OpenRaiser/Intern-Atlas huggingface dataset:
A Cause-Focused Query Optimizer Alert System
Authors: Runfan Ye, Zibo Liang, Xu Chen, Shuncheng Liu, Kai Zheng
Venue: CIKM
Year: 2024

A series of studies apply machine learning to assist cost-based query optimizers in DBMS, emphasizing incorporating uncertainty predictions to guide decision-making. While these approaches have demonstrated advancement in some benchmarks, their drawbacks, such as unstable performance, stem from the inherent challenges of using machine learning models to predict the cost of execution plans and the lack of exploration of the intrinsic characteristics of suboptimal plans. In this paper, we introduce an alert system for query optimization, which is built upon cost models to reduce the selection of regressed plans. The key insight is that there are differences in the predictive uncertainty that lead to query optimization and the regression of execution plans. We investigate the causes of these differences in uncertainty and design a discriminator to filter out execution plans with higher risks of regression. The alert system can be integrated with various cost models, enhancing the robustness of query optimizers. In our experiments, the system further reduces execution time by 20% compared to learned optimizers. Meanwhile, the proportion of optimized queries reduced by the alert system is just 15% of the proportion of regressed queries diminished.
Automation of Text-Based Economic Indicator Construction: A Pilot Exploration on Economic Policy Uncertainty Index
Authors: Hsiu-Hsuan Yeh, Yu-Lieh Huang, Ziho Park, Chung-Chi Chen
Venue: CIKM
Year: 2024

The growing popularity of text-as-data in various domain-specific applications and research has often relied on manually selected keywords or annotations. Although labor-intensive, expensive and time-consuming, the effectiveness of these efforts is not always guaranteed, especially in the early stages of research. This predicament raises the question of the extent to which large language models (LLMs) can aid in verifying the potential of a nascent research idea. This paper seeks to explore the reliability of LLM-suggested keywords in the automatic construction of the Economic Policy Uncertainty (EPU) index. Our findings confirm that LLMs can effectively automate the construction of EPU index. Furthermore, we delve into the potential of LLMs in enhancing the indicator construction process.
myCADI: my Contextual Anomaly Detection using Isolation
Authors: Véronne Yepmo, Grégory Smits
Venue: CIKM
Year: 2024

myCADI is a machine learning framework associated with a graphical interface for discovering and understanding the internal structure of an unsupervised dataset. It is an intuitive end-user interface to the CADI approach, which uses a revised version of the Isolation Forest method to both 1) identify local anomalies, 2) reconstruct the cluster-based internal structure of the data, and 3) provide end-users with explanations of how anomalies deviate from the found clusters. myCADI takes numerical data as input and is structured around several interfaces, each of which displays a ranked list of the found anomalies, a description of the subspaces in which the different clusters lie, and feature attribution explanations to ease the interpretation of anomalies. These explanations make explicit why a selected point is considered to be a local anomaly of one (or more) cluster(s). The framework also provides dataset and trees visualizations.
CKNN: Cleansed k-Nearest Neighbor for Unsupervised Video Anomaly Detection
Authors: Jihun Yi, Sungroh Yoon
Venue: CIKM
Year: 2024

In this paper, we address the problem of unsupervised video anomaly detection (UVAD). The task aims to detect abnormal events in test video using unlabeled videos as training data. The presence of anomalies in the training data poses a significant challenge in this task, particularly because they form clusters in the feature space. We refer to this property as the "Anomaly Cluster" issue. The condensed nature of these anomalies makes it difficult to distinguish between normal and abnormal data in the training set. Consequently, training conventional anomaly detection techniques using an unlabeled dataset often leads to sub-optimal results. To tackle this difficulty, we propose a new method called Cleansed k-Nearest Neighbor (CKNN), which explicitly filters out the Anomaly Clusters by cleansing the training dataset. Following the k-nearest neighbor algorithm in the feature space provides powerful anomaly detection capability. Although the identified Anomaly Cluster issue presents a significant challenge to applying k-nearest neighbor in UVAD, our proposed cleansing scheme effectively addresses this problem. We evaluate the proposed method on various benchmark datasets and demonstrate that CKNN outperforms the previous state-of-the-art UVAD method by up to 8.5% (from 82.0 to 89.0) in terms of AUROC. Moreover, we emphasize that the performance of the proposed method is comparable to that of the state-of-the-art method trained using anomaly-free data.
Prioritized Binary Transformation Method for Efficient Multi-label Classification of Data Streams with Many Labels
Authors: Onur Yildirim, Sepehr Bakhshi, Fazli Can
Venue: CIKM
Year: 2024

Real-time data processing systems generate huge amounts of data that need to be classified. The volume, variety, velocity, and veracity (uncertainty) of this data necessitate new approaches and the adaptation of existing classification methods. Moreover, the arriving data can belong to more than one class at the same time. As the number of labels grows larger, a significant portion of the multi-label data stream classification methods become computationally inefficient. We propose a novel online approach: the Prioritized Binary Transformation (PBT) method, which can classify data with large numbers of labels by ordering the labels using Principal Component Analysis (PCA) within a fixed-size window. This order is then used to transform the label vectors for classification. We perform an empirical analysis on 12 datasets and compare PBT to four prominent baselines using four evaluation metrics. PBT achieves the best average ranking in three of the four evaluation metrics. Moreover, we investigate efficiency under average execution time per data item and memory consumption where PBT achieves second and first average rankings, respectively.
Voting with Generative AI for German Compound Splitting in E-commerce Search
Authors: Ümit Yilmaz, Kilian Merkelbach, Daniel Stein, Hasan Oezkan
Venue: CIKM
Year: 2024

Compound words are a grammatical structure that allows forming new words by composing existing words. For e-commerce search in German, it is essential to split these compounds into meaningful parts because item titles often use the joint form while search queries are often split. We propose a method for German compound splitting leveraging a large language model (LLM) with a voting mechanism and a hyperparameter search for automatically optimizing prompt and parameter combinations. Our evaluation of the proposed method on human-created gold standard data for e-commerce shows that it outperforms existing methods for compound splitting in this domain.
Revolutionizing Biomarker Discovery: Leveraging Generative AI for Bio-Knowledge-Embedded Continuous Space Exploration
Authors: Wangyang Ying, Dongjie Wang, Xuanming Hu, Ji Qiu, Jin Park, Yanjie Fu
Venue: CIKM
Year: 2024

Biomarker discovery is vital in advancing personalized medicine, offering insights into disease diagnosis, prognosis, and therapeutic efficacy. Traditionally, the identification and validation of biomarkers heavily depend on extensive experiments and statistical analyses. These approaches are time-consuming, demand extensive domain expertise, and are constrained by the complexity of biological systems. These limitations motivate us to ask: Can we automatically identify the effective biomarker subset without substantial human efforts? Inspired by the success of generative AI, we think that the intricate knowledge of biomarker identification can be compressed into a continuous embedding space, thus enhancing the search for better biomarkers. Thus, we propose a new biomarker identification framework with two important modules:1) training data preparation and 2) embedding-optimization-generation. The first module uses a multi-agent system to automatically collect pairs of biomarker subsets and their corresponding prediction accuracy as training data. These data establish a strong knowledge base for biomarker identification. The second module employs an encoder-evaluator-decoder learning paradigm to compress the knowledge of the collected data into a continuous space. Then, it utilizes gradient-based search techniques and autoregressive-based reconstruction to efficiently identify the optimal subset of biomarkers. Finally, we conduct extensive experiments on three real-world datasets to show the efficiency, robustness, and effectiveness of our method.
Forecasting Live Chat Intent from Browsing History
Authors: Se-eun Yoon, Ahmad Bin Rabiah, Zaid Alibadi, Surya Kallumadi, Julian J. McAuley
Venue: CIKM
Year: 2024

Customers reach out to online live chat agents with various intents, such as asking about product details or requesting a return. In this paper, we propose the problem of predicting user intent from browsing history and address it through a two-stage approach. The first stage classifies a user's browsing history into high-level intent categories. Here, we represent each browsing history as a text sequence of page attributes and use the ground-truth class labels to fine-tune pretrained Transformers. The second stage provides a large language model (LLM) with the browsing history and predicted intent class to generate fine-grained intents. For automatic evaluation, we use a separate LLM to judge the similarity between generated and ground-truth intents, which closely aligns with human judgments. Our two-stage approach yields significant performance gains compared to generating intents without the classification stage.
GaQR: An Efficient Generation-augmented Question Rewriter
Authors: Oliver Young, Yixing Fan, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Xueqi Cheng
Venue: CIKM
Year: 2024

Query understanding is an essential part in search systems to improve the recall. Unlike prior works focusing on word expansions, in this paper, we leverage the comprehension ability of LLM to generate detailed queries from a global semantic perspective. To this end, we introduce an efficient GaQR to reformulate a question into several queries using Chain of Thought (CoT) and make it more efficient through knowledge distillation. Specifically, we first prompt a teacher model to generate indicative queries by considering answer generation one step ahead. Then, we filter out low-quality queries by validating the effectiveness of all generated queries in retrieving useful passages. Finally, we distill a student rewriter based on the verified results to improve efficiency. Our experimental results demonstrate that the rewriter improves the retrieval performance by 3% to 15% on the Miracl and NFCorpus datasets and shows good generalisation ability across different retrieval methods. Moreover, the efficiency of the rewriter after knowledge distillation is improved by as much as 5 times. Code is available at https://github.com/youngbeauty250/GaQR.
GraphCBAL: Class-Balanced Active Learning for Graph Neural Networks via Reinforcement Learning
Authors: Chengcheng Yu, Jiapeng Zhu, Xiang Li
Venue: CIKM
Year: 2024

Graph neural networks (GNNs) have recently demonstrated significant success. Active learning for GNNs aims to query the valuable samples from the unlabeled data for annotation to maximize the GNNs' performance at a low cost. However, most existing methods for reinforced active learning in GNNs may lead to a highly imbalanced class distribution, especially in highly skewed class scenarios. This further adversely affects the classification performance. To tackle this issue, in this paper, we propose a novel reinforced class-balanced active learning framework for GNNs, namely, GraphCBAL. It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes. GraphCBAL designs class-balance-aware states, as well as a reward function that achieves trade-off between model performance and class balance. We further upgrade GraphCBAL to GraphCBAL++ by introducing a punishment mechanism to obtain a more class-balanced labeled set. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed approaches, achieving superior performance over state-of-the-art baselines. In particular, our methods can strike the balance between classification results and class balance. We provide our code and data at https://github.com/cici-chengcheng/GraphCBAL.
Rethinking Attention Mechanism for Spatio-Temporal Modeling: A Decoupling Perspective in Traffic Flow Prediction
Authors: Qi Yu, Weilong Ding, Hao Zhang, Yang Yang, Tianpu Zhang
Venue: CIKM
Year: 2024

The attention mechanism has the advantage of handling long-term correlations, and has been widely adopted in multivariate time series (MTS) prediction. As an important application of MTS, traffic flow prediction has the most popular solution using transformer-based prediction models nowadays. Just with attention mechanism, those models can learn the spatio-temporal correlations from traffic data. However, the up-to-date linear prediction models have questioned the effectiveness of current transformer-based models in certain conditions, which provides new possibilities for more efficient work. We rethink the role of the attention mechanism during spatio-temporal modeling from a decoupling perspective, and propose DEC-Former for traffic flow prediction. Specifically, the trend and seasonal parts of the time series data, the geographical adjacency of the nodes in the road network, and the traditional encoder-decoder architecture, are respectively decoupled. Such decoupling leverages the attention mechanism's advantage to capture long-term and long-range correlations.From extensive experiments on four real-world datasets, our work proves better predictive performance and efficiency than state-of-the-art attention-based models. Two case studies further show the distinct real effects.
Time-Series Representation Learning via Dual Reference Contrasting
Authors: Rui Yu, Yongshun Gong, Shoujin Wang, Jiasheng Si, Xueping Peng, Bing Xu, Wenpeng Lu
Venue: CIKM
Year: 2024

The inherent complexity of real-world time series data, combined with the cost and infeasibility of manual labeling, presents considerable challenges to time series representation learning. Most existing studies tend to utilize data augmentation techniques to construct positive and negative samples and leverage a comparative learning framework to generate time series representations. However, they typically employ simple data augmentation techniques, such as jitter and cropping, to construct positive samples while randomly selecting irrelevant samples as negative ones, which are easily distinguished and unable to guide comparative learning to capture subtle discriminative features. Furthermore, they usually employ only a single positive sample for comparative learning, which is insufficient to model the diversity and hurts the robustness. To address these issues, this paper proposes a Time Series representation learning framework via Dual Reference Contrasting (TS-DRC). Specifically, we first utilize Markov transition field or Gramian angular field to transform the anchor sample of time series into image representations, which are adopted as positive samples. Then, we incorporate two positive samples (dual references) and one negative sample into the comparative learning framework, and devise a novel optimization objective to guide the model to capture more discriminate features, mitigate overfitting, and enhance the robustness. Extensive experiments conducted on four public real-world datasets demonstrate that our TS-DRC outperforms other state-of-the-art baselines.Our code is available at: https://github.com/yurui12138/TS-DRC.
XRDMamba: Large-scale Crystal Material Space Group Identification with Selective State Space Model
Authors: Liheng Yu, Pengkun Wang, Zhe Zhao, Zhongchao Yi, Sun Nan, Di Wu, Yang Wang
Venue: CIKM
Year: 2024

In material science, the properties of crystalline materials largely depend on their structures, and space group is a key descriptor of crystal structure. With the rapid advancement of deep learning, the traditional artificial structure analysis method based on X-ray diffraction (XRD) has become cumbersome and is being gradually supplanted by neural networks. However, existing models are too simplistic and lack a comprehensive understanding of material structure. Our approach XRDMamba integrates chemical knowledge and presents a fresh crystal planes perspective on XRD data. We also introduce a knowledge-driven model for space group identification tasks. We have thoroughly analyzed our approach through numerous experiments, observing its SOTA performance and excellent generalization capabilities. The code is available in ~https://github.com/baigeiguai/XRDMamba.
MMLRec: A Unified Multi-Task and Multi-Scenario Learning Benchmark for Recommendation
Authors: Guanghu Yuan, Jieyu Yang, Shujie Li, Mingjie Zhong, Ang Li, Ke Ding, Yong He, Min Yang, Liang Zhang, Xiaolu Zhang, Linjian Mo
Venue: CIKM
Year: 2024

In recent years, there has been a trend in the field of recommender systems towards multi-task modeling and multi-scenario modeling. The aim is to enhance the performance of various tasks and scenarios by jointly training on multiple tasks or scenarios to learn common patterns and features. Joint modeling of tasks and scenarios has also received widespread attention recently. However, despite the rich proposals of methods for Multi-Task Learning (MTL), Multi-Scenario Learning (MSL), and Multi-Task-Multi-Scenario Learning (MTMSL) in recent years, there still lacks a comprehensive benchmark to evaluate these methods. Previous studies often employed different datasets, data processing techniques, data partitioning strategies, and hyperparameter settings, making replication of existing research and fair comparison of experimental results challenging. To address this challenge, we introduce MMLRec, the first unified comprehensive benchmark for evaluating MTL, MSL and MTMSL, featuring consistent dataset processing and identical parameter settings. This benchmark implements a range of MTL, MSL, and MTMSL algorithms, and evaluates them on multiple commonly used recommender systems datasets. Through fair comparative experiments, we find that some structurally simplistic recommendation algorithms are underestimated, as they can achieve comparable results to more complex algorithms while maintaining lower complexity. Furthermore, our experimental analysis indicates that more complex methods exhibit better robustness when there are significant differences between tasks or scenarios. By providing a unified framework (MMLRec), our goal is to promote rapid evaluation and inspire innovative research in this continuously evolving field. We hope that our open-source benchmark can facilitate swift, equitable evaluations, while also fostering further breakthrough research in the domains of MTL, MSL, and MTMSL.
Transformer Based Bayesian Network Embedding for Efficient Multiple Probabilistic Inferences
Authors: Kun Yue, Zhiwei Qi, Liang Duan
Venue: CIKM
Year: 2024

Bayesian network (BN) is a directed acyclic graph (DAG) representing the dependence relations among random variables with conditional probability tables (CPTs). The efficiency and accuracy of multiple probabilistic inferences in BN could not be guaranteed by most of the existing approximate inference methods. To address this issue, we propose the methods of Transformer based BN embedding (TBNE) and TBNE based probabilistic inferences. Specifically, we first adopt mutual information to measure the weight of parent-child node pairs and transform BN into multiple bidirectional weighted graphs (BWGs), while preserving the DAG and CPTs. Then, we redesign the Transformer model by incorporating the node importance and shortest path encodings, and extend the self-attention module of Transformer to generate node embeddings of BWGs. Following, we cast the probabilistic inference as the decoding information maximization of the path in BN from the perspective of information theory. Finally, we give an efficient algorithm for multiple probabilistic inferences by calculating embedding similarities between evidence and query nodes in BN. Experimental results show that our inference method is more efficient than the state-of-the-art competitors by several orders of magnitude while maintaining almost the same results.
Using Distributed Ledgers To Build Knowledge Graphs For Decentralized Computing Ecosystems
Authors: Tarek Zaarour, Ahmed Khalid, Preeja Pradeep, Ahmed H. Zahran
Venue: CIKM
Year: 2024

Knowledge graphs have proven vital for efficient data management, enhanced search capabilities, and improved decision-making in various information technology domains. However, constructing reliable knowledge graphs in decentralized ecosystems, with distributed autonomous actors, poses significant challenges related to asynchronous transmission, out-of-order knowledge-sharing, device heterogeneity, and trust issues. These challenges are also present in resource orchestration within multi-cloud edge ecosystems where multiple stakeholders must collaborate and share information to enable next-gen smart applications. In this paper, we propose a novel system design that utilizes Distributed Ledger Technology to build knowledge graphs. This approach ensures consistent and trustworthy knowledge sharing among orchestrators in a cloud-edge continuum. Our solution accommodates diverse requirements of both cloud and edge servers, allowing clients to construct complete historic graphs or build filtered sub-graphs. We deploy our solution in a multi-cloud edge environment and construct knowledge graphs representing the system state, including clusters, servers, microservices, and various resources. We validate the feasibility and performance of our solution through a real-world deployment and experiments in a smart shopping use case. Results demonstrate that the proposed solution achieves the claimed benefits with minimal or acceptable delays in comparison to traditional event streaming services.
Causal Discovery from Heterogenous Multivariate Time Series
Authors: Lei Zan
Venue: CIKM
Year: 2024

This paper explores three key aspects of causal discovery from heterogeneous time series. First, it introduces a method that uses (conditional) mutual information to determine (conditional) independence among diverse qualitative and quantitative variables, alongside a novel local permutation test. Next, the paper presents a new algorithm for identifying event-based causal relations in threshold-based IT systems for root cause analysis. This method is effective when root causes are not causally related, and an extension involving agent intervention is proposed to address this limitation. Both the algorithm and its extension utilize causal discovery from offline data and subgraph traversal for new anomalies in online data. Finally, the paper addresses time series with multiple piecewise consistent regimes, each having distinct causal mechanisms. The proposed method segments the time series into appropriate regimes and identifies the correct window causal graph, capturing both instantaneous and lagged connections within each regime. Experiments with synthetic data confirm the effectiveness of the proposed methods.