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Data formats in analytical DBMSs: performance trade-offs and future directions

VLDB JOURNAL 2025年第3期34卷 1-23页

作者： Liu, Chunwei Pavlenko, Anna Interlandi, Matteo Haynes, Brandon MIT CSAIL Cambridge MA 02139 USA Microsoft Redmond WA USA

This paper evaluates the suitability of Apache Arrow, Parquet, and ORC as formats for subsumption in an analytical DBMS. We systematically identify and explore the high-level features that are important to support efficient querying in modern OLAP DBMSs and evaluate the ability of each format to support these features. We find that each format has trade-offs that make it more or less suitable for use as a format in a DBMS and identify opportunities to more holistically co-design a unified in-memory and on-disk data representation. Notably, for certain popular machine learning tasks, none of these formats perform optimally, highlighting significant opportunities for advancing format design. Our hope is that this study can be used as a guide for system developers designing and using these formats, as well as provide the community with directions to pursue for improving these common open formats.

关键词： structured query language

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Minerva: Decentralized Collaborative query Processing Over InterPlanetary File System

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IEEE TRANSACTIONS ON BIG DATA 2025年第2期11卷 669-683页

作者： Yao, Zhiyi Ding, Bowen Bai, Qianlan Xu, Yuedong Fudan Univ Sch Informat Sci & Technol Shanghai 200437 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200437 Peoples R China

Data silos create barriers to accessing and utilizing data dispersed over networks. Directly sharing data easily suffers from the long downloading time, the single point failure and the untraceable data usage. In this paper, we present Minerva, a peer- to-peer cross-cluster data query system based on the InterPlanetary File System (IPFS). Minerva makes use of the distributed Hash table (DHT) lookup to pinpoint the locations that store content chunks. We theoretically model the DHT query delay and introduce a fat Merkle tree structure as well as the DHT caching to reduce it. We design the query plan for read and write operations on top of Apache Drill that enables the collaborative query with decentralized workers. We conduct comprehensive experiments on Minerva, and the results show that Minerva achieves up to 2.08x query performance acceleration compared to the original IPFS data query, and can complete data analysis queries on the Internet-like environments within an average latency of 0.615 second. With a collaborative query, Minerva could perform up to 1.39x performance acceleration than the centralized query with raw data shipment.

关键词： Distributed databases InterPlanetary File System Memory Soft sensors Big Data structured query language Engines IPFS decentralized query distributed hash table Merkle tree

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Efficient and Secure Spatial Fuzzy Keyword query

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IEEE Internet of Things Journal 2025年第10期12卷 14752-14770页

作者： Xie, Qingqing Zhu, Fatong Feng, Xia Jiangsu University School of Computer Science and Communication Engineering China Hainan University School of Cyberspace Security China

With the popularity of Location-based services and the explosive growth of spatial data, massive spatial data has been outsourced to cloud servers for storage and query services, such as spatial range and fuzzy keyword query. To protect data and user privacy, it is essential to encrypt the data and query requests before uploading them. However, encrypting the data makes search on ciphertext more challenging. Moreover, the existing schemes do not support spatial range and fuzzy keyword query (SFKQ for short) simultaneously. To this end, we first leverage the indecomposable property of primes and the Geohash algorithm to create an index vector and query trapdoor vector. We then propose concrete constructions for SFKQ, which support both spatial range query and fuzzy multi-keyword query in a single interaction. Additionally, we carefully design a novel Geohash-based index structure (referred to as GeoTree) along with a pruning strategy to accelerate search efficiency. Finally, we provide the formal security analysis to demonstrate that the proposed SFKQ scheme is indistinguishable under Chosen-Plaintext attacks (IND-CPA), and we conduct comprehensive experiments to validate the accuracy and efficiency. © 2025 IEEE.

关键词： structured query language

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The Role of Accuracy and Validation Effectiveness in Conversational Business Analytics

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IEEE ACCESS 2025年 13卷 29279-29291页

作者： Alparslan, Adem FOM Univ Appl Sci Econ & Management Dept Business Analyt D-45127 Essen Germany

This study examines how conversational business analytics can bridge the skill gap of end users that hinders traditional self-service analytics. By leveraging generative AI, conversational business analytics enables end users to independently retrieve data, process it, and generate information. Using Text-to-SQL as an example, this study proposes theoretical models grounded in expected utility theory to examine two levels of AI support: partial support, where AI translates natural language requests into SQL and the generated information serves directly as the basis for decision-making, and full support, which includes an additional validation step. The models define conditions where AI-driven information generation surpasses human delegation. These conditions underscore the critical interplay between AI accuracy and validation effectiveness as pivotal factors for the successful integration of AI. The findings suggest that partial support is viable when the AI accuracy is sufficiently high. In contrast, full support necessitates both adequate accuracy and robust validation. Insufficient validation impairs decisions, highlighting the need for effective validation techniques to fully leverage conversational business analytics. Moreover, the dependence on user-driven validation introduces additional risks, as its effectiveness is contingent on the user's experience or familiarity with SQL and underlying data structures. This insight challenges conventional validation techniques for AI-generated information and highlights the need to use techniques that reduce the reliance on the technical expertise of end users.

关键词： Business Self-service structured query language Natural languages Data models Analytical models Artificial intelligence Data retrieval Conversational business analytics self-service analytics large language models accuracy validation effectiveness Text-to-SQL expected utility theory fine-tuning retrieval augmented generation business intelligence retrieval augmented generation business intelligence

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Blending Static and Dynamic Analysis for Web Application Vulnerability Detection: Methodology and Case Study

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IEEE ACCESS 2025年 13卷 3139-3153页

作者： Nunes, Paulo Fonseca, Jose Vieira, Marco Univ Coimbra Polytech Guarda CISUC P-3004531 Coimbra Portugal Univ North Carolina Charlotte Charlotte NC 28223 USA

Static Analysis (SA) and Dynamic Analysis (DA) are complementary techniques for searching web application vulnerabilities. Typically, SA detects more vulnerabilities but reports a higher number of false positives, whereas DA finds less but with better precision. In this paper, we blend SA and DA to simultaneously improve the detection and decrease the false alarms. Our approach starts with SA to identify an initial set of potential vulnerabilities. Then, the target application is executed to obtain specific runtime information. These data are used to automatically configure the DA, improving its ability to confirm if the vulnerabilities reported by the SA are indeed exploitable. We evaluated the proposed approach using 49 WordPress plugins with more than 450 SQLi vulnerabilities. Our approach was able to confirm either as a vulnerability or a false alarm 76.7% of the results reported by the SA, decreasing tremendously the usual need for manual work, which is a huge improvement for security practitioners.

关键词： Runtime Codes Security Vectors Static analysis Source coding Protection Input variables Uniform resource locators structured query language dynamic analysis vulnerability detection execution traces SQLi blend analysis

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LaGR-SEQ: language-guided reinforcement learning with sample-efficient querying

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Neural Computing and Applications 2025年 1-24页

作者： George, Thommen Karimpanal Semage, Buddhika Laknath Rana, Santu Le, Hung Tran, Truyen Gupta, Sunil Venkatesh, Svetha School of Information Technology Deakin University 75 Pigdons Road Waurn PondsVIC3216 Australia Applied Artificial Intelligence Institute Deakin University 75 Pigdons Road Waurn PondsVIC3216 Australia

Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. For example, by observing a partial stack of cubes, LLMs can predict the correct sequence in which the remaining cubes should be stacked by extrapolating the observed patterns (e.g., cube sizes, colors or other attributes) in the partial stack. In this work, we introduce LaGR (language-guided reinforcement learning), which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent, in order to subsequently guide the latter’s training. However, as RL training is generally not sample-efficient, deploying this approach would inherently imply that the LLM be repeatedly queried for solutions;a process that can be expensive and infeasible. To address this issue, we introduce SEQ (sample-efficient querying), where we simultaneously train a secondary RL agent to decide when the LLM should be queried for solutions. Specifically, we use the quality of the solutions emanating from the LLM as the reward to train this agent. We show that our proposed framework LaGR-SEQ enables more efficient primary RL training, while simultaneously minimizing the number of queries to the LLM. We demonstrate our approach on a series of tasks and highlight the advantages of our approach, along with its limitations and potential future research directions. © The Author(s) 2025.

关键词： structured query language

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Efficient Ontology-Mediated query Answering: : Extending DL-liteR and Linear εLH

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JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH 2025年 82卷 851-899页

作者： Dimartino, Mirko M. Wood, Peter T. Cali, Andrea Poulovassilis, Alexandra Prima Assicurazioni 71-73 Carter Lane London EC4V 5EQ England Birkbeck Univ London Knowledge Lab Malet St London WC1E 7HX England Univ Napoli Federico II Dipartimento Ingn Elettr & Tecnol Informaz Naples Italy

The OWL 2 QL profile of the OWL 2 Web Ontology language, based on the family of description logics called DL-Lite, is designed so that data stored in a standard relational database system (RDBMS) can be queried through an ontology via a rewriting mechanism, i.e., by rewriting the query into an SQL query that is then answered by the RDBMS system, without any changes to the data. In this paper we propose a language whose expressive power goes beyond that of DL-Lite while still allowing query answering via rewriting of queries into unions of conjunctive two-way regular path queries (UC2RPQs) instead of SQL queries. Our language is an extension of both OWL 2 QL and linear epsilon LH: OWL 2 QL is extended by allowing qualified existential quantification on the left-hand side of concept inclusion axioms, and linear epsilon LH by allowing inverses in role inclusion axioms. We identify a syntactic property of the extended language that guarantees UC2RPQ-rewritability. We propose a novel rewriting technique for conjunctive queries (CQs) under our ontology language that makes use of nondeterministic finite state automata. We show that CQ answering in our setting is NLOGSPACE-complete with respect to data complexity and NP-complete for combined complexity;we also show that answering instance queries is NLOGSPACE-complete for data complexity and in PTIME for combined complexity.

关键词： structured query language

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NALSpatial: A Natural language Interface for Spatial Databases

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第4期37卷 2056-2070页

作者： Liu, Mengyi Wang, Xieyang Xu, Jianqiu Lu, Hua Tong, Yongxin Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Roskilde Univ Dept People & Technol DK-4000 Roskilde Denmark Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100190 Peoples R China

Spatial databases play a vital role in a number of applications ranging from geographic information systems to location-based services. Application tasks typically access underlying spatial data to answer queries. However, non-experts lack the expertise necessary for formulating spatial queries. To fill in this gap, we propose an effective framework that translates natural language queries over spatial data into executable database queries, called NALSpatial. The framework consists of two core phases: (i) natural language understanding and (ii) natural language translation. Phase (i) extracts key entity information, comprehends the query intent and determines the query type by employing natural language processing techniques and deep learning algorithms. The key entities and query type are passed to phase (ii), which makes use of entity mapping rules and structured language models to construct executable database queries. NALSpatial supports dealing with five types of queries including (i) basic queries (e.g. distance and area), (ii) range queries, (iii) nearest neighbor queries, (iv) spatial join queries and (v) aggregation queries. We develop NALSpatial in an open-source extensible database system SECONDO. Extensive experiments show that NALSpatial on average achieves response time of about 2.5 seconds, translatability of 95% and translation precision of 92%, outperforming three state-of-the-art methods.

关键词： Spatial databases structured query language Translation Chatbots Semantics Natural language processing Data models Computational modeling Indexing Distributed databases Natural language interface query processing semantic parsing spatial database

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Leveraging Large language Model for Enhanced Text-to-SQL Parsing

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IEEE ACCESS 2025年 13卷 30497-30504页

作者： Zhan, Zecheng Haihong, E. Song, Meina ByteDance Inc Beijing 100098 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

Text-to-SQL conversion, the process of generating SQL queries from natural language input, has gained significant attention due to its potential to simplify database interaction. Although benchmarks in this task have driven advancements in the field, the challenges posed by complex join logic and the rich diversity of natural language expressions remain significant obstacles. These complexities underscore the ongoing difficulty of accurately bridging the gap between natural language and structured query representations, particularly in cross-domain and real-world scenarios. Recent research, including intermediate representations, relation-aware transformers, and large language models such as T5 and LLaMA, has improved performance by addressing the semantic gap between natural language and SQL. In this work, we propose SLENet, a novel approach that uses state-of-the-art large language models (LLMs) to enhance semantic understanding and SQL generation. Our method integrates three core innovations: (1) the use of advanced LLMs for context-aware representations, (2) syntax-constrained SQL decoder to ensure grammatical correctness, and (3) search-based prompt optimization utilizing external knowledge sources like WikiSQL. These innovations collectively address schema comprehension and SQL generation complexities. Evaluations on the Spider benchmark demonstrate that SLENet significantly outperforms existing methods, achieving higher exact matching accuracy and effectively handling complex SQL components. Our contributions highlight the importance of combining LLMs with syntax constraints and external data for advancing cross-domain semantic parsing.

关键词： structured query language Decoding Semantics Natural languages Databases Syntactics Benchmark testing Large language models Technological innovation Complexity theory Semantic parsing SQL generation deep learning LLM

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FlexIM: Efficient and Verifiable Index Management in Blockchain

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3399-3412页

作者： Li, Binhong Lin, Licheng Zhang, Shijie Xu, Jianliang Xiao, Jiang Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Wuhan430074 China Hong Kong Baptist University School of Computer Science China Department of Computer Science and Engineering Hong Kong999077 Hong Kong

Blockchain-based query with its traceability and data provenance has become increasingly popular and widely adopted in numerous applications. Yet existing index-based query approaches are only efficient under static blockchain query workloads where the query attribute or type must be fixed. It turns out to be particularly challenging to construct an efficient index for dynamic workloads due to prohibitively long construction time and excessive storage consumption. In this paper, we present FlexIM, the first efficient and verifiable index management system for blockchain dynamic queries. The key innovation in FlexIM is to uncover the inherent characteristics of blockchain, i.e., data distribution and block access frequency, and then to optimally choose the index by utilizing reinforcement learning technique under varying workloads. In addition, we enhance and facilitate verifiability with low storage overhead by leveraging Root Merkle Tree (RMT) and Bloom Filter Merkle Tree (BMT). Our comprehensive evaluations demonstrate that FlexIM outperforms the state-of-the-art blockchain query mechanism, vChain+, by achieving a 26.5% speedup while consuming 94.2% less storage, on average, over real-world Bitcoin datasets. 1989-2012 IEEE.

关键词： structured query language

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