Data Aggregation and Visuali-zation for the Torrent Measure-ment Software Kraken

Bachelor Thesis - Final Presentation

Sebastian Schrepfer

Overview

1. Introduction

2. Design

3. Architecture & Implementation

4. Evaluation

5. Demonstration

live slides: http://goo.gl/yxVrC4

How does the worldwide distribution of torrent downloads look like on 2014-09-15?

country_iso_code | observed_peers
---------------------------------
 ES              |          2302
 IT              |          2038
 AR              |           689
 RU              |           633
 PH              |           598
 US              |           521
 CL              |           349
 CN              |           332
 MX              |           312
 GB              |           283
 PK              |           254
 IN              |           253
 BR              |           241
 SA              |           208
 CA              |           178
 AU              |           177
 CO              |           154
 FR              |           125
 AE              |           120
 UA              |           112
 DO              |           108
 TW              |            94
 JP              |            84
                 |
        ...      |     ...

And how many people from within Switzerland were downloading in movie torrent networks on 2014-09-15?

Modules

Mobile Optimization

Architecture

Data Aggregation

18.12.2014 | Prisoners | 100.5.5.5 | CH
18.12.2014 | Prisoners | 92.21.1.4 | DE
18.12.2014 | Prisoners | 100.5.5.5 | CH
18.12.2014 | Prisoners |  81.1.1.5 | CH
18.12.2014 | Prisoners | 100.5.5.5 | CH
18.12.2014 | Prisoners | 100.5.5.5 | CH
18.12.2014 | Prisoners | 100.5.5.5 | CH
18.12.2014 | Taxi      |  81.1.1.5 | CH
18.12.2014 | Taxi      | 92.21.1.4 | DE
18.12.2014 | Taxi      | 92.21.1.4 | DE
18.12.2014 | Taxi      | 1.23.3.55 | FR
18.12.2014 | Taxi      | 1.23.3.55 | FR
18.12.2014 | Taxi      | 92.21.1.4 | DE
18.12.2014 | Taxi      | 92.21.1.4 | DE

KRAKEN CORE DB

Technologies

Sass

Compass

HTML5

CSS3

normalize.css

FRONT-END

Usability Evaluation

Context Questions

Demographic Questions

SUS Questions

• 10 simple questions (postive/negative alternating)

• Technology agnostic: applicable for multiple technologies

• SUS Score can be calculated with provided algorithm

• SUS Score is highly reliable

• According to study: score of 71.4 is «good».

Demographic Questions

Modules

Click here

Modules

Modules

Data Aggregation and Visuali-zation for the Torrent Measure-ment Software Kraken

Bachelor Thesis - Final Presentation

Sebastian Schrepfer

Data Aggregation: SQL

DELIMITER $$
CREATE EVENT `CREATE_DAILY_STATISTICS`
ON SCHEDULE EVERY 1 DAY STARTS '2014-09-30 00:03:00'
DO BEGIN

   -- yesterday's date
   SET @QUERYDATE = DATE(DATE_SUB(NOW(), INTERVAL 1 DAY));

   INSERT INTO `kraken-statistics`.statistics_peers
      (ip_address, country_iso_code, info_hash, date)

      (SELECT PEERS.IP_ADDRESS AS ip_address,
         PEERS.COUNTRY AS country_iso_code,
         ANNOUNCE_RESULT.INFO_HASH AS info_hash,
         @QUERYDATE AS date
      
      FROM `kraken-master`.ANNOUNCE_RESULT,
         `kraken-master`.PEERS
      WHERE ANNOUNCE_RESULT.ID = PEERS.ID
      AND DATE(ANNOUNCE_RESULT.TIMESTAMP) = @QUERYDATE
      GROUP BY PEERS.IP_ADDRESS,
         ANNOUNCE_RESULT.INFO_HASH);

   INSERT INTO `kraken-statistics`.statistics_torrentmeta
      (observed_peers, max_swarm_size, seeder_quota, info_hash, date)
      
      (SELECT observed_peers,
         max_swarm_size,
         seeder_quota,
         table1.info_hash,
         date
         
      FROM (
         SELECT MAX(TOTAL_PEERS) AS max_swarm_size,
            AVG(SEEDERS/TOTAL_PEERS) AS seeder_quota,
            INFO_HASH AS info_hash,
            @QUERYDATE AS date
         
         FROM `kraken-master`.`ANNOUNCE_RESULT`
         WHERE ANNOUNCE_COMPLETED = 1
         AND DATE(TIMESTAMP) = @QUERYDATE
         GROUP BY INFO_HASH
      ) AS table1, (
         SELECT COUNT(*) AS observed_peers,
            info_hash AS info_hash
         FROM `kraken-statistics`.statistics_peers
         WHERE date = @QUERYDATE
         GROUP BY info_hash
      ) AS table2
      
      WHERE table1.info_hash = table2.info_hash);

   INSERT INTO `kraken-statistics`.statistics_torrents
      (info_hash, title, filesize, publish_date)
      
      (SELECT INFO_HASH AS info_hash,
         TORRENT_TITLE AS title,
         TORRENT_SIZE_KB AS filesize,
         DATE(PUBLISH_DATE) AS publish_date
         
      FROM `kraken-master`.TORRENTS
      WHERE INFO_HASH NOT IN (
         SELECT info_hash
         FROM `kraken-statistics`.statistics_torrents)
      AND INFO_HASH IN (
         SELECT info_hash
         FROM `kraken-statistics`.statistics_torrentmeta
         WHERE date = @QUERYDATE));
      
END $$
DELIMITER ;

Module Pattern

kraken.systemstatus = ( function($, moment, d3, errorHandling) {

   "use strict";

   var servletUrl = null;

   function convertTimestamp(timestamp) {
      [...]
   }

   function init(servletUrlNew) {
      servletUrl = servletUrlNew;
	  [...]
   }

   [...]
   
   return {
      init: init
   };

}(jQuery, moment, d3, kraken.errorHandling));