Disseminate

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

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

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

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 PonderYou can find Aditya on:TwitterLinkedInGoogle Scholar

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

In this High Impact episode we talk to Ali Dasdan, CTO at Zoominfo. Tune in to hear Ali's story and learn about some of his most impactful work such as his work on "Map-Reduce-Merge".The podcast is proudly sponsored by Pometry the developers behind Raphtory, the open source temporal graph analytics engine for Python and Rust.Materials mentioned on this episode:Map-Reduce-Merge: Simplified Relational Data Processing on Large Clusters (SIGMOD'07)The Art of Doing Science and Engineering: Learning to Learn, Richard HammingHow to Solve It, George PolyaSystems Architecting: Creating & Building Complex Systems, Eberhardt RechtinYou can find Ali on:TwitterLinkedIn