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X-ORIGINAL-URL:https://isg.ics.uci.edu
X-WR-CALDESC:Events for Information Systems Group
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TZID:America/Los_Angeles
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DTSTART:20200308T100000
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DTSTART:20201101T090000
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200207T150000
DTEND;TZID=America/Los_Angeles:20200207T160000
DTSTAMP:20261108T021133
CREATED:20200130T224250Z
LAST-MODIFIED:20200130T224250Z
UID:1272-1581087600-1581091200@isg.ics.uci.edu
SUMMARY:Event Detection with Temporal Predicates
DESCRIPTION:Speaker: Fabio Persia (Free University of Bozen-Bolzano\, Italy) \nAbstract: Human perception tends to group individual values into larger structures\, this is also the case for time series data. This tendency inspired us to define an event-detection language based on time intervals\, which combines timepoint-based events into larger structures. Complex events can then be defined on a more abstract level by specifying temporal relationships between different time intervals. As a result\, we propose a system based on an extension of relational algebra (ISEQL\, Interval-based Surveillance Event Query Language)\, enriched with powerful temporal operators and additional constraints\, which is able to: (i) extend Allen’s interval relationships; (ii) support overlap percentage and cardinality constraints over the interval relationships; (iii) provide robustness with respect to small variations in the intervals; (iv) be more efficient than state-of-the-art approaches. With the help of intuitive and interactive graphical user interfaces\, a user can also have a look at the different layers of our system to gain insights into the inner workings of the system\, as well as create new events on the fly and track their processing through the system. \nBio: Fabio Persia is currently (since 2015) a Researcher/Assistant Professor (RTD/A) at the Faculty of Computer Science of the Free University of Bozen-Bolzano\, Italy. He received the master’s and PhD degrees in Computer Engineering from the University of Naples “Federico II”\, Italy\, in 2009 and 2013\, respectively\, under the supervision of Professors Antonio Picariello and Vincenzo Moscato. During his PhD\, he was visiting scholar at the University of Maryland\, College Park (USA) for six months\, under the supervision of Prof. V.S. Subrahmanian. He was also Postdoctoral Research Fellow at the Department of Electrical Engineering and Information Technology of the University of Naples “Federico II” from 2013 to 2015. His research interests include the fields of event detection and analysis in large scale databases applied to multimedia\, semantic analysis and security\, as demonstrated by several papers published in top journals (such as TKDE and TOIT) and conferences (IJCAI\, EDBT\, CIKM).
URL:https://isg.ics.uci.edu/event/event-detection-with-temporal-predicates/
LOCATION:DBH 3011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200214T150000
DTEND;TZID=America/Los_Angeles:20200214T160000
DTSTAMP:20261108T021133
CREATED:20200201T005425Z
LAST-MODIFIED:20200214T225332Z
UID:1274-1581692400-1581696000@isg.ics.uci.edu
SUMMARY:Effective Filters and Linear Time Verification for Tree Similarity Joins
DESCRIPTION:Speaker:\nThomas Hütter (University of Salzburg) \nAbstract:\nThe tree similarity join computes all similar pairs in a collection of trees. Two trees are similar if their edit distance falls within a user-defined threshold. Previous algorithms\, which are based on a filter-verify approach\, suffer from the following two issues. First\, ineffective filters produce a large number of candidates that must be further verified. Second\, the candidates are verified by computing the tree edit distance\, which is cubic in the number of tree nodes. Thus\, these techniques fail to scale to large tree collections and are infeasible even for small collections when the trees are large. In this talk\, a scalable solution for the tree similarity join\, called TJoin\, is presented that is based on (1) an effective indexing technique that leverages both the labels and the structure of trees to reduce the number of candidates\, (2) an efficient upper bound filter that moves many of the candidates directly to the join result without additional verification\, and (3) a linear time verification technique for the remaining candidates that avoids the expensive tree edit distance computation. Unlike previous solutions\, TJoin scales to collections with millions of large trees and improves the overall join time by up to two orders of magnitude w.r.t. the state of the art. \nShort-Bio:\nThomas Hütter received his bachelor’s and master’s degrees in Computer Sciences from the University of Salzburg\, Austria\, in 2014 and 2017\, respectively. During his master’s degree\, he spent a semester abroad as an exchange student at École Polytechnique Fédérale de Lausanne (EPFL)\, Switzerland. His research career started in 2013 when he became a member of the Computational Systems Group supervised by Prof. Christoph Kirsch at the University of Salzburg. His work focused on memory management optimizations which resulted in two projects in collaboration with Google Munich.\nCurrently\, he is pursuing his Ph.D. degree in the Database Research Group supervised by Prof. Nikolaus Augsten in Salzburg. His research interests include efficient algorithms for similarity queries on complex data structures. In 2019\, Thomas Hütter won the “Young Investigators Award” for his work on tree similarity joins (published at ICDE 2019) and was awarded with the “Austrian Marshallplan Scholarship”.
URL:https://isg.ics.uci.edu/event/effective-filters-and-linear-time-verification-for-tree-similarity-joins/
LOCATION:DBH 3011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200221T110000
DTEND;TZID=America/Los_Angeles:20200221T120000
DTSTAMP:20261108T021133
CREATED:20200219T175125Z
LAST-MODIFIED:20200219T175125Z
UID:1281-1582282800-1582286400@isg.ics.uci.edu
SUMMARY:Systems and ML at RISELab (CS Distinguished Seminar Series)
DESCRIPTION:Speaker: Prof. Ion Stoica (University of California at Berkeley) \nAbstract: In this talk\, I will present several of the projects we are developing at RISELab\, a two-year old lab at UC Berkeley that focuses on building platforms and algorithms for real-time intelligent decisions\, decisions that are secure and explainable. These projects include both systems to better support machine learning (ML) workloads\, and leveraging ML to build better systems. In the first category\, I will present\, Ray\, a general-purpose distributed system which provides both task-parallel and actor abstractions. Ray is highly scalable employing an in-memory storage system and a distributed scheduler. Ray already supports several popular libraries\, including a reinforcement learning library (RLlib) and a hyperparameter search library (Tune)\, and it is deployed in production at tens of organizations. In the second category\, I will present Autopandas\, a system that synthesizes snippets of API calls from input-output examples for Pandas\, the most popular data science library today\, and NeuroCuts\, a tool to generate decision trees that implement network packet classifiers. \nBio: Ion Stoica is a Professor in the EECS Department at the University of California at Berkeley\, and the Director of RISELab (https://rise.cs.berkeley.edu/). He is currently doing research on cloud computing and AI systems. Past work includes Apache Spark\, Apache Mesos\, Tachyon\, Chord DHT\, and Dynamic Packet State (DPS). He is an ACM Fellow and has received numerous awards\, including the Mark Weiser Award (2019)\, SIGOPS Hall of Fame Award (2015)\, the SIGCOMM Test of Time Award (2011)\, and the ACM doctoral dissertation award (2001). He also co-founded three companies\, Anyscale (2019)\, Databricks (2013) and Conviva (2006).
URL:https://isg.ics.uci.edu/event/systems-and-ml-at-riselab-cs-distinguished-seminar-series/
LOCATION:DBH 6011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200225T110000
DTEND;TZID=America/Los_Angeles:20200225T120000
DTSTAMP:20261108T021133
CREATED:20200219T174827Z
LAST-MODIFIED:20210409T181309Z
UID:1279-1582628400-1582632000@isg.ics.uci.edu
SUMMARY:Dr. Alfred Spector(Two Sigma) : Opportunities and Perils of Data Science: A Roadmap (ICS Distinguished Lecture)
DESCRIPTION:Speaker: Dr. Alfred Spector \nAbstract: Data-driven approaches have led to powerful prediction\, optimization and automation techniques.  Powered by large-scale\, networked computer systems and machine learning algorithms\, these have been very impactful to-date and hold great promise in many disciplines\, even in the humanities and social sciences. However\, no new technology arrives without complications\, and we have recently seen the press and various political circles illustrating real\, potential\, and fictional implications of Big Data. \nThis presentation aims to balance the opportunities provided by Big Data and its associated artificial intelligence techniques with a discussion of the various challenges that have ensued. I review eleven types of challenges\, including those which are technical (resilience and complexity)\, societal (difficulties in setting objective functions or understanding causation)\, and humanist (issues relating to free will or privacy). I provide example problems and suggest ways to address some of the unanticipated consequences of Big Data. \nBio: Dr. Alfred Spector is Chief Technology Officer at Two Sigma\, a firm dedicated to using information to undertake many forms of economic optimization. His career has led him from innovation in large scale\, networked computing systems (as a professor at CMU and founder of his company\, Transarc) to broad research leadership: five years leading IBM Software Research and eight years leading Google Research. Recently\, Spector has lectured widely on the growing importance of computer science across all disciplines (CS+X) and on the Societal Implications of Data Science. He  received an AB in Applied Mathematics from Harvard and a Ph.D. in Computer Science from Stanford\, where he was a Hertz Fellow. He is a Fellow of the ACM and IEEE\, and a member of the National Academy of Engineering and the American Academy of Arts and Sciences. Dr. Spector won the 2001 IEEE Kanai Award for Distributed Computing\, was co-awarded the 2016 ACM Software Systems Award\, and was a 2018-19 Phi Beta Kappa Scholar.
URL:https://isg.ics.uci.edu/event/opportunities-and-perils-of-data-science-a-roadmap-ics-distinguished-lecture/
LOCATION:DBH 6011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200228T150000
DTEND;TZID=America/Los_Angeles:20200228T160000
DTSTAMP:20261108T021133
CREATED:20200219T174631Z
LAST-MODIFIED:20210409T181320Z
UID:1277-1582902000-1582905600@isg.ics.uci.edu
SUMMARY:Pat Helland (Salesforce.com) : Depending on Appending
DESCRIPTION:Speaker:  Pat Helland (Salesforce.com) \nAbstract: \n    Increasingly\, we see “Gray Failures” in the datacenter and public cloud.  This happens when a server\, router\, or other device just plain goes slow.  This may result in severe problems in the user perceived performance as the slowness cascades\, sometimes not slow enough to cause the exclusion of the bad devices. \n    In this talk\, we briefly examine Gray Failures and consider the use of  “append” to support our work.  How has append been used in distributed systems?  What are some novel ways that new cloud-centric databases use forms of append to partially address these issues?  We conclude that it seems likely we can get relief by “Depending on Appending”. \nBio: \n    Pat Helland has been working building databases\, transaction systems\, and distributed systems since 1978\, a couple of years after dropping out of UC Irvine to feed his new family. \n    He is keenly interested in strong consistency\, eventual consistency\, scale\, and making complex systems easy for application developers.  Pat writes regularly for ACM Queue and Communications of the ACM.
URL:https://isg.ics.uci.edu/event/depending-on-appending/
LOCATION:DBH 3011
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