BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Pacific BEGIN:DAYLIGHT DTSTART:20250309T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20250821T022644Z UID:5088B763-8CFB-42F9-AFE2-0A23D3D9080E DTSTART;TZID=US/Pacific:20250819T180000 DTEND;TZID=US/Pacific:20250819T203000 DESCRIPTION:Free Registration (with a Zoom account\; you can get one for fr ee if you don't already have it. This requirement is to avoid Zoom bombing . Please sign in using the email address tied to your Zoom account — not necessarily the one you used to register for the event.):\n\nhttps://sjsu .zoom.us/webinar/register/WN_DIDfdbkgRFeEd3JbzURIUQ\n\nSynopsis:\n\nHow ar e machine learning algorithms able to answer questions from any nook and c orner of the World Wide Web? How are trending hashtags from the near infin ite microblog posts\, unique visitors and other distinct counts in the nea r infinite website traffic determined? How do blogging websites avoid reco mmending articles a user has previously read? In general\, how can we answ er complex queries about enormous data streams without storing them entire ly\, in real-time? The answer often lies in clever approximation algorithm s and data "sketches" that capture essential properties using vastly reduc ed space. The relentless flow of data in modern systems indeed presents si gnificant challenges. These data streams are often too large to store and too fast to process exhaustively with traditional methods. This talk intro duces key sketching and approximation techniques that help generate real-t ime data insights by processing data streams.\n--------------------------- ------------------------------------\n\nBy registering for this event\, yo u agree that IEEE and the organizers are not liable to you for any loss\, damage\, injury\, or any incidental\, indirect\, special\, consequential\, or economic loss or damage (including loss of opportunity\, exemplary or punitive damages). The event will be recorded and will be made available f or public viewing.\n\nSpeaker(s): Dr. Vishnu S. Pendyala\n\nVirtual: https ://events.vtools.ieee.org/m/482936 LOCATION:Virtual: https://events.vtools.ieee.org/m/482936 ORGANIZER:pendyala@ieee.org SEQUENCE:58 SUMMARY:The Sketches of Infinite Data and Algorithms for Real-Time Data Ins ights URL;VALUE=URI:https://events.vtools.ieee.org/m/482936 X-ALT-DESC:Description: <br /><p>Free Registration (with a Zoom account\; y ou can get one for free if you don't already have it. This requirement is to avoid Zoom bombing. Please sign in using the email address tied to your Zoom account &mdash\; not necessarily the one you used to register for th e event.):&nbsp\;</p>\n<p><a href="https://sjsu.zoom.us/webinar/register/W N_DIDfdbkgRFeEd3JbzURIUQ">https://sjsu.zoom.us/webinar/register/WN_DIDfdbk gRFeEd3JbzURIUQ</a>&nbsp\;</p>\n<p><em><strong>Synopsis:<br></strong></em> </p>\n<p class="MsoNormal">How are machine learning algorithms able to ans wer questions from any nook and corner of the World Wide Web? How are tren ding hashtags from the near infinite microblog posts\, unique visitors and other distinct counts in the near infinite website traffic determined? Ho w do blogging websites avoid recommending articles a user has previously r ead? In general\, how can we answer complex queries about enormous data st reams without storing them entirely\, in real-time? The answer often lies in clever approximation algorithms and data "sketches" that capture essent ial properties using vastly reduced space. The relentless flow of data in modern systems indeed presents significant challenges. These data streams are often too large to store and too fast to process exhaustively with tra ditional methods. This talk introduces key sketching and approximation tec hniques that help generate real-time data insights by processing data stre ams.</p>\n<hr>\n<p class="MsoNormal"><span style="font-size: 10pt\;"><em>B y registering for this event\, you agree that IEEE and the organizers are not liable to you for any loss\, damage\, injury\, or any incidental\, ind irect\, special\, consequential\, or economic loss or damage (including lo ss of opportunity\, exemplary or punitive damages). The event will be reco rded and will be made available for public viewing.</em></span></p> END:VEVENT END:VCALENDAR