Web Performance Optimization: Analytics Wim Leers Promotor: Prof. - - PowerPoint PPT Presentation

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Web Performance Optimization: Analytics Wim Leers Promotor: Prof. - - PowerPoint PPT Presentation

Nov 29, 2022 161 likes •757 views

Web Performance Optimization: Analytics Wim Leers Promotor: Prof. dr. Jan Van den Bussche Web Performance Optimization Speed matters! Source: http://www.useit.com/alertbox/response-times.html, Jakob Nielsen, June 21, 2010 Web Performance

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Web Performance Optimization: Analytics

Wim Leers Promotor: Prof. dr. Jan Van den Bussche

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Web Performance Optimization

  • Speed matters!

Source: http://www.useit.com/alertbox/response-times.html, Jakob Nielsen, June 21, 2010

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Web Performance Optimization

  • Speed matters!
  • 0.1 s → direct manipulation

Source: http://www.useit.com/alertbox/response-times.html, Jakob Nielsen, June 21, 2010

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Web Performance Optimization

  • Speed matters!
  • 0.1 s → direct manipulation
  • 1 s → good navigation

Source: http://www.useit.com/alertbox/response-times.html, Jakob Nielsen, June 21, 2010

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Web Performance Optimization

  • Speed matters!
  • 0.1 s → direct manipulation
  • 1 s → good navigation
  • 10 s → attention kept

Source: http://www.useit.com/alertbox/response-times.html, Jakob Nielsen, June 21, 2010

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Web Performance Optimization

  • Speed matters!
  • 0.1 s → direct manipulation
  • 1 s → good navigation
  • 10 s → attention kept
  • >10 s → bye bye!

Source: http://www.useit.com/alertbox/response-times.html, Jakob Nielsen, June 21, 2010

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How to Measure? Episodes

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How to Measure? Episodes

  • Measures “episodes” during page loading
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How to Measure? Episodes

  • Measures “episodes” during page loading
  • Real measurements: JS in browser, for each visitor
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How to Measure? Episodes

  • Measures “episodes” during page loading
  • Real measurements: JS in browser, for each visitor
  • Result: Episodes log file
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Analytics

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Analytics

  • Automatically pinpoint causes of slow page loads
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Analytics

  • Automatically pinpoint causes of slow page loads
  • e.g.:
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Analytics

  • Automatically pinpoint causes of slow page loads
  • e.g.:
  • “http://uhasselt.be/ is slow in Belgium, for users of the ISP Telenet”
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Analytics

  • Automatically pinpoint causes of slow page loads
  • e.g.:
  • “http://uhasselt.be/ is slow in Belgium, for users of the ISP Telenet”
  • “http://uhasselt.be/studenten/dossier has slowly loading CSS”
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Analytics

  • Automatically pinpoint causes of slow page loads
  • e.g.:
  • “http://uhasselt.be/ is slow in Belgium, for users of the ISP Telenet”
  • “http://uhasselt.be/studenten/dossier has slowly loading CSS”
  • “http://uhasselt.be/bib has slowly loading JS in Firefox 3”
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Analytics

  • Automatically pinpoint causes of slow page loads
  • e.g.:
  • “http://uhasselt.be/ is slow in Belgium, for users of the ISP Telenet”
  • “http://uhasselt.be/studenten/dossier has slowly loading CSS”
  • “http://uhasselt.be/bib has slowly loading JS in Firefox 3”
  • …
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Literature Study Subjects

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Literature Study Subjects

  • Data Stream Mining
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Literature Study Subjects

  • Data Stream Mining
  • Anomaly Detection
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Literature Study Subjects

  • Data Stream Mining
  • Anomaly Detection}

Data Mining: finding patterns in data

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Literature Study Subjects

  • Data Stream Mining
  • Anomaly Detection
  • OLAP: Data Cube }

Data Mining: finding patterns in data

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Literature Study Subjects

  • Data Stream Mining
  • Anomaly Detection
  • OLAP: Data Cube }

Data Mining: finding patterns in data

}

OLAP: querying multidimensional data

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Data Stream Mining

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Data Stream Mining

  • Constraints
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Data Stream Mining

  • Constraints
  • Possibly infinite data stream ⇒ approximation
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Data Stream Mining

  • Constraints
  • Possibly infinite data stream ⇒ approximation
  • Window model
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Data Stream Mining

  • Constraints
  • Possibly infinite data stream ⇒ approximation
  • Window model
  • Landmark: from beginning until now
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Data Stream Mining

  • Constraints
  • Possibly infinite data stream ⇒ approximation
  • Window model
  • Landmark: from beginning until now
  • Tilted-time: per-hour window, 24 “hour windows” → “day window”, etc.
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Data Stream Mining

  • Constraints
  • Possibly infinite data stream ⇒ approximation
  • Window model
  • Landmark: from beginning until now
  • Tilted-time: per-hour window, 24 “hour windows” → “day window”, etc.
  • Algorithms studied
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Data Stream Mining

  • Constraints
  • Possibly infinite data stream ⇒ approximation
  • Window model
  • Landmark: from beginning until now
  • Tilted-time: per-hour window, 24 “hour windows” → “day window”, etc.
  • Algorithms studied
  • Frequent Item Mining: 7
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Data Stream Mining

  • Constraints
  • Possibly infinite data stream ⇒ approximation
  • Window model
  • Landmark: from beginning until now
  • Tilted-time: per-hour window, 24 “hour windows” → “day window”, etc.
  • Algorithms studied
  • Frequent Item Mining: 7
  • Frequent Pattern Mining: 2
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Data Stream Mining: FP-Stream

Source: Mining Frequent Patterns in Data Streams at Multiple Time Granularities, Giannella; Han et al., 2003

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Data Stream Mining: FP-Stream

Source: Mining Frequent Patterns in Data Streams at Multiple Time Granularities, Giannella; Han et al., 2003

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Data Stream Mining: FP-Stream

Source: Mining Frequent Patterns in Data Streams at Multiple Time Granularities, Giannella; Han et al., 2003

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Data Stream Mining: FP-Stream

Source: Mining Frequent Patterns in Data Streams at Multiple Time Granularities, Giannella; Han et al., 2003

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Anomaly Detection

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Anomaly Detection

  • Types
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Anomaly Detection

  • Types
  • Point: e.g. rainfall in mm
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Anomaly Detection

  • Types
  • Point: e.g. rainfall in mm
  • Contextual: point + contextual attributes, e.g. rainfall in mm + lat/lon
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Anomaly Detection

  • Types
  • Point: e.g. rainfall in mm
  • Contextual: point + contextual attributes, e.g. rainfall in mm + lat/lon
  • Contextual anomaly detection algorithms categories
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Anomaly Detection

  • Types
  • Point: e.g. rainfall in mm
  • Contextual: point + contextual attributes, e.g. rainfall in mm + lat/lon
  • Contextual anomaly detection algorithms categories
  • Reduction: 1) certain context, 2) point anomaly algorithm
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Anomaly Detection

  • Types
  • Point: e.g. rainfall in mm
  • Contextual: point + contextual attributes, e.g. rainfall in mm + lat/lon
  • Contextual anomaly detection algorithms categories
  • Reduction: 1) certain context, 2) point anomaly algorithm
  • Model: 1) learn through training, 2) compare: observed vs. expected
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Anomaly Detection

  • Types
  • Point: e.g. rainfall in mm
  • Contextual: point + contextual attributes, e.g. rainfall in mm + lat/lon
  • Contextual anomaly detection algorithms categories
  • Reduction: 1) certain context, 2) point anomaly algorithm
  • Model: 1) learn through training, 2) compare: observed vs. expected
  • Algorithms studied: 2
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Anomaly Detection: Vilalta/Ma

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Anomaly Detection: Vilalta/Ma

  • Based on frequent pattern mining
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Anomaly Detection: Vilalta/Ma

  • Based on frequent pattern mining
  • Find all frequent itemsets that precede anomalies
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OLAP: Data Cube

Source: Introduction to Data Mining, Tan; Steinbach; Kumar, 2006

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OLAP: Data Cube

Source: Introduction to Data Mining, Tan; Steinbach; Kumar, 2006

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OLAP: Data Cube: Range-Sum Performance

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OLAP: Data Cube: Range-Sum Performance

  • Very common type of query
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OLAP: Data Cube: Range-Sum Performance

  • Very common type of query
  • Algorithms studied: 3
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OLAP: Data Cube: Dynamic Data Cube

Source: Data Cubes in Dynamic Environments, Geffner; Riedewald; Agrawal, 1999

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OLAP: Data Cube: Dynamic Data Cube

Source: Data Cubes in Dynamic Environments, Geffner; Riedewald; Agrawal, 1999

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Outlook

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Outlook

  • Further literature study, especially: data cubes over data streams
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Outlook

  • Further literature study, especially: data cubes over data streams
  • Implementation
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Outlook

  • Further literature study, especially: data cubes over data streams
  • Implementation
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Questions?

Thanks for your time!