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.

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.

Log-based anomaly detection plays a crucial role in ensuring the reliability of systems. While deep learning-based small detection models (SDMs) are efficient, the large language models (LLMs) are accurate and capable of providing explanations. Intuitively, a compelling question arises: Can we seamlessly combine the advantages of both approaches? In this work, we delve into this underexplored research direction and propose CoLA, a novel collaborative log anomaly detection framework. During collaborative inference, an SDM serves as a filter to select potentially anomalous instances, while a downstream LLM acts as an expert to detect anomalies, offer explanations, and refine the SDM. Extensive experiments on three large real-world datasets demonstrate that CoLA significantly outperforms state-of-the-art methods in terms of effectiveness, efficiency, and explainability, while also greatly reducing labor costs.

Link prediction, which aims to predict the existence of a link between two nodes in a network, has various applications ranging from friend recommendation to protein interaction prediction. Recently, Graph Neural Network (GNN)-based link prediction has demonstrated its advantages and achieved the state-of-the-art performance. Typically, GNN-based link prediction can be formulated as a binary classification problem. However, in link prediction, we only have positive data (observed links) and unlabeled data (unobserved links), but no negative data. Therefore, Positive Unlabeled (PU) learning naturally fits the link prediction scenario. Unfortunately, the unknown class prior and data imbalance of networks impede the use of PU learning in link prediction. To deal with these issues, this paper proposes a novel model-agnostic PU learning algorithm for GNN-based link prediction by means ofPositive-Unlabeled Area Under the Receiver Operating Characteristic Curve(PU-AUC) optimization. The proposed method is free of class prior estimation and able to handle the data imbalance. Moreover, we propose an accelerated method to reduce the operational complexity of PU-AUC optimization from quadratic to approximately linear. Extensive experiments back up our theoretical analysis and validate that the proposed method is capable of boosting the performance of the state-of-the-art GNN-based link prediction models.

Distributed transaction processing often involves multiple rounds of cross-node communications, and therefore, tends to be slow. To improve performance, existing approaches convert distributed transactions into single-node transactions by either migrating co-accessed partitions onto the same nodes or establishing a super node housing replicas of the entire database. However, migration-based methods might cause transactions to be blocked due to waiting for data migration, while the super node can become a bottleneck. In this paper, we present Lion, a novel transaction processing protocol that utilizes partition-based replication to reduce the occurrence of distributed transactions. Inspired by the fact that modern distributed databases horizontally partition data, with each partition having multiple replicas, Lion aims to assign a node with one replica from each partition involved in a given transaction's read or write operations. To ensure such a node is available, we propose an adaptive replica provision mechanism, enhanced with an LSTM-based workload prediction algorithm, to determine the appropriate node for locating replicas of co-accessed partitions. The adaptation of replica placement is conducted preemptively and asynchronously, thereby minimizing its impact on performance. By employing this adaptive replica placement strategy, we ensure that the majority of transactions can be efficiently processed on a single node without additional overhead. Only a small fraction of transactions will need to be treated as regular distributed transactions when such a node is unavailable. Consequently, Lion effectively minimizes distributed transactions, while avoiding any disruption caused by data migration or the creation of a super node. We conduct extensive experiments to compare Lion against various transaction processing protocols. The results show that Lion achieves up to 2.7x higher throughput and 76.4% better scalability against these state-of-the-art approaches.

Multi-label text classification (MLTC) allows a given text to be associated with multiple labels, which well suits many real-world data mining scenarios. However, the annotation effort of MLTC is inevitably expensive and time-consuming. Although multi-label active learning provides a cost-effective solution, it still faces two major challenges: (i) constructing decent feature space to distinguish the confusing semantics of different labels; (ii) defining proper sampling criteria to measure a sample's joint effect over the entire label space. To bridge these gaps, we propose a Contrastive Multi-label Active Learning framework (CoMAL) that gives an effective data acquisition strategy. Specifically, a contrastive decoupling mechanism is introduced to fully release the semantic information of multiple labels into the latent space. Then, we devise a hybrid criterion that balances two data value measures: (i) similarity-enhanced label cardinality inconsistency reflects the uncertainty of data predictions. (ii) positive feature diversity evaluates the positive-propensity semantic diversity to handle the label sparsity. Extensive experiments demonstrate that our CoMAL outperforms the current state-of-the-art multi-label active learning approaches. Code for CoMAL is available at https://github.com/chengzju/CoMAL.

CLIP-based models have made significant advancements in text-to-image retrieval tasks. However, these retrieval models are typically trained on public datasets with optimizing all parameters, which limits their ability to generalize and adapt quickly to personalized private datasets. In this paper, we introduce a lightweight personalized federated learning solution, namely Federated Personalized Augmentation Model (FedPAM), to achieve personalized text-to-image retrieval from multiple private database. Specifically, for the query text, we fetch the top-k most similar text-image pairs from the private database. We then use an attention-based module to generate personalized representations for different clients. The updated representation includes client-specific information for text-to-image matching, resolving issues of data heterogeneity. Additionally, we ensure efficient and secure communication by fine-tuning a small portion of network parameters. Our experiments demonstrate the effectiveness of the proposed framework, exhibiting a significant performance improvement over recently proposed methods: +5.36 on IAPR TC-12, +2.86 on CC3M, and +1.72 on Flickr30k.

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.

Interaction learning plays an essential role in learning patients' comprehensive representations that contribute to improved performance in many analytical tasks. In healthcare, interactions among medical features (i.e., feature-level interactions) can exhibit different abnormal patterns in detail, while interactions among time steps (i.e., time-level interactions) can indicate the dynamic changes in patients' health conditions. Therefore, it is necessary to capture and analyze both types of interactions when conducting healthcare analytics, In this paper, we propose a general framework ELDA that is supported by the novel model ELDA-Net to learn dual-interactions for healthcare analytics in an explicit manner. Specifically, we devise a Feature-level Interaction Learning Module that can enrich a separately processed medical feature by learned interactions among medical features, and a Time-level Interaction Learning Module that can enhance the representations of the patients' health conditions by learned interactions among time steps. In both levels, ELDA can provide explicit and intuitive interpretations via explaining through the designed attention mechanism. Further, to facilitate the feature-level interaction learning, we propose a novel Bi-directional Embedding Module in ELDA-Net which can efficiently embed the medical features recorded in numerical values. We evaluate the effectiveness and interpretability of ELDA over two public real-world clinical datasets. The experimental results confirm that ELDA consistently outperforms existing state-of-the-art methods with a significant margin, and supports fine-grained interpretability in both the feature level and the time level with medical insights.

By maintaining the data in main memory, in-memory databases dramatically reduce the I/O cost of transaction processing. However, for recovery purposes, in-memory systems still need to flush the log to disk, which incurs a substantial number of I/Os. Recently, command logging has been proposed to replace the traditional data log (e.g., ARIES logging) in in-memory databases. Instead of recording how the tuples are updated, command logging only tracks the transactions that are being executed, thereby effectively reducing the size of the log and improving the performance. However, when a failure occurs, all the transactions in the log after the last checkpoint must be redone sequentially and this significantly increases the cost of recovery. In this paper, we first extend the command logging technique to a distributed system, where all the nodes can perform their recovery in parallel. We show that in a distributed system, the only bottleneck of recovery caused by command logging is the synchronization process that attempts to resolve the data dependency among the transactions. We then propose an adaptive logging approach by combining data logging and command logging. The percentage of data logging versus command logging becomes a tuning knob between the performance of transaction processing and recovery to meet different OLTP requirements, and a model is proposed to guide such tuning. Our experimental study compares the performance of our proposed adaptive logging, ARIES-style data logging and command logging on top of H-Store. The results show that adaptive logging can achieve a 10x boost for recovery and a transaction throughput that is comparable to that of command logging.

The widely adopted single-threaded OLTP model assigns a single thread to each static partition of the database for processing transactions in a partition. This simplifies concurrency control while retaining parallelism. However, it suffers performance loss arising from skewed workloads as well as transactions that span multiple partitions. In this paper, we present a dynamic single-threaded in-memory OLTP system, called LADS, that extends the simplicity of the single-threaded model. The key innovation in LADS is the separation of dependency resolution and execution into two non-overlapping phases for batches of transactions. After the first phase of dependency resolution, the record actions of the transactions are partitioned and ordered. Each independent partition is then executed sequentially by a single thread, avoiding the need for locking. By careful mapping of the tasks to be performed to threads, LADS is able to achieve a high degree of balanced parallelism. We evaluate LADS against H-Store, a partition-based database; DORA, a data-oriented transaction processing system; and SILO, a multi-core in-memory OLTP engine. The experimental study shows that LADS achieves up to 20x higher throughput than existing systems and exhibits better robustness with various workloads.