CVFDT are found to have advantages over Hoeffding Tree in accuracy while maintaining speed, but CVFDT can additionally detect and respond to concept drift.
A New Data Stream Mining Algorithm for Interestingness-rich Association RulesVenu Madhav
Frequent itemset mining and association rule generation is
a challenging task in data stream. Even though, various algorithms
have been proposed to solve the issue, it has been found
out that only frequency does not decides the significance
interestingness of the mined itemset and hence the association
rules. This accelerates the algorithms to mine the association
rules based on utility i.e. proficiency of the mined rules. However,
fewer algorithms exist in the literature to deal with the utility
as most of them deals with reducing the complexity in frequent
itemset/association rules mining algorithm. Also, those few
algorithms consider only the overall utility of the association
rules and not the consistency of the rules throughout a defined
number of periods. To solve this issue, in this paper, an enhanced
association rule mining algorithm is proposed. The algorithm
introduces new weightage validation in the conventional
association rule mining algorithms to validate the utility and
its consistency in the mined association rules. The utility is
validated by the integrated calculation of the cost/price efficiency
of the itemsets and its frequency. The consistency validation
is performed at every defined number of windows using the
probability distribution function, assuming that the weights are
normally distributed. Hence, validated and the obtained rules
are frequent and utility efficient and their interestingness are
distributed throughout the entire time period. The algorithm is
implemented and the resultant rules are compared against the
rules that can be obtained from conventional mining algorithms
Apache Flink: Real-World Use Cases for Streaming AnalyticsSlim Baltagi
This face to face talk about Apache Flink in Sao Paulo, Brazil is the first event of its kind in Latin America! It explains how Apache Flink 1.0 announced on March 8th, 2016 by the Apache Software Foundation (link), marks a new era of Big Data analytics and in particular Real-Time streaming analytics. The talk maps Flink's capabilities to real-world use cases that span multiples verticals such as: Financial Services, Healthcare, Advertisement, Oil and Gas, Retail and Telecommunications.
In this talk, you learn more about:
1. What is Apache Flink Stack?
2. Batch vs. Streaming Analytics
3. Key Differentiators of Apache Flink for Streaming Analytics
4. Real-World Use Cases with Flink for Streaming Analytics
5. Who is using Flink?
6. Where do you go from here?
IRJET- AC Duct Monitoring and Cleaning Vehicle for Train CoachesIRJET Journal
This document summarizes research on techniques for handling concept drift in data stream mining. It begins with an introduction to the challenges of concept drift in data streams and the two main approaches for handling concept drift using ensembles: online and block-based. It then reviews several existing studies on concept drift detection and handling in data streams. Finally, it proposes an adaptive online ensemble approach that uses an internal change detector to dynamically determine block sizes and capture concept drifts in a timely manner. Experimental results show this approach outperforms other ensemble techniques, especially on datasets with sudden concept changes.
IRJET- A Data Stream Mining Technique Dynamically Updating a Model with Dynam...IRJET Journal
This document summarizes several techniques for handling concept drift in data stream mining. It discusses how ensemble methods are commonly used to deal with concept drift and categorizes ensemble approaches into online and block-based. It also reviews several existing studies on handling concept drift, including methods that use adaptive windowing and online learning as well as techniques for detecting concept drift and efficiently updating models. The document concludes by discussing the need for approaches that can adapt to different types of concept drift and changes in non-stationary data streams.
Odam an optimized distributed association rule mining algorithm (synopsis)Mumbai Academisc
This document proposes ODAM, an optimized distributed association rule mining algorithm. It aims to discover rules based on higher-order associations between items in distributed textual documents that are neither vertically nor horizontally distributed, but rather a hybrid of the two. Modern organizations have geographically distributed data stored locally at each site, making centralized data mining infeasible due to high communication costs. Distributed data mining emerged to address this challenge. ODAM reduces communication costs compared to previous distributed ARM algorithms by mining patterns across distributed databases without requiring data consolidation.
Streaming data involves the continuous analysis of data as it is generated in real-time. It allows for data to be processed and transformed in memory before being stored. Popular streaming technologies include Apache Storm, Apache Flink, and Apache Spark Streaming, which allow for processing streams of data across clusters. Each technology has its own approach such as micro-batching but all aim to enable real-time analysis of high-velocity data streams.
This document provides an overview of data stream processing. It discusses what streaming data is and examples like IoT sensors, social media, and website monitoring. It also outlines the typical components of a streaming data pipeline including collecting and ingesting data from various sources, processing the data in real-time, storing the processed data, and serving it for analytics, search, and dashboards. Key streaming technologies mentioned include Apache Kafka, Apache NiFi, and various stream processing frameworks. It also introduces Stishovite as a console for managing an entire streaming data platform built from open source components.
Adaptive Real Time Data Mining Methodology for Wireless Body Area Network Bas...acijjournal
This document discusses adaptive real-time data mining techniques for wireless body area networks used in healthcare applications. It presents an innovative framework called Wireless Mobile Real-time Health care Monitoring (WMRHM) that applies data mining to physiological signals acquired through wireless sensors to predict a patient's health risk. Key challenges addressed include the continuous and changing nature of real-time data streams, which require efficient concept-adapting algorithms to handle concept drift. The paper reviews state-of-the-art approaches and introduces five algorithms for tasks like ensemble classification, concept drift detection and adaptation that are suitable for mining real-time physiological signals to support healthcare predictions and decisions.
The document discusses data stream mining and summarizes some key challenges and techniques. It describes how traditional data mining cannot be directly applied to data streams due to their continuous, rapid arrival. It then outlines several techniques used for summarizing and extracting knowledge from data streams, including sampling, sketching, load shedding, synopsis data structures, and algorithms modified from basic data mining to handle streams.
Grid computing is the sharing of computer resources from multiple administrative domains to achieve common goals. It allows for independent, inexpensive access to high-end computational capabilities. Grid computing federates resources like computers, data, software and other devices. It provides a single login for users to access distributed resources for tasks like drug discovery, climate modeling and other data-intensive applications. Current grids are used for distributed supercomputing, high-throughput computing, on-demand computing and other methods. Grids benefit scientists, engineers and other users who need to solve large problems or collaborate globally.
The document discusses essential elements to look for in a high-performance real-time streaming analytics platform. It identifies 7 key features: open source architecture, low latency, data integration using Lambda architecture, rapid application development through visual tools and pre-built operators, elastic scaling, future-proof design, and data visualization. The document argues that an ideal platform would incorporate these features to handle massive streaming data volumes with low latency and flexibility.
This document presents an analytical framework for classifying data stream mining techniques based on their approaches to challenges. It discusses how data streams pose computational challenges due to their continuous, massive, and potentially infinite nature. It classifies data stream mining challenges and techniques for addressing them, such as approaches that modify existing data mining algorithms or develop new ones. The document proposes an analytical framework to evaluate how data mining applications can help develop novel data stream mining algorithms to handle different tasks.
RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics.
The server will record large web log files when user visits the website. Extracting knowledge from such
huge data demands for new methods. In this paper, we propose a web usage mining method with
RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which
has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by
processing and analyzing log file, and mines unusual rules, and provides the reference for the policy
decision and construction of website. Experimental result analysis show that, applying RapidMiner in web
usage mining, will obtain frequent model which user visits the website, manage to optimize the website
structure and recommends for users.
RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics. The server will record large web log files when user visits the website. Extracting knowledge from such huge data demands for new methods. In this paper, we propose a web usage mining method with RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which
has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by processing and analyzing log file, and mines unusual rules, and provides the reference for the policy decision and construction of website. Experimental result analysis show that, applying RapidMiner in web usage mining, will obtain frequent model which user visits the website, manage to optimize the website structure and recommends for users.
RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics.
The server will record large web log files when user visits the website. Extracting knowledge from such
huge data demands for new methods. In this paper, we propose a web usage mining method with
RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which
has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by
processing and analyzing log file, and mines unusual rules, and provides the reference for the policy
decision and construction of website. Experimental result analysis show that, applying RapidMiner in web
usage mining, will obtain frequent model which user visits the website, manage to optimize the website
structure and recommends for users.
Big Data Serving with Vespa - Jon Bratseth, Distinguished Architect, OathYahoo Developer Network
Offline and stream processing of big data sets can be done with tools such as Hadoop, Spark, and Storm, but what if you need to process big data at the time a user is making a request? Vespa (http://www.vespa.ai) allows you to search, organize and evaluate machine-learned models from e.g TensorFlow over large, evolving data sets with latencies in the tens of milliseconds. Vespa is behind the recommendation, ad targeting, and search at Yahoo where it handles billions of daily queries over billions of documents.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
A review on techniques and modelling methodologies used for checking electrom...
Uint-4 Mining Data Stream.pdf
1. Page | 1
Unit-IV
Mining Data Streams: Streams: Concepts – Stream Data Model and Architecture - Sampling data in a
stream – Mining Data Streams and Mining Time-series data - Real Time Analytics Platform (RTAP)
Applications - Case Studies - Real Time Sentiment Analysis, Stock Market Predictions
Stream Processing
Stream processing is a method of data processing that involves continuously processing data in
real-time as it is generated, rather than processing it in batches. In stream processing, data is
processed incrementally and in small chunks as it arrives, making it possible to analyze and act
on data in real-time.
Stream processing is particularly useful in scenarios where data is generated rapidly, such as in
the case of IoT devices or financial markets, where it is important to detect anomalies or patterns
in data quickly. Stream processing can also be used for real-time data analytics, machine
learning, and other applications where real-time data processing is required.
There are several popular stream processing frameworks, including Apache Flink, Apache
Kafka, Apache Storm, and Apache Spark Streaming. These frameworks provide tools for
building and deploying stream processing pipelines, and they can handle large volumes of data
with low latency and high throughput.
Mining data streams
Mining data streams refers to the process of extracting useful insights and patterns from
continuous and rapidly changing data streams in real-time. Data streams are typically
highvolume and high-velocity, making it challenging to analyze them using traditional data
mining techniques.
Mining data streams requires specialized algorithms that can handle the dynamic nature of data
streams, as well as the need for real-time processing. These algorithms typically use techniques
such as sliding windows, online learning, and incremental processing to adapt to changing data
patterns over time.
Applications of mining data streams include fraud detection, network intrusion detection,
predictive maintenance, and real-time recommendation systems. Some popular algorithms for
mining data streams include Frequent Pattern Mining (FPM), clustering, decision trees, and
neural networks.
Mining data streams also requires careful consideration of the computational resources required
to process the data in real-time. As a result, many mining data stream algorithms are designed
to work with limited memory and processing power, making them well-suited for deployment
on edge devices or in cloud-based architectures.
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Introduction to Streams Concepts
In computer science, a stream refers to a sequence of data elements that are continuously
generated or received over time. Streams can be used to represent a wide range of data, including
audio and video feeds, sensor data, and network packets.
Streams can be thought of as a flow of data that can be processed in real-time, rather than being
stored and processed at a later time. This allows for more efficient processing of large volumes
of data and enables applications that require real-time processing and analysis.
Some important concepts related to streams include:
1. Data Source: A stream's data source is the place where the data is generated or received.
This can include sensors, databases, network connections, or other sources.
2. Data Sink: A stream's data sink is the place where the data is consumed or stored. This
can include databases, data lakes, visualization tools, or other destinations.
3. Streaming Data Processing: This refers to the process of continuously processing data as
it arrives in a stream. This can involve filtering, aggregation, transformation, or analysis
of the data.
4. Stream Processing Frameworks: These are software tools that provide an environment
for building and deploying stream processing applications. Popular stream processing
frameworks include Apache Flink, Apache Kafka, and Apache Spark Streaming.
5. Real-time Data Processing: This refers to the ability to process data as soon as it is
generated or received. Real-time data processing is often used in applications that require
immediate action, such as fraud detection or monitoring of critical systems.
Overall, streams are a powerful tool for processing and analyzing large volumes of data in real-
time, enabling a wide range of applications in fields such as finance, healthcare, and the Internet
of Things.
Stream Data Model and Architecture
Stream data model is a data model used to represent the continuous flow of data in a stream
processing system. The stream data model typically consists of a series of events, which are
individual pieces of data that are generated by a data source and processed by a stream
processing system.
The architecture of a stream processing system typically involves three main components: data
sources, stream processing engines, and data sinks.
1. Data sources: The data sources are the components that generate the events that make up
the stream. These can include sensors, log files, databases, and other data sources.
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2. Stream processing engines: The stream processing engines are the components
responsible for processing the data in real-time. These engines typically use a variety of
algorithms and techniques to filter, transform, aggregate, and analyze the stream of
events.
3. Data sinks: The data sinks are the components that receive the output of the stream
processing engines. These can include databases, data lakes, visualization tools, and other
data destinations.
The architecture of a stream processing system can be distributed or centralized, depending on
the requirements of the application. In a distributed architecture, the stream processing engines
are distributed across multiple nodes, allowing for increased scalability and fault tolerance. In
a centralized architecture, the stream processing engines are run on a single node, which can
simplify deployment and management.
Some popular stream processing frameworks and architectures include Apache Flink, Apache
Kafka, and Lambda Architecture. These frameworks provide tools and components for building
scalable and fault-tolerant stream processing systems, and can be used in a wide range of
applications, from real-time analytics to internet of things (IoT) data processing.
Stream Computing
Stream computing is the process of computing and analyzing data streams in real-time. It
involves continuously processing data as it is generated, rather than processing it in batches.
Stream computing is particularly useful for scenarios where data is generated rapidly and needs
to be analyzed quickly.
Stream computing involves a set of techniques and tools for processing and analyzing data
streams, including:
1. Stream processing frameworks: These are software tools that provide an environment for
building and deploying stream processing applications. Popular stream processing
frameworks include Apache Flink, Apache Kafka, and Apache Storm.
2. Stream processing algorithms: These are specialized algorithms that are designed to
handle the dynamic and rapidly changing nature of data streams. These algorithms use
techniques such as sliding windows, online learning, and incremental processing to adapt
to changing data patterns over time.
3. Real-time data analytics: This involves using stream computing techniques to perform
real-time analysis of data streams, such as detecting anomalies, predicting future trends,
and identifying patterns.
4. Machine learning: Machine learning algorithms can also be used in stream computing to
continuously learn from the data stream and make predictions in real-time.
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Stream computing is becoming increasingly important in fields such as finance, healthcare, and
the Internet of Things (IoT), where large volumes of data are generated and need to be processed
and analyzed in real-time. It enables businesses and organizations to make more informed
decisions based on real-time insights, leading to better operational efficiency and improved
customer experiences.
Sampling Data in a Stream
Sampling data in a stream refers to the process of selecting a subset of data points from a
continuous and rapidly changing data stream for analysis. Sampling is a useful technique for
processing data streams when it is not feasible or necessary to process all data points in realtime.
There are various sampling techniques that can be used for stream data, including:
1. Random sampling: This involves selecting data points from the stream at random
intervals. Random sampling can be used to obtain a representative sample of the entire
stream.
2. Systematic sampling: This involves selecting data points at regular intervals, such as
every tenth or hundredth data point. Systematic sampling can be useful when the stream
has a regular pattern or periodicity.
3. Cluster sampling: This involves dividing the stream into clusters and selecting data points
from each cluster. Cluster sampling can be useful when there are multiple subgroups
within the stream.
4. Stratified sampling: This involves dividing the stream into strata or sub-groups based on
some characteristic, such as location or time of day. Stratified sampling can be useful
when there are significant differences between the sub-groups.
When sampling data in a stream, it is important to ensure that the sample is representative of
the entire stream. This can be achieved by selecting a sample size that is large enough to capture
the variability of the stream and by using appropriate sampling techniques.
Sampling data in a stream can be used in various applications, such as monitoring and quality
control, statistical analysis, and machine learning. By reducing the amount of data that needs to
be processed in real-time, sampling can help improve the efficiency and scalability of stream
processing systems.
Filtering Streams
Filtering streams refers to the process of selecting a subset of data from a data stream based on
certain criteria. This process is often used in stream processing systems to reduce the amount
of data that needs to be processed and to focus on the relevant data.
There are various filtering techniques that can be used for stream data, including:
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1. Simple filtering: This involves selecting data points from the stream that meet a specific
condition, such as a range of values, a specific text string, or a certain timestamp.
2. Complex filtering: This involves selecting data points from the stream based on multiple
criteria or complex logic. Complex filtering can involve combining multiple conditions
using Boolean operators such as AND, OR, and NOT.
3. Machine learning-based filtering: This involves using machine learning algorithms to
automatically classify data points in the stream based on past observations. This can be
useful in applications such as anomaly detection or predictive maintenance.
When filtering streams, it is important to consider the trade-off between the amount of data
being filtered and the accuracy of the filtering process. Too much filtering can result in valuable
data being discarded, while too little filtering can result in a large volume of irrelevant data
being processed.
Filtering streams can be useful in various applications, such as monitoring and surveillance,
real-time analytics, and Internet of Things (IoT) data processing. By reducing the amount of
data that needs to be processed and analyzed in real-time, filtering can help improve the
efficiency and scalability of stream processing systems.
Counting Distinct Elements in a Stream
Counting distinct elements in a stream refers to the process of counting the number of unique
items in a continuous and rapidly changing data stream. This is an important operation in stream
processing because it can help detect anomalies, identify trends, and provide insights into the
data stream.
There are various techniques for counting distinct elements in a stream, including:
1. Exact counting: This involves storing all the distinct elements seen so far in a data
structure such as a hash table or a bloom filter. When a new element is encountered, it is
checked against the data structure to determine if it is a new distinct element.
2. Approximate counting: This involves using probabilistic algorithms such as the Flajolet-
Martin algorithm or the HyperLogLog algorithm to estimate the number of distinct
elements in a data stream. These algorithms use a small amount of memory to provide an
approximate count with a known level of accuracy.
3. Sampling: This involves selecting a subset of the data stream and counting the distinct
elements in the sample. This can be useful when the data stream is too large to be
processed in real-time or when exact or approximate counting techniques are not feasible.
Counting distinct elements in a stream can be useful in various applications, such as social
media analytics, fraud detection, and network traffic monitoring. By providing real-time
insights into the data stream, counting distinct elements can help businesses and organizations
make more informed decisions and improve operational efficiency.
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Estimating Moments
In statistics, moments are numerical measures that describe the shape, central tendency, and
variability of a probability distribution. They are calculated as functions of the random variables
of the distribution, and they can provide useful insights into the underlying properties of the
data.
There are different types of moments, but two of the most commonly used are the mean (the
first moment) and the variance (the second moment). The mean represents the central tendency
of the data, while the variance measures its spread or variability.
To estimate the moments of a distribution from a sample of data, you can use the following
formulas:
Sample mean (first moment):
where n is the sample size, and x_i are the individual observations.
Sample variance (second moment):
where n is the sample size, x_i are the individual observations, and s^2 is the sample variance.
These formulas provide estimates of the population moments based on the sample data. The
larger the sample size, the more accurate the estimates will be. However, it's important to note
that these formulas only work for certain types of distributions (e.g., normal distribution), and
for other types of distributions, different formulas may be required.
Counting Oneness in a Window
Counting the number of times a number appears exactly once (oneness) in a window of a given
size in a sequence is a common problem in computer science and data analysis. Here's one way
you could approach this problem:
1. Initialize a dictionary to store the counts of each number in the window.
2. Initialize a count variable to zero.
3. Iterate through the first window and update the counts in the dictionary.
4. If a count in the dictionary is 1, increment the count variable.
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5. For the remaining windows, slide the window by one element to the right and update the
counts in the dictionary accordingly.
6. If the count of the number that just left the window is 1, decrement the count variable.
7. If the count of the number that just entered the window is 1, increment the count variable.
8. Repeat steps 5-7 until you reach the end of the sequence.
Here's some Python code that implements this approach:
This function takes in a sequence seq and a window size window_size, and returns the number
of times a number appears exactly once in a window of size window_size in the sequence. Note
that this code assumes that all the elements in the sequence are integers. If the elements are not
integers, you may need to modify the code accordingly.
Decaying Window
A decaying window is a common technique used in time-series analysis and signal processing
to give more weight to recent observations while gradually reducing the importance of older
observations. This can be useful when the underlying data generating process is changing over
time, and more recent observations are more relevant for predicting future values.
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Here's one way you could implement a decaying window in Python using an exponentially
weighted moving average (EWMA):
This function takes in a Pandas Series data, a window size window_size, and a decay rate
decay_rate. The decay rate determines how much weight is given to recent observations relative
to older observations. A larger decay rate means that more weight is given to recent
observations.
The function first creates a series of weights using the decay rate and the window size. The
weights are calculated using the formula decay_rate^(window_size - i) where i is the index of
the weight in the series. This gives more weight to recent observations and less weight to older
observations.
Next, the function normalizes the weights so that they sum to one. This ensures that the weighted
average is a proper average.
Finally, the function applies the rolling function to the data using the window size and a custom
lambda function that calculates the weighted average of the window using the weights.
Note that this implementation uses Pandas' built-in rolling and apply functions, which are
optimized for efficiency. If you're working with large datasets, this implementation should be
quite fast. If you're working with smaller datasets or need more control over the implementation,
you could implement a decaying window using a custom function that calculates the weighted
average directly.
Real time Analytics Platform (RTAP) Applications
Real-time analytics platforms (RTAPs) are becoming increasingly popular as businesses strive
to gain insights from streaming data and respond quickly to changing conditions. Here are some
examples of RTAP applications:
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1. Fraud detection: Financial institutions and e-commerce companies use RTAPs to detect
fraud in real-time. By analyzing transactional data as it occurs, these companies can
quickly identify and prevent fraudulent activity.
2. Predictive maintenance: RTAPs can be used to monitor the performance of machines and
equipment in real-time. By analyzing data such as temperature, pressure, and
vibration, these platforms can predict when equipment is likely to fail and alert
maintenance teams to take action.
3. Supply chain optimization: RTAPs can help companies optimize their supply chain by
monitoring inventory levels, shipment tracking, and demand forecasting. By analyzing
this data in real-time, companies can make better decisions about when to restock
inventory, when to reroute shipments, and how to allocate resources.
4. Customer experience management: RTAPs can help companies monitor customer
feedback in real-time, enabling them to respond quickly to complaints and improve the
customer experience. By analyzing customer data from various sources, such as social
media, email, and chat logs, companies can gain insights into customer behavior and
preferences.
5. Cybersecurity: RTAPs can help companies detect and prevent cyberattacks in realtime.
By analyzing network traffic, log files, and other data sources, these platforms can
quickly identify suspicious activity and alert security teams to take action.
Overall, RTAPs can be applied in various industries and domains where real-time monitoring
and analysis of data is critical to achieving business objectives. By providing insights into
streaming data as it happens, RTAPs can help businesses make faster and more informed
decisions.
Case Studies - Real Time Sentiment Analysis
Real-time sentiment analysis is a powerful tool for businesses that want to monitor and respond
to customer feedback in real-time. Here are some case studies of companies that have
successfully implemented real-time sentiment analysis:
1. Airbnb: The popular home-sharing platform uses real-time sentiment analysis to monitor
customer feedback and respond to complaints. Airbnb's customer service team uses the
platform to monitor social media and review sites for mentions of the brand, and to track
sentiment over time. By analyzing this data in real-time, Airbnb can quickly respond to
complaints and improve the customer experience.
2. Coca-Cola: Coca-Cola uses real-time sentiment analysis to monitor social media for
mentions of the brand and to track sentiment over time. The company's marketing team
uses this data to identify trends and to create more targeted marketing campaigns. By
analyzing real-time sentiment data, Coca-Cola can quickly respond to changes in
consumer sentiment and adjust its marketing strategy accordingly.
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3. Ford: Ford uses real-time sentiment analysis to monitor customer feedback on social
media and review sites. The company's customer service team uses this data to identify
issues and to respond to complaints in real-time. By analyzing real-time sentiment data,
Ford can quickly identify and address customer concerns, improving the overall customer
experience.
4. Hootsuite: Social media management platform Hootsuite uses real-time sentiment
analysis to help businesses monitor and respond to customer feedback. Hootsuite's
sentiment analysis tool allows businesses to monitor sentiment across social media
channels, track sentiment over time, and identify trends. By analyzing real-time
sentiment data, businesses can quickly respond to customer feedback and improve the
overall customer experience.
5. Twitter: Twitter uses real-time sentiment analysis to identify trending topics and to
monitor sentiment across the platform. The company's sentiment analysis tool allows
users to track sentiment across various topics and to identify emerging trends. By
analyzing real-time sentiment data, Twitter can quickly identify issues and respond to
changes in user sentiment.
Overall, real-time sentiment analysis is a powerful tool for businesses that want to monitor and
respond to customer feedback in real-time. By analyzing real-time sentiment data, businesses
can quickly identify issues and respond to changes in customer sentiment, improving the overall
customer experience.
Case Studies - Stock Market Predictions
Predicting stock market performance is a challenging task, but there have been several
successful case studies of companies using machine learning and artificial intelligence to make
accurate predictions. Here are some examples of successful stock market prediction case
studies:
1. Kavout: Kavout is a Seattle-based fintech company that uses artificial intelligence and
machine learning to predict stock performance. The company's system uses a
combination of fundamental and technical analysis to generate buy and sell
recommendations for individual stocks. Kavout's AI algorithms have outperformed
traditional investment strategies and consistently outperformed the S&P 500 index.
2. Sentient Technologies: Sentient Technologies is a San Francisco-based AI startup that
uses deep learning to predict stock market performance. The company's system uses a
combination of natural language processing, image recognition, and genetic algorithms
to analyze market data and generate investment strategies. Sentient's AI algorithms have
consistently outperformed the S&P 500 index and other traditional investment strategies.
3. Quantiacs: Quantiacs is a California-based investment firm that uses machine learning to
develop trading algorithms. The company's system uses machine learning algorithms to
analyze market data and generate trading strategies. Quantiacs' trading algorithms have
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consistently outperformed traditional investment strategies and have delivered returns
that are significantly higher than the S&P 500 index.
4. Kensho Technologies: Kensho Technologies is a Massachusetts-based fintech company
that uses artificial intelligence to predict stock market performance. The company's
system uses natural language processing and machine learning algorithms to analyze
news articles, social media feeds, and other data sources to identify patterns and generate
investment recommendations. Kensho's AI algorithms have consistently outperformed
the S&P 500 index and other traditional investment strategies.
5. AlphaSense: AlphaSense is a New York-based fintech company that uses natural
language processing and machine learning to analyze financial data. The company's
system uses machine learning algorithms to identify patterns in financial data and
generate investment recommendations. AlphaSense's AI algorithms have consistently
outperformed traditional investment strategies and have delivered returns that are
significantly higher than the S&P 500 index.
Overall, these case studies demonstrate the potential of machine learning and artificial
intelligence to make accurate predictions in the stock market. By analyzing large volumes of
data and identifying patterns, these systems can generate investment strategies that outperform
traditional methods. However, it is important to note that the stock market is inherently
unpredictable, and past performance is not necessarily indicative of future results.