BEGIN:VCALENDAR
PRODID:-//SiteBuilder 2//University of ĚÇĐÄTV ITS Web Team//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-TIMEZONE:Europe/London
X-LIC-LOCATION:Europe/London
BEGIN:VTIMEZONE
TZID:Europe/London
LAST-MODIFIED:20201010T011803Z
TZURL:http://tzurl.org/zoneinfo/Europe/London
X-LIC-LOCATION:Europe/London
X-PROLEPTIC-TZNAME:LMT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+000115
TZOFFSETTO:+0000
DTSTART:18471201T000000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19160521T020000
RDATE:19170408T020000
RDATE:19180324T020000
RDATE:19190330T020000
RDATE:19200328T020000
RDATE:19210403T020000
RDATE:19220326T020000
RDATE:19230422T020000
RDATE:19240413T020000
RDATE:19270410T020000
RDATE:19300413T020000
RDATE:19330409T020000
RDATE:19340422T020000
RDATE:19350414T020000
RDATE:19380410T020000
RDATE:19390416T020000
RDATE:19400225T020000
RDATE:19460414T020000
RDATE:19470316T020000
RDATE:19480314T020000
RDATE:19490403T020000
RDATE:19530419T020000
RDATE:19540411T020000
RDATE:19570414T020000
RDATE:19600410T020000
RDATE:19680218T020000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19161001T030000
RDATE:19170917T030000
RDATE:19180930T030000
RDATE:19190929T030000
RDATE:19201025T030000
RDATE:19211003T030000
RDATE:19221008T030000
RDATE:19391119T030000
RDATE:19471102T030000
RDATE:19481031T030000
RDATE:19491030T030000
RDATE:19711031T030000
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19230916T030000
RRULE:FREQ=YEARLY;UNTIL=19240921T020000Z;BYMONTH=9;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19250419T020000
RRULE:FREQ=YEARLY;UNTIL=19260418T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19251004T030000
RRULE:FREQ=YEARLY;UNTIL=19381002T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19280422T020000
RRULE:FREQ=YEARLY;UNTIL=19290421T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19310419T020000
RRULE:FREQ=YEARLY;UNTIL=19320417T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19360419T020000
RRULE:FREQ=YEARLY;UNTIL=19370418T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BDST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19410504T020000
RDATE:19450402T020000
RDATE:19470413T020000
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19410810T030000
RRULE:FREQ=YEARLY;UNTIL=19430815T010000Z;BYMONTH=8;BYMONTHDAY=9,10,11,12,
 13,14,15;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BDST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19420405T020000
RRULE:FREQ=YEARLY;UNTIL=19440402T010000Z;BYMONTH=4;BYMONTHDAY=2,3,4,5,6,7
 ,8;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19440917T030000
RDATE:19450715T030000
RDATE:19470810T030000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19451007T030000
RRULE:FREQ=YEARLY;UNTIL=19461006T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19500416T020000
RRULE:FREQ=YEARLY;UNTIL=19520420T020000Z;BYMONTH=4;BYMONTHDAY=14,15,16,17
 ,18,19,20;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19501022T030000
RRULE:FREQ=YEARLY;UNTIL=19521026T020000Z;BYMONTH=10;BYMONTHDAY=21,22,23,2
 4,25,26,27;BYDAY=SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19531004T030000
RRULE:FREQ=YEARLY;UNTIL=19601002T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19550417T020000
RRULE:FREQ=YEARLY;UNTIL=19560422T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19580420T020000
RRULE:FREQ=YEARLY;UNTIL=19590419T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19610326T020000
RRULE:FREQ=YEARLY;UNTIL=19630331T020000Z;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19611029T030000
RRULE:FREQ=YEARLY;UNTIL=19671029T020000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19640322T020000
RRULE:FREQ=YEARLY;UNTIL=19670319T020000Z;BYMONTH=3;BYMONTHDAY=19,20,21,22
 ,23,24,25;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:BST
TZOFFSETFROM:+0100
TZOFFSETTO:+0100
DTSTART:19681026T230000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19720319T020000
RRULE:FREQ=YEARLY;UNTIL=19800316T020000Z;BYMONTH=3;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19721029T030000
RRULE:FREQ=YEARLY;UNTIL=19801026T020000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19810329T010000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19811025T020000
RRULE:FREQ=YEARLY;UNTIL=19891029T010000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19901028T020000
RRULE:FREQ=YEARLY;UNTIL=19951022T010000Z;BYMONTH=10;BYDAY=4SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
DTSTART:19960101T000000
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19961027T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260428T040159Z
DTSTART;VALUE=DATE-TIME:20190219T100000
DTEND;VALUE=DATE-TIME:20190219T110000
SUMMARY:Aleksandr Kolotkov (Soft-Service LLC): Tutorial: Real time big da
 ta handling
TZID:Europe/London
UID:20190219-8a17841a66c4e3b70166c974bbd83e5b@warwick.ac.uk
CREATED:20190131T102331Z
DESCRIPTION:A problem of handling and analysing big data attracts growing
  attention in different branches of science and economics. Important ele
 ments of big data handling are the reporting and analytics of a dataset 
 of interest. As per a textbook\, reporting is “the process of organising
  data into informational summaries in order to monitor how different are
 as of a business are performing”. This includes the identification of th
 e intrinsic parameters of the data cloud (its core metrics)\, presenting
  them in e.g. a spreadsheet or online dashboard\, and aggregation of the
 se parameters according to their properties. Likewise\, analytics is “th
 e process of exploring data and reports in order to extract meaningful i
 nsights\, which can be used to better understand and improve business pe
 rformance”. For example\, in some cases the global behaviour of some dat
 a parameter can be better understood by breaking the dataset down toward
 s smaller scales\, which is a form of analytics. Thus\, both reporting a
 nd analytics are valuable forms of business intelligence. Reporting prov
 ides the information\, analytics gives the insights. Reporting raises qu
 estions\, analytics attempts to answer them. For big data constituted of
 \, e.g.\, millions or billions of events\, performing of the reporting a
 nd analytics is naturally complicated by the amount of data\, leading to
  the following principal issues: 1) involvement of reasonable computatio
 nal resources\; and 2) adequate time-scales needed for the report making
  (reporting) and hypothesis testing (analytics). In this tutorial\, I wi
 ll be discussing big data handling\, addressing one of the tools allowin
 g for an effective solution of these tasks\, ClickHouse: a new open sour
 ce column-oriented database management system developed by Yandex\, Russ
 ia (it currently powers Yandex.Metrica\, the world’s third-largest web a
 nalytics platform). In the tutorial format the following features of Cli
 ckHouse will be considered: its performance\, scalability\, hardware eff
 iciency\, fault tolerance\, and more. Also\, the installation procedure 
 and loading of a sample dataset from open sources and querying will be d
 iscussed. A sample dataset of the USA civil flights data since 1987 till
  2015 from the open sources (contains 166 millions rows\, 63 Gb of uncom
 pressed data) will be used for the demonstration. As an example\, we are
  going to obtain new knowledge after querying a sample dataset to find t
 he most popular destinations in 2015\, the most popular cities of depart
 ure\, cities of departure which offer maximum variety of destinations\, 
 flight delay dependence on the day of week\, cities of departure with mo
 st frequent delays for 1 hour or longer\, flights of maximum duration\, 
 distribution of arrival time delays split by air companies\, air compani
 es which stopped flights operation\, most trending destination cities in
  2015\, destination cities with maximum popularity-season dependency. Re
 porting and analytics processes and tools capable to implement them\, in
 cluding ClickHouse\, are mainly positioned as an important constituent p
 art of business intelligence. However\, one can consider the demonstrate
 d features from an interdisciplinary point of view and readily implement
  them for data science purposes. For example\, ClickHouse has already be
 en successfully implemented at CERN's LHCb experiment to store and proce
 ss metadata on 10 billion events with over 1000 attributes per event.
LOCATION:PS017a
CATEGORIES:CFSA Seminar
LAST-MODIFIED:20190131T102331Z
ORGANIZER;CN=Anne-Marie Broomhall:
END:VEVENT
END:VCALENDAR