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:20251009T041235Z
UID:6EEBE15C-7CE3-4B3F-A955-EE1E8A64352B
DTSTART;TZID=US/Pacific:20251008T180000
DTEND;TZID=US/Pacific:20251008T200000
DESCRIPTION:#ieeeday Free Registration (with a Zoom account\; you can get o
 ne for free if you don't already have it. This requirement is to avoid Zoo
 m bombing. Please sign in using the email address tied to your Zoom accoun
 t — not necessarily the one you used to register for the event.):\n\nhtt
 ps://sjsu.zoom.us/meeting/register/GGH84KjiSTiIr_W1K8MJVg\n\nSynopsis:\n\n
 In an era where data never sleeps\, streaming algorithms offer a powerful 
 toolkit for extracting meaningful insights from high-velocity data flows. 
 This talk explores some foundational techniques that enable efficient\, re
 al-time analytics with minimal memory requirements. The algorithms covered
  include a clever bit-based strategy for approximating the count of 1s in 
 a sliding window\, ideal for binary streams where space efficiency is para
 mount. Another algorithm helps estimate statistical moments (mean\, varian
 ce\, skewness) using compact sketches\, enabling a deeper understanding of
  stream distributions without storing the entire dataset. One other algori
 thm identifies trending items with exponential decay\, giving more weight 
 to recent data\, a crucial method for dynamic environments like social med
 ia or sensor networks. Techniques like these form the backbone of intellig
 ent stream processing. Through intuitive examples and practical applicatio
 ns\, this session will demystify how these algorithms work\, why they matt
 er\, and how they can be used to monitor\, summarize\, and react to data i
 n motion.\n---------------------------------------------------------------
 \n\nBy registering for this event\, you 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 (includi
 ng loss of opportunity\, exemplary or punitive damages). The event will be
  recorded and will be made available for public viewing.\n\nSpeaker(s): Dr
 . Vishnu S. Pendyala\n\nVirtual: https://events.vtools.ieee.org/m/497694
LOCATION:Virtual: https://events.vtools.ieee.org/m/497694
ORGANIZER:pendyala@ieee.org
SEQUENCE:58
SUMMARY:IEEE Day event: Sensing the Pulse of a Data Stream in Real Time
URL;VALUE=URI:https://events.vtools.ieee.org/m/497694
X-ALT-DESC:Description: <br /><p>&nbsp\;<strong>#ieeeday </strong>Free Regi
 stration (with a Zoom account\; you can get one for free if you don't alre
 ady have it. This requirement is to avoid Zoom bombing. Please sign in usi
 ng the email address tied to your Zoom account &mdash\; not necessarily th
 e one you used to register for the event.):&nbsp\;</p>\n<p><a href="https:
 //sjsu.zoom.us/meeting/register/GGH84KjiSTiIr_W1K8MJVg">https://sjsu.zoom.
 us/meeting/register/GGH84KjiSTiIr_W1K8MJVg</a>&nbsp\;</p>\n<p><em><strong>
 Synopsis:<br></strong></em></p>\n<p class="MsoNormal">In an era where data
  never sleeps\, streaming algorithms offer a powerful toolkit for extracti
 ng meaningful insights from high-velocity data flows. This talk explores s
 ome foundational techniques that enable efficient\, real-time analytics wi
 th minimal memory requirements. The algorithms covered include a clever bi
 t-based strategy for approximating the count of 1s in a sliding window\, i
 deal for binary streams where space efficiency is paramount. Another algor
 ithm helps estimate statistical moments (mean\, variance\, skewness) using
  compact sketches\, enabling a deeper understanding of stream distribution
 s without storing the entire dataset. One other algorithm identifies trend
 ing items with exponential decay\, giving more weight to recent data\, a c
 rucial method for dynamic environments like social media or sensor network
 s. Techniques like these form the backbone of intelligent stream processin
 g. Through intuitive examples and practical applications\, this session wi
 ll demystify how these algorithms work\, why they matter\, and how they ca
 n be used to monitor\, summarize\, and react to data in motion.&nbsp\;</p>
 \n<hr>\n<p class="MsoNormal"><span style="font-size: 10pt\;"><em>By regist
 ering for this event\, you agree that IEEE and the organizers are not liab
 le to you for any loss\, damage\, injury\, or any incidental\, indirect\, 
 special\, consequential\, or economic loss or damage (including loss of op
 portunity\, exemplary or punitive damages). The event will be recorded and
  will be made available for public viewing.</em></span></p>
END:VEVENT
END:VCALENDAR