BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Information Systems Group - ECPv6.4.0.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Information Systems Group
X-ORIGINAL-URL:https://isg.ics.uci.edu
X-WR-CALDESC:Events for Information Systems Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20190310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20191103T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191108T123000
DTEND;TZID=America/Los_Angeles:20191108T140000
DTSTAMP:20261108T024110
CREATED:20191103T212621Z
LAST-MODIFIED:20191103T212621Z
UID:1215-1573216200-1573221600@isg.ics.uci.edu
SUMMARY:Gift Sinthong: AsterixDB Meets Data Science
DESCRIPTION:Abstract:\nIn the last few years\, Data Science has become an increasingly important use case for data platforms. To support the full Big Data analysis lifecycle\, we have examined one of the most popular exploratory data analytics tools\, Pandas\, which has a serious problem: scalability. Exploratory tools such as Pandas only work well against locally stored data that fits in the memory of a single machine. Our plan is to integrate a Pandas-like user experience with AsterixDB to provide analysts with a familiar working environment while scaling out the evaluation of the analytical operations over a large data cluster to enable Big Data analysis. The two main components that we use to enable such a workflow are the AsterixDB UDF framework and our new Python data analytics library (“AFrame”) that operates against AsterixDB. AFrame allows users to interact with a very large volume of semi-structured data in the same way that Pandas DataFrames work against locally stored tabular data. Influenced by Spark SQL and Spark DataFrames\, our AFrame prototype leverages lazy evaluation and only performs operations once an action is invoked. AFrame operations are incrementally translated into AsterixDB SQL++ queries that are executed only when final results are called for.  In this talk\, we will demonstrate our approach using a restaurant review analytics use case.
URL:https://isg.ics.uci.edu/event/gift-sinthong-asterixdb-meets-data-science/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191115T123000
DTEND;TZID=America/Los_Angeles:20191115T140000
DTSTAMP:20261108T024110
CREATED:20191115T192124Z
LAST-MODIFIED:20191115T192124Z
UID:1219-1573821000-1573826400@isg.ics.uci.edu
SUMMARY:Multistage Adaptive Load Balancing in Big Active Data Publish Subscribe Systems
DESCRIPTION:Speaker: Hang\nTime: 12:30pm\nRoom: 3011 \nWe address issues in the design and operation of a Big Active Data Publish Subscribe (BAD Pub/Sub) systems to enable the next generation of enriched notification systems that can scale to societal levels. The proposed BAD Pub/Sub systems aim to ingest massive amounts of data from heterogeneous publishers and sources and deliver customized\, enriched notifications to end users that express interests in these data items via parameterized channels. To support scalability\, we employ a hierarchical architecture that combines a back-end big data cluster (to receive publications and data feeds\, store data and process subscriptions) with a client-facing distributed broker network that manages user subscriptions and scales the delivery process. The skewed distribution of subscribers\, their interests and the dynamic nature of societal scale publications\, create load imbalance in the distributed broker network. We propose a staged approach for broker load balancing that executes in multiple stages — initial placement of brokers to subscribers\, dynamic subscriber migration during operation to handle transient and instantaneous loads and occasional shuffles to re-stabilize the system.  
URL:https://isg.ics.uci.edu/event/multistage-adaptive-load-balancing-in-big-active-data-publish-subscribe-systems/
LOCATION:DBH 3011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191122T130000
DTEND;TZID=America/Los_Angeles:20191122T140000
DTSTAMP:20261108T024110
CREATED:20191115T192950Z
LAST-MODIFIED:20191122T203428Z
UID:1223-1574427600-1574431200@isg.ics.uci.edu
SUMMARY:Texera: Supporting Big Data Analytics for Domain Experts through GUI-based workflows
DESCRIPTION:Speakers:  Avinash Kumar\,  Shengquan Ni\, Zuozhi Wang \n  \nAbstract: Big data analytics is a daunting task for domain experts such as doctors and teachers. Their non-IT background makes it challenging for them to write analytics code and maintain computing infrastructures to efficiently process large amounts of data. Existing data analytics frameworks that offer GUI-based alternatives are mostly limited to a single machine. In light of these facts\, we are developing Texera\, a scalable data-processing system that supports interaction and debugging and is easy to use by non-IT people. One of the challenges of the system is supporting real-time interactions without compromising on scalability. We discuss how we solve the problem in Amber\, the backend engine of Texera. Amber\, built using the actor model\, is a cluster-based distributed engine that can process workflows while supporting interactions. We will show a demonstration of using Texera to analyze social media data.
URL:https://isg.ics.uci.edu/event/amber-a-debuggable-dataflow-system-based-on-theactor-model/
LOCATION:DBH 3011
END:VEVENT
END:VCALENDAR