Uint-4 Mining Data Stream.pdf

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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.

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