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X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20220727T110000
DTEND;TZID=America/Los_Angeles:20220727T123000
DTSTAMP:20260606T165356
CREATED:20220726T052542Z
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SUMMARY:Anand Deshpahde (Persistent Technologies): How to build your own Business
DESCRIPTION:How to build your own Business\nlocation: \nDonald Bren Hall 3011 \nZoom info: the meeting will be hybrid and will also be available on zoom\nhttps://uci.zoom.us/j/96160303043 \nSkype for Business \nhttps://uci.zoom.us/skype/96160303043 \n  \nSpeaker: Anand Deshpande \nFounder\, Chairman and Managing Director\, Persistent Technologies \nHost: Prof. Sharad Mehrotra \nAbstract: In this talk Dr. Deshpande will provide insight into entrepreneurship based on his experience with Persistent Technologies highlighting what goes into starting a successful business from idea conception to a publicly traded company. He will also discuss the ongoing focus of the IT industry in India as it rapidly transforms itself into a modern economy. \nBio: \nDr. Anand Deshpande is the Founder\, Chairman\, and Managing Director of Persistent Systems since inception and is responsible for the overall leadership of the Company. Anand holds a Bachelor of Technology (B. Tech.) with Honours (Hons.) in Computer Science and Engineering from the Indian Institute of Technology (IIT)\, Kharagpur\, and an M.S. and a Ph.D. in Computer Science from Indiana University\, Bloomington\, Indiana\, USA. He has been recognized by both his alma maters — as a Distinguished Alumnus in 2012 by IIT Kharagpur and by the School of Informatics of Indiana University with the Career Achievement Award in 2007. \nAnand is a true technology visionary and has been the driving force in growing Persistent Systems from its inception in 1990 to the publicly traded global Company of today. Prior to founding Persistent Systems\, Anand began his professional career at Hewlett-Packard Laboratories in Palo Alto\, California\, where he worked as a Member of Technical Staff from May 1989 to October 1990. He is a founding trustee of Persistent Foundation and has served numerous positions at various professional and non-profit organizations — NASSCOM’s Executive Council\, founding President of Association for Computing Machinery (ACM) India\, Software Exporters’ Association of Pune (SEAP)\, Pune Chapter of Computer Society of India (CSI)\, CII’s Pune Zonal Council\, Trustee in the Computer History Museum\, founding member of Indian Software Products Industry Round Table (iSPIRT)\, founding member of I4C\, a member of the Dean’s Advisory Council in the School of Informatics\, Computing and Engineering of Indiana University. \nAfter transitioning from the role of CEO at Persistent\, Anand is committed to making a broader impact and is focused on data\, higher education\, and entrepreneurship.He is a part-time member of the Unique Identification Authority of India (UIDAI)\, trustee of the VLDB Foundation\, and is actively working on projects to create a data platform for Indian patients suffering from cancer and diabetes. He is an honorary Adjunct Professor of Practice at the Desai Sethi School of Entrepreneurship at IIT Bombay\, Chairman of the Board of Governors of IIT Patna and the interim Chairman of the Board of Governors at IIIT Allahabad. In addition\, he is on the governing board of the College of Engineering\, Pune and on the board of Gokhale Institute of Politics and Economics\, Pune. \nWith his family members\, Anand has established the DeAsra Foundation. This non-profit entity focuses on creating self-employment at scale and through the Second Orbit program\, in collaboration with Dr. Ashok Korwar\, he has helped hundreds of entrepreneurs scale their businesses. Anand is married to Sonali and they have a daughter and a son. \n  \n \nDr. Anand Deshpande\, Founder\, Chairman and Managing Director at Persistent Systems \n 
URL:https://isg.ics.uci.edu/event/anand-deshpahde-persistent-technologies-how-to-build-your-own-business/
LOCATION:Hybrid: DBH3011 & Zoom
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220930T140000
DTEND;TZID=America/Los_Angeles:20220930T150000
DTSTAMP:20260606T165356
CREATED:20220926T230737Z
LAST-MODIFIED:20220926T230807Z
UID:1462-1664546400-1664550000@isg.ics.uci.edu
SUMMARY:ISG talks: Welcome Back
DESCRIPTION:
URL:https://isg.ics.uci.edu/event/isg-talks-welcome-back/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221007T123000
DTEND;TZID=America/Los_Angeles:20221007T140000
DTSTAMP:20260606T165356
CREATED:20220926T231018Z
LAST-MODIFIED:20221007T045854Z
UID:1464-1665145800-1665151200@isg.ics.uci.edu
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221014T123000
DTEND;TZID=America/Los_Angeles:20221014T140000
DTSTAMP:20260606T165356
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:20260606T165356
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:20260606T165356
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221104T123000
DTEND;TZID=America/Los_Angeles:20221104T140000
DTSTAMP:20260606T165356
CREATED:20220926T231639Z
LAST-MODIFIED:20221028T174302Z
UID:1476-1667565000-1667570400@isg.ics.uci.edu
SUMMARY:Juncheng Fang: PeloPartition- Improving Blockchain Resilience to Partitioning by Sharding
DESCRIPTION:Abstract:\nBlockchain has gained considerable traction over the last few years and plays a critical role in realizing decentralized and cryptocurrency applications. A challenge that has been overlooked in prior blockchain algorithms is that they do not consider large-scale network outages and relied on the assumption of reliable global network connectivity. In the event of a large-scale network partition\, forks may occur between partitioned regions. After the partition ends they will be discarded\, leading to the loss of many blocks and a considerable amount of wasted work.\nThis paper presents PeloPartition\, which provides a sharding mechanism to improve blockchain’s resilience to the possibility of a global internet outage. In PeloPartition we form consensus groups dynamically and consider the partitioning of the group as a hint to split the blockchain into branches and guarantee that all of them will be merged after the network is recovered. We indicate different methodologies to ensure blockchain security while partitioning occurs. Our experiments use simulations to show how this approach can improve the performance of blockchain algorithms and prevent wasted computational power during partitioning.\n\nBio:\nJuncheng Fang is a 2nd-year Ph.D. student in the Computer Science Department at UC Irvine\, supervised by Prof. Faisal Nawab. His current research focuses on blockchain and distributed systems.
URL:https://isg.ics.uci.edu/event/juncheng-fang-pelopartition-improving-blockchain-resilience-to-partitioning-by-sharding/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221118T123000
DTEND;TZID=America/Los_Angeles:20221118T140000
DTSTAMP:20260606T165356
CREATED:20220926T231824Z
LAST-MODIFIED:20260405T013614Z
UID:1478-1668774600-1668780000@isg.ics.uci.edu
SUMMARY:Peeyush Gupta: A Demonstration of TippersDB
DESCRIPTION:Abstract: In the talk\, I’ll present TippersDB\, a middleware system designed to build\nsensor-based smart space analytical applications. TippersDB supports a powerful data model that decouples semantic data about the application domain from sensor data using which the semantic data is derived. By supporting mechanisms to map/translate\ndata\, concepts\, and queries between the two levels\, TippersDB relieves the application developers from having to know or reason about either the type or location of sensors or write sensor-specific code. In addition\, it allows for multiple optimizations based on smart space semantics to improve query processing.\nIn the talk\, I will present TippersDB’s data model\, query-driven translation of sensor data\, a summary of the system implementation\, and a demonstration of the TippersDB system. \n \nBio: Peeyush Gupta is a Postdoc in the Computer Science Department at UC Irvine\, advised by Prof. Sharad Mehrotra. His research interests include IoT data management\, time series database systems\, and data security and privacy.
URL:https://isg.ics.uci.edu/event/peeyush-gupta-a-demonstration-of-tippersdb/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221202T123000
DTEND;TZID=America/Los_Angeles:20221202T140000
DTSTAMP:20260606T165356
CREATED:20220926T232050Z
LAST-MODIFIED:20221028T174456Z
UID:1480-1669984200-1669989600@isg.ics.uci.edu
SUMMARY:Glenn Galvizo: Navigational Pattern Matching w/ Graphix
DESCRIPTION:Abstract:\nUsers aiming to perform scalable graph analytics on large datasets are stuck between a rock and a hard place. On one side\, a user works with an intuitive data model and query language chained to a system that cannot gracefully scale across multiple machines (i.e. the rock). On the other side\, a user works against a system that is able to gracefully scale but whose data model does match the model you are querying against (i.e. the hard place). We present Graphix\, which bridges the gap between scalable and graph-focused. Graphix enables (property) graph views of your existing document data in AsterixDB\, a Big Data management system boasting a partitioned-parallel query execution engine. \nIn this talk\, we’ll walk through the process of creating and querying a property graph view\, we’ll explain the architecture behind Graphix\, as well as some on-going work to support cyclic plans in our execution engine. \nSpeaker Bio:\nGlenn Justo Galvizo is a 4th-year Ph.D. candidate in the Computer Science Department at UC Irvine. He received his M.S. in CS at UC Irvine\, and his B.S. in CS at the University of Hawaii\, Manoa. His research interests include query languages\, graph data management\, and data modeling. \nPhotograph:
URL:https://isg.ics.uci.edu/event/glenn-galvizo-navigational-pattern-matching-w-graphix/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230113T123000
DTEND;TZID=America/Los_Angeles:20230113T140000
DTSTAMP:20260606T165356
CREATED:20230110T182620Z
LAST-MODIFIED:20230110T182620Z
UID:1506-1673613000-1673618400@isg.ics.uci.edu
SUMMARY:Andrew Chio: SmartSPEC: Customizable Smart Space Datasets via Event-Driven Simulations
DESCRIPTION:Bio – Andrew is a 4th year Ph.D. student in the Distributed Systems Middleware (DSM) group under the supervision of Professor Nalini Venkatasubramanian. His general research interests revolve around middleware\, data mining and analytics\, optimization\, and machine learning. \nAbstract – In this talk\, we present SmartSPEC\, an approach to generate customizable smart space datasets using sensorized spaces in which people and events are embedded. Smart space datasets are critical to design\, deploy and evaluate robust systems and applications to ensure cost-effective operation and safety/comfort/convenience of the space occupants. Often\, real-world data is difficult to obtain due to the lack of fine-grained sensing; privacy/security concerns prevent the release and sharing of individual and spatial data. SmartSPEC is a smart space simulator and data generator that can create a digital representation(twin) of a smart space and its activities. SmartSPEC uses a semantic model and ML-based approaches to characterize and learn attributes in a sensorized space\, and applies an event-driven simulation strategy to generate realistic simulated data about the space (events\, trajectories\, sensor datasets\, etc). To evaluate the realism of the data generated by SmartSPEC\, we develop a structured methodology and metrics to assess various aspects of smart space datasets\, including trajectories of people and occupancy of spaces. Our experimental study looks at two real-world settings/datasets: an instrumented smart campus building and a city-wide GPS dataset. Our results show that the trajectories produced by SmartSPEC are 1.4x to 4.4x more realistic than the best synthetic data baseline when compared to real-world data\, depending on the scenario and configuration.
URL:https://isg.ics.uci.edu/event/andrew-chio-smartspec-customizable-smart-space-datasets-via-event-driven-simulations/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230120T130000
DTEND;TZID=America/Los_Angeles:20230120T140000
DTSTAMP:20260606T165356
CREATED:20230117T181251Z
LAST-MODIFIED:20230117T181251Z
UID:1509-1674219600-1674223200@isg.ics.uci.edu
SUMMARY:Tung-Chun Chang: SmartParcels: Cross-Layer IoT Planning for Smart Communities
DESCRIPTION:Abstract:\nThe emergence of IoT-aided smart communities has created the need for a new set of urban planning tools. The extra design process includes instrumenting infrastructures (sensing\, networking\, and computing devices) in smartspaces to generate information units (from data analytics) to realize a range of required services. We propose SmartParcels\, a framework that generates a comprehensive and cost-effective plan for instrumenting designated regions of smart communities (often called parcels). SmartParcels embeds an approach to solve the cross-layer IoT planning problem (shown to be NP-hard) that must consider applications\, information/data\, infrastructure\, and geophysical layout as interdependent layers in the overall design. We develop a suite of algorithms (optimal\, partial optimal\, heuristic) for the problem; urban planners can compose these techniques in a plug-and-play manner to achieve performance trade-offs (optimality\, timeliness). SmartParcels can be utilized for clean-slate planning (from scratch) or for retrofit of communities with existing smart infrastructure. We evaluate Smart- Parcels in two real-world settings: National Tsing Hua University in Taiwan and Irvine in California\, USA\, for clean-slate and retrofit. The evaluation results reveal that SmartParcels can enable a 2X – 7X improvement in cost/performance metrics as compared to the baseline algorithm in the clean-slate and retrofit cases.\n\nBio:\nTung-Chun Chang is a 4th year Ph.D. student in the Distributed Systems Middleware (DSM) group under the supervision of Professor Nalini Venkatasubramanian. His general research interests include the Internet of Things\, wireless networking\, network analysis\, social networks\, optimization\, and machine learning.
URL:https://isg.ics.uci.edu/event/tung-chun-chang-smartparcels-cross-layer-iot-planning-for-smart-communities/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230126T153000
DTEND;TZID=America/Los_Angeles:20230126T170000
DTSTAMP:20260606T165356
CREATED:20230123T193002Z
LAST-MODIFIED:20230123T193002Z
UID:1513-1674747000-1674752400@isg.ics.uci.edu
SUMMARY:Aaron Elmore: Adventures in Database Compression
DESCRIPTION:Prof. Aaron Elmore\n\nUniversity of Chicago\nAbstract: Columnar databases enable effective compression by improving entropy through attribute locality and provides opportunities for fast query execution directly on compressed data. In this talk I will briefly overview how compressed query execution works in columnar systems and discuss techniques developed by our group over the past several years. This includes a pattern-inferred attribute decomposition for improved string compression and query performance\, a bounded float compression technique for fast filtering on limited precision numeric data\, and partially ordered dictionary compression.
URL:https://isg.ics.uci.edu/event/aaron-elmore-adventures-in-database-compression/
LOCATION:TBD
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230127T110000
DTEND;TZID=America/Los_Angeles:20230127T120000
DTSTAMP:20260606T165356
CREATED:20230123T192752Z
LAST-MODIFIED:20230123T192752Z
UID:1511-1674817200-1674820800@isg.ics.uci.edu
SUMMARY:Aaron Elmore: CrocodileDB: Resource Efficient Database Execution (CS Seminar)
DESCRIPTION:Prof. Aaron Elmore\nUniversity of Chicago\n\nAbstract: The coming end of Moore’s law requires that data systems be more judicious with computation and resources as the growth in data outpaces the availability of computational resources. Current database systems are eager and aggressively consume resources to immediately and quickly complete the task at hand. Intelligently deferring a task to a later point in time can increase result reuse\, reduce work that might later be invalidated\, or avoid unnecessary work altogether. In this talk I will introduce CrocodileDB\, a resource-efficient database system that automatically optimizes deferment based on user-specification and workload prediction. CrocodileDB integrates new ways of specifying timing information\, new query execution policies\, new task schedulers\, and new data loading schemes. In particular\, this talk will highlight two new query execution paradigms\, Intermittent Query Processing and Incremental-Aware Query Execution.
URL:https://isg.ics.uci.edu/event/aaron-elmore-crocodiledb-resource-efficient-database-execution-cs-seminar/
LOCATION:DBH 6011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230210T130000
DTEND;TZID=America/Los_Angeles:20230210T140000
DTSTAMP:20260606T165356
CREATED:20230207T054301Z
LAST-MODIFIED:20230217T192348Z
UID:1517-1676034000-1676037600@isg.ics.uci.edu
SUMMARY:Yiming Lin: QUIP: Query-driven Missing Value Imputation
DESCRIPTION:QUIP: Query-driven Missing Value Imputation\n\n\n\n\n\n\n\nThis paper develops a query-time missing value imputation frame- work\, entitled QUIP\, that minimizes the joint costs of imputation and query execution. QUIP achieves this by modifying how rela- tional operators are processed. It adds a cost-based decision function in each operator that checks whether the operator should invoke imputation prior to execution or to defer the imputations for down- stream operators to resolve. QUIP implements a new approach to evaluating outer join that preserve missing values during query processing\, and a bloom filter based index structure to optimize the space and running overhead. We have implemented QUIP using ImputeDB – a specialized database engine for data cleaning. Exten- sive experiments on both real and synthetic data sets demonstrates the effectiveness and efficiency of QUIP\, which outperforms the state-of-the-art ImputeDB by 2 to 10 times on different query sets and data sets\, and achieves the order-of-magnitudes improvement over offline approach. \n\n\n\n\n\n\n\n\nBio:\n\nYiming is a final year PhD student working with Prof. Sharad Mehrotra. His research area focuses on data management\, and especially on efficient query processing\, query optimization\, data quality and data integration.
URL:https://isg.ics.uci.edu/event/yiming-lin-quip-query-driven-during-missing-value-imputation/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230217T130000
DTEND;TZID=America/Los_Angeles:20230217T140000
DTSTAMP:20260606T165356
CREATED:20230217T190636Z
LAST-MODIFIED:20230217T190657Z
UID:1529-1676638800-1676642400@isg.ics.uci.edu
SUMMARY:Shanshan Han: Veil: Storage and Communication Efficient Volume Hiding Algorithms
DESCRIPTION:February 17\, 2023\, Friday\, 1:00 PM – 2 PM\nDonald Bren Hall 4011\, ICS\, UC Irvine\n\n\n\nZoom: https://uci.zoom.us/j/92445274511 (UCI only)\n\n\nAbstract \n\n\nVolume leakage is a major threat to searchable encryption and data outsourcing\, where an adversary can obtain the number of values in response to a query and deduce additional information about the data\, such as the query key. This work deals with the problem of volume leakage in the context of key-value datasets\, and develops solutions\, entitled Veil\, that partition the dataset into buckets and achieve a tradeoff between the storage and communication overheads. We design bucketing approaches that consider two types adversaries\, weak adversaries (i.e.\, passive adversaries) that passively observes the query processing\, and strong adversaries (i.e.\, active adversaries) that knows some keys in the dataset can actively query the encrypted database to gain information about other keys in the dataset. We then propose two strategies to add fake values to buckets\, including a straightforward disjoint strategy\, and an advanced overlapping strategy that further reduces the storage overhead.\n\n\n \n \nBio\n\nShanshan Han is a 4th year PhD student in Prof. Sharad Mehrotra’s group. Her research interests include data security and privacy.
URL:https://isg.ics.uci.edu/event/shanshan-han-veil-storage-and-communication-efficient-volume-hiding-algorithms/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230224T130000
DTEND;TZID=America/Los_Angeles:20230224T140000
DTSTAMP:20260606T165356
CREATED:20230208T191259Z
LAST-MODIFIED:20260405T013550Z
UID:1519-1677243600-1677247200@isg.ics.uci.edu
SUMMARY:Babak Salimi (UCSD): Certifying the Fairness of Predictive Models in the Face of Selection Bias
DESCRIPTION:The Department of Computer Science\, UC Irvine \nWELCOMES \nProf. Babak Salimi \nUCSD \nHosts: Prof. Chen Li \nCertifying the Fairness of Predictive Models in the Face of Selection Bias\n  \nAbstract: The widespread use of data-driven algorithmic decision making in crucial areas such as hiring\, loan assessments\, medical diagnoses\, and pretrial release has raised questions about the accuracy and fairness of these algorithms. Selection bias\, a prevalent data quality issue in sensitive domains\, is a major obstacle to creating fair predictive models. Most existing fair predictive modeling approaches are unable to address selection bias. To overcome this challenge\, we introduce a new framework called CRAB that leverages principles of data management and query answering from inconsistent and incomplete databases to produce certifiably fair predictive models. \n  \nIn this talk\, we will delve into the concept of consistent range approximation\, which plays a critical role in approximating the fairness of predictive models on a target population using biased data. We will also discuss the difficulties in achieving consistent range approximation when limited or no external data is available. With the help of our framework\, CRAB\, we can train predictive models that are certifiably fair on the target population\, even in the presence of selection bias. This talk will provide valuable insights for those working in data management\, ML\, and responsible data science and emphasize the importance of addressing selection bias in algorithmic decision making. \n  \nBio: Babak Salimi is an Assistant Professor in the HDSI department at UC San Diego. Prior to this\, he was a postdoctoral researcher in the Computer Science and Engineering Department at the University of Washington\, where he collaborated with Prof. Dan Suciu and the database group. Salimi received his Ph.D. from Carleton University’s School of Computer Science\, where he was advised by Prof. Leopoldo Bertossi. His research focuses on responsible data management and causal inference\, including algorithmic fairness and transparency. He has made several significant contributions to the understanding of responsible data management and analysis\, including explainability\, fairness\, reliability\, and robustness. Salimi also has a strong interest in database theory and data management. His research achievements have been acknowledged with awards such as the Postdoc Research Award at the University of Washington\, the Best Demonstration Paper Award at VLDB 2018\, the Best Paper Award at SIGMOD 2019\, and the Research Highlight Award at SIGMOD 2020. \n 
URL:https://isg.ics.uci.edu/event/babak-salimi-ucsd-certifying-the-fairness-of-predictive-models-in-the-face-of-selection-bias/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230303T130000
DTEND;TZID=America/Los_Angeles:20230303T140000
DTSTAMP:20260606T165356
CREATED:20230215T180806Z
LAST-MODIFIED:20260417T192125Z
UID:1524-1677848400-1677852000@isg.ics.uci.edu
SUMMARY:Alex Behm (Databricks): Photon: How to think vectorized
DESCRIPTION:The Department of Computer Science\, Information Systems Group\, UC Irvine \nWELCOMES \nDr. Alex Behm \nDatabricks \nPhoton: How to think vectorized \n3/3/2023\, Friday\, 1:00 – 2 pm \nPlace DBH 4011 \nI’m presenting Photon\, a new vectorized execution engine powering Databricks written from scratch in C++. I will introduce you to its basic building blocks by walking you through the evaluation of an example query with code snippets. You will learn about expression evaluation\, compute kernels\, runtime adaptivity\, filter evaluation\, and vectorized operations against hash tables. After the talk\, you will understand why vectorization is not just about SIMD for database people! \nBio: Alex has been building databases for over a decade in academia and industry and maintains a passion for speed and quality. He is the tech lead for Photon\, a new vectorized engine written from scratch in C++ that powers Databricks. Before joining Databricks\, Alex helped build Apache Impala as the second engineer on the project. Alex holds a PhD in databases from UC Irvine.
URL:https://isg.ics.uci.edu/event/photon-how-to-think-vectorized/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230310T130000
DTEND;TZID=America/Los_Angeles:20230310T140000
DTSTAMP:20260606T165356
CREATED:20230307T042058Z
LAST-MODIFIED:20230307T042058Z
UID:1533-1678453200-1678456800@isg.ics.uci.edu
SUMMARY:Fangqi Liu: DOME: Drone-assisted Monitoring of Emergent Events For Wildland Fire Resilience
DESCRIPTION:Abstract:\n\nBy serving as “eyes in the sky\,” data obtained from a carefully coordinated set of drones equipped with sensors have the potential to enable continuous monitoring of mission-critical events. We develop a Drone-assisted Monitoring system\, DOME\, that gathers real-time data for situational awareness in emergent and evolving events. The driving use case for this work is a prescribed burn event (Rx fire)\, often used to reduce hazardous fuels in forests. DOME coordinates the use of multiple heterogeneous drone platforms to support the observation of emergent physical phenomena (e.g.\, fire spread) by leveraging domain expert input and physics-based modeling/simulation methods. We propose an executable rule-based system for drone task generation; here\, a high-level mission specification utilizes physics-based models for fire spread prediction and automatically generates detailed monitoring instructions with locations\, periods\, and frequency for individual drones. DOME integrates algorithms for task allocation (mapping tasks to drones) and flight path planning while considering trade-offs between sensing coverage and accuracy. In addition\, DOME will guide in-flight drones to store and upload data under challenged communication settings (out of transmission range\, external signal blocking by trees). We evaluate the performance of DOME in real events (with expert-developed burn plans for a forest in North America). We test the applicability of the DOME system using simulated Rx burns at the Blodgett Forest Research Station and evaluate our proposed algorithms by comparing their performance with multiple baseline algorithms. Our experiments illustrate the effectiveness of the composite mechanisms in DOME that outperforms other approaches with higher rewards (capturing data of higher quality) and coverage (reduction of missed tasks).\n\nBio:\n\nFangqi Liu is a final year Ph.D. student in the Distributed Systems Middleware (DSM) group led by Professor Nalini Venkatasubramanian. Her research interests include wireless mobile networks\, the Internet of things\, motion planning and scheduling of mobile vehicles\, and drone-based monitoring applications.
URL:https://isg.ics.uci.edu/event/fangqi-liu-dome-drone-assisted-monitoring-of-emergent-events-for-wildland-fire-resilience/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230413T120000
DTEND;TZID=America/Los_Angeles:20230413T130000
DTSTAMP:20260606T165356
CREATED:20230420T175420Z
LAST-MODIFIED:20260401T210127Z
UID:1541-1681387200-1681390800@isg.ics.uci.edu
SUMMARY:C. Mohan: A Survey of Cloud Database Systems
DESCRIPTION:C. Mohan\nDistinguished Visiting Professor\, Tsinghua University\, China & Member\, Board of Governors (Digital University Kerala\, India) & Retired IBM Fellow (IBM Research\, USA)\n“A Survey of Cloud Database Systems” \nABSTRACT:  In this talk\, I will first introduce traditional (non-cloud) parallel and distributed database systems. Concepts like SQL and NoSQL systems\, data replication\, distributed and parallel query processing\, and data recovery after different types of failures will be covered. Then\, I will discuss how the emergence of the (public) cloud has introduced new requirements on parallel and distributed database systems\, and how such requirements have necessitated fundamental changes to the architectures of such systems. I will illustrate the related developments by discussing some of the details of systems like Alibaba POLARDB\, Microsoft Azure SQL DB\, Microsoft Socrates\, Azure Synapse POLARIS\, Google Spanner\, Google AlloyDB\, CockroachDB\, Amazon Aurora and Snowflake. \nBio: Dr. C. Mohan is currently a Distinguished Visiting Professor at Tsinghua University in China\, a Member of the inaugural Board of Governors of Digital University Kerala\, and an Advisor of the Kerala Blockchain Academy (KBA) and the Tamil Nadu e-Governance Agency (TNeGA) in India. He retired in June 2020 from being an IBM Fellow at the IBM Almaden Research Center in Silicon Valley. He was an IBM researcher for 38.5 years in the database\, blockchain\, AI and related areas\, impacting numerous IBM and non-IBM products\, the research and academic communities\, and standards\, especially with his invention of the well-known ARIES family of database locking and recovery algorithms\, and the Presumed Abort distributed commit protocol. This IBM (1997-2020)\, ACM (2002-) and IEEE (2002-) Fellow has also served as the IBM India Chief Scientist (2006-2009). In addition to receiving the ACM SIGMOD Edgar F. Codd Innovations Award (1996)\, the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards\, Mohan was elected to the United States and Indian National Academies of Engineering (2009)\, and named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. More information can be found in the Wikipedia page and his homepage.
URL:https://isg.ics.uci.edu/event/c-mohan-a-survey-of-cloud-database-systems/
LOCATION:DBH 3011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230414T130000
DTEND;TZID=America/Los_Angeles:20230414T140000
DTSTAMP:20260606T165356
CREATED:20230417T220007Z
LAST-MODIFIED:20230417T220007Z
UID:1537-1681477200-1681480800@isg.ics.uci.edu
SUMMARY:Zuozhi Wang: Texera: A System for Collaborative and Interactive Data Analytics Using Workflows (PhD Final Defense)
DESCRIPTION:Abstract\nIn the world of data analytics\, domain experts\, such as public health scientists and medical researchers\, play a crucial role as their domain knowledge can unlock valuable insights from data. However\, they face several challenges in the current landscape of data analytics tools. They often lack the technical skills necessary to analyze large datasets\, requiring collaboration with technical experts who may not have relevant domain knowledge. Moreover\, when processing large volumes of data\, processing times can be lengthy\, and non-technical users are left in the dark without feedback.Over the past six years\, our team has been developing Texera\, a workflow-based data analytics system specifically designed to enable non-technical users to perform data analytics tasks with ease by promoting seamless collaboration and responsive interactions. Texera enables multiple users to collaboratively construct workflows\, offering an experience similar to that of Google Docs. Furthermore\, Texera allows users to interact with the workflow execution\, enabling them to pause/resume workflows\, inspect execution states\, and modify logic as needed. In this talk\, we will explore the design choices and the associated tradeoffs of several key components within Texera that enable these powerful features. \n  \nBio\nZuozhi Wang is a sixth year PhD student at UC Irvine\, under the supervision of Professor Chen Li. His main research focuses are on the areas of distributed big data processing and query optimization.
URL:https://isg.ics.uci.edu/event/zuozhi-wang-texera-a-system-for-collaborative-and-interactive-data-analytics-using-workflows-phd-final-defense/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230428T130000
DTEND;TZID=America/Los_Angeles:20230428T140000
DTSTAMP:20260606T165356
CREATED:20230418T205830Z
LAST-MODIFIED:20230420T003058Z
UID:1539-1682686800-1682690400@isg.ics.uci.edu
SUMMARY:Quishi Bai: Maliva: Using Machine Learning to Rewrite Visualization Queries Under Time Constraints
DESCRIPTION:Abstract:\nAs a powerful way for people to gain insights from data quickly and intuitively\,  visualization is becoming increasingly important in the Big Data era. Considering data-visualization systems where a middleware layer translates a frontend request to a SQL query to a backend database to compute visual results.  In this talk\, we study the problem of answering a visualization request within a limited time due to the responsiveness requirement.  We propose a novel middleware solution called Maliva based on machine learning (ML) techniques.  Maliva applies the Markov Decision Process (MDP) model to decide how to rewrite queries and uses instances to train an agent to make a sequence of decisions judiciously for an online request.  Our experiments on both real and synthetic datasets show that Maliva performs significantly better than a baseline solution that does not do any rewriting\, in terms of both the probability of serving requests interactively and query execution time.\n\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/quishi-bai-maliva-using-machine-learning-to-rewrite-visualization-queries-under-time-constraints/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230505T130000
DTEND;TZID=America/Los_Angeles:20230505T140000
DTSTAMP:20260606T165356
CREATED:20230502T052932Z
LAST-MODIFIED:20230512T193154Z
UID:1553-1683291600-1683295200@isg.ics.uci.edu
SUMMARY:Farzad Habibi: Metastable Failures in Consensus Algorithms
DESCRIPTION:Abstract\nMetastable failure is a recent abstraction of a pattern of failures in distributed systems. A metastable failure is characterized as “permanent overload with an ultra-low goodput.”\nPrior research has proposed a framework for understanding metastable failure and has observed various cases of such failures in real-world settings.\nIn this talk\, we discuss the challenge of metastable fault tolerance in replication systems\, focusing specifically on a basic problem in distributed systems—consensus. Consensus is a basic building block for many distributed systems and protocols which means that such a focus would have an impact on a large class of systems.\nThe main points covered in this talk include (1) an introduction to metastable failures\, (2) a comprehensive analysis of various metastable failure cases in replication systems\, (3) a reproduction of a metastable failure case study\, (4) modeling of these failures using queuing theory\, and (5) a machine learning approach for predicting metastable failures. \nBio:\nFarzad Habibi is a second-year Ph.D. student in Computer Science at the University of California\, Irvine. He earned his Bachelor’s degree in Computer Engineering from the University of Tehran. Prior to his current research focus\, Farzad explored the resiliency of blockchain systems in the face of network partitioning. His current research endeavors are centered around investigating and addressing metastable failures in distributed systems.
URL:https://isg.ics.uci.edu/event/farzad-habibi-metastable-failures-in-consensus-algorithms/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230512T110000
DTEND;TZID=America/Los_Angeles:20230512T120000
DTSTAMP:20260606T165356
CREATED:20230512T193453Z
LAST-MODIFIED:20230522T212929Z
UID:1564-1683889200-1683892800@isg.ics.uci.edu
SUMMARY:CS Seminar: Prof. Arun Kumar: The New DBfication of ML/AI
DESCRIPTION:The Department of Computer Science\, UC Irvine  \nWELCOMES \nProf. Arun Kumar \nUCSD \n5/12/2023\, Friday\, 11:00 am – noon \nPlace DBH 6011 \nAbstract: \nThe recent boom in ML/AI applications has brought into sharp focus the pressing need for tackling the concerns of scalability\, usability\, and manageability across the entire lifecycle of ML/AI applications. The ML/AI world has long studied the concerns of accuracy\, automation\, etc. from theoretical and algorithmic vantage points. But to truly democratize ML/AI\, the vantage point of building and deploying practical systems is equally critical. \nIn this talk\, I will make the case that it is high time to bridge the gap between the ML/AI world and a world that exemplifies successful democratization of data technology: databases. I will show how new bridges rooted in the principles\, techniques\, and tools of the database world are helping tackle the above pressing concerns and in turn\, posing new research questions to the world of ML/AI. As case studies of such bridges\, I will briefly describe two lines of work from my group: query optimization for scalable deep learning systems and benchmarking data preparation in AutoML platforms. \nBio: \nArun Kumar is an Associate Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute and an HDSI Faculty Fellow at the University of California\, San Diego. He is a member of the Database Lab and Center for Networked Systems and an affiliate member of the AI Group. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part of the Apache MADlib open-source library\, shipped as part of products from Cloudera\, IBM\, Oracle\, and Pivotal\, and used internally by Facebook\, Google\, LogicBlox\, Microsoft\, and other companies. He is a recipient of three SIGMOD research paper awards\, five distinguished reviewer/meta reviewer awards from SIGMOD/VLDB\, the IEEE TCDE Rising Star Award\, an NSF CAREER Award\, a UCSD oSTEM Faculty of the Year Award\, and research award gifts from Amazon\, Google\, Oracle\, and VMware.
URL:https://isg.ics.uci.edu/event/the-new-dbfication-of-ml-ai/
LOCATION:DBH 6011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230519T130000
DTEND;TZID=America/Los_Angeles:20230519T140000
DTSTAMP:20260606T165356
CREATED:20230516T180316Z
LAST-MODIFIED:20230516T180316Z
UID:1567-1684501200-1684504800@isg.ics.uci.edu
SUMMARY:Yiming Lin: Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph
DESCRIPTION:Abstract: \nBusiness Intelligence (BI) is crucial in modern enterprises and billion-dollar business. Traditionally\, technical experts like database administrators would manually prepare BI-models (e.g.\, in star or snowflake schemas) that join tables in data warehouses\, before less-technical business users can run analytics using end-user dashboarding tools. However\, the popularity of self-service BI (e.g.\, Tableau and Power-BI) in recent years creates a strong demand for less technical end-users to build BI-models themselves. We develop an Auto-BI system that can accurately predict BI models given a set of input tables\, using a principled graph-based optimization problem we propose called k-Min-Cost-Arborescence (k-MCA)\, which holistically considers both local join prediction and global schema-graph structures\, leveraging a graph-theoretical structure called arborescence. While we prove k-MCA is intractable and inapproximate in general\, we develop novel algorithms that can solve k-MCA optimally\, which is shown to be efficient in practice with sub-second latency and can scale to the largest BI-models we encounter (with close to 100 tables). Auto-BI is rigorously evaluated on a unique dataset with over 100K real BI models we harvested\, as well as on 12 popular TPC benchmarks. It is shown to be both efficient and accurate\, achieving over 0.9 F1-score on both real and synthetic benchmarks.\n\n\nBio:\nYiming is a final year PhD student working with Prof. Sharad Mehrotra. His research area focuses on data management\, and especially on efficient query processing\, query optimization\, data quality and data integration.
URL:https://isg.ics.uci.edu/event/yiming-lin-auto-bi-automatically-build-bi-models-leveraging-local-join-prediction-and-global-schema-graph/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230526T130000
DTEND;TZID=America/Los_Angeles:20230526T140000
DTSTAMP:20260606T165356
CREATED:20230522T213103Z
LAST-MODIFIED:20230522T213103Z
UID:1577-1685106000-1685109600@isg.ics.uci.edu
SUMMARY:Qiushi Bai: Improving SQL Performance Using Middleware-Based Query Rewriting
DESCRIPTION:Abstract: \nQuery performance is critical in database-supported applications where users need answers quickly to make timely decisions. Traditional databases rely on rewriting queries to improve SQL performance. With the emergence of business intelligence and interactive visualization applications\, databases often miss opportunities to rewrite their queries\, due to reasons such as failure to adopt high-accuracy time estimators to choose efficient plans\, and missing domain-specific rewriting rules valid only for specific datasets. We focus on middleware-based query-rewriting solutions to address the problem since\, in many cases\, both the application and database layer are black boxes. First\, we develop Maliva\, a machine-learning-based technique that leverages high-accuracy query-time estimators to rewrite queries under time constraints. Second\, we present QueryBooster\, a human-centered query rewriting framework that provides an easy-to-use language for users to formulate rewriting rules based on their domain knowledge. Finally\, to make it easy for users to optimize their application queries\, we propose a middleware-based system called Squidster that provides query rewriting as a service. Our experiments in real and synthetic datasets show the effectiveness of the proposed solutions in improving end-to-end SQL query performance. \n  \nIn this talk\, we will focus on the QueryBooster and Squidster work. We will show a demo to motivate the problem and demonstrate the effectiveness and convenience of using the QueryBooster system to improve SQL query performance. \n  \nBio: \nQiushi Bai is a final year 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.
URL:https://isg.ics.uci.edu/event/qiushi-bai-improving-sql-performance-using-middleware-based-query-rewriting/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230602T130000
DTEND;TZID=America/Los_Angeles:20230602T140000
DTSTAMP:20260606T165356
CREATED:20230531T194408Z
LAST-MODIFIED:20230531T194408Z
UID:1580-1685710800-1685714400@isg.ics.uci.edu
SUMMARY:Saeed Kargar: Hamming Tree: The case for Energy-Aware Indexing for NVMs
DESCRIPTION:Zoom Link: https://uci.zoom.us/j/8045933305\n\nAbstract\nNVM technologies play a crucial role in data storage solutions as well as in battery-powered mobile and IoT devices. However\, the challenges of wear-out and energy efficiency need to be addressed for the widespread adoption of NVM. In this presentation\, I will discuss our research endeavors aimed at enhancing various aspects of NVMs and seamlessly integrating these technologies into the memory hierarchy.I will particularly focus on our latest work\, “Hamming Tree\,” which recently got accepted at SIGMOD 2023. The Hamming Tree introduces a novel software-level memory-aware solution designed to intelligently select the memory segment for write operations\, thereby minimizing bit flipping. By reducing bit flips\, we can significantly improve energy consumption and enhance the write endurance of NVMs.To demonstrate the effectiveness of the Hamming Tree approach\, we conducted real evaluations on an Optane memory device. The results revealed substantial improvements in both energy consumption and write endurance for NVMs. These findings underscore the practical benefits that can be achieved by implementing the Hamming Tree technique in NVM technologies.\n\nBio\nSaeed is a sixth-year PhD student at UCSC\, under the supervision of Professor Faisal Nawab. His main research area focuses on storage systems\, Non-Volatile Memory (NVM) technology\, and machine learning for systems.
URL:https://isg.ics.uci.edu/event/saeed-kargar-hamming-tree-the-case-for-energy-aware-indexing-for-nvms/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230609T130000
DTEND;TZID=America/Los_Angeles:20230609T140000
DTSTAMP:20260606T165356
CREATED:20230607T211742Z
LAST-MODIFIED:20230607T211806Z
UID:1582-1686315600-1686319200@isg.ics.uci.edu
SUMMARY:Hari Kishore Chaparala: When (Apache) AsterixDB Hit An (Apache) Iceberg
DESCRIPTION:Abstract\nApache Iceberg is an open-source table format with rich data management capabilities\, including schema evolution\, time travel\, and efficient data pruning. It offers a reliable foundation for storing and organizing data in a data lake environment. Iceberg specification allows multiple query engines to safely operate on the same data simultaneously. In this talk\, we see how we have introduced Apache AsterixDB to the family of query engines that support Iceberg tabe format specification. Apache AsterixDB is an open-source scalable Big Data Management System (BDMS) targeted to efficiently handle large amounts of semi-structured data. AsterixDB uses Hyracks\, a partitioned-parallel platform to perform data-intensive computations and analytics. With AsterixDB’s highly parallel execution capabilities and rich analytics support through SQL++ and flexible data model\, querying on external datasets in data lake environments becomes seamless. By integrating AsterixDB with Iceberg\, we can leverage Iceberg’s data management features and AsterixDB’s querying features for efficient data lake management and advanced analytics.\n\nBio\nHari Kishore is a second-year Master of Science student in Computer Science. His main research interests are in distributed systems.
URL:https://isg.ics.uci.edu/event/when-apache-asterixdb-hit-an-apache-iceberg/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231006T130000
DTEND;TZID=America/Los_Angeles:20231006T140000
DTSTAMP:20260606T165356
CREATED:20231004T193718Z
LAST-MODIFIED:20231004T193757Z
UID:1631-1696597200-1696600800@isg.ics.uci.edu
SUMMARY:Glenn Galvizo: Removing the 'A' in DAG: Navigational Queries in Hyracks
DESCRIPTION:Abstract \n\n\nThe need to “view” existing data under different models (e.g. JSON to graph) is a requirement seen in many modern applications. A naive solution involves utilizing narrow-purposed systems to handle each model\, however\, this multi-DBMS architecture significantly increases the cost of owning one’s data. For Apache AsterixDB users\, we offer Graphix as a way to issue synergistic document-graph queries on their existing Big Data\, in-situ (i.e. in partition-parallel).\n\nIn this talk\, we’ll be walking through how we modified Hyracks\, the runtime platform for AsterixDB\, to execute a recursive Graphix query. We’ll first talk about how tuple-pipelineable recursion occurs in a non-distributed setting. We will then extend our discussion for the distributed setting\, and conclude with optimizations we take to handle dense graphs.\n\n\n\nBio \nGlenn Justo Galvizo is a 5th-year Ph.D. candidate in the Computer Science Department at UC Irvine. He received his M.S. in CS at UC Irvine\, and his B.S. in CS at the University of Hawaii\, Manoa. His research interests include query languages\, graph data management\, and data modelling.
URL:https://isg.ics.uci.edu/event/removing-the-a-in-dag-navigational-queries-in-hyracks/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231013T130000
DTEND;TZID=America/Los_Angeles:20231013T170000
DTSTAMP:20260606T165356
CREATED:20231007T181612Z
LAST-MODIFIED:20231007T181612Z
UID:1637-1697202000-1697216400@isg.ics.uci.edu
SUMMARY:Suyash Gupta(UC Berkeley): Dissecting BFT Consensus: In Trusted Components we Trust!
DESCRIPTION:The Information Systems Group (ISG) at UC Irvine welcomes \nSuyash Gupta \nUC Berkeley  \n\nDissecting BFT Consensus: In Trusted Components we Trust! \n  \nABSTRACT \nThe growing interest in reliable multi-party applications has fostered widespread adoption of Byzantine Fault-Tolerant (bft) consensus protocols. Existing bft protocols need f more replicas than Paxos-style protocols to prevent equivocation attacks. trust-bft protocols seek to minimize this cost by making use of trusted components at replicas. This paper makes two contributions. First\, we analyze the design of existing trust-bft protocols and uncover three fundamental limitations that preclude most practical deployments. Some of these limitations are fundamental\, while others are linked to the state of trusted components today. Second\, we introduce a novel suite of consensus protocols\, FlexiTrust\, that attempts to sidestep these issues. We show that our FlexiTrust protocols achieve up to 185% more throughput than their trust-bft counterparts. \nBIO \nSuyash Gupta is a postdoctoral researcher at the SkyLab\, University of California\, Berkeley. He is also the Lead Architect of ResilientDB fabric. Prior to joining Berkeley\, he received his Ph.D. degree from University of California\, Davis. He also holds two Master of Science degrees; one from Purdue University and another from Indian Institute of Technology Madras. His current research focuses on attaining safe and efficient\, fault tolerant distributed consensus and communication. He has also co-authored a book on fault-tolerant distributed transaction processing at Morgan & Claypool. He has been awarded the Best Graduate Researcher Award for 2021 by UC Davis and Best Paper Award at EuroSys’23. In his free time\, Suyash likes to code and his team won Best Hacker Award at BostonHacks\, HackIllinois\, and HackPrinceton\, among others.
URL:https://isg.ics.uci.edu/event/suyash-guptauc-berkeley-dissecting-bft-consensus-in-trusted-components-we-trust/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231020T110000
DTEND;TZID=America/Los_Angeles:20231020T120000
DTSTAMP:20260606T165356
CREATED:20231016T182746Z
LAST-MODIFIED:20231016T182746Z
UID:1640-1697799600-1697803200@isg.ics.uci.edu
SUMMARY:Boon Thau Loo(UPenn): Towards Full-Stack Adaptivity in Permissioned Blockchain Systems
DESCRIPTION:  \nThe Computer Science Department and Information Systems Group (ISG) \nat UC Irvine welcomes \n\n \nBoon Thau Loo \nUniversity of Pennsylvania \n\nTowards Full-Stack Adaptivity in Permissioned Blockchain Systems \nOctober 20\, 2023 at 11:00AM \nDBH 6011 \n  \nABSTRACT \nPermissioned blockchain systems are an emerging instance of untrustworthy distributed databases. As novel smart contracts\, modern hardware\, and new cloud platforms arise\, future-proof permissioned blockchain systems need to be designed with full-stack adaptivity in mind.  At the application level\, a future-proof system must adaptively learn the best transaction processing paradigm in order to maximize performance for dynamic workloads\, and quickly adapt to new hardware as well as unanticipated workload changes on-the-fly. Likewise\, the Byzantine consensus layer must dynamically adjust itself to the workloads\, faulty conditions\, and network configuration while maintaining compatibility with the transaction processing paradigm. At the infrastructure level\, cloud providers must enable cross-layer adaptation\, which identifies performance bottlenecks and possible attacks\, and determines at runtime the degree of resource disaggregation that best meets application requirements. \n  \nThis talk presents four preliminary building blocks towards our vision of full-stack adaptivity: (1) FlexChain\, a novel permissioned blockchain system that physically disaggregating CPUs\, DRAM\, and storage devices to process different blockchain workloads efficiently; (2) AdaChain\, a learning-based framework that adaptively chooses the best permissioned blockchain architecture to optimize effective throughput for dynamic transaction workloads; (3) Bedrock\, a unified platform for Byzantine consensus protocol analysis\, implementation\, and experimentation; and (4) DeCon\, a declarative programming language for implementing\, optimizing\, and verifying smart contracts deployed on Blockchain systems. We conclude the talk with our ongoing work towards the goal of full-stack adaptivity across transaction processing\, consensus protocols\, and hardware infrastructure layers.  \nBIO \nBoon Thau Loo is the RCA Professor in the Computer and Information Science (CIS) department at the University of Pennsylvania. He is also the Associate Dean for Graduate Programs\, where he oversees all academic and admissions operations for doctoral\, master’s and professional programs at the School of Engineering and Applied Science. He received his Ph.D. degree in Computer Science from the University of California at Berkeley in 2006. Prior to his Ph.D.\, he received his M.S. degree from Stanford University in 2000\, and his B.S. degree with highest honors from University of California-Berkeley in 1999. His research focuses on distributed data management systems\, Internet-scale query processing\, and the application of data-centric techniques and formal methods to the design\, analysis and implementation of networked systems.  He leads the NetDB@Penn research team\, and is also the director of the Distributed Systems Laboratory (DSL)\, an inter-disciplinary systems research lab bringing together researchers in networking\, distributed systems\, and security.  \nLoo is the recipient of the David J. Sakrison Memorial Prize (2006) for the most outstanding dissertation research in the Department of EECS at University of California-Berkeley\, the ACM SIGMOD Dissertation Award (2007)\, NSF CAREER award (2009)\, the Air Force Office of Scientific Research (AFOSR) Young Investigator Award (2012)\, Penn’s Emerging Inventor of the year award (2018)\, the Ruth and Joel Spira award for Excellence in Teaching (2021)\, and the University Lindback award for distinguished teaching (2022). He has published 160+ peer reviewed publications and has graduated sixteen Ph.D. students and three postdocs\, including three tenured professors\, four current tenure-track professors\, and winners of five dissertation awards. As an entrepreneur\, he co-founded two companies: Netsil\, a cloud microservices analytics company acquired by public cloud company Nutanix Inc.\, and Termaxia\, an energy-efficient big data storage company acquired by Frontiir.  
URL:https://isg.ics.uci.edu/event/boon-thau-looupenn-towards-full-stack-adaptivity-in-permissioned-blockchain-systems/
LOCATION:DBH 6011
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