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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:20221007T123000
DTEND;TZID=America/Los_Angeles:20221007T140000
DTSTAMP:20260711T114825
CREATED:20220926T231018Z
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SUMMARY:Sadeem Alsudais: Drove: Tracking Execution Results of Workflows on Large Data
DESCRIPTION:Abstract:\n\nData analytics using workflows is an iterative process\, in which an analyst makes many iterations of changes\, such as additions\, deletions\, and alterations of operators and their links. In many cases\, the analyst wants to compare these workflow versions and their execution results to help decide the next iteration of changes. To this end\, we introduce Drove\, a framework that manages the end-to-end lifecycle of constructing\, refining\, and executing workflows on large data sets and provides a dashboard to monitor these execution results. In many cases\, the result of an execution is equivalent to a prior one. Identifying such equivalence between the execution results of different workflow versions is important to find reuse opportunities. In Drove\, we reason the semantic equivalence of the workflow versions to reuse previously-stored results by leveraging existing Equivalence Verifiers (EV). In this talk\, I will discuss a novel technique called a “covering window\,” which covers the edits between workflow versions to reason their effect on the results. This technique can be applied not only to find final result reuse opportunities but also to find intermediate ones. Finally\, I will demonstrate in this talk a prototype of Drove’s dashboard in Texera. \n\nBio:\n\nSadeem Alsudais is a Ph.D. student in the Computer Science department at UC Irvine. She received her M.Sc. in Software Engineering from USC and B.Sc. in Information Technology from King Saud University. Her research interests lie in the fields of Big Data processing and visualization. She is a recipient of the KSU scholarship award 2018.
URL:https://isg.ics.uci.edu/event/sadeem-alsudais-shengquan-ni-drove-tracking-execution-results-of-workflows-on-large-data/
LOCATION:DBH 4011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221014T123000
DTEND;TZID=America/Los_Angeles:20221014T140000
DTSTAMP:20260711T114825
CREATED:20220926T231206Z
LAST-MODIFIED:20221011T230328Z
UID:1469-1665750600-1665756000@isg.ics.uci.edu
SUMMARY:Qiushi Bai: QueryBooster-Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting + Demo
DESCRIPTION:Title: \nQueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting \nAbstract: \nQuery latency is critical in many database-backed applications where users need answers quickly to gain timely insights and make mission-critical decisions.  “Query rewriting” is one of the query optimization techniques which transforms SQL queries to more efficient formats based on pre-defined rewriting rules.  However\, with the emergence of different domain-specific applications such as visualization and business intelligence\, existing database optimizers lack support for developers to leverage their domain knowledge to rewrite queries. \nWe propose QueryBooster\, a middleware query rewriting service that sits between the application and the database.  It requires few or no modifications to the application or the database and allows developers to introduce their own rewriting rules to optimize their SQL queries.  We call this rewriting “human-centered.”  QueryBooster aims to provide a powerful interface for users to either compose rewriting rules using a rule language or show their rewriting intentions by providing examples.  Furthermore\, with the wisdom accumulated from the crowd\, QueryBooster can also automatically recommend rewritings for new queries to the user. \nIn this talk\, I will first show a demo where QueryBooster can accelerate Tableau queries on top of the PostgreSQL database up to 100 times faster.  I then discuss the research questions we are working on in QueryBooster\, such as how to develop a rule language\, how to generate rewriting rules automatically from user-given examples\, and how to leverage crowdsources to recommend rewriting rules. \nBio: \nQiushi Bai is a Ph.D. candidate in the Computer Science Department at UC Irvine. He received his Master’s and Bachelor’s degrees in CS from Northeastern University in China. His research interests have focused on improving query performance for big data analytics and visualizations.
URL:https://isg.ics.uci.edu/event/qiushi-bai-querybooster-improving-sql-performance-using-middleware-services-for-human-centered-query-rewriting-demo/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221021T123000
DTEND;TZID=America/Los_Angeles:20221021T140000
DTSTAMP:20260711T114825
CREATED:20220926T231335Z
LAST-MODIFIED:20221019T171018Z
UID:1471-1666355400-1666360800@isg.ics.uci.edu
SUMMARY:Xiaozhen Liu: Demonstration of Collaborative and Interactive Workflow-based Data Analytics in Texera
DESCRIPTION:Abstract: \nCollaborative data analytics is becoming increasingly important due to the higher complexity of data science\, more diverse skills from different disciplines\, more common asynchronous schedules of team members\, and the global trend of working remotely. In this demo we will show how Texera supports this emerging computing paradigm to achieve high productivity among collaborators with various backgrounds. Based on our active joint projects on the system\, we use a scenario of social media analysis to show how a data science task can be conducted on a user friendly yet powerful platform by a multi-disciplinary team including domain scientists with limited coding skills and experienced machine learning experts. We will present how to do collaborative editing of a workflow and collaborative execution of the workflow in Texera. We will then show the technical details of how we support these features in our system. First we will show how collaborative editing is achieved in Texera\, then we will focus on data-centric features such as synchronization of operator schemas among the users during the construction phase\, and monitoring and controlling the shared runtime during the execution phase. \nBio: \nXiaozhen Liu is a 2nd-year Ph.D. student in the Computer Science Department at UC Irvine. He received his B.E. in Computer Science from Southeast University\, China. His current research interests include big data processing and collaborative data analytics systems.
URL:https://isg.ics.uci.edu/event/xiaozhen-liu-demonstration-of-collaborative-and-interactive-workflow-based-data-analytics-in-texera/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221028T123000
DTEND;TZID=America/Los_Angeles:20221028T140000
DTSTAMP:20260711T114825
CREATED:20220926T231451Z
LAST-MODIFIED:20260324T060756Z
UID:1473-1666960200-1666965600@isg.ics.uci.edu
SUMMARY:Abhishek Singh: WedgeBlock - An Off-Chain Secure Logging Platform for Blockchain Applications
DESCRIPTION:Abstract\n\n\nIn recent years\, there has been a growing interest in building blockchain-based decentralized applications (DApps). DApps typically consist of two components: an on-chain component that implements the logic of the application and runs on blockchain as a smart contract\, and an off-chain component that runs on a regular server to receive and process user requests while coordinating with the on-chain component. Developing DApps faces many challenges due to the cost and high latency of writing to a blockchain smart contract. In addition to the cost and latency\, DApps also face security challenges in maintaining logs and other data. \nWe propose WedgeBlock\, a secure data logging infrastructure for DApps. Logging is one of the most essential building blocks of data management systems and applications. Thus we envision that WedgeBlock would be used as a foundation to implement various DApps. WedgeBlock’s design reduces the performance and monetary cost of DApps with its main technical innovation called lazy-minimum trust (LMT). In LMT\, we show that we can combine the following features in one design: (1) it has an off-chain component for storage\, (2) it lazily writes digests of data—rather than all data—on-chain to minimize costs\, and (3) it integrates a trust mechanism to ensure the detection and punishment of malicious acts performed by the Offchain Node.\n\nSpeaker Bio\n\nAbhishek Singh is a 5th-year PhD student in the Computer Science Department at UC Irvine. His current research interests include transaction processing in decentralized data management systems.
URL:https://isg.ics.uci.edu/event/speaker-abhishek-singh-wedgeblock-an-off-chain-secure-logging-platform-for-blockchain-applications/
LOCATION:DBH 4011
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