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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


  1. Web Performance Optimization: Analytics Wim Leers Promotor: Prof. dr. Jan Van den Bussche

  2. Web Performance Optimization • Speed matters! Source: http://www.useit.com/alertbox/response-times.html, Jakob Nielsen, June 21, 2010

  3. Web Performance Optimization • Speed matters! • 0.1 s → direct manipulation Source: http://www.useit.com/alertbox/response-times.html, Jakob Nielsen, June 21, 2010

  4. 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

  5. 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

  6. 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

  7. How to Measure? Episodes

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

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

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

  11. Analytics

  12. Analytics • Automatically pinpoint causes of slow page loads

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

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

  15. 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”

  16. 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”

  17. 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” • …

  18. Literature Study Subjects

  19. Literature Study Subjects • Data Stream Mining

  20. Literature Study Subjects • Data Stream Mining • Anomaly Detection

  21. Literature Study Subjects • Anomaly Detection } • Data Stream Mining Data Mining: finding patterns in data

  22. Literature Study Subjects • OLAP: Data Cube } • Data Stream Mining Data Mining: finding patterns in data • Anomaly Detection

  23. Literature Study Subjects • OLAP: Data Cube } • Data Stream Mining Data Mining: finding patterns in data • Anomaly Detection } OLAP: querying multidimensional data

  24. Data Stream Mining

  25. Data Stream Mining • Constraints

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

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

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

  29. 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.

  30. 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

  31. 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

  32. 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

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

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

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

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

  37. Anomaly Detection

  38. Anomaly Detection • Types

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

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

  41. 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

  42. 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

  43. 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

  44. 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

  45. Anomaly Detection: Vilalta/Ma

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

  47. Anomaly Detection: Vilalta/Ma • Based on frequent pattern mining • Find all frequent itemsets that precede anomalies

  48. OLAP: Data Cube Source: Introduction to Data Mining, Tan; Steinbach; Kumar, 2006

  49. OLAP: Data Cube Source: Introduction to Data Mining, Tan; Steinbach; Kumar, 2006

  50. OLAP: Data Cube: Range-Sum Performance

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

  52. OLAP: Data Cube: Range-Sum Performance • Very common type of query • Algorithms studied: 3

  53. OLAP: Data Cube: Dynamic Data Cube Source: Data Cubes in Dynamic Environments, Geffner; Riedewald; Agrawal, 1999

  54. OLAP: Data Cube: Dynamic Data Cube Source: Data Cubes in Dynamic Environments, Geffner; Riedewald; Agrawal, 1999

  55. Outlook

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

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

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

  59. Questions? Thanks for your time!

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