Papers

Continuous graph-query engines must process edge updates while making current query results available to concurrent consumers. Existing dynamic constant-delay enumeration methods provide strong per-answer delay guarantees, but are commonly formulated as a maintenance-then-enumeration process. Conversely, multicore graph-stream engines emphasize update throughput and match discovery without a query-level snapshot guarantee for concurrent full-result enumeration. We present TrieGS, a shared-memory engine that maintains a query-specific CDE state over a streaming graph. Logically, TrieGS uses the classical free-connex witness-subtree enumerator; dynamically, it maintains multiplicity payloads using exact signed deltas; physically, RDF terms are dictionary-encoded and the required access structures are realized as versioned LFNT relations (Lock-Free Nested Trie). The new systems problem is not relation-level snapshotting alone: an enumeration job must observe one consistent state across all interdependent base relations, projections, views, and indexes. TrieGS therefore publishes an atomic root vector only after an update epoch has fully propagated. Enumeration threads pin one published root vector and traverse immutable versions while later updates continue.

Tool-calling LLM agents navigate unfamiliar codebases with a handful of generic primitives for listing, reading and searching files (ls, cat, grep). A knowledge graph admits the same interface: listing neighbours, reading node content and searching descriptions are the same operations on a different substrate. Building on this correspondence, we present GRA, a Graph Reasoning Agent that explores hybrid knowledge graphs, whose nodes are either textual concepts or relational tables, with seven generic tools, discovering everything domain-specific at run time. On UFK-M (Unified Factory Knowledge Model), an industrial benchmark of 258 analytical questions whose gold answers are produced by executing validated SQL programs, GRA beats a full-context agent by 5.1 pp (88.4% vs. 83.3%), while reading under a third of its input tokens. A graph-free control shows the gain comes chiefly from selective agentic access rather than graph topology, and that the effect depends on a model able to drive tools reliably. Seeing less, the agent answers better: selective navigation over a structured substrate beats exhaustive context.

SQL rewrite verification must account for duplicate rows, observable row order, and typed value semantics. Existing verifiers have yet to combine proofs over database instances of arbitrary finite cardinality with an ordered-list semantics for nested, tie-sensitive top-k. Unbounded systems reason primarily over bags or handle ordering through syntax-directed restrictions, whereas bounded systems either support only restricted top-k forms or impose a deterministic ordering rather than retain all legal tie-induced outcomes. Support for typed expression and aggregate semantics, observable runtime errors, and integrity constraints also remains partial. In Rocq, we mechanize a compositional logical semantics for a typed SQL core with order-sensitive operators, capturing all possible ordered lists and observable SQL failures in the supported fragment. To our knowledge, this is the first mechanized SQL semantics to combine nested, tie-sensitive top-k with a closure-based lifting from bag equivalence to ordered-list equivalence, enabling sound reuse of bag-theoretic reasoning while preserving compositionality across order-sensitive and correlated contexts. The formalization further provides executable semantics for PostgreSQL-oriented scalar and aggregate evaluation and an explicit account of integrity constraints. Building on this semantics, we present Logos, an LLM-guided Rocq verifier for unbounded SQL rewrite equivalence. Its agent uses a verified SQL-specific lemma library to construct query-specific Rocq proofs. Our evaluation covers 389 query pairs from Apache Calcite optimizer tests, TPC-H and TPC-DS rewrites, and WeTune's real-application workloads. Logos solves 86.9% of them, compared with 64.0% for SQLSolver, the strongest baseline.

Building approximate nearest neighbor (ANN) indexes at billion scale is often dominated by expensive global clustering or graph construction, making time-to-index a first-order systems concern. We present NeuRoute, a learned hashing index that turns short binary codes into an effective routing primitive for large-scale vector search. NeuRoute trains a lightweight neural network encoder with a selective similarity-preserving objective to produce well-balanced binary addresses. During construction, NeuRoute organizes vectors into buckets by their codes and performs bucket-local clustering in the encoder's low-dimensional space to form centroids. At query time, NeuRoute exploits the encoder logits as an uncertainty signal: it uses deviation-to-threshold scores to prioritize uncertain-bit perturbations for query-adaptive multi-bucket probing, scores bucket-local centroids by their distances to the query to form a compact candidate cluster set, and applies centroid-stage gating with heap-quality-driven early stopping to prune low-value clusters before exact refinement. On billion-scale benchmarks, NeuRoute achieves strong accuracy-throughput trade-offs with fast index construction: on BigANN-1B it reaches $90.3\%$ Recall@10 at 2,414 QPS and is $1.7\times$ faster than OPQ+IVF-PQ (refine) at comparable accuracy, while completing end-to-end training+construction in under an hour on both BigANN-1B and Deep1B-1B. These results show that logit-guided neural routing can make hashing competitive as a lightweight ANN indexing framework at billion scale. Source code and artifacts are available at https://github.com/XingqiaoWang/NeuRoute.

If a table violates its required set of functional dependencies (FDs), what is the minimum number of cell changes needed to restore consistency? This fundamental problem, known as finding an optimal update repair (U-repair), is known to admit polynomial-time algorithms only for a small number of specific FD sets. Whether additional tractable cases exist has remained open. The only established hardness result for this problem is due to Kolahi and Lakshmanan (2009); subsequent attempts to prove hardness for additional cases have failed, leaving these cases unresolved. In this work, we make substantial progress on this open problem by completely resolving the case of unary FDs, in which every FD has a single attribute on its left-hand side. We show that every set of unary FDs either falls into one of the previously known tractable classes or makes the problem of finding an optimal U-repair NP-hard.

We found that a heavy target such as Pb is most suitable for determining the proton distribution radii of unstable nuclei through charge-changing cross-section ($\sigma_\text{cc}$) measurements. As a heavy ion probe, low-$Z$ targets are routinely used to determine nucleon distribution radii of unstable isotopes. This approach has recently been extended to study proton distribution radii from $\sigma_\text{cc}$ measurements. However, empirical scaling factors have to be introduced to apply the Glauber models. In the present work, we systematically investigated the scaling factor using 39 new $\sigma_\text{cc}$ data of 18 $p$-shell nuclei on hydrogen, carbon, silver, and lead targets at around 240 MeV/nucleon. Together with the existing data, we reveal a universal dependence of the scaling factor on both the masses of target nuclei and the separation energies of projectile nuclei. The scaling factors decrease with increasing target-nucleus mass and converge to 1 for the highest-$Z$ target, making the scaling unnecessary. We conclude that instead of a low-$Z$ target, employing a heavy target such as Pb in $\sigma_\text{cc}$ measurements is the best option to determine the proton distribution radii of unstable nuclei.

Data cleaning without a trusted clean reference is challenging because unusual values may represent either genuine errors or valid observations. This paper studies how different agent capabilities affect reference-free data cleaning and proposes an evidence-grounded framework that combines structured context, profiling, LLM reasoning, executable checks, controlled evidence retrieval, source ranking, citation alignment, conservative repair, reversible scripts, and provenance logging. Seven configurations are evaluated across financial, clinical, and environmental-monitoring datasets using controlled synthetic corruption and original-data descriptive analysis, resulting in 126 completed runs. The evaluation includes two comparison baselines and a progressive LLM-based sequence that adds executable tools, evidence retrieval, evidence controls, and conservative repair. In the synthetic evaluation, the deterministic profiling baseline achieved the highest detection F1-score of 0.561. Among the LLM-based configurations, the full conservative configuration achieved the highest F1-score of 0.421, but no configuration performed best across all evaluation criteria. The source-ranked configurations achieved the lowest unsupported-rule rates, while decision-level citation alignment remained weak. The full conservative configuration produced no unsafe or unnecessary modifications, although these rates were already zero before the conservative policy was added, and it performed no direct repairs. Overall, the results show that additional capabilities introduce trade-offs among detection, repair, evidence grounding, conservative behaviour, reproducibility, and operational cost rather than producing consistent improvements. The study provides a structured framework and empirical methodology for evaluating these trade-offs in reference-free agentic data cleaning.

Accurate prediction of undrained shear strength (su) is crucial for geotechnical design, but is often hampered by substantial uncertainty in traditional empirical methods. This study uses the CLAY/10/7490 global database to develop probabilistic indirect models to predict su based on Atterberg limits and piezocone cone penetration (CPTU) measurements. Firstly, the dataset has a high missing data rate and variability. We test three imputation methods - multivariate normal (MN), multiple imputation by chained equations (MICE), and miss forest (MF) - to fill the missing values. To validate their effectiveness, a Probabilistic Extreme Gradient Boosting (PXGB) model is developed, and the imputation methods are evaluated by comparing the PXGB's performance when trained on the imputed datasets against that on the original incomplete data. Secondly, the indirect model is built by integrating a multi-head attention (MHA) mechanism into an artificial neural network (ANN) to enhance information extraction from limited data, which leads to the MHA-based probabilistic neural networks (MHA-PNN) model. The models' performance, alongside a conventional MN-based prediction model, was evaluated using root mean square error (RMSE), coefficient of determination (R2), mean absolute percentage error (MAPE), conditional interval width (wCI), and coverage rate (CR). Results demonstrate that the proposed MN-enhanced MHA-PNN model substantially outperforms other models in both prediction accuracy and uncertainty quantification. These findings highlight the potential of this integrated strategy for building robust probabilistic indirect models in geotechnical applications, particularly when confronted with sparse and incomplete datasets.

Large language models have made natural language interfaces to databases (NLIDB) newly credible, but LLM text-to-SQL systems fail in a way that matters for deployment: a hallucinated column or a mis-aggregated total yields a fluent wrong answer, indistinguishable at the point of use from a right one. Where the consumer cannot inspect the generated query, as in enterprise AI deployments and operational dashboards, and increasingly where the consumer is a tool-using agent rather than a person, accuracy alone is insufficient: nothing marks which answers to distrust. This is a reliability problem before it is an accuracy problem. We propose an architectural pattern for such systems, a trusted kernel with a generative shell, resting on one invariant: a component that can fabricate may influence which question the system answers, never which value it returns. A generative shell interprets underspecified input and phrases replies; a deterministic kernel matches fully specified questions against a bounded set of answerable question shapes and compiles them to queries by deterministic execution. The two meet at a confirmation the user reads before any value is computed, and requests the kernel cannot express are declined rather than approximated. We call this structural abstention, and distinguish it from the statistical abstention of selective prediction and calibrated confidence: refusal here needs no confidence estimate, because unanswerable requests are unrepresentable. We specify the pattern implementation-independently, give a five-decision recipe and work it across three domains, extend the invariant from returned values to the actions of agentic systems, and report a two-year production case study alongside two generative alternatives, a fine-tuned parser and a tool-retrieval agent. We close against enterprise and reliability benchmarks published since.

Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation. As agents increasingly operate over persistent environments and multi-step workflows, they face challenges analogous to those addressed by transactional database systems: reliable execution, consistent outcomes, safe concurrency, and durable state management. We introduce the concept of an agentic transaction and propose an ACID-compliant agent system framework that reinterprets the classical ACID properties for agent execution through four semantic guarantees: Semantic Atomicity, Semantic Consistency, Semantic Isolation, and Semantic Durability. Together, these properties provide a principled foundation for building reliable agent systems despite model uncertainty and dynamic execution environments. To instantiate this framework, we develop an ACID-compliant data agent that realizes these guarantees through transactional exploration-execution-validation cycles, transactional skill hubs, confidence divergence-based validation, semantic dependency-aware isolation, and transaction-aware semantic state management. Experimental results on widely used benchmarks show that our system achieves a 10.6% improvement over state-of-the-art agents, including Claude Code. This work opens a broader research agenda on extending transactional principles and system architectures toward building trustworthy, scalable, and self-evolving AI agent systems.

Datacenter GPUs have seen an order-of-magnitude increase in memory bandwidth with the adoption of newer generations of HBM. Meanwhile, GPU database systems are gaining traction, many building on cuDF, an open-source library of GPU relational operators. Previously, query performance was bound by memory bandwidth, but the increase in memory bandwidth has not resulted in a proportional speedup of cuDF kernels. To investigate why performance has not kept up, we built Valk, a performance analysis tool that combines data from multiple profilers. We profile cuDF running TPC-H in-memory on two extremes of hardware capability, the L4 and GH200 GPUs. The GH200 has 13.4$\times$ the memory bandwidth and 2.5$\times$ the instruction throughput of the L4, yet is only 5.2$\times$ faster in running TPC-H. Our analysis shows that when memory bandwidth is increased, kernels become compute bound. From our analysis, we make three recommendations to fully utilize the GPUs' potential for relational workloads when the memory wall is removed: kernels need to 1) make more efficient use of caches, and 2) increase occupancy and/or instruction level parallelism, and 3) execute fewer instructions per memory access.

Unstructured documents constitute the majority of enterprise and web data. With the rapid development of large language models(LLMs), researchers have started to build data systems that analyze unstructured textual documents like operating on databases. However, because mainstream retrieval methods still relies on fuzzy matching based on vector similarity, accurately obtaining information and performing structured analysis and reasoning remains a major challenge. To address these limitations, AnnoIndex introduces two core fundamental components. The first is Annotation Index. The system uses a module called SchemaLoop to automatically create hierarchical annotation schemas from the raw corpus, and then uses lightweight language model to extract specific values. It turns scattered unstructured text into a materialized, structured index that enables low-cost filtering and querying. The annotation index avoids the black-box matching of vector similarity and amortizes attribute extraction costs from online queries to a one-time build. The second innovation is a Structured Query Engine. It compiles user questions into execution plans based on SQL extension. It first uses the Annotation Index for precise documents filtering, then gradually applies extraction operations in ascending order of cost, resorting to LLMs only for the remaining minimal fraction of the corpus that require deep semantic understanding. The extracted attributions are merged into the annotation index, reducing the cost of future queries. Experiments on three real-world datasets demonstrate that AnnoIndex consistently outperforms state-of-the-art baselines, achieving the highest average F1 score (0.87) while maintaining robust performance on complex multi-hop join and progressive reasoning queries.

With the rapid advancement of large language models (LLMs), research idea generation has attracted increasing attention. Existing approaches enable LLMs to retrieve relevant literature and propose novel ideas for research areas. However, current evaluation practices for idea generation remain fragmented and lack objective standards, often relying on direct LLM scoring, which limits their ability to provide unified and reliable assessments across a coherent distribution of generated ideas. To address this challenge, we propose LigBench, an automated evaluation benchmark that enables fine-grained and reliable evaluation of AI research ideas, consistently applicable across different generation distributions. In addition, we introduce PAIR-IQ, a dataset tailored for training pairwise idea judgment models and serving as an auxiliary reference to support more objective comparative evaluation. Extensive experiments demonstrate that LigBench achieves stable and interpretable evaluations, significantly improving alignment with expert judgments. Furthermore, models trained on PAIR-IQ exhibit enhanced ranking accuracy and robustness, establishing a principled standard for scalable and objective research idea assessment.

Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning. However, prevailing RDL evaluation practices rely on static, single-episode dataset snapshots, overlooking the continuous, time-evolving nature of real-world databases. Consequently, current RDL benchmarks fail to capture how model performance changes as new data accumulates over time. To address this limitation, we introduce an incremental, multi-episode evaluation and training paradigm to assess and improve the temporal robustness and adaptability of state-of-the-art RDL models. Using established large-scale datasets, we examine data evolution and model training dynamics, demonstrating that temporal concept drifts occur in the majority of predictive tasks. We present multiple incremental training regimes for fine-tuning the models and demonstrate that transfer learning is both feasible and highly effective in the RDL setting. Alongside a new temporal evaluation metric that prioritizes near-future accuracy, we show that our incrementally fine-tuned models consistently outperform the standard, expensive, from-scratch trained baselines.

This work presents the PreventCSA@EU ontology, a semantically grounded framework designed to support the identification, classification, annotation, and analysis of online Child Sexual Abuse and Child Sexual Exploitation Material (CSAM/CSEM). The growing circulation and dissemination of CSAM/CSEM across digital environments, combined with inconsistencies in legal definitions and classification practices across jurisdictions, highlights the need for semantically interoperable frameworks capable of supporting cross-organizational cooperation and automated processing. The proposed ontology is developed through a systematic review and comparative analysis of existing CSA/CSE-related, metadata oriented, and investigative ontologies and taxonomies, with its primary design aimed at addressing the operational needs and domain-specific requirements of national LEA Directorates. It introduces a hierarchical semantic model built around core entities such as Media Object, Content, Person, Depiction, and Investigative Report, while enabling structured alignment with INHOPE UCS labels, Dublin Core-DMCI Metadata Terms, and Schema.org. The proposed framework emphasizes ontology-driven interoperability for structured annotation and analysis of CSA/CSE-related data, supporting consistent classification, child identification, and investigative processes for offender prosecution. The design aims extend existing classification approaches with additional conceptual structures for database conceptualization, process modeling, and ontology-driven data management. By integrating established classification standards with a novel hierarchical ontology, the proposed framework enhances cross-system compatibility, with particular relevance to emerging EU-level data infrastructures, including the envisaged EU Center database under the proposed Child Sexual Abuse Regulation (CSAR).

Across many domains (e.g., observability, networking, security), data processing pipelines face what we refer to as the CSP problem: achieving low Cost at large Scale, while maintaining high Performance. In response, we see several efforts to tackle CSP in various stages of the Collect-Transmit-Store-Analyze data lifecycle; such as approximate query processing in databases or sketches in network routers. Our work is driven by the simple insight: "seeing the forest for the trees". These proposed solutions (e.g., AQP, sketching, compression, rollups) addressing CSP share a common property - they exploit semantic-preserving opportunities to support application needs. In this paper, we make a case for ASAP, a paradigm that makes Application Semantic-Aware Processing (ASAP) a first-class design principle in data processing pipelines. We argue that by taking a unified view across ASAP primitives developed in different domains, across the entire data lifecycle, we can unlock new opportunities to tackle the CSP problem. In particular, we can: (i) enable novel cross-lifecycle optimizations such as analytics run directly on sketches computed at the source; (ii) leverage primitives developed in other application domains; and (iii) enable widespread adoption of these powerful techniques. We discuss research challenges in socializing the benefits of the ASAP paradigm, and show preliminary evidence that adopting ASAP can yield up to 3 orders of magnitude improvements in the CSP tradeoff for many application domains.

Relational databases are the systems of record for business applications, and there is growing demand to query them through natural language interfaces. A key challenge is that AI models need appropriate query context, i.e., sample questions paired with their corresponding data-model fragments, to generate accurate SQL. Today, creating this context is a manual, time-consuming process that requires expertise in both SQL and the database schema, leading to a cold start problem for new databases and an ongoing maintenance burden as schemas and query patterns evolve. We present an automated approach for generating query context for relational databases. Our method defines query flows that capture common patterns of data retrieval and analysis questions, systematically generates data models by traversing these flows, and instantiates them with specific values sampled from the database using weighted strategies that maximize diversity and coverage. The resulting question–data-model pairs can be used to guide natural language interfaces in accurately querying relational databases. We report on our experience deploying this approach across more than 50 real-world databases connected to Tursio.

Motivation: LinkML is a suitable language for the representation of the structural and content constraints of different kinds of biomedical data. Even if it is a quite recent proposal, it has been applied in several biomedical contexts. Developing and maintaining LinkML schemas presents several challenges, particularly for novice curators. Non-expert bio-curators may struggle with LinkML syntax and best practices, requiring significant time and effort to develop well-structured schemas. Results: In this paper we propose SchemaLink, a web-based environment for the graphical construction and enhancement of LinkML schemas that address the following requirements: $(i)$ introduce a graphical language for the specification of LinkML schemas, $(ii)$ make uniform the specification of schemas in similar contexts, $(iii)$ simplify the design and curation processes by exploiting a RAG-based approach to assist curators in creating new schemas from scratch and editing already developed ones. Several experimental analyses show the quality of the produced LinkML schemas through the AI-based editing facilities. Availability and Implementation: SchemaLink is available online at: https://SchemaLink.biodata.di.unimi.it. SchemaLink code and testing data are available as open-source on GitHub at: https://github.com/AnacletoLAB/{schemalink-webapp,schemalink-api}.

Relational query languages have been studied and used for more than 50 years, with SQL dominant in practice. Today, queries are increasingly generated by machines and read by humans. At the same time, the landscape also includes dataframe, pipeline, logical, functional, graph, and relational programming notations. These developments invite two related questions beyond expressive power: which relational structures do languages make explicit, and how well can notation support users in reading and revising queries? This 3-hour tutorial extends an earlier SIGMOD'26 tutorial in three directions: recursive and path queries (connecting relational and graph query languages), nested relational data, and relational languages for problems beyond PTIME. Rather than beginning from formal definitions, we start from example queries and compare how different languages express the same intent. To compare recurring structure across notations, we use Abstract Relational Calculus (ARC) and Relational Diagrams as reference representations. From these examples, we develop a vocabulary for relational language design, including information need, query mapping, relational pattern structure, relational pattern denotation, and semantic conventions. Participants will leave with a framework for comparing existing and future relational languages, a precise vocabulary for articulating design trade-offs, and a concrete set of examples connecting classical database languages with alternative proposals.

Prompting-based (i.e., non-fine-tuning) Text-to-SQL methods, where underlying large language model parameters are not changed for the task, face three problems: (i) relying on coarse-grained schema information that may not reveal the fine-grained relationships needed to distinguish ambiguous columns, (ii) failing to capture recurring SQL-generation failures, and (iii) suffering from omission or hallucination of components in complex questions. This paper develops DexterSQL, a prompting/non-fine-tuning-based Text-to-SQL system that improves SQL generation with three novel components: (i) deep schema explorator that identifies ambiguous columns, analyzes their individual and joint data distributions to uncover their relationships and the distinct role of each, (ii) database-agnostic rule creator that mines mismatches between generated and gold SQL only on the training database and converts them into database-agnostic corrective rules that capture recurring LLM failure patterns; and (iii) multi-path SQL generation that introduces a dependency-tree-based intermediate representation that uses the question's sentence structure to guide its decomposition into an SQL skeleton for final SQL generation. DexterSQL achieves a higher accuracy compared to the state-of-the-art using both open-source/weight and closed-source/weight models. Particularly, DexterSQL shows a high improvement of at least 5.5% using an open-weight model (GPT-OSS-120B) on BIRDDev, with total accuracy 70.4%. DexterSQL also shows better improvement of at least 1.4% using closed-weight models, with total accuracy 72.1% and 72.9% on BIRD-Dev with GPT-4o and GPT-5.2.

Elasticity is commonly presented as the default response to capacity loss after failures, since replacement replicas can compensate for failed nodes and restore pre-incident service levels. Replacement capacity entails both delay and additional resource commitment, as replicas must be provisioned and synchronized before they can serve traffic. Under fixed budgets or constrained operating conditions, capacity restoration cannot be treated as the immediate recovery path. Failure handling must instead define how the service continues while capacity remains reduced. When the service exposes differentiated service levels, capacity loss cannot be handled uniformly. Degradation becomes part of the service behavior, requiring explicit control over how reduced capacity affects each class without erasing the intended differentiation. We study differentiated QoS degradation in replicated database services with service-class-aware sessions. We present a repair-to-target policy, implemented in PLB, a PostgreSQL JDBC middleware load balancer for service-class-aware routing. When fail-stop failures remove part of the available capacity, PLB updates the role assignment of healthy replicas into Premium, Mixed, and Freemium roles. This keeps the replica pool shared while ensuring that new session assignments continue to reflect the service class. We evaluate PLB under single and cascading replica failures across two deployment strategies: isolated perclass replica pools and shared, priority-agnostic routing. The results show that PLB improves median Premium goodput retention by 26-28 percentage points under a Premium-side fault, achieves more than 2x higher Premium goodput in the most severe cascading-failure phase, and reduces Premium p95 latency by 18.2% relative to shared round-robin.

Answering natural language questions over structured databases requires identifying the relevant tables and determining how to join them—a task that demands both schema knowledge and semantic understanding of the user's intent. We present guided table retrieval, a four-phase pipeline that combines deterministic grounding via hash-based predictors, structural exploration of join-graph reachability, LLM-powered disambiguation of sources and targets, and algorithmic merging into minimal, topologically ordered join trees. By decomposing the problem into phases with distinct responsibilities— determinism, coverage, semantic reasoning, and coherence— the pipeline avoids the brittleness of end-to-end LLM approaches while leveraging LLMs where their contextual judgment is most needed. We evaluate on BIRD-DEV and the enterprise-scale BEAVER benchmark, achieving 94% and 70% precision respectively, with 92% and 53% F1—substantially outperforming existing baselines on precision and F1 while producing exact join trees that can be directly consumed by downstream query compilers.

Cloud has become ubiquitous to modern applications due to its agility and scalability. However, regulated industries still prefer to deploy on-premises due to security and compliance reasons. This creates a paradox for vendors who need to develop in the cloud but deploy on-premises, leading to long release cycles and complex maintenance. In this paper, we present Diel, the Tursio On-premises Migrator, a tool that automates reverse migration of cloud applications to on-premises environments. Diel applies a combination of simulate, replicate, and delegate strategies to transform cloud services into on-premises counterparts. We describe the design and implementation of Diel, along with lessons learned from using it in practice. With Diel, we have been able to keep Tursio AI's cloud and on-premises versions in sync, releasing new stable versions every three weeks.

The paper considers the construction of two new orthogonal multiwavelets with supercompact support by using the Fast Bauer's method for matrix spectral factorization on the matrix product filter of the orthogonal CL multiwavelet filter. The new multiwavelets possess orthogonality, symmetry/antisymmetry, and one of them provides better coding and smoothness than other supercompact multiwavelets. The performance of the new multiwavelet filters in subband-based edge detection, grayscale and color image compression and 1D and 2D signal denoising is compared with the GHM, SA4, CL, Integer Haar and Alpert multifilters. The comparative analysis shows that new multiwavelets can provides better human visual measures, SSIM and MS-SSIM in image compression and denoising applications.

A data management server that combines the relational data model found in traditional databases with the graphical models being developed by newer data products can provide its knowledge model to generative tools for large language models. Both models have been reconciled at the implementation level by replacing foreign keys by reference values. This boosts performance and allows effectively following links instead of lookup joins. The reference value will never change as it uses the record address in the log file. This paper reports on progress in providing such a synthesis and includes an example of a knowledge base that is triple-based and would also be supported in such a system.

Current developments in standardization activities for database management systems include the development of the next versions of the Structured Query Language (SQL), and related standards for labelled property graphs. This short paper explores mechanisms for unified implementation of record-based relational and property graph databases: its contribution is that modeling SQL foreign keys as reference keys facilitates the graph insert and advanced pattern matching mechanisms of Property Graph Database Management Systems (PGDMS) and allows the construction of multiple type graph models on a single relational database.

SQL is the database community's success story in terms of language design. The key reason for its success is its declarativeness: it gives rise to optimizability, reducing the programmer's burden significantly. However, given the evolving complexity of problems to solve with query languages, our community needs to re-think some of the fundamental early query language design decisions. Our experience in having worked on the design of Rel (a language for end-to-end relational programming) tells us that it is possible to design, implement, and successfully deploy a language based on fully normalized relations. Such relations avoid what Codd called corrupted relations and what we commonly refer to as bags, and the 'harmful' 'billion dollar mistake' that we know as nulls. In the SQL world, it is accepted that bags and nulls are tolerated as an unavoidable evil. We argue that the evil is completely avoidable: reasons offered for justifying bags and nulls evaporate at a closer examination. In addition to debunking them, we also describe opportunities offered by a null-free language with set semantics.

In this vision paper, we propose a novel architectural paradigm for accelerated AI query execution via a unified compiled execution strategy. By compiling the hybrid AI Query as a whole – integrating both standard SQL relational constructs and LLM inference layers into a single, unified tensor compute graph – we completely alleviate PCIe data movement bottlenecks across execution boundaries and enable global compiler optimizations and efficient automatic sharding. We demonstrate the viability of this unified execution paradigm on select and extended AI queries on SemBench Reviews and Movies datasets, achieving up to 5.3x latency speedup and 9.8x throughput speedup on TPUs, and outline a research roadmap of open technical challenges to realize this vision.

Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents. However, the latter operates as an opaque black box, hiding its intermediate reasoning and data retrieval steps, and failing to expose controls for managing API costs and execution latency. Meanwhile, the former can be prohibitively expensive for enterprise-scale data lakes. Consequently, analysts using these systems lack the agency to intercept hallucinated premises, verify intermediate results, or correct the system's trajectory. We present Carnot, an interactive execution engine for AI-driven analytics. Carnot compiles natural language requests into physical execution graphs and surfaces them through an interactive notebook interface. Rather than waiting blindly for a final output, users can critique the plan, incrementally execute operators, inspect intermediate data, or directly edit the underlying code or semantic operator instructions. Carnot's query optimizer will optimize the query with respect to cost or latency constraints provided by the user. Our demo will showcase how Carnot helps users achieve efficient and verifiable insights on workloads motivated by real enterprise use cases.

Process discovery is one of the central challenges in process mining. Petri nets are particularly attractive because simple local constructs can express complex behavior, including concurrency. While their global behavior may be difficult to analyze, individual places can be efficiently characterized using monotonic properties, enabling bottom-up discovery. Unlike top-down approaches such as the Inductive Miner, which rely on predefined constructs for sequences, choices, loops, and concurrency, our approach allows such structures to emerge organically and can exploit the full expressive power of Petri nets, including free-choice constructs and long-term dependencies. The main challenge is the exponential number of candidate places and their combinations. We present the SPECpp framework which implements strategies to obtain high-quality models under time and resource constraints. SPECpp supports rapid experimentation and is used to evaluate these strategies using both synthetic and real-life event data.