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X-ORIGINAL-URL:https://isg.ics.uci.edu
X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20191206T123000
DTEND;TZID=America/Los_Angeles:20191206T140000
DTSTAMP:20261108T023252
CREATED:20191113T025449Z
LAST-MODIFIED:20191122T162630Z
UID:1217-1575635400-1575640800@isg.ics.uci.edu
SUMMARY:AquaEIS: Middleware Support for Event Identification in CommunityWater Infrastructures
DESCRIPTION:Speaker: Quing Han \n  \nAbstract: Real-time event identification is critical in complex distributed infrastructures\, e.g.\, water systems\, where failures are difficult to isolate. We present AquaEIS\, an event-based middleware tailored to the problem of locating sources of failure (e.g.\, contamination) in community water infrastructures. The inherent complexity of underground hydraulic systems combined with aging infrastructure presents unique challenges. AquaEIS combines online learning techniques\, model-driven simulators and data from limited sensing networks to intelligently guide human participants (e.g.\, staff) in identifying contaminant sources. The framework integrates the necessary abstractions with event processing methods into a workflow that iteratively selects and refines the set of potential failure points for human-driven grab sampling. The integrated platform utilizes Hidden Markov Model (HMM) based representations along with field reports for event inference; reinforcement learning (RL) methods have also shown promise for further refining event locations and reducing the cost of human engagement. Our approach is evaluated in real-world water systems under a range of distinct events. The results show that AquaEIS can significantly reduce the number of sampling cycles while ensuring localization accuracy (detected 100% of the failure events as compared to a baseline that can only identify 38% of the events).
URL:https://isg.ics.uci.edu/event/aquaeis-middleware-support-for-event-identification-in-communitywater-infrastructures/
LOCATION:DBH 3011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191213T150000
DTEND;TZID=America/Los_Angeles:20191213T160000
DTSTAMP:20261108T023252
CREATED:20191202T202712Z
LAST-MODIFIED:20191204T182807Z
UID:1242-1576249200-1576252800@isg.ics.uci.edu
SUMMARY:Scalable transaction and polystore data management in LeanXcale
DESCRIPTION:Speaker: Ricardo Jimenez-Péris (LeanXcale\, Spain)\, Patrick Valduriez (Inria\, France) \nAbstract:\nHybrid Transaction Analytical Processing (HTAP) is poised to revolutionize data management. By providing online analytics over operational data\, HTAP systems open up new opportunities in many application domains where real-time decision is critical. Important use cases are proximity marketing\, real-time pricing\, risk monitoring\, real-time fraud detection\, etc. HTAP also simplifies data management\, by removing the traditional separation between operational database and data warehouse/ data lake (no more ETLs!). However\, a hard problem is scaling out transactions in mixed operational and analytical workloads over big data\, possibly coming from different data stores (HDFS\, SQL\, NoSQL\, …).Today\, only a few NewSQL systems have solved this problem. In this talk\, we introduce the solution for scalable transaction and polystore data management in LeanXcale\, a new HTAP DBMS. \nBio:\nDr. Ricardo Jimenez-Peris was a professor at TU Madrid (UPM) for over 25 years. Four years ago he abandoned his scientific career to incorporate LeanXcale and became an entrepreneur. As a researcher he co-authored over 100 scientific articles and papers. He is co-author of the book of “Replicated databases”. He is also co-inventor of two patents granted in US and Europe being exploited by LeanXcale. Already at the University he transferred technology to industry\, an in memory database to Ericsson\, a federated database to Teléfonica\, a replication system for J2EE to Bull\, and an orchestration engine for web services and its visual composer to Bull. He has been invited speaker at top tech companies to present LeanXcale technology such as Facebook\, Twitter\, Salesforce\, Heroku\, Cloudera\, Hortonworks\, Mapr\, MongoDB\, Greenplum\, Microsoft\, IBM\, HP\, etc. \nPatrick Valduriez is a senior researcher at Inria\, working on distributed data management. He has authored over 300 technical papers and several textbooks\, among which one is “Principles of Distributed Database Systems”. He has been associate editor of major journals such as VLDBJ and DAPD and has served as PC chair or general chair of major conferences such as EDBT\, SIGMOD and VLDB. He received the best paper award at VLDB 2000. He was the recipient of the 1993 IBM scientific prize in Computer Science in France and the 2014 Innovation Award from Inria – French Academy of Science. He is an ACM Fellow.
URL:https://isg.ics.uci.edu/event/scalable-transaction-and-polystore-data-management-in-leanxcale/
LOCATION:DBH 5011
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