Disseminate: The Computer Science Research Podcast

Jack Waudby
  • 5.0 (6)
  • EDUCATION
Disseminate: The Computer Science Research Podcast

This podcast features interviews with Computer Science researchers. Hosted by Dr. Jack Waudby researchers are interviewed, highlighting the problem(s) they tackled, solutions they developed, and how their findings can be applied in practice. This podcast is for industry practitioners, researchers, and students, aims to further narrow the gap between research and practice, and to generally make awesome Computer Science research more accessible. We have 2 types of episode: (i) Cutting Edge (red/blue logo) where we talk to researchers about their latest work, and (ii) High Impact (gold/silver logo) where we talk to researchers about their influential work. You can support the show through Buy Me a Coffee. A donation of $3 will help us keep making you awesome Computer Science research podcasts.  Hosted on Acast. See acast.com/privacy for more information.

  1. Disseminate x DuckDB Coming Soon...

    SEASON 10, EPISODE 1 TRAILER

    Disseminate x DuckDB Coming Soon...

    Hey folks! We have been collaborating with everyone's favourite in-process SQL OLAP database management system DuckDB to bring you a new podcast series - the DuckDB in Research series! At Disseminate our mission is to bridge the gap between research and industry by exploring research that has a real-world impact. DuckDB embodies this synergy—decades of research underpin its design, and now it’s making waves in the research community as a platform for others to build on and this is what the series will focus on! Join us as we kick off the series with: 📌 Daniel ten Wolde – DuckPGQ, a graph workload extension for DuckDB supporting SQL/PGQ 📌 David Justen – POLAR: Adaptive, non-invasive join order selection 📌 Till Döhmen – DuckDQ: A Python library for data quality checks in ML pipelines 📌 Arjen de Vries – FAISS extension for vector similarity search in DuckDB 📌 Harry Gavriilidis – SheetReader: Efficient spreadsheet parsing Whether you're a researcher, engineer, or just curious about the intersection of databases and innovation we are sure you will love this series. Subscribe now and stay tuned for our first episode! 🚀 Hosted on Acast. See acast.com/privacy for more information.

    3 min
  2. High Impact in Databases with... Anastasia Ailamaki

    MAR 3

    High Impact in Databases with... Anastasia Ailamaki

    In this High Impact in Databases episode we talk to Anastasia Ailamaki. Anastasia is a Professor of Computer and Communication Sciences at the École Polytechnique Fédérale de Lausanne (EPFL). Tune in to hear Anastasia's story! The podcast is proudly sponsored by Pometry the developers behind Raphtory, the open source temporal graph analytics engine for Python and Rust. You can find Anastasia on: HomepageGoogle ScholarLinkedIn Hosted on Acast. See acast.com/privacy for more information.

    46 min
  3. Anastasiia Kozar | Fault Tolerance Placement in the Internet of Things | #61

    12/16/2024

    Anastasiia Kozar | Fault Tolerance Placement in the Internet of Things | #61

    In this episode, we chat with Anastasiia Kozar about her research on fault tolerance in resource-constrained environments. As IoT applications leverage sensors, edge devices, and cloud infrastructure, ensuring system reliability at the edge poses unique challenges. Unlike the cloud, edge devices operate without persistent backups or high availability standards, leading to increased vulnerability to failures. Anastasiia explains how traditional methods fall short, as they fail to align resource allocation with fault tolerance needs, often resulting in system underperformance. To address this, Anastasiia introduces a novel resource-aware approach that combines operator placement and fault tolerance into a unified process. By optimizing where and how data is backed up, her solution significantly improves system reliability, especially for low-end edge devices with limited resources. The result? Up to a tenfold increase in throughput compared to existing methods. Tune to learn more! Links: Fault Tolerance Placement in the Internet of Things [SIGMOD'24]The NebulaStream Platform: Data and Application Management for the Internet of Things [CIDR'20]nebula.stream Hosted on Acast. See acast.com/privacy for more information.

    49 min
  4. Liana Patel | ACORN: Performant and Predicate-Agnostic Hybrid Search | #60

    11/11/2024

    Liana Patel | ACORN: Performant and Predicate-Agnostic Hybrid Search | #60

    In this episode, we chat with with Liana Patel to discuss ACORN, a groundbreaking method for hybrid search in applications using mixed-modality data. As more systems require simultaneous access to embedded images, text, video, and structured data, traditional search methods struggle to maintain efficiency and flexibility. Liana explains how ACORN, leveraging Hierarchical Navigable Small Worlds (HNSW), enables efficient, predicate-agnostic searches by introducing innovative predicate subgraph traversal. This allows ACORN to outperform existing methods significantly, supporting complex query semantics and achieving 2–1,000 times higher throughput on diverse datasets. Tune in to learn more! Links: ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data [SIGMOD'24]Liana's LinkedInLiana's X Hosted on Acast. See acast.com/privacy for more information.

    53 min
  5. High Impact in Databases with... David Maier

    11/04/2024

    High Impact in Databases with... David Maier

    In this High Impact episode we talk to David Maier. David is the Maseeh Professor Emeritus of Emerging Technologies at Portland State University. Tune in to hear David's story and learn about some of his most impactful work. The podcast is proudly sponsored by Pometry the developers behind Raphtory, the open source temporal graph analytics engine for Python and Rust. You can find David on: HomepageGoogle Scholar Hosted on Acast. See acast.com/privacy for more information.

    1h 2m
  6. Raunak Shah | R2D2: Reducing Redundancy and Duplication in Data Lakes | #59

    10/28/2024

    Raunak Shah | R2D2: Reducing Redundancy and Duplication in Data Lakes | #59

    In this episode, Raunak Shah joins us to discuss the critical issue of data redundancy in enterprise data lakes, which can lead to soaring storage and maintenance costs. Raunak highlights how large-scale data environments, ranging from terabytes to petabytes, often contain duplicate and redundant datasets that are difficult to manage. He introduces the concept of "dataset containment" and explains its significance in identifying and reducing redundancy at the table level in these massive data lakes—an area where there has been little prior work. Raunak then dives into the details of R2D2, a novel three-step hierarchical pipeline designed to efficiently tackle dataset containment. By utilizing schema containment graphs, statistical min-max pruning, and content-level pruning, R2D2 progressively reduces the search space to pinpoint redundant data. Raunak also discusses how the system, implemented on platforms like Azure Databricks and AWS, offers significant improvements over existing methods, processing TB-scale data lakes in just a few hours with high accuracy. He concludes with a discussion on how R2D2 optimally balances storage savings and performance by identifying datasets that can be deleted and reconstructed on demand, providing valuable insights for enterprises aiming to streamline their data management strategies. Materials: SIGMOD'24 Paper - R2D2: Reducing Redundancy and Duplication in Data LakesICDE'24 - Towards Optimizing Storage Costs in the Cloud Hosted on Acast. See acast.com/privacy for more information.

    31 min
  7. High Impact in Databases with... Aditya Parameswaran

    10/21/2024

    High Impact in Databases with... Aditya Parameswaran

    In this High Impact episode we talk to Aditya Parameswaran about his some of his most impactful work. Aditya is an Associate Professor at the University of California, Berkeley. Tune in to hear Aditya's story! The podcast is proudly sponsored by Pometry the developers behind Raphtory, the open source temporal graph analytics engine for Python and Rust. Links: EPIC Data LabAnswering Queries using Humans, Algorithms and Databases (CIDR'11)Potter’s Wheel: An Interactive Data Cleaning System (VLDB'01)Online Aggregation (SIGMOD'97)Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases (INFOVIS'00)Coping with Rejection Ponder You can find Aditya on: TwitterLinkedInGoogle Scholar Hosted on Acast. See acast.com/privacy for more information.

    59 min
  8. Marco Costa | Taming Adversarial Queries with Optimal Range Filters | #58

    10/14/2024

    Marco Costa | Taming Adversarial Queries with Optimal Range Filters | #58

    In this episode, we sit down with Marco Costa to discuss the fascinating world of range filters, focusing on how they help optimize queries in databases by determining whether a range intersects with a given set of keys. Marco explains how traditional range filters, like Bloom filters, often result in high false positives and slow query times, especially when dealing with adversarial inputs where queries are correlated with the keys. He walks us through the limitations of existing heuristic-based solutions and the common challenges they face in maintaining accuracy and speed under such conditions. The highlight of our conversation is Grafite, a novel range filter introduced by Marco and his team. Unlike previous approaches, Grafite comes with clear theoretical guarantees and offers robust performance across various datasets, query sizes, and workloads. Marco dives into the technicalities, explaining how Grafite delivers faster query times and maintains predictable false positive rates, making it the most reliable range filter in scenarios where queries are correlated with keys. Additionally, he introduces a simple heuristic filter that excels in uncorrelated queries, pushing the boundaries of current solutions in the field. SIGMOD' 24 Paper - Grafite: Taming Adversarial Queries with Optimal Range Filters Hosted on Acast. See acast.com/privacy for more information.

    37 min
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Ratings & Reviews

5
out of 5
6 Ratings

About

This podcast features interviews with Computer Science researchers. Hosted by Dr. Jack Waudby researchers are interviewed, highlighting the problem(s) they tackled, solutions they developed, and how their findings can be applied in practice. This podcast is for industry practitioners, researchers, and students, aims to further narrow the gap between research and practice, and to generally make awesome Computer Science research more accessible. We have 2 types of episode: (i) Cutting Edge (red/blue logo) where we talk to researchers about their latest work, and (ii) High Impact (gold/silver logo) where we talk to researchers about their influential work. You can support the show through Buy Me a Coffee. A donation of $3 will help us keep making you awesome Computer Science research podcasts.  Hosted on Acast. See acast.com/privacy for more information.

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