Anadi Shakya | DIPr Lab at PSU

Winter 2026 Week 2

Fri, 16 Jan 2026 00:00:00 +0000

Title	LLM-Driven Auto Configuration for Transient IoT Device Collaboration
Authors	Hetvi Shastri, Walid A. Hanafy, Li Wu, David Irwin, Mani Srivastava, Prashant Shenoy
Abstract	Today's Internet of Things (IoT) has evolved from simple sensing and actuation devices to those with embedded processing and intelligent services, enabling rich collaborations between users and their devices. However, enabling such collaboration becomes challenging when transient devices need to interact with host devices in temporarily visited environments. In such cases, fine-grained access control policies are necessary to ensure secure interactions; however, manually implementing them is often impractical for non-expert users. Moreover, at run-time, the system must automatically configure the devices and enforce such fine-grained access control rules. Additionally, the system must address the heterogeneity of devices. In this paper, we present CollabIoT, a system that enables secure and seamless device collaboration in transient IoT environments. CollabIoT employs a Large language Model (LLM)-driven approach to convert users' high-level intents to fine-grained access control policies. To support secure and seamless device collaboration, CollabIoT adopts capability-based access control for authorization and uses lightweight proxies for policy enforcement, providing hardware-independent abstractions. We implement a prototype of CollabIoT's policy generation and auto configuration pipelines and evaluate its efficacy on an IoT testbed and in large-scale emulated environments. We show that our LLM-based policy generation pipeline is able to generate functional and correct policies with 100% accuracy. At runtime, our evaluation shows that our system configures new devices in ~150 ms, and our proxy-based data plane incurs network overheads of up to 2 ms and access control overheads up to 0.3 ms.

Summer 2025 Week 2

Wed, 23 Jul 2025 00:00:00 +0000

Title	An Elephant Under the Microscope: Analyzing the Interaction of Optimizer Components in PostgreSQL
Authors	Rico Bergmann, Claudio Hartmann, Dirk Habich, Wolfgang Lehner
Abstract	Despite an ever-growing corpus of novel query optimization strategies, the interaction of the core components of query optimizers is still not well understood. This situation can be problematic for two main reasons: On the one hand, this may cause surprising results when two components influence each other in an unexpected way. On the other hand, this can lead to wasted effort in regard to both engineering and research, e.g., when an improvement for one component is dwarfed or entirely canceled out by problems of another component. Therefore, we argue that making improvements to a single optimization component requires a thorough understanding of how these changes might affect the other components. To achieve this understanding, we present results of a comprehensive experimental analysis of the interplay in the traditional optimizer architecture using the widely-used PostgreSQL system as prime representative. Our evaluation and analysis revisit the core building blocks of such an optimizer, i.e. per-column statistics, cardinality estimation, cost model, and plan generation. In particular, we analyze how these building blocks influence each other and how they react when faced with faulty input, such as imprecise cardinality estimates. Based on our results, we draw novel conclusions and make recommendations on how these should be taken into account.

Spring 2025 Week 6

Fri, 09 May 2025 00:00:00 +0000

Title	Grouping, Subsumption, and Disjunctive Join Optimizations in Oracle
Authors	Rafi Ahmed, Krishna Kantikiran Pasupuleti, Sriram Tirupattur, Lei Sheng, Hong Su, Mohamed Ziauddin
Abstract	Query optimization must evolve with new workloads. As analytic and data warehouse workloads become more ubiquitous, optimization techniques that reduce the amount of data processed during query execution, enable shared computation and avoid expensive data access and joins must be rigorously explored. In this paper, we present aggregate-decomposition techniques as enhancements to an existing query transformation that performs grouping before joins. Consequently, the transformation generates more query rewrite candidates and can also be applied to a larger set of queries. Further, we introduce two new query transformations, i) subsumption of views and subqueries that explores opportunities for sharing computation and ii) union-all duplicator transformation for queries with disjunctive join predicates that removes the need for multiple data access and joins. These techniques are applicable to commonly noticed query patterns in customer workloads and provide significant performance benefit as indicated in our performance study. They have been implemented in Oracle RDBMS.

Sieve

Sat, 01 Jun 2024 00:00:00 +0000

SIEVE is a versatile middleware that enhances access control in DBMS, enabling efficient query processing even with a large number of access control policies. We’re currently integrating caching to further improve query performance. Additionally, we’ve developed a workload generator that simulates various scenarios to test policy models and ensure access control compliance, reflecting real-world conditions.