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DTSTART;TZID=America/Los_Angeles:20230310T130000
DTEND;TZID=America/Los_Angeles:20230310T140000
DTSTAMP:20261106T204059
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:20230303T130000
DTEND;TZID=America/Los_Angeles:20230303T140000
DTSTAMP:20261106T204059
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:20230224T130000
DTEND;TZID=America/Los_Angeles:20230224T140000
DTSTAMP:20261106T204059
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:20230217T130000
DTEND;TZID=America/Los_Angeles:20230217T140000
DTSTAMP:20261106T204059
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:20230210T130000
DTEND;TZID=America/Los_Angeles:20230210T140000
DTSTAMP:20261106T204059
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:20230127T110000
DTEND;TZID=America/Los_Angeles:20230127T120000
DTSTAMP:20261106T204059
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:20230126T153000
DTEND;TZID=America/Los_Angeles:20230126T170000
DTSTAMP:20261106T204059
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:20230120T130000
DTEND;TZID=America/Los_Angeles:20230120T140000
DTSTAMP:20261106T204059
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:20230113T123000
DTEND;TZID=America/Los_Angeles:20230113T140000
DTSTAMP:20261106T204059
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:20221202T123000
DTEND;TZID=America/Los_Angeles:20221202T140000
DTSTAMP:20261106T204059
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:20221118T123000
DTEND;TZID=America/Los_Angeles:20221118T140000
DTSTAMP:20261106T204059
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:20221104T123000
DTEND;TZID=America/Los_Angeles:20221104T140000
DTSTAMP:20261106T204059
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:20221028T123000
DTEND;TZID=America/Los_Angeles:20221028T140000
DTSTAMP:20261106T204059
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:20221021T123000
DTEND;TZID=America/Los_Angeles:20221021T140000
DTSTAMP:20261106T204059
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:20221014T123000
DTEND;TZID=America/Los_Angeles:20221014T140000
DTSTAMP:20261106T204059
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:20221007T123000
DTEND;TZID=America/Los_Angeles:20221007T140000
DTSTAMP:20261106T204059
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:20220930T140000
DTEND;TZID=America/Los_Angeles:20220930T150000
DTSTAMP:20261106T204059
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:20220727T110000
DTEND;TZID=America/Los_Angeles:20220727T123000
DTSTAMP:20261106T204059
CREATED:20220726T052542Z
LAST-MODIFIED:20220726T052542Z
UID:1456-1658919600-1658925000@isg.ics.uci.edu
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:20220606T120000
DTEND;TZID=America/Los_Angeles:20220606T133000
DTSTAMP:20261106T204059
CREATED:20220531T175900Z
LAST-MODIFIED:20220531T182239Z
UID:1446-1654516800-1654522200@isg.ics.uci.edu
SUMMARY:Jayant Haritsa (IISc Bangalore): Shedding Light on Opaque Database Queries
DESCRIPTION:Shedding Light on Opaque Database Queries\nlocation: \nDonald Bren Hall 3011 \nSpeaker:  Jayant Haritsa \n                 Database Systems Lab \n                 Indian Institute of Science\, Bangalore \nAbstract: We have recently defined a new query reverse-engineering problem of unmasking SQL queries hidden within opaque database applications. The diverse industrial use-cases for this problem range from resurrecting legacy code to query rewriting.  It poses hard and novel intellectual challenges due to the presence of a variety of complexities such as dependencies between the query clauses\, schematic renaming in the output attributes\, result consolidation due to aggregates\, and presence of computed column functions. \nIn this talk\, we motivate the hidden query extraction problem\, and then present UNMASQUE\, an extraction algorithm based on an active learning approach\, that successfully exposes a complex class of hidden warehouse queries. A special feature of its design is that the extraction is completely non-invasive with respect to the application\, examining only the results obtained from repeated executions on databases derived with a combination of data mutation and data generation techniques. Further\, potent optimizations are incorporated to minimize the extraction overheads. A detailed evaluation over applications hosting hidden SQL queries\, or their imperative versions\, demonstrates that UNMASQUE correctly and efficiently extracts these queries. \nWhile the above represents a first step in query extraction\, a suite of open research problems that remain to be solved will be covered at the end of the talk. \nBio: Jayant Haritsa is on the computer science faculty at the Indian Institute of Science\, Bangalore\, since 1993. He received a BTech degree from the Indian Institute of Technology (Madras)\, and MS and PhD degrees from the University of Wisconsin (Madison).  He is a Fellow of ACM and IEEE\, and currently the President of ACM India.
URL:https://isg.ics.uci.edu/event/jayant-haritsa-iisc-bangalore-shedding-light-on-opaque-database-queries/
LOCATION:DBH 3011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220531T170000
DTEND;TZID=America/Los_Angeles:20220531T174500
DTSTAMP:20261106T204059
CREATED:20220531T182515Z
LAST-MODIFIED:20220531T182557Z
UID:1451-1654016400-1654019100@isg.ics.uci.edu
SUMMARY:Matt Ingenthron (Couchbase): Couchbase and Distributed Computing Backends for big data processing
DESCRIPTION:Location:  DBH 3011  \nCouchbase and Distributed Computing Backends for big data processing\nSpeaker:   Matt Ingenthron\,  Engineering Director\, Couchbase \nBiography:  Matt is a Couchbase co-founder and Engineering Director who leads SDK and Connector development at Couchbase. He has a deep software development background with extensive experience scaling Java\, Ruby on Rails\, and AMP web applications. He is a contributor to the memcached project\, one of the maintainers of the Java spymemcached client\, and a core developer on Couchbase. He heads up Couchbase’s efforts to help developers be most effective and continues to lead the team in getting the right bits needed for Node.js\, Java\, .NET\, and PHP developers among others.
URL:https://isg.ics.uci.edu/event/matt-ingenthron-couchbase-couchbase-and-distributed-computing-backends-for-big-data-processing/
LOCATION:DBH 3011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220422T110000
DTEND;TZID=America/Los_Angeles:20220422T120000
DTSTAMP:20261106T204059
CREATED:20220324T185928Z
LAST-MODIFIED:20260405T013443Z
UID:1413-1650625200-1650628800@isg.ics.uci.edu
SUMMARY:Tim Kraska (MIT): Towards instance-optimized data systems
DESCRIPTION:Location:  \nDBH 6011 \nhttps://uci.zoom.us/j/94559511434 (for UCI users only) \n\nSpeaker: Tim Kraska\, MIT \nAbstract: Recently\, there has been a lot of excitement around ML-enhanced (or learned) algorithms and data structures. For example\, there has been work on applying machine learning to improve query optimization\, indexing\, storage layouts\, scheduling\, log-structured merge trees\, sorting\, compression\, sketches\, among many other data management tasks. Arguably\, the ideas behind these techniques are similar: machine learning is used to model the data and/or workload in order to derive a more efficient algorithm or data structure. Ultimately\, what these techniques will allow us to build are “instance-optimized” systems; systems that self-adjust to a given workload and data distribution to provide unprecedented performance and avoid the need for tuning by an administrator. \nIn this talk\, I will first provide an overview of the opportunities and limitations of current ML-enhanced algorithms and data structures\, present initial results of SageDB\, a first instance-optimized system we are building as part of DSAIL@CSAIL at MIT\, and finally outline remaining challenges and future directions. \nTim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT’s Computer Science and Artificial Intelligence Laboratory\, co-director of the Data System and AI Lab at MIT (DSAIL@CSAIL)\, and co-founder of Einblick Analytics. Currently\, his research focuses on building systems for machine learning\, and using machine learning for systems. Before joining MIT\, Tim was an Assistant Professor at Brown\, spent time at Google Brain\, and was a PostDoc in the AMPLab at UC Berkeley after he got his PhD from ETH Zurich. Tim is a 2017 Alfred P. Sloan Research Fellow in computer science and received several awards including the VLDB Early Career Research Contribution Award\,  the VMware Systems Research Award\, the university-wide Early Career Research Achievement Award at Brown University\, an NSF CAREER Award\, as well as several best paper and demo awards at VLDB\, SIGMOD\, and ICDE.
URL:https://isg.ics.uci.edu/event/tim-kraska-mit-towards-instance-optimized-data-systems/
LOCATION:DBH 6011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220311T150000
DTEND;TZID=America/Los_Angeles:20220311T170000
DTSTAMP:20261106T204059
CREATED:20220225T223925Z
LAST-MODIFIED:20220226T002759Z
UID:1392-1647010800-1647018000@isg.ics.uci.edu
SUMMARY:Nandit Soparkar (Ubiquiti): Data-driven AI technologies for a Consumer webapp
DESCRIPTION:Title:  Data-driven AI technologies for a Consumer webapp \n  \nAbstract: We discuss the challenges\, and the opportunity\, in providing a consumer-facing data-driven AI webapp. Our presentation will include a demo\, make available access to the audience\, and cover the technical as also relevant business challenges being addressed. Our webapp is the new CarBeast.com (about 2 months since launch) which uses well-established technologies\, and the experienced team\, from Ubiquiti.com (about 2 decades since launch). Our technical considerations will cover both\, the data & software challenges\, and our business considerations will cover both\, the marketing & sales challenges. Attendees will get to try CarBeast for free! \n\n \n Speaker Bio-sketch:  Nandit Soparkar leads business and technical development at Ubiquiti. Previously\, he was a tenure-track professor in the College of Engineering at the University of Michigan\, Ann Arbor. Many of his former students now hold senior faculty\, research and industry positions\, and he also advises small & large software and other technical companies. His research focused on time-constrained concurrent computing\, data mining & analysis technology\, and logic-enhanced memory for data-intensive computing. He has worked at the AT&T Bell Laboratories in Murray Hill\, NJ\, and at the IBM TJ Watson Research Center in Yorktown Heights NY. He has published books and research papers\, holds multiple pending & issued patents\, and done peer-reviewed research in several fields. He holds a B.Tech in Computer Science & Engineering from the Indian Institute of Technology\, Delhi\, as well as M.S. and Ph.D. degrees in Computer Science from the University of Texas at Austin.\n\n\n\nRelated Links:\n\nwww.CarBeast.com\nwww.Ubiquiti.com
URL:https://isg.ics.uci.edu/event/nandit-soparkar-ubiquiti-data-driven-ai-technologies-for-a-consumer-webapp/
LOCATION:Zoom
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211025T120000
DTEND;TZID=America/Los_Angeles:20211025T130000
DTSTAMP:20261106T204059
CREATED:20211025T161310Z
LAST-MODIFIED:20211025T161310Z
UID:1374-1635163200-1635166800@isg.ics.uci.edu
SUMMARY:Yannis Chronis (University of Wisconsin-Madison): Analytic Query Processing using Associative Computing
DESCRIPTION:Speaker:\nYannis Chronis (University of Wisconsin-Madison) \nTitle:\nAnalytic Query Processing using Associative Computing \nAbstract:\nWe are in the midst of a “Cambrian” hardware evolution in which a variety of architectures are being invented with a flurry that we haven’t seen in a long time. The associative computing paradigm enables designs that utilize memories in new ways and packs storage and computing closely together.  Associative computing processors offer massive data-level parallelism and single-cycle search and update primitives. The focus of our work is on exploring how database analytic workloads can run efficiently on this new computing substrate. Our vehicle for this research is the Castle project in which we are building a data processing engine to run on the CAPE hardware\, which is a specific SRAM-based associative processor. To fully exploit CAPE’s potential\, we adapt data query processing operators and query optimization methods to leverage the style of data-parallel computing that can be carried out natively inside CAPE. We experiment with both individual query processing operators and end-to-end queries and show that the performance gains using our methods are significant\, and up to 128X in some cases. Tentatively\, these results point to the exciting potential in accelerating database analytic workloads using associative computing. \nBio:\nYannis is a fifth-year Ph.D. student in the Computer Sciences Department at the University of Wisconsin-Madison advised by Prof. Jignesh Patel. His research focuses on efficient data processing using modern and emerging hardware.
URL:https://isg.ics.uci.edu/event/yannis-chronis-university-of-wisconsin-madison-analytic-query-processing-using-associative-computing/
LOCATION:DBH 4011
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210423T150000
DTEND;TZID=America/Los_Angeles:20210423T160000
DTSTAMP:20261106T204059
CREATED:20210318T225801Z
LAST-MODIFIED:20210409T181102Z
UID:1355-1619190000-1619193600@isg.ics.uci.edu
SUMMARY:Jerry Power (I3 Systems): Managing Digital Flows in a Data Driven World
DESCRIPTION:Speaker:\nJerry Power \nBio:\nJerry Power is the CEO of I3 Systems and a founder of the I3 Consortium. I3 Systems creates real-time data networks that span organizational and geographical constraints. Prior to the formation of I3 Systems\, Jerry was the Executive Director of The Institute for Communication Technology Management (CTM) at USC. Jerry has over 30 years’ experience in strategic marketing and business analysis in the communications industry. Jerry is also the co-author of the book “The Real Time Revolution: Transforming your organization to value customer Time.”
URL:https://isg.ics.uci.edu/event/managing-digital-flows-in-a-data-driven-world/
LOCATION:Zoom
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20210402T110000
DTEND;TZID=America/Los_Angeles:20210402T120000
DTSTAMP:20261106T204059
CREATED:20210318T224510Z
LAST-MODIFIED:20210409T181049Z
UID:1348-1617361200-1617364800@isg.ics.uci.edu
SUMMARY:Phil Bernstein (MSR): Adding Data Management to Orleans – A Journey
DESCRIPTION:Speaker:\nPhil Bernstein\nMicrosoft Research \nAbstract:\nI spent eight years adding database features to the Orleans object-oriented programming framework: replication\, geo-distribution\, transactions\, and indexing. The challenge is how to do it when storage is a plug-in service that you don’t control. In this talk\, I’ll describe the journey\, summarizing the main technical ideas and recounting the ups and downs of a friendly collaboration with a product group. Along the way\, I’ll discuss where database system research problems come from and the special challenges of industrial research. You can learn more about Orleans at https://dotnet.github.io/orleans/ \nSpeaker Bio:\nPhilip A. Bernstein is a Distinguished Scientist at Microsoft Research. He has published over 150 papers and two books on the theory and implementation of database systems\, especially on transaction processing and data integration\, which are still a major focus of his research. He is an ACM Fellow\, a AAAS Fellow\, a winner of ACM SIGMOD’s Codd Innovations Award\, a member of the Washington State Academy of Sciences\, and the U.S. National Academy of Engineering. He received a B.S. degree from Cornell and M.Sc. and Ph.D. from University of Toronto.
URL:https://isg.ics.uci.edu/event/adding-data-management-to-orleans-a-journey/
LOCATION:Zoom
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201120T150000
DTEND;TZID=America/Los_Angeles:20201120T160000
DTSTAMP:20261106T204059
CREATED:20201120T091507Z
LAST-MODIFIED:20210409T181135Z
UID:1340-1605884400-1605888000@isg.ics.uci.edu
SUMMARY:Yiming Lin (UCI): LOCATER - Cleaning WiFi Connectivity Datasets for Semantic Localization
DESCRIPTION:Speaker:\nYiming Lin\, UCI \nAbstract:\nSensor data is abundant in our life but often dirty to generate services with high quality. This talk explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings\, regions\, rooms. WiFi connectivity data consists of sporadic connections between devices and nearby WiFi access points (APs)\, each of which may cover a relatively large area within a building. Our system\, entitled semantic LOCATion cleanER (LOCATER)\, postulates semantic localization as a series of data cleaning tasks – first\, it treats the problem of determining the AP to which a device is connected between any two of its connection events as a missing value detection and repair problem. It then associates the device with the semantic subregion (e.g.\, a conference room in the region) by postulating it as a location disambiguation problem. LOCATER uses a bootstrapping semi-supervised learning method for  coarse localization and a probabilistic method to achieve finer localization. Evaluation on both real and synthetic datasets shows that LOCATER can achieve significantly high accuracy  at both the coarse and fine levels. \nBio: \nYiming Lin is a Ph.D. student in the Department of Computer Science at the University of California\, Irvine. He is currently doing research on data cleaning and data stream analysis. He is the recipient of Hasso Plattner Institute Fellowship since 2020.  \nZoom Link:\nhttps://uci.zoom.us/j/95066121155
URL:https://isg.ics.uci.edu/event/locater-cleaning-wifi-connectivity-datasets-for-semantic-localization/
LOCATION:https://uci.zoom.us/j/95066121155
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201023T150000
DTEND;TZID=America/Los_Angeles:20201023T160000
DTSTAMP:20261106T204059
CREATED:20201017T155305Z
LAST-MODIFIED:20210409T181153Z
UID:1337-1603465200-1603468800@isg.ics.uci.edu
SUMMARY:Karthik Ranganathan: YugabyteDB - Bringing Together the Best of Amazon Aurora and Google Spanner
DESCRIPTION:Speaker: \n\nKarthik Ranganathan\n\nAbstract: \nPostgreSQL\, a single-node open-source RDBMS\, is widely adopted for its powerful set of features. However\, PostgreSQL is not built to be used as a cloud-native database\, and therefore cannot inherently survive failures\, scale horizontally or support geo-distributed deployments. While Amazon Aurora has modified the subsystem of PostgreSQL that writes to disk along with simplifying async replication to make the database resilient to failures\, it does not address horizontal scalability or geo-distribution. Google Spanner is a distributed SQL database that addresses these features as well\, however it does not offer many of the powerful features of PostgreSQL. YugabyteDB\, a fully open-source distributed SQL database\, aims to combine the best of these in an attempt to build a very compelling\, cloud-native database. \n  \nIn this talk\, we will look at the architecture of YugabyteDB that enables it to support all PostgreSQL features along with distributed transactions\, resilience\, scalability\, and geo-distribution of data. \n\n\n\n\n\n\n\n\n\n\nBio:\n\nKarthik was one of the original database engineers at Facebook responsible for building distributed databases including Cassandra and HBase. He is an Apache HBase committer\, and also an early contributor to Cassandra\, before it was open-sourced by Facebook. He is currently the co-founder and CTO of the company behind YugabyteDB\, a fully open-source distributed SQL database for building cloud-native and geo-distributed applications. \nZoom link \n\n\n\n\n\n\n\n\n\n\n\nhttps://uci.zoom.us/j/97063664357
URL:https://isg.ics.uci.edu/event/yugabytedb-bringing-together-the-best-of-amazon-aurora-and-google-spanner/
LOCATION:CA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201016T140000
DTEND;TZID=America/Los_Angeles:20201016T150000
DTSTAMP:20261106T204059
CREATED:20200929T234710Z
LAST-MODIFIED:20210409T181216Z
UID:1333-1602856800-1602860400@isg.ics.uci.edu
SUMMARY:Aaron J. Elmore: CrocodileDB - Resource Efficient Database Execution
DESCRIPTION:Abstract:\nThe 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. \nZoom link:\nhttps://uci.zoom.us/j/92895672890 \nAuthor bio:\nAaron J. Elmore is an Assistant Professor in the Department of Computer Science\, and the College of the University of Chicago. Aaron was previously a Postdoctoral Associate at MIT. Aaron’s thesis on Elasticity Primitives for Database-as-a-Service was completed at the University of California\, Santa Barbara. His recent research interests focus on building data systems that address the growing data deluge. This includes building a resource-efficient database system (CrocodileDB)\, new compression techniques for databases (DenseStore and EdgeTSD)\, bringing git-like functionality to Databases (Datahub\, Decibel\, and OrpheusDB)\, and data discovery (DataSwamp and Relic). His previous projects included polystores (BigDAWG)\, elastic databases (E-Store)\, and database migration (Squall and Zephyr). He is currently an associate editor for SIGMOD record\, and has served as co-chair for SIGMOD demonstration track\, the inaugural SIGMOD student research competition\, and VLDB proceeding editor.
URL:https://isg.ics.uci.edu/event/crocodiledb-resource-efficient-database-execution/
LOCATION:https://uci.zoom.us/j/92895672890
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200427T110000
DTEND;TZID=America/Los_Angeles:20200427T120000
DTSTAMP:20261106T204059
CREATED:20200424T192059Z
LAST-MODIFIED:20200424T192059Z
UID:1291-1587985200-1587988800@isg.ics.uci.edu
SUMMARY:Lei Cao: Toward an End-to-end Anomaly Discovery Paradigm
DESCRIPTION:ABSTRACT: \nAnomaly detection is critical in enterprises\, with applications ranging from preventing financial fraud\, and defending network intrusions\, to detecting imminent device failures. \nAlthough previously developed research offers a plethora of stand-alone methods for detecting particular types of anomalies\, there is no end-to-end solution for data scientists to effectively discover anomalies over large volumes of varied data. To build such a system\, several critical challenges have to be solved: How to determine which among many alternative anomaly detection algorithms is the best for a given task and to find the proper parameter settings? How to leverage a small amount of end-user feedback to improve the anomaly extraction process? How to best present the anomaly detection results such that users do not have to evaluate the potentially large number of anomaly candidates one by one? \nThis talk will present our ADP solution that solves all above problems. ADP supports all stages of anomaly discovery by seamlessly integrating anomaly-related services within one integrated platform. It enables the tuning free anomaly detection and achieves the power of sense making afforded by anomaly summarization and explanation services\, while allowing the users to easily steer the discovery process with human ingenuity. \nBio: \nDr. Lei Cao is a Postdoc Associate at MIT CSAIL\, working with Prof. Samuel Madden and Prof. Michael Stonebraker. Before that he worked at IBM T.J. Watson Research Center as a Research Staff Member. He received his Ph.D. in Computer Science from Worcester Polytechnic Institute\, supervised by Prof. Elke Rundensteiner. His recent research is focused on developing end-to-end tools for data scientists to effectively make sense of data. \n\nZoom Linj: https://uci.zoom.us/j/808173003
URL:https://isg.ics.uci.edu/event/lei-cao-toward-an-end-to-end-anomaly-discovery-paradigm/
LOCATION:CA
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200420T110000
DTEND;TZID=America/Los_Angeles:20200420T120000
DTSTAMP:20261106T204059
CREATED:20200222T010700Z
LAST-MODIFIED:20200222T010700Z
UID:1285-1587380400-1587384000@isg.ics.uci.edu
SUMMARY:Redesigning Storage Systems for Future Workloads\, Hardware\, and Performance Requirements (CS Faculty Candidate Seminar)
DESCRIPTION:Speaker: Oana Balmau (University of Sydney) \nAbstract: Cloud storage stacks are being challenged by new workloads\, new hardware and new performance requirements. First\, workloads evolved from following a read-heavy pattern (e.g.\, a static web-page) to a write-heavy profile where the read:write ratio is closer to 1:1 (e.g.\, as in the Internet of Things). Second\, the hardware is undergoing rapid changes.  The divide between fine-grained volatile memory and slow block-level storage is rapidly being bridged by the emerging byte-addressable non-volatile memory devices and the fast block-addressable NVMe SSDs (e.g.\, Intel Optane NVMe SSDs). Third\, performance requirements in storage systems now emphasize low tail latency\, in addition to high throughput.  \nIn this talk I will argue that existing storage systems have fundamental limitations that do not allow them to fully meet these challenges\, and that therefore the storage stack needs to undergo radical change. In particular\, using state-of-the-art key-value stores I will demonstrate that with modern workloads and hardware the bottleneck shifts from I/O to CPU\, invalidating an assumption that has underpinned all past storage system design. In line with this observation I will then present a new design paradigm for key-value stores that departs from the conventional wisdom of optimizing disk usage and instead optimizes CPU usage. To do so\, we keep data unsorted on disk\, reduce contention for shared data structures\, and do away with expensive maintenance operations. \nThis design has been implemented in the KVell key-value store. KVell outperforms state-of-the-art key-value stores such as RocksDB in both read- and write-heavy workloads\, running on modern NVMe SSDs.  Thanks to its novel design\, KVell achieves up to 5x better throughput\, and up to two orders of magnitude lower tail latency. \nBio: Oana Balmau is a PhD candidate in the School of Computer Science at the University of Sydney\, advised by Prof. Willy Zwaenepoel. She earned her Bachelors and Masters degrees in Computer Science from EPFL\, Switzerland. Her research interests are computer systems and storage technologies. Currently\, she is working towards redesigning storage systems\, aimed at future workloads and hardware. (Homepage: oanabalmau.com)
URL:https://isg.ics.uci.edu/event/redesigning-storage-systems-for-future-workloads-hardware-and-performance-requirements-cs-faculty-candidate-seminar/
LOCATION:DBH 3011
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END:VCALENDAR