Nandit Soparkar (Ubiquiti): Data-driven AI technologies for a Consumer webapp
2022-03-11 15:00 - 17:00 (America/Los_Angeles) | Zoom
Title: Data-driven AI technologies for a Consumer webapp

Abstract: 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!

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.

Related Links:

www.CarBeast.com
www.Ubiquiti.com

Tim Kraska (MIT): Towards instance-optimized data systems
2022-04-22 11:00 - 12:00 | DBH 6011
Location:
DBH 6011
https://uci.zoom.us/j/94559511434 (for UCI users only)

Speaker: Tim Kraska, MIT
Abstract: 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.
In 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.
Tim 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.

Matt Ingenthron (Couchbase): Couchbase and Distributed Computing Backends for big data processing
2022-05-31 17:00 - 17:45 | DBH 3011
Location: DBH 3011
Couchbase and Distributed Computing Backends for big data processing
Speaker: Matt Ingenthron, Engineering Director, Couchbase
Biography: 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.

Jayant Haritsa (IISc Bangalore): Shedding Light on Opaque Database Queries
2022-06-06 12:00 - 13:30 | DBH 3011
Shedding Light on Opaque Database Queries
location:
Donald Bren Hall 3011
Speaker: Jayant Haritsa
Database Systems Lab
Indian Institute of Science, Bangalore
Abstract: 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.
In 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.
While 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.
Bio: 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.

Anand Deshpahde (Persistent Technologies): How to build your own Business
2022-07-27 11:00 - 12:30 | Hybrid: DBH3011 & Zoom
How to build your own Business
location:
Donald Bren Hall 3011
Zoom info: the meeting will be hybrid and will also be available on zoom
https://uci.zoom.us/j/96160303043
Skype for Business
https://uci.zoom.us/skype/96160303043

Speaker: Anand Deshpande
Founder, Chairman and Managing Director, Persistent Technologies
Host: Prof. Sharad Mehrotra
Abstract: 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.
Bio:
Dr. 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.
Anand 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.
After 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.
With 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.

Dr. Anand Deshpande, Founder, Chairman and Managing Director at Persistent Systems

ISG talks: Welcome Back
2022-09-30 14:00 - 15:00 | DBH 4011

Sadeem Alsudais: Drove: Tracking Execution Results of Workflows on Large Data
2022-10-07 12:30 - 14:00 | DBH 4011
Abstract:

Data 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.

Bio:

Sadeem 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.

Qiushi Bai: QueryBooster-Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting + Demo
2022-10-14 12:30 - 14:00 | DBH 4011
Title:
QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting
Abstract:
Query 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.
We 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.
In 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.
Bio:
Qiushi 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.

Xiaozhen Liu: Demonstration of Collaborative and Interactive Workflow-based Data Analytics in Texera
2022-10-21 12:30 - 14:00 | DBH 4011
Abstract:
Collaborative 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.
Bio:
Xiaozhen 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.

Abhishek Singh: WedgeBlock - An Off-Chain Secure Logging Platform for Blockchain Applications
2022-10-28 12:30 - 14:00 | DBH 4011
Abstract

In 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.

We 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.

Speaker Bio

Abhishek 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.

Juncheng Fang: PeloPartition- Improving Blockchain Resilience to Partitioning by Sharding
2022-11-04 12:30 - 14:00 | DBH 4011
Abstract:
Blockchain 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.
This 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.

Bio:
Juncheng 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.

Peeyush Gupta: A Demonstration of TippersDB
2022-11-18 12:30 - 14:00 | DBH 4011
Abstract: In the talk, I’ll present TippersDB, a middleware system designed to build
sensor-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
data, 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.
In 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.

Bio: 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.

Glenn Galvizo: Navigational Pattern Matching w/ Graphix
2022-12-02 12:30 - 14:00 | DBH 4011
Abstract:
Users 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.
In 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.
Speaker Bio:
Glenn 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.
Photograph:

Andrew Chio: SmartSPEC: Customizable Smart Space Datasets via Event-Driven Simulations
2023-01-13 12:30 - 14:00 | DBH 4011
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.
Abstract – 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.

Tung-Chun Chang: SmartParcels: Cross-Layer IoT Planning for Smart Communities
2023-01-20 13:00 - 14:00 | DBH 4011
Abstract:
The 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.

Bio:
Tung-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.

Aaron Elmore: Adventures in Database Compression
2023-01-26 15:30 - 17:00 | TBD
Prof. Aaron Elmore

University of Chicago
Abstract: 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.

Aaron Elmore: CrocodileDB: Resource Efficient Database Execution (CS Seminar)
2023-01-27 11:00 - 12:00 | DBH 6011
Prof. Aaron Elmore
University of Chicago

Abstract: 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.

Yiming Lin: QUIP: Query-driven Missing Value Imputation
2023-02-10 13:00 - 14:00 | DBH 4011
QUIP: Query-driven Missing Value Imputation

This 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.

Bio:

Yiming 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.

Shanshan Han: Veil: Storage and Communication Efficient Volume Hiding Algorithms
2023-02-17 13:00 - 14:00 | DBH 4011
February 17, 2023, Friday, 1:00 PM – 2 PM
Donald Bren Hall 4011, ICS, UC Irvine

Zoom: https://uci.zoom.us/j/92445274511 (UCI only)

Abstract

Volume 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.

Bio

Shanshan Han is a 4th year PhD student in Prof. Sharad Mehrotra’s group. Her research interests include data security and privacy.

Babak Salimi (UCSD): Certifying the Fairness of Predictive Models in the Face of Selection Bias
2023-02-24 13:00 - 14:00 | DBH 4011
The Department of Computer Science, UC Irvine
WELCOMES
Prof. Babak Salimi
UCSD
Hosts: Prof. Chen Li
Certifying the Fairness of Predictive Models in the Face of Selection Bias

Abstract: 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.

In 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.

Bio: 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.

Alex Behm (Databricks): Photon: How to think vectorized
2023-03-03 13:00 - 14:00 | DBH 4011
The Department of Computer Science, Information Systems Group, UC Irvine
WELCOMES
Dr. Alex Behm
Databricks
Photon: How to think vectorized
3/3/2023, Friday, 1:00 – 2 pm
Place DBH 4011
I’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!
Bio: 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 vectoried 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.

Fangqi Liu: DOME: Drone-assisted Monitoring of Emergent Events For Wildland Fire Resilience
2023-03-10 13:00 - 14:00 | DBH 4011
Abstract:

By 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).

Bio:

Fangqi 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.

C. Mohan: A Survey of Cloud Database Systems
2023-04-13 12:00 - 13:00 | DBH 3011
C. Mohan
Distinguished Visiting Professor, Tsinghua University, China & Member, Board of Governors (Digital University Kerala, India) & Retired IBM Fellow (IBM Research, USA)
“A Survey of Cloud Database Systems”
ABSTRACT: 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.
Bio: 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.

Zuozhi Wang: Texera: A System for Collaborative and Interactive Data Analytics Using Workflows (PhD Final Defense)
2023-04-14 13:00 - 14:00
Abstract
In 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.

Bio
Zuozhi 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.

Quishi Bai: Maliva: Using Machine Learning to Rewrite Visualization Queries Under Time Constraints
2023-04-28 13:00 - 14:00 | DBH 4011
Abstract:
As 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.

Bio:
Qiushi 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.

Farzad Habibi: Metastable Failures in Consensus Algorithms
2023-05-05 13:00 - 14:00 | DBH 4011
Abstract
Metastable 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.”
Prior research has proposed a framework for understanding metastable failure and has observed various cases of such failures in real-world settings.
In 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.
The 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.
Bio:
Farzad 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.

CS Seminar: Prof. Arun Kumar: The New DBfication of ML/AI
2023-05-12 11:00 - 12:00 | DBH 6011
The Department of Computer Science, UC Irvine
WELCOMES
Prof. Arun Kumar
UCSD
5/12/2023, Friday, 11:00 am – noon
Place DBH 6011
Abstract:
The 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.
In 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.
Bio:
Arun 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.

Yiming Lin: Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph
2023-05-19 13:00 - 14:00 | DBH 4011
Abstract:
Business 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.

Bio:
Yiming 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.

Qiushi Bai: Improving SQL Performance Using Middleware-Based Query Rewriting
2023-05-26 13:00 - 14:00 | DBH 4011
Abstract:
Query 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.

In 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.

Bio:
Qiushi 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.

Saeed Kargar: Hamming Tree: The case for Energy-Aware Indexing for NVMs
2023-06-02 13:00 - 14:00 | DBH 4011
Zoom Link: https://uci.zoom.us/j/8045933305

Abstract
NVM 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.

Bio
Saeed 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.
