A precise workload forecaster is the key to effective resource management, system scalability, and overall operational efficiency in cloud environments. However, real-world cloud systems frequently operate in dynamic and unpredictable settings, causing workloads that exhibit significant diversity and fluctuations. To address these problems, we introduce OMCR, a novel online multivariate forecaster for cloud resource management, that overcomes the limitations of existing static forecasting methods through online learning. OMCR integrates long-term memory with a rapid response mechanism to short-term changes in cloud systems, while also considering the impact of multivariate relationships on workload prediction. OMCR minimizes its reliance on historical data, thereby reducing training difficulty and maintaining lower prediction loss in the long run. OMCR also offers an adaptive approach to forecasting peak workloads in a certain time span, which helps cloud resource management. Experimental results demonstrate the superior performance of our proposed framework compared to state-of-the-art methods in MAE and MSE metrics when forecasting cloud workloads.

Abstract Combinatorial optimization problems over graphs, such as the traveling salesman problem, longest path problem, and maximum independent set problem, are well-known for being computationally costly, some even NP-hard problems. In this paper, we propose a general quantum algorithm framework searching for approximate solutions to combinatorial optimization problems with linear objective functions. Our framework provides APIs (application programming interfaces) that enable developers to encode weighted graph structures onto quantum circuits and utilize variational algorithms to generate approximate solutions. One key advantage of our framework is that it allows developers to design new graph algorithms for the graph problem represented as linear combinations of edge weights without requiring expertise in quantum programming. Besides, it only uses a logarithmic level of quantum bit scale, making our framework work on quantum computers with limited physical resources. Our experimental results demonstrate that our framework can provide good approximations for the traveling salesman problem compared to current quantum algorithm.

Hybrid storage solutions have emerged as potent strategies to alleviate the data storage bottlenecks prevalent in blockchain systems. These solutions harness off-chain Storage Services Providers (SP) in conjunction with Authenticated Data Structures (ADS) to ensure data integrity and accuracy. Despite these advancements, the reliance on centralized SPs raises concerns about query correctness, as the integrity of query results depends on the SPs' trustworthiness. Although ADS can verify the integrity of individual data points, they fall short of preventing SPs from omitting valid results. In this paper, we delineate the fundamental distinctions between data retrieval in blockchains and traditional database systems. Drawing upon these insights, we introduce the BPI framework, which employs a suite of validation models that ascertain the inclusion of all valid content in retrieval outcomes, with low overhead. We further present ''Articulated Search'', a query pattern specifically tailored for blockchain environments, which not only enhances retrieval efficiency but also substantially reduces costs during data user updates. Extensive experimental evaluations demonstrate that the BPI framework achieves outstanding scalability and performance in keyword searches within blockchain environments, surpassing EthMB+ and state-of-the-art search databases commonly used in mainstream hybrid storage blockchains (HSB). Notably, the Articulated Search pattern improves query performance by over three orders of magnitude, highlighting its potential as a transformative approach to blockchain query optimization.

Querying and analyzing data in data lakes requires substantial manual intervention, including numerous data preprocessing steps, and often demands complex domain expertise. However, the advent of Large Language Models (LLMs) has introduced a promising solution to these challenges by providing a unified framework for interpreting the heterogeneous datasets within data lakes. In this paper, we demonstrate QueryArtisan, a novel LLM-powered analytical system tailored for data lakes. It enables users to issue complex queries in natural language without the need for domain-specific expertise. The system automatically executes user-submitted queries and performs data processing and analysis based on the query results. QueryArtisan extends beyond traditional ETL (Extract, Transform, Load) processes by generating just-in-time code customized for dataset-specific tasks. A suite of heterogeneous operators is developed to process data across various modalities. In addition, a cost-based query optimization mechanism is integrated to improve the efficiency of the generated code. Furthermore, QueryArtisan can dynamically instantiate multiple agents in response to user-defined analytical requirements to perform further in-depth analysis of the retrieved data.

The scarcity of training data restricts a neural network from capturing schema diversity and intricacies, hindering schema-matching models' generalization capabilities. In this paper, we propose ISResMat, a framework specifically designed to match the schemas of relational tables by fine-tuning a pre-trained language model. We first offer a training data construction method, Pairwise Sampling, which could generate the training dataset with table data. Next, we design two loss functions (i.e., Meta-Matching Loss and Agent-Delegating Loss) to learn representations of table columns. With those representations, we could calculate matching scores between different table columns for deducing the matching candidates, which provides a novel approach to schema matching. Finally, we present two optimizations (i.e., Matching Rectification Loss and Distribution-Aware Fingerprint) to handle the problems of matching cardinality constraints and numerical columns, respectively. ISResMat is a flexible framework supporting instance-based, schema-based, and hybrid matching without significant modification. Experiments on 500+ fabricated and human-curated relation pairs spanning diverse domains and matching scenarios showcase that our approach outperforms existing state-of-the-art methods.

Query processing over data lakes is a challenging task, often requiring extensive data pre-processing activities such as data cleaning, transformation, and loading. However, the advent of Large Language Models (LLMs) has illuminated a new pathway to address these complexities by offering a unified approach to understanding the diverse datasets submerged in data lakes. In this paper, we introduce QueryArtisan, a novel LLM-powered analytic tool specifically designed for data lakes. QueryArtisan transcends traditional ETL (Extract, Transform, Load) processes by generating just-intime code for dataset-specific queries. It eliminates the need for an intermediary schema, enabling users to query the data lake directly using natural language. To achieve this, we have developed a suite of heterogeneous operators capable of processing data across various modalities. Additionally, QueryArtisan incorporates a cost model-based query optimization technique, significantly enhancing its code generation capabilities for efficient query resolution. Our extensive experimental evaluations, conducted with real-life datasets, demonstrate that QueryArtisan markedly outperforms existing solutions in terms of effectiveness, efficiency and usability.

Quantum computing represents a next-generation technology in data processing, promising to transcend the limitations of traditional computation. In this paper, we undertake an early exploration of the potential integration of quantum computing with database query optimization. We introduce a pioneering hybrid classical-quantum algorithm for sampling-based approximate query processing (AQP). The core concept of the algorithm revolves around identifying rare groups, which often follow a long-tail distribution, and applying distinct sampling methodologies to normal and rare groups. By leveraging the quantum capabilities of the diffusion gate and QRAM, the algorithm defines a novel quantum sampling approach that iteratively amplifies the signals of these infrequent groups. The algorithm operates without the need for preprocessing or prior knowledge of workloads or data. It utilizes the power of quadratic acceleration to achieve well-balanced sampling across various data categories. Experimental results demonstrate that in the context of AQP, the new sampling scheme provides higher accuracy at the same sampling cost. Additionally, the benefits of quantum computing become more pronounced as query selectivity increases.

Database management systems (DBMSs) are prone to logic bugs that can result in incorrect query results. Current debugging tools are limited to single table queries and struggle with issues like lack of ground-truth results and repetitive query space exploration. In this paper, we demonstrate DLBD, a system that automatically detects logic bugs in databases. DLBD offers holistic logic bug detection by providing automatic schema and query generation and ground-truth query result retrieval. Additionally, DLBD provides minimal test cases and root cause analysis for each bug to aid developers in reproducing and fixing detected bugs. DLBD incorporates heuristics and domain-specific knowledge to efficiently prune the search space and employs query space exploration mechanisms to avoid the repetitive search. Finally, DLBD utilizes a distributed processing framework to test database logic bugs in a scalable and efficient manner. Our system offers developers a reliable and effective way to detect and fix logic bugs in DBMSs.

How to manage various data in a unified way is a significant research topic in the field of databases. To address this problem, researchers have proposed multi-model databases to support multiple data models in a uniform platform with a single unified query language. However, since relational databases are predominant in the current market, it is expensive to replace them with others. Besides, due to the theories and technologies of RDBMSs having been enhanced over decades, it is hard to use few years to develop a multi-model database that can be compared with existing RDBMSs in handling security, query optimization, transaction management, etc. In this paper, we reconsider employing relational databases to store and query multi-model data. Unfortunately, the mismatch between the complexity of multi-model data structure and the simplicity of flat relational tables makes this difficult. Against this challenge, we utilize the reinforcement learning (RL) method to learn a relational schema by interacting with an RDBMS. Instead of using the classic Q-learning algorithm, we propose a variant Q-learning algorithm, called Double Q-tables, to reduce the dimension of the original Q-table and improve learning efficiency. Experimental results show that our approach could learn a relational schema outperforming the existing multi-model storage schema in terms of query time and space consumption.

With the trend of increasing vendors to develop various multi-model databases, people have reaped benefits from using a single and unified platform to manage both well-structured and NoSQL data. However, it causes a steep learning curve of mastering a multi-model query language for the specific multi-model database, not to mention various languages for different databases. Therefore, this research discusses the motivations of performing keyword searches on multi-model databases and then presents our current research. Methodologically, we attempt to use the quantum-inspired framework to query and explore multi-model databases. Firstly, we apply non-classical probabilities to estimate the relevance between a keyword query and candidate answers for guaranteeing getting good accuracy. Then we use the Principle Component Analysis (PCA) method to optimize the quantum language model for capturing good scalability. Finally, experiments show that our approaches are effective and our framework outperforms the state-of-the-art approaches.