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X-WR-CALDESC:Events for Information Systems Group
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DTSTART;TZID=America/Los_Angeles:20230210T130000
DTEND;TZID=America/Los_Angeles:20230210T140000
DTSTAMP:20260722T010732
CREATED:20230207T054301Z
LAST-MODIFIED:20230217T192348Z
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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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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:20260722T010732
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231027T110000
DTEND;TZID=America/Los_Angeles:20231027T120000
DTSTAMP:20260722T010732
CREATED:20231016T183414Z
LAST-MODIFIED:20231016T183414Z
UID:1643-1698404400-1698408000@isg.ics.uci.edu
SUMMARY:Ken Birman (Cornell): Cascade:  A Platform for Fast Edge Intelligence
DESCRIPTION:The Computer Science Department and Information Systems Group (ISG) \nat UC Irvine welcomes \n \nKen Birman \nCornell University \nCascade:  A Platform for Fast Edge Intelligence \nOctober 27\, 2023 at 11:00AM \nDBH 6011 \n  \nABSTRACT \nThere is a growing need to apply machine intelligence and learning at the edge of the cloud.  Doing so would reduce delays for interactive LLM queries\, enable expanded use of assistive technology for drivers\, and let us bring AI into settings like factories and hospitals.  However\, the cloud was designed mostly to support scalable web applications\, and many of those design choices are mismatched relative to the needs of edge intelligence applications.   Our new system\, Cascade\, overcomes these issues.  Cascade enables a fast reactive edge in which AIs always see consistent\, current data.  The work centers on a rethinking of the lowest levels of modern computing frameworks and entails eliminating copying\, locking\, and synchronous distributed interactions.  This leads to an asynchronous flow model that yields dramatic reductions in delay\, improved throughput\, and even saves power.     \nBIO \nKen has worked in distributed systems since getting his PhD at UC Berkeley in 1991.  He is currently the N. Rama Rao Professor in the Department of Computer Science at Cornell.  Past projects including building the infrastructure that operated the New York and Swiss Stock Exchanges\, developing the core architecture and software for the French Air Traffic Control System\, and inventing some of the techniques that enabled today’s scalable\, self-managed\, cloud computing settings.  Ken is a fellow of the ACM and IEEE\, and won the IEEE Kanai Award for his research in distributed systems.  
URL:https://isg.ics.uci.edu/event/ken-birman-cornell-cascade-a-platform-for-fast-edge-intelligence/
LOCATION:DBH 6011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231103T130000
DTEND;TZID=America/Los_Angeles:20231103T140000
DTSTAMP:20260722T010732
CREATED:20231031T202318Z
LAST-MODIFIED:20231031T202333Z
UID:1649-1699016400-1699020000@isg.ics.uci.edu
SUMMARY:Nada Lahjouji: ProBE: Proportioning Privacy Budget for Complex Exploratory Decision Support
DESCRIPTION:ProBE: Proportioning Privacy Budget for Complex Exploratory Decision Support\nNada Lahjouji \n\nPhD Student\, UC\, Irvine\n\n\n\nAbstract\nDecision support (DS) applications play a crucial role in analyzing large volumes of data to produce valuable insights that facilitate informed decision-making. Such data can\, however\, contain sensitive information about individuals that requires privacy-preserving mechanisms to prevent data leaks\, but this is done at the expense of data utility. In this talk\, we explore the trade-off between privacy and utility in the context of DS and define its utility requirements. Specifically\, we observe that DS classifications tasks are characterized by asymmetric utility\, where the false negatives and false positives resulting from added noise hold different weights depending on the application. Previous work in decision support models this asymmetric utility\, but only addresses simple DS queries with a single aggregate condition. We consider complex queries consisting of the conjunction and disjunction of multiple conditions on different aggregate statistics which encompass the full scope of exploratory data analysis for DS. We formally define such queries and their utility requirements\, and provide algorithms that apportion the preset budget to optimally minimize privacy loss while supporting a mathematical bound on the utility. Our experimental results on multiple real-life datasets show that our algorithms successfully maintain utility guarantees for complex decision support queries\, while also minimizing privacy loss.\n\nBio\nNada Lahjouji is a fourth-year Ph.D. student in the Computer Science Department at UC Irvine. Her research interests include differential privacy\, IoT\, and database systems.
URL:https://isg.ics.uci.edu/event/nada-lahjouji-probe-proportioning-privacy-budget-for-complex-exploratory-decision-support/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20231201T130000
DTEND;TZID=America/Los_Angeles:20231201T140000
DTSTAMP:20260722T010732
CREATED:20231129T053803Z
LAST-MODIFIED:20260417T191706Z
UID:1653-1701435600-1701439200@isg.ics.uci.edu
SUMMARY:Vishal Chakraborty: Much Ado About Data-Undo: Semantically Meaningful Data Erasure
DESCRIPTION:Title: Much Ado About Data-Undo: Semantically Meaningful Data Erasure\n \n\nAbstract:  \n\nData regulations\, such as GDPR and CCPA\, are increasingly being adopted globally to protect against unsafe data management practices. Such regulations are\, often ambiguous (with multiple valid interpretations) when it comes to defining the expected dynamic behaviour of data processing systems. We will argue and show that it is possible to represent regulations such as GDPR formally as invariants using a (small set of) data processing concepts that capture system behaviour. When such concepts are grounded\, i.e.\, they are provided with a single unambiguous interpretation\, systems can achieve compliance by demonstrating that the system actions they implement maintain the invariants (representing the regulations). To illustrate our vision\, we propose Data-CASE\, a simple yet powerful model that (a) captures key data processing concepts and (b) a set of invariants that describe regulations in terms of these concepts. \nNext\, we use Data-CASE to study different interpretations of data erasure\, a key component of almost all data regulations that exist today. We present a taxonomy of data erasure from the perspective of databases. Recent work has shown that in social media platforms and other applications where extensive data dependencies are present\, data erasure is often implemented incorrectly/incompletely. Motivated by this\, we formulate data erasure as a mechanism for preventing data leakage in databases by accounting for data dependencies such as logs\, AI/ML models\, materialized views\, etc. We propose a SQL-like language to express such data dependencies which are an input to the data erasure mechanism. We show that the decision variant of our problem is NP-complete and present some algorithms to optimize overheads such as the cost of data\, time taken\, and additional number of erasures. We evaluate our implementations in PostgreSQL by analyzing the overheads (time\, space\, additional erasures and computation) of offering semantically meaningful data erasure. \n\n\n\nBio: Vishal Chakraborty is a Ph.D. student advised by Professor Sharad Mehrotra. He works on data management with a focus on privacy and efficient policy management. For more info\, visit https://www.vishalc.com.
URL:https://isg.ics.uci.edu/event/vishal-chakraborty-much-ado-about-data-undo-semantically-meaningful-data-erasure/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240112T110000
DTEND;TZID=America/Los_Angeles:20240112T120000
DTSTAMP:20260722T010732
CREATED:20240122T190433Z
LAST-MODIFIED:20240122T190629Z
UID:1661-1705057200-1705060800@isg.ics.uci.edu
SUMMARY:Shahram Ghandeharizadeh(USC): Intelligent 3D Multimedia Displays using Flying Light Specks
DESCRIPTION:The Computer Science Department and Information Systems Group (ISG) \nat UC Irvine welcomes \nShahram Ghandeharizadeh \nUniversity of Southern California \n  \nIntelligent 3D Multimedia Displays using Flying Light Specks \nJanuary 12 at 11:00AM \nDBH 6011 \n\nAbstract: A Flying Light Speck\, FLS\, is a miniature sized drone equipped with one or more light sources to generate different colors and textures with adjustable brightness. Synchronized swarms of FLSs illuminate complex 2D and 3D shapes in a  fixed volume\, a 3D display. An FLS display may be a cuboid that sits on a table or hangs on a wall\, the dashboard of a self-driving vehicle\, a room\, etc. Its illuminations provide haptic feedback by detecting a user’s touch and flying to exert force back. This presentation describes use of an FLS display in two diverse multimedia applications that generate 3D data\, entertainment and healthcare. We present an architecture for a 3D FLS display and novel algorithms to localize FLSs to illuminate complex 2D and 3D shapes\, compute FLS flight paths to render a motion illumination\, use dark standby FLSs to maintain the quality of an illumination in the presence of FLS failures\, and STAG as a novel battery charging framework for FLSs.  \nBio: Shahram Ghandeharizadeh received his Ph.D. degree in Computer Science from the University of Wisconsin\, Madison\, in 1990. Since then\, he has been on the faculty at the University of Southern California. His research interests include design\, implementation and evaluation of novel architectures for high performance data intensive applications\, multimedia based social networking systems\, parallel and cache-augmented database systems. His research team designed and implemented one of the first video-on-demand servers prior to the introduction of DVDs. This system was licensed by Panasonic for research and development purposes. His current efforts with FLS displays is an extension of this prior work to realize a 3D display. His research efforts have been recognized by numerous awards including the NSF Young investigator award\, ACM Software Systems award\, USC School of Engineering research award\, among others.  \n 
URL:https://isg.ics.uci.edu/event/shahram-ghandeharizadeh-intelligent-3d-multimedia-displays-using-flying-light-specks/
LOCATION:DBH 6011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240119T110000
DTEND;TZID=America/Los_Angeles:20240119T120000
DTSTAMP:20260722T010732
CREATED:20240122T190712Z
LAST-MODIFIED:20240122T190712Z
UID:1663-1705662000-1705665600@isg.ics.uci.edu
SUMMARY:Henry F. Korth (Lehigh University): Blockchain: Computer Science Foundations\, Positive Social and Business Impact\, and Research Opportunities
DESCRIPTION:The Computer Science Department and Information Systems Group (ISG) \nat UC Irvine welcomes \nHenry F. Korth \nLehigh University \nBlockchain: Computer Science Foundations\, Positive Social and Business Impact\, and Research Opportunities \nJanuary 19 at 11:00AM \nDBH 6011 \n  \nAbstract: To start\, basic concepts of blockchain systems will be introduced assuming only a basic background in computing. The key properties that distinguish blockchains from traditional databases will be explained along with an introduction to the mathematical concepts that make them possible.   \nWe next survey briefly current and emerging blockchain applications and their benefits to business and to society as a whole.   \nThe latter and main part of the talk presents research currently underway at Lehigh: \n\nAcceleration of the generation of zero-knowledge proofs on modern parallel architectures\,\nA consensus mechanism for the implementation of global-scale central-bank digital currencies in a manner that allows privacy along with transparency and regulatability\, \nA framework for benchmarking blockchain systems\, including current work extending the existing framework to layer-2 blockchain systems\n\nWe conclude with a brief survey of other development projects in Lehigh Blockchain with industry partners \n  \nBio: Korth’s current research is focused in blockchain systems\, with an emphasis on blockchain system internals and applications in financial systems. For full details on his blockchain work\, spanning research and education\, see blockchain.cse.lehigh.edu. More broadly\, Korth’s research focuses on database systems\, information systems and distributed systems. He is co-author of Database Systems Concepts\, now in its seventh edition. Previously\, Korth was Director of Database Principles Research at Bell Laboratories\, Vice President of Panasonic Technologies\, and Associate Professor at the University of Texas at Austin. Korth is a Fellow of the ACM and the IEEE. At the 1995 International Conference on Very Large Data Bases\, his paper “A Model of CAD Transactions” was chosen as “Most Influential Paper from the Proceedings of Ten Years Ago.”  At Lehigh\, he has served as department chair and directed the Lehigh-unique Computer Science and Business degree.  He currently holds a concurrent courtesy appointment in the Decision and Technology Analytics Department in Lehigh’s College of Business and is director of the Blockchain Lab in Lehigh’s Center for Financial Services. \n 
URL:https://isg.ics.uci.edu/event/henry-f-korth-lehigh-university-blockchain-computer-science-foundations-positive-social-and-business-impact-and-research-opportunities/
LOCATION:DBH 6011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240126T130000
DTEND;TZID=America/Los_Angeles:20240126T140000
DTSTAMP:20260722T010732
CREATED:20240122T191108Z
LAST-MODIFIED:20240122T191108Z
UID:1666-1706274000-1706277600@isg.ics.uci.edu
SUMMARY:Volker Markl (TU Berlin): Mosaics of Big Data: Database Systems and Information Management – Trends and a Vision
DESCRIPTION:Prof. Dr. Volker Markl \nChair of the Database Systems and Information Management (DIMA) Group at TU Berlin \nDirector of the Berlin Institute for the Foundations of Learning and Data (BIFOLD) \nChief Scientist and Head of the Intelligent Analytics for Massive Data Research Group at German Research Center for Artificial Intelligence (DFKI) \n \nMosaics of Big Data  Database Systems and Information Management – Trends and a Vision \n\nAbstract: The global database research community has greatly impacted the functionality and performance of data storage and processing systems along the dimensions that define “big data”\, i.e.\, volume\, velocity\, variety\, and veracity. Locally\, over the past five years\, we have also been working on varying fronts. Among our contributions are: (1) establishing a vision for a database-inspired big data analytics system\, which unifies the best of database and distributed systems technologies\, and augments it with concepts drawn from compilers (e.g.\, iterations) and data stream processing\, as well as (2) forming a community of researchers and institutions to create the Stratosphere platform to realize our vision. One major result from these activities was Apache Flink\, an open-source big data analytics platform and its thriving global community of developers and production users. Although much progress has been made\, when looking at the overall big data stack\, a major challenge for database research community still remains. That is\, how to maintain the ease-of-use despite the increasing heterogeneity and complexity of data analytics\, involving specialized engines for various aspects of an end-to-end data analytics pipeline\, including\, among others\, graph-based\, linear algebra-based\, and relational-based algorithms\, and the underlying\, increasingly heterogeneous hardware and computing infrastructure. At TU Berlin\, DFKI\, and the Berlin Institute for Foundations of Learning and Data (BIFOLD) we currently aim to advance research in this field via the NebulaStream and Agora projects. Our goal is to remedy some of the heterogeneity challenges that hamper developer productivity and limit the use of data science technologies to just the privileged few\, who are coveted experts. In this talk\, we will outline how state-of-the-art SPEs have to change to exploit the new capabilities of the IoT and showcase how we tackle IoT challenges in our own system\, NebulaStream. We will also present our vision for Agora\, an asset ecosystem that provides the technical infrastructure for offering and using data and algorithms\, as well as physical infrastructure components.  \n  \nBio: Volker Markl is a German Professor of Computer Science. He leads the Chair of Database Systems and Information Management at TU Berlin and the Intelligent Analytics for Massive Data Research Department at DFKI. In addition\, he is Director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD). He is a database systems researcher\, conducting research at the intersection of distributed systems\, scalable data processing\, and machine learning. Volker led the Stratosphere project\, which resulted in the creation of Apache Flink. Volker has received numerous honors and prestigious awards\, including best paper awards at ACM SIGMOD\, VLDB\, and ICDE as well as the ACM SIGMOD Systems Award. In 2014\, he was elected one of Germany‘s leading “Digital Minds“ (Digitale Köpfe) by the German Informatics Society and is a member of the Berlin-Brandenburg Academy of Sciences. He was elected an ACM Fellow for his contributions to query optimization\, scalable data processing\, and data programmability. He served President of the VLDB Endowment\, and serves as advisor to academic institutions\, governmental organizations\, and technology companies. Volker holds eighteen patents and has been co-founder and mentor to several startups. \n 
URL:https://isg.ics.uci.edu/event/volker-markl-tu-berlin-mosaics-of-big-data-database-systems-and-information-management-trends-and-a-vision/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240202T130000
DTEND;TZID=America/Los_Angeles:20240202T140000
DTSTAMP:20260722T010732
CREATED:20240131T055243Z
LAST-MODIFIED:20240131T055243Z
UID:1674-1706878800-1706882400@isg.ics.uci.edu
SUMMARY:Shengquan Ni: Supporting time-travel debugging in Texera
DESCRIPTION:Title: Supporting time-travel debugging in Texera \nSpeaker: Shengquan Ni \nAbstract: Dataflow systems\, traditionally used for relational analysis\, now support a variety of tasks including complex user-defined functions. As dataflow jobs become more diverse and complex\, there is an increasing need for better debugging support to understand their runtime behaviors and identify issues either in data or the analysis. To achieve the goal in the Texera system\, we develop techniques to support “time-travel debugging.” In particular\, the system allows users to interact with an execution during runtime to retrieve an execution state\, which is a consistent snapshot of the engine. The user has the ability to “travel back to the past” to access the execution state of a previous interaction\, thus retrospectively explore and analyze a previous execution state.  We will show a demo of this powerful feature\, and give an overview of the underlying techniques. \nBio: Shengquan Ni is a Ph.D. student in the Department of Computer Science advised by Professor Chen Li. His current research interests include big data processing\, distributed systems\, data analytics and data science. He was a summer intern at Google.
URL:https://isg.ics.uci.edu/event/shengquan-ni-supporting-time-travel-debugging-in-texera/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240209T110000
DTEND;TZID=America/Los_Angeles:20240209T120000
DTSTAMP:20260722T010732
CREATED:20240122T191410Z
LAST-MODIFIED:20240122T191430Z
UID:1669-1707476400-1707480000@isg.ics.uci.edu
SUMMARY:Joseph Hellerstein (UC Berkeley): Hydro: A Compiler Stack for Distributed Programs
DESCRIPTION:The Computer Science Department and Information Systems Group (ISG) \nat UC Irvine welcomes \n \nJoseph Hellerstein \nUC Berkeley and Sutter Hill Ventures \nTITLE: Hydro: A Compiler Stack for Distributed Programs \nABSTRACT:   \nNearly all programs of interest today are distributed. Unfortunately\, the traditional languages and compilers in common use today offer little assistance in ensuring the correctness of distributed programs. This state of affairs makes infrastructure development and tuning unduly expensive\, and hampers the ability of less-technical but highly creative individuals to invent new applications that take advantage of the ubiquity of cloud and mobile computing. \n  \nThe Hydro project at Berkeley is an effort to build a compiler stack to address these issues\, taking lessons from the success of scaling data management software. The foundation of the Hydro stack is Hydroflow\, a Rust-based dataflow runtime with an IR based on algebraic dataflow. Hydroflow enables a compiler to make correct program transformations that are natural in the context of distributed systems. Transformations include: \n– Refactoring: Given an arbitrary block of code\, refactor it into smaller blocks that can be launched on independent machines \n– Replication: Given an arbitrary block of code\, determine whether it can be safely replicated in deployment \n– Partitioning: Given an arbitrary block of code\, determine how its inputs can be safely partitioned (“sharded”) to multiple machines in deployment \n  \nThese transformations in turn allow distributed programs to be optimized for various goals\, including parallelism (both pipelines and partitioning)\, memory scaling\, performance isolation\, geoproximity and physical security. \n  \nAlthough the Hydro project is still in early stages\, I will present case studies showing correctness\, latency and scaling results when optimizing programs ranging from infrastructure like key-value stores\, applications like shopping carts and messaging systems\, and tricky consensus protocols. \n  \nJoint work with colleagues at UC Berkeley and Sutter Hill Ventures. \n  \nBIO: Joseph M. Hellerstein is the Jim Gray Professor of Computer Science at UC Berkeley\, and a Faculty Fellow at Sutter Hill Ventures. His academic recognition includes the ACM SIGMOD Codd Innovations Award\, ACM Fellow and Sloan Research Fellow awards\, and six “Test of Time” awards for his papers. Hellerstein is a longtime participant in the computing industry\, co-founding startups\, advising companies and venture funds\, and directing industry research. He also enjoys playing music\, and has performed live with legendary musicians including Joe Henderson\, Joshua Redman and Michael J. Carey. \n 
URL:https://isg.ics.uci.edu/event/joseph-hellerstein-uc-berkeley-hydro-a-compiler-stack-for-distributed-programs/
LOCATION:DBH 6011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240212T130000
DTEND;TZID=America/Los_Angeles:20240212T140000
DTSTAMP:20260722T010732
CREATED:20240206T000852Z
LAST-MODIFIED:20260405T013527Z
UID:1676-1707742800-1707746400@isg.ics.uci.edu
SUMMARY:Raul Castro Fernandez (U. Chicago): On Data Ecology\, Data Markets\, the Value of Data\, and Dataflow Governance
DESCRIPTION:Abstract: \nData shapes our social\, economic\, cultural\, and technological environments. Data is valuable\, so people seek it\, inducing data to flow. The resulting dataflows distribute data and thus value. For example\, large Internet companies profit from accessing data from their users\, and engineers of large language models seek large and diverse data sources to train powerful models. It is possible to judge the impact of data in an environment by analyzing how the dataflows in that environment impact the participating agents. My research hypothesizes that it is also possible to design (better) data environments by controlling what dataflows materialize; not only can we analyze environments but also synthesize them. In this talk\, I present the research agenda on “data ecology\,” which seeks to build the principles\, theory\, algorithms\, and systems to design beneficial data environments. I will also present examples of data environments my group has designed\, including data markets for machine learning\, data-sharing\, and data integration. I will conclude by discussing the impact of dataflows in data governance and how the ideas are interwoven with the concepts of trust\, privacy\, and the elusive notion of “data value.” As part of the technical discussion\, I will complement the data market designs with the design of a data escrow system that permits controlling dataflows. \nBio (Raul Castro Fernandez): \nIn my research\, I ask what is the value of data and explore the potential of data markets to unlock that value. My group collaborates with economists\, legal scholars\, statisticians\, and domain scientists. We build systems to share\, discover\, prepare\, integrate\, and process data. I have traditionally worked on distributed query processing systems and continue to do so. I have received a SIGMOD’23 Test-of-time-Award. I am an assistant professor in the Department of Computer Science and on the Committee of Data Science at The University of Chicago. Before UChicago\, I did a postdoc at MIT with Sam Madden and Mike Stonebraker. And before that\, I completed a PhD at Imperial College London with Peter Pietzuch.
URL:https://isg.ics.uci.edu/event/raul-castro-fernandez-u-chicago-on-data-ecology-data-markets-the-value-of-data-and-dataflow-governance/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240301T130000
DTEND;TZID=America/Los_Angeles:20240301T140000
DTSTAMP:20260722T010732
CREATED:20240228T200048Z
LAST-MODIFIED:20240228T200048Z
UID:1685-1709298000-1709301600@isg.ics.uci.edu
SUMMARY:Yunyan Ding: Efficient Mouse Brain Image Processing Using Collaborative Data Workflows on Texera
DESCRIPTION:Abstract:\nIn the field of neuroscience\, accurately mapping the complex three-dimensional (3D) neural circuitry and architecture of the brain is crucial for advancing our understanding of brain functions and disorders. In this study\, we introduce a distributed computational pipeline designed for processing high-resolution mouse brain tile images captured by TissueCyte. This pipeline efficiently and accurately stitches these tiles and stacks 2D sections to construct detailed whole mouse brain models in 3D. Our high-quality 3D volumes can facilitate secondary analyses such as whole-brain 3-dimensional registration and segmentation\, cell counting\, and high-resolution volumetric visualization. By utilizing specialized optimization methods within Texera platform to distribute computational resources\, our pipeline achieves an over 80\% reduction in processing times\, enhancing the efficiency significantly. Additionally\, this pipeline is designed with scalability and flexibility\, enabling it to process large volumes of high-resolution neuroimaging data across various computational environments. Our work is developed through a collaborative effort among neuroscience\, computer vision\, and data processing teams\, exemplifying the power of creating tools for interdisciplinary collaboration in addressing complex research challenges.\n\nBio:\nYunyan Ding is a second-year Ph.D. student in the Computer Science Department at UC Irvine. Her research interests include data processing systems and big data analytics.
URL:https://isg.ics.uci.edu/event/yunyan-ding-efficient-mouse-brain-image-processing-using-collaborative-data-workflows-on-texera/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240304T110000
DTEND;TZID=America/Los_Angeles:20240304T120000
DTSTAMP:20260722T010732
CREATED:20240304T175318Z
LAST-MODIFIED:20250211T004006Z
UID:1689-1709550000-1709553600@isg.ics.uci.edu
SUMMARY:Bratin Saha (AWS Amazon): Scaling Generative AI in the Enterprise
DESCRIPTION:Abstract: Machine learning (ML) and generative artificial intelligence (AI) is one of the most transformational technologies that is opening up new opportunities for innovation in every domain across software\, finance\, health care\, manufacturing\, media\, entertainment and others. This talk will discuss the key trends that are driving AI/ML innovation\, how enterprises are using AI/ML today to innovate how they run their businesses\, the key technology challenges in scaling out ML and generative AI across the enterprise\, some of the key innovations from Amazon\, and how this field is likely to evolve in the future. \nBio: Dr. Bratin Saha is the Vice President of Machine Learning and AI services at AWS where he leads all the ML and AI services and helped build one of the fastest growing businesses in AWS history. In 2022 Harvard Business School wrote three case studies on how he built the machine learning business at AWS. He is an alumnus of Harvard Business School (General Management Program)\, Yale University (PhD Computer Science)\, and Indian Institute of Technology (BS Computer Science). He has more than 70 patents granted (with another 50+ pending) and more than 30 papers in conferences/journals. Prior to Amazon he worked at Nvidia and Intel leading different product groups spanning imaging\, analytics\, media processing\, high performance computing\, machine learning\, and software infrastructure. Bratin received the Distinguished Alumnus Award from the Indian Institute of Technology and is an Executive Fellow at the Harvard Business School. 
URL:https://isg.ics.uci.edu/event/bratin-saha-aws-amazon-scaling-generative-ai-in-the-enterprise/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240315T130000
DTEND;TZID=America/Los_Angeles:20240315T140000
DTSTAMP:20260722T010732
CREATED:20240311T193926Z
LAST-MODIFIED:20240311T193926Z
UID:1693-1710507600-1710511200@isg.ics.uci.edu
SUMMARY:Yinan Zhou: SpendableDB: A UTxO-based decentralized Database
DESCRIPTION:Abstract: \nBlockchain technology has attracted a significant amount of attention ever since the Bitcoin blockchain’s success. Currently\, most of the research and engineering efforts have been centered around monetary transactions such as token exchange protocols. The potential of building databases on top of blockchains is largely overlooked and remains an open problem. The literature on blockchain databases is divided into permissioned blockchains and permissionless account-based blockchains. However\, the former is not fully decentralized\, and the latter suffers from challenges in performance and cost. We propose SpendableDB\, a permissionless UTxO-based blockchain database as a novel approach to the problem of data decentralization. Our design integrates data into individual UTxOs to achieve true decentralization of data ownership that can be securely transferred and traded\, similar to how the regular monetary UTxOs are protected by the underlying blockchain’s decentralization protocol. Additionally\, SpendableDB provides cryptographically secured data integrity and immutable data lineage that can be easily verified. Our implementation and experiments show that our design is economically practical as it incurs a small amount of blockchain transaction fees. \nBio: \nYinan Zhou is a second-year Ph.D. student in the Computer Science Department at UC Irvine. His primary research focus is on blockchain infrastructure and application developments.
URL:https://isg.ics.uci.edu/event/yinan-zhou-spendabledb-a-utxo-based-decentralized-database/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20240405T130000
DTEND;TZID=America/Los_Angeles:20240405T140000
DTSTAMP:20260722T010732
CREATED:20240401T183244Z
LAST-MODIFIED:20240401T183244Z
UID:1696-1712322000-1712325600@isg.ics.uci.edu
SUMMARY:Lukasz Golab (University of Waterloo): Understanding models and the data they learn from
DESCRIPTION:Lukasz Golab (U. Waterloo) \nUnderstanding models and the data they learn from \nAbstract: The modern world is powered by data. However\, as the capabilities of data-intensive systems grow\, so does their complexity\, making them hard to understand and troubleshoot. I will discuss my lab’s efforts towards understanding models and the data they learn from\, including local and global model explanations as well as model diagnostics for fairness and bias avoidance. \n  \nBio: Lukasz Golab is a Professor and Canada Research Chair at the University of Waterloo. From 2006 to 2011\, he was a Senior Member of Research Staff at AT&T Labs. He obtained a BSc in Computer Science from the University of Toronto (with High Distinction) and a PhD in Computer Science from the University of Waterloo (with Alumni Gold Medal). His long-term research agenda of Data for Good calls for building data-intensive systems with societal impact. His recent projects focus on systems for managing high-speed data events such as data stream engines and blockchains\, understanding complex models and the data they learn from\, and applications including online safety\, education\, and sustainability.
URL:https://isg.ics.uci.edu/event/lukasz-golab-university-of-waterloo-understanding-models-and-the-data-they-learn-from/
LOCATION:DBH 4011
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END:VCALENDAR