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arXiv 2025年

作者： Loizou, Andreas Tsoumakos, Dimitrios Database and Knowledge Systems Lab School of ECE National Technical University of Athens Greece

The massive increase in the data volume and dataset availability for analysts compels researchers to focus on data content and select high-quality datasets to enhance the performance of analytics operators. While selecting the highest quality data for analysis highly increases task accuracy and efficiency, it is still a hard task, especially when the number of available inputs is very large. To address this issue, we propose a novel methodology that infers the outcome of analytics operators by creating a model from datasets similar to the queried one. Dataset similarity is performed via projecting each dataset to a vector embedding representation. The vectorization process is performed using our proposed deep learning model NumTabData2Vec, which takes a whole dataset and projects it into a lower vector embedding representation space. Through experimental evaluation, we compare the prediction performance and the execution time of our framework to another state-of-the-art modelling operator framework, illustrating that our approach predicts analytics outcomes accurately. Furthermore, our vectorization model can project different real-world scenarios to a lower vector embedding representation and distinguish between them. Copyright © 2025, The Authors. All rights reserved.

关键词： Data quality

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Data-Aware Adaptive Compression for Stream Processing

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2024年第9期36卷 4531-4549页

作者： Zhang, Yu Zhang, Feng Li, Hourun Zhang, Shuhao Guo, Xiaoguang Chen, Yuxing Pan, Anqun Du, Xiaoyong Renmin Univ China Sch Informat Key Lab Data Engn & Knowledge Engn MOE Beijing 100872 Peoples R China Nanyang Technol Univ NTU Sch Comp Sci & Engn SCSE Singapore 639798 Singapore Tencent Inc Database R&D Dept Shenzhen Peoples R China

Stream processing has been in widespread use, and one of the most common application scenarios is SQL query on streams. By 2021, the global deployment of IoT endpoints reached 12.3 billion, indicating a surge in data generation. However, the escalating demands for high throughput and low latency in stream processing systems have posed significant challenges due to the increasing data volume and evolving user requirements. We present a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, to significantly enhance the performance of existing stream processing solutions. CompressStreamDB utilizes nine diverse compression methods tailored for different stream data types and integrates a cost model to automatically select the most efficient compression schemes. CompressStreamDB provides high throughput with low latency in stream SQL processing by identifying and eliminating redundant data among streams. Our evaluation demonstrates that CompressStreamDB improves average performance by 3.84x and reduces average delay by 68.0% compared to the state-of-the-art stream processing solution for uncompressed streams, along with 68.7% space savings. Besides, our edge trials show an average throughput/price ratio of 9.95x and a throughput/power ratio of 7.32x compared to the cloud design.

关键词： Encoding Compression algorithms Real-time systems Low latency communication Data processing Costs Delays Data compaction and compression stream processing edge computing

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SP³: Enhancing Structured Pruning via PCA Projection 62

SP<SUP>3</SUP>: Enhancing Structured Pruning via PCA Project...

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62nd Annual Meeting of the Association-for-Computational-Linguistics (ACL) / Student Research Workshop (SRW)

作者： Hu, Yuxuan Zhang, Jing Zhao, Zhe Zhao, Chen Chen, Xiaodong Li, Cuiping Chen, Hong Renmin Univ China Sch Informat Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China MOE Engn Res Ctr Database & Business Intelligence Beijing Peoples R China

ISBN: (纸本)9798891760998

Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidden dimension (d) in PLMs, a dimension critical to model size and efficiency. This paper introduces a novel structured pruning approach, Structured Pruning with PCA Projection (SP3), targeting the effective reduction of d by projecting features into a space defined by principal components before masking. Extensive experiments on benchmarks (GLUE and SQuAD) show that SP3 can reduce d by 70%, compress 94% of the BERTbase model, and maintain over 96% accuracy and outperform other methods that compress d by 6% in accuracy at the same compression ratio. SP3 has also proven effective with other models, including OPT and Llama. Our data and code are available at ours repo.

关键词： Computational linguistics

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FOSS: A Self-Learned Doctor for Query Optimizer 40

FOSS: A Self-Learned Doctor for Query Optimizer

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhong, Kai Sun, Luming Ji, Tao Li, Cuiping Chen, Hong Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Shanghai Yunxi Technology Co. Ltd. China Engineering Research Center of Database and Business Intelligence MOE China

ISBN: (纸本)9798350317152

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks. © 2024 IEEE.

关键词： Reinforcement learning

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VEnOM: A Vector Embedding Operator Modelling Framework 25

VEnOM: A Vector Embedding Operator Modelling Framework

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Proceedings of the 19th ACM International Conference on Distributed and Event-based Systems

作者： Andreas Loizou Nikolaos Andriotis Dimitrios Tsoumakos School of Electrical and Computer Engineering/ Database and Knowledge Systems Lab National Technical University of Athens Athens Greece

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A Generation-based Deductive Method for Math Word Problems

A Generation-based Deductive Method for Math Word Problems

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Conference on Empirical Methods in Natural Language Processing (EMNLP)

作者： Hu, Yuxuan Zhang, Jing Li, Haoyang Li, Cuiping Chen, Hong Renmin Univ China Sch Informat Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China MOE Engn Res Ctr Database & Business Intelligence Beijing Peoples R China

ISBN: (纸本)9798891760608

Math word problems (MWP) involving advanced operators such as linear equation solver cannot be easily tackled by earlier MWP methods, because the existing generation methods suffer from repeated sub-expression generation and deductive methods are restricted to dealing with binary operations. This paper propose a new multivariate directed acyclic graph (mDAG) as an alternative to the generation methods' binary expression tree or the deductive methods' binary directed acyclic graph. Then to produce the topological ordering of mDAG, we propose a generation-based deductive (GeDe) model, which equips a generation model with a re-encoder to keep the deductive property but avoid the expensive enumeration of the deductive methods. GeDe performs well on math problems with many operators on the widely used benchmarks as well as solving multivariate operators on our own CMWPA benchmark. Our code is available at https://***/hyx1999/GeDe

关键词： Directed graphs

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Unsupervised consumer intention and sentiment mining from microblogging data as a business intelligence tool

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OPERATIONAL RESEARCH 2022年第5期22卷 6007-6036页

作者： Symeonidis, Symeon Peikos, Georgios Arampatzis, Avi Democritus Univ Thrace Dept Elect & Comp Engn Database & Informat Retrieval Res Unit Xanthi 67100 Greece Univ Milano Bicocca Dept Informat Syst & Commun DISCo Informat & Knowledge Representat Retrieval & Reas Milan Italy

The present study aims to create a framework that analyses user posts related to a product of interest on social networking platforms. More precisely, by applying information mining techniques, posts are categorised according to the intention they express, the sentiment polarisation, and the type of opinion. The model operates based on linguistic rules, machine learning, and combinations. Six different methodologies are implemented to extract intent, sentiment, and type of opinion from a tweet. The final model automatically detects intention to buy or not to buy the product, intention to compare the product with other competitors, and finally, intention to search for information about the product. It then categorises the text according to the sentiment and depending on their expressed opinion. The dataset comprises tweets for each day of the iPhone 5's life cycle, corresponding to 365 days. Additionally, it demonstrated that the business's external or internal decisions affect the public purchasing audience's opinions, sentiments, and intentions expressed on social media. Lastly, as a Business Intelligence tool, the framework recognises and analyses these points, which contribute substantially to the company's decision-making through the findings.

关键词： Intention mining Sentiment analysis Business intelligence Microblogging

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RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL 37

RESDSQL: Decoupling Schema Linking and Skeleton Parsing for ...

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37th AAAI Conference on Artificial Intelligence (AAAI) / 35th Conference on Innovative Applications of Artificial Intelligence / 13th Symposium on Educational Advances in Artificial Intelligence

作者： Li, Haoyang Zhang, Jing Li, Cuiping Chen, Hong Renmin Univ China Key Lab Data Engn & Knowledge Engn Minist Educ Beijing Peoples R China Minist Educ Database & BI Engn Res Ctr Beijing Peoples R China Renmin Univ China Informat Sch Beijing Peoples R China

ISBN: (纸本)9781577358800

One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tables and columns) and the skeleton (i.e., SQL keywords). Such coupled targets increase the difficulty of parsing the correct SQL queries especially when they involve many schema items and logic operators. This paper proposes a ranking-enhanced encoding and skeleton-aware decoding framework to decouple the schema linking and the skeleton parsing. Specifically, for a seq2seq encoder-decode model, its encoder is injected by the most relevant schema items instead of the whole unordered ones, which could alleviate the schema linking effort during SQL parsing, and its decoder first generates the skeleton and then the actual SQL query, which could implicitly constrain the SQL parsing. We evaluate our proposed framework on Spider and its three robustness variants: Spider-DK, Spider-Syn, and Spider-Realistic. The experimental results show that our framework delivers promising performance and robustness. Our code is available at https://***/RUCKBReasoning/RESDSQL.

关键词： Decoding

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Federated Incremental Named Entity Recognition 31

Federated Incremental Named Entity Recognition

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Zesheng Zhu, Qiannan Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Artificial Intelligence Beijing Normal University Beijing China Engineering Research Center of Intelligent Technology and Educational Application MOE China

ISBN: (纸本)9798891761964

Federated learning-based Named Entity Recognition (FNER) has attracted widespread attention through decentralized training on local clients. However, most FNER models assume that entity types are pre-fixed, so in practical applications, local clients constantly receive new entity types without enough storage to access old entity types, resulting in severe forgetting on previously learned knowledge. In addition, new clients collecting only new entity types may join the global training of FNER irregularly, further exacerbating catastrophic forgetting. To overcome the above challenges, we propose a Forgetting-Subdued Learning (FSL) model which solves the forgetting problem on old entity types from both intra-client and inter-client two aspects. Specifically, for intra-client aspect, we propose a prototype-guided adaptive pseudo labeling and a prototypical relation distillation loss to surmount catastrophic forgetting of old entity types with semantic shift. Furthermore, for inter-client aspect, we propose a task transfer detector. It can identify the arrival of new entity types that are protected by privacy and store the latest old global model for relation distillation. Qualitative experiments have shown that our model has made significant improvements compared to several baseline methods. © 2025 Association for Computational Linguistics.

关键词： Federated learning

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A Query Optimization Method Utilizing Large Language Models

arXiv

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arXiv 2025年

作者： Yao, Zhiming Li, Haoyang Zhang, Jing Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China

Query optimization is a critical task in database systems, focused on determining the most efficient way to execute a query from an enormous set of possible strategies. Traditional approaches rely on heuristic search methods and cost predictions, but these often struggle with the complexity of the search space and inaccuracies in performance estimation, leading to suboptimal plan choices. This paper presents LLMOpt, a novel framework that leverages Large Language Models (LLMs) to address these challenges through two innovative components: (1) LLM for Plan Candidate Generation (LLMOpt(G)), which eliminates heuristic search by utilizing the reasoning abilities of LLMs to directly generate high-quality query plans, and (2) LLM for Plan Candidate Selection (LLMOpt(S)), a list-wise cost model that compares candidates globally to enhance selection accuracy. To adapt LLMs for query optimization, we propose fine-tuning pre-trained models using optimization data collected offline. Experimental results on the JOB, JOB-EXT, and Stack benchmarks show that LLMOpt(G) and LLMOpt(S) outperform state-of-the-art methods, including PostgreSQL, BAO, and HybridQO. Notably, LLMOpt(S) achieves the best practical performance, striking a balance between plan quality and inference efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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