BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20261015T192926Z UID:7F42D8AA-63D8-4937-A1C3-8417D601B17A DTSTART;TZID=America/New_York:20261014T190000 DTEND;TZID=America/New_York:20261014T200000 DESCRIPTION:[]\n\nDid you know that in physical manufacturing\, a batch sys tem (or batch production) is a method where identical products move throug h each manufacturing stage together before the entire group advances to th e next step. These systems can be both large and complex. When you are eit her in charge of such a system or your livelyhood depends on one\, you rea lly want to make sure that its always up and running.\n\nMany of today's m odern enterprise workflow still depend on scheduled batch jobs\, file tran sfers\, and data pipelines. Failures in such systems are often identified only after downstream business impact happens. Let us agree that that's no t good for anyone!\n\nIn this seminar we'll be revealing the Data Freshnes s Reliability (DFR) framework for monitoring delivery-time reliability in batch-oriented systems. Instead of relying only on scheduler success or fa ilure\, this new technique evaluates whether expected data arrives within a defined time window. If something goes wrong\, now you'll know about it sooner and will be able to do something about it before things get out of hand.\n\nThe framework introduces freshness Service Level Indicators (SLIs )\, Service Level Objectives (SLOs)\, burn rate calculations\, and classif ication of delivery events as fresh\, late\, or missing. A reference archi tecture together with a synthetic evaluation is used for validating the ap proach.\n\nAttend this seminar and you can find out how freshness-based mo nitoring can expose delivery problems which may not be visible in traditio nal job-centric monitoring systems.\n\nIf your job currently or in the fut ure may depend on a batch system\, then this is the seminar that you must come to!\n\nWe'll see you there!\n\nVirtual: https://events.vtools.ieee.or g/m/580509 LOCATION:Virtual: https://events.vtools.ieee.org/m/580509 ORGANIZER:jim.anderson@ieee.org SEQUENCE:40 SUMMARY:Data Freshness as a Reliability Signal: A New Way Of Creating Batch Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/580509 X-ALT-DESC:Description: <br /><p><img style="display: block\; margin-left: auto\; margin-right: auto\;" src="https://events.vtools.ieee.org/vtools_ui /media/display/859f540f-18a9-4c69-b5ae-367151b67977" alt="" width="472" he ight="265"></p>\n<p>Did you know that in physical manufacturing\, a <stron g data-path-to-node="0" data-index-in-node="29">batch system</strong> (or batch production) is a method where identical products move through each m anufacturing stage together before the entire group advances to the next s tep. These systems can be both large and complex. When you are either in c harge of such a system or your livelyhood depends on one\, you really want to make sure that its always up and running.</p>\n<p>Many of today's mode rn enterprise workflow still depend on scheduled batch jobs\, file transfe rs\, and data pipelines. Failures in such systems are often identified onl y after downstream business impact happens. Let us agree that that's not g ood for anyone!</p>\n<p>In this seminar we'll be revealing the Data Freshn ess Reliability (DFR) framework for monitoring delivery-time reliability i n batch-oriented systems. Instead of relying only on scheduler success or failure\, this new technique evaluates whether expected data arrives withi n a defined time window. If something goes wrong\, now you'll know about i t sooner and will be able to do something about it before things get out o f hand.</p>\n<p>The framework introduces freshness Service Level Indicator s (SLIs)\, Service Level Objectives (SLOs)\, burn rate calculations\, and classification of delivery events as fresh\, late\, or missing. A referenc e architecture together with a synthetic evaluation is used for validating the approach.</p>\n<p>Attend this seminar and you can find out how freshn ess-based monitoring can expose delivery problems which may not be visible in traditional job-centric monitoring systems.</p>\n<p>If your job curren tly or in the future may depend on a batch system\, then this is the semin ar that you must come to!</p>\n<p>We'll see you there!</p> END:VEVENT END:VCALENDAR