Anime, as a popular medium, has attracted hundreds of millions audience, especially the young followers. In recent years, various anime-related AI tasks like anime character classification (ACC), retrieval (ACR), tag-prediction (ACTP), question answering (ACQA), and generation (ACG) have been proposed to meet the requirements of various applications. However, there is still a lack of large-scale datasets for these tasks. Such a situation is definitely not beneficial to anime-related academic research and industrial applications. In this paper, to boost anime-related AI technical research and application development, we present Anime-2026, a new and large-scale anime character dataset, which can support various anime-related AI tasks, including ACC, ACR, ACTP, ACQA and ACG etc. Anime-2026 consists of 1.5M anime character images, 14k different characters, 16k unique semantic keyword tags, 10k question-answer pairs, and 4k manually designed text queries by crowdsourcing for the ACR and ACG tasks. Furthermore, to assess the dataset, we re-implement a number of generic and anime-specific AI baseline models, and conduct extensive experiments to evaluate these models on Anime-2026. In summary, as a general benchmark dataset, Anime-2026 provides the largest free anime character resource to support future anime-related AI research and development. We expect that Anime-2026 will promote the R&D of new and more advanced models and methods of various anime-related AI tasks. The dataset is available on https://huggingface.co/datasets/miaojiemiao/Anime-2026.

Watch time prediction (WTP) has emerged as a pivotal task in short video recommendation systems, designed to quantify user engagement through continuous interaction modeling. Predicting users' watch times on videos often encounters fundamental challenges, including wide value ranges and imbalanced data distributions, which can lead to significant estimation bias when directly applying regression techniques. Recent studies have attempted to address these issues by converting the continuous watch time estimation into an ordinal regression task. While these methods demonstrate partial effectiveness, they exhibit notable limitations: (1) The discretization process frequently relies on bucket partitioning, inherently reducing prediction flexibility and accuracy. (2) The interdependencies among different partition intervals remain underutilized, missing opportunities for effective error correction.

This article proposes a new method for causal discovery based on a novel dependence measurement criterion, namely, Multi-level Wavelet Mapping Correlation (MWMC). MWMC captures nonlinear dependencies between variables by measuring their correlations across multiple levels of wavelet mappings. From a theoretical perspective, we show that the empirical estimate of MWMC converges exponentially fast to its population quantity. Under the null hypothesis of independence, we further design a permutation-based independence testing procedure, termed the Wavelet Independence Test (WIT), built upon MWMC. We prove that WIT not only effectively controls the Type I error rate (false positives), but also guarantees that the Type II error rate (false negatives) is upper bounded by \(\mathcal{O}(n^{-1})\) , where \( n \) denotes the sample size, even with a finite number of permutations. Building on these theoretical guarantees, we derive a causal discovery method by integrating MWMC-based WIT into standard causal discovery pipelines. Extensive experiments on (conditional) independence testing and causal discovery using both synthetic and real-world datasets with varying sample sizes demonstrate that our approach consistently outperforms existing independence testing and causal discovery methods in terms of reduced Type II error rates and statistically validated performance improvements. Impact Statement —Causal discovery is a fundamental task in knowledge discovery, aiming to uncover the underlying data-generating mechanisms in order to support more accurate and interpretable predictions. Statistical independence tests and conditional independence (CI) tests have long served as core tools in this area. To improve the reliability of independence testing, we propose a novel test, WIT, which achieves lower Type II error rates in 19 out of 25 distinct experimental scenarios involving diverse data distributions, compared to 15 out of 25 for the strongest existing baseline. We further apply WIT to CI testing and causal discovery, and extensive empirical results show that it consistently improves the performance of multiple causal discovery algorithms across a range of experimental settings.

Sea surface temperature (SST) prediction is a critical task in ocean science, supporting various applications, such as weather forecasting, fisheries management, and storm tracking. While existing data-driven methods have demonstrated significant success, they often neglect to leverage the rich domain knowledge accumulated over the past decades, limiting further advancements in prediction accuracy. The recent emergence of large language models (LLMs) has highlighted the potential of integrating domain knowledge for downstream tasks. However, the application of LLMs to SST prediction remains under explored, primarily due to the challenge of integrating ocean domain knowledge and numerical data. To address this issue, we propose Ocean Knowledge Graph-enhanced LLM (OKG-LLM), a novel framework for global SST prediction. To the best of our knowledge, this work presents the first systematic effort to construct an Ocean Knowledge Graph (OKG) specifically designed to represent diverse ocean knowledge for SST prediction. We then develop a graph embedding network to learn the comprehensive semantic and structural knowledge within the OKG, capturing both the unique characteristics of individual sea regions and the complex correlations between them. Finally, we align and fuse the learned knowledge with fine-grained numerical SST data and leverage a pre-trained LLM to model SST patterns for accurate prediction. Extensive experiments on the real-world dataset demonstrate that OKG-LLM consistently outperforms state-of-the-art methods, showcasing its effectiveness, robustness, and potential to advance SST prediction. The codes are available in the online repository.

Temporal causal discovery aims to uncover causal relations in time series data. Current deep learning-based methods usually analyze the parameters of some components of the trained models, which is an incomplete mapping process from the model parameters to the causality and fails to investigate the other components. To address this, this paper presents an interpretable transformer-based causal discovery model termed CausalFormer, which consists of: 1) the causality-aware transformer which learns the causal representation with the multi-kernel causal convolution under the temporal priority constraint, and 2) the decomposition-based causality detector which identifies causality by interpreting the global structure of the trained transformer with the regression relevance propagation.

Determining conditional independence (CI) relationships between random variables is a fundamental yet challenging task in machine learning and statistics, especially in high-dimensional settings. Existing generative model-based CI testing methods, such as those utilizing generative adversarial networks (GANs), often struggle with undesirable modeling of conditional distributions and training instability, resulting in subpar performance. To address these issues, we propose a novel CI testing method via score-based generative modeling, which achieves precise Type I error control and strong testing power. Concretely, we first employ a sliced conditional score matching scheme to accurately estimate conditional score and use Langevin dynamics conditional sampling to generate null hypothesis samples, ensuring precise Type I error control. Then, we incorporate a goodness-of-fit stage into the method to verify generated samples and enhance interpretability in practice. We theoretically establish the error bound of conditional distributions modeled by score-based generative models and prove the validity of our CI tests. Extensive experiments on both synthetic and real-world datasets show that our method significantly outperforms existing state-of-the-art methods, providing a promising way to revitalize generative model-based CI testing.

Accurate prediction of Sea Surface Temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SST usually shows diverse SST patterns in different sea areas due to the changes of temperature zones and the dynamics of ocean currents. However, existing studies on SST prediction often focus on small-area predictions and lack the consideration of diverse SST patterns. Furthermore, SST shows an annual periodicity, but the periodicity is not strictly adherent to an annual cycle. Existing SST prediction methods struggle to adapt to this non-strict periodicity. To address these two issues, we proposed the Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation (RGCN-PSA) model which is equipped with the Cross-Region Graph Convolutional Network module and the Periodicity Shift Adaption module. The Cross-Region Graph Convolutional Network module enhances wide-area SST prediction by learning and incorporating diverse SST patterns. Meanwhile, the periodicity Shift Adaptation module accounts for the annual periodicity and enable the model to adapt to the possible temporal shift automatically. We conduct experiments on two real-world SST datasets, and the results demonstrate that our RGCN-PSA model obviously outperforms baseline models in terms of prediction accuracy. The code of RGCN-PSA model is available at https://github.com/ADMIS-TONGJI/RGCN-PSA/ .

With the growing diversity of data formats on social media, such as text, images, and videos, there is a growing need to analyze sentiment from multiple modalities. Multimodal sentiment detection, which aims to identify users’ sentiment by jointly modeling information from different modalities, has thus attracted increasing attention. However, most existing multimodal sentiment detection methods fuse multimodal information directly after the unimodal encoding and overlook the modality consistency of multimodal vector spaces before the fusion, which may damage the accuracy of multimodal sentiment detection. To address this issue, we propose a contrastive learning-based multimodal sentiment detection model termed EPMC which can map the representations of different modalities into a unified semantic space before fusion. EPMC operates in two stages, i.e., pre-training stage and fine-tuning stage. At the pre-training stage, we designed a cross-modal transformation module to map different modalities into a unified feature space. Meanwhile, to further capture the relationship between the cross-modal transformation vectors and the unimodal encoding vectors, we propose a multimodal consistency contrastive learning task that helps the model discern and amplify the cross-modal similarity between different modalities, thereby learning more discriminative features for sentiment detection. At the fine-tuning stage, EPMC is iteratively refined using the learned multimodal representation and guided by the cross-entropy loss. Extensive experiments conducted on three public multimodal datasets validate the effectiveness of EPMC model. The official implementation of EPMC is released at https://github.com/ADMIS-TONGJI/EPMC .

Given a specific region, crime prediction aims to predict the occurrence of various crime events within a certain period of time in future, which is of high significance for guaranteeing urban safety. In practice, crime events are usually affected by a variety of factors from different views, e.g., the attributes of the region, the correlations between different regions, and the correlations between different categories of crime events. Moreover, these correlations are dynamically changing over time, which makes it difficult to learn the regularity and patterns in crime data for achieving accurate prediction. To address this issue, we proposed a new M ulti- V iew S patial- T emporal (MVST) model for fine-grained crime prediction. MVST model first builds a static region graph to capture the similarity between regions in terms of region attributes such as census records and economy statistics, and creates a time-dependent graph to capture the dynamic correlations between regions based on human mobility data. Meanwhile, both static and dynamic graphs are created to capture the correlations between different categories of crime events. After that, those graphs created from different views are fused together with a multi-view graph fusion module to achieve crime prediction with fine-grained time granularities, e.g., 4 hours and 12 hours. According to the experiments on two real crime datasets, our MVST model obviously outperforms existing crime prediction methods. The code of MVST model is available at https://github.com/weichang811/MVST .

Inductive relation prediction, an important task for knowledge graph completion, is to predict the relations between entities that are unseen at the training stage. The latest methods use Pre-Trained Language Models (PLMs) to encode the paths between the head entity and tail entity and achieve state-of-the-art prediction performance. However, these methods cannot handle no-path scenarios well and lack the capability to learn comprehensive relation representations for distinguishing different relations. To tackle this issue, we propose a novel R elation- a ware k nowledg e r easoning model entitled Raker, which introduces an adaptive reasoning information extraction method to identify relation-aware reasoning neighbors of entities in the target triple to handle no-path scenarios and enables the PLM to better distinguish different relations via the relation-specific soft prompting. Raker is evaluated on three public datasets and achieves SOTA performance in inductive relation prediction when compared with the baseline methods. Notably, the absolute improvement of Raker is even more than 5% on the FB15k-237 dataset in the inductive setting. Moreover, Raker also demonstrates the superiority in transductive, few-shot, and unseen relation settings. The code of Raker is available at https://github.com/ADMIS-TONGJI/Raker .

With the rapid amassing of spatial-temporal (ST) ocean data, many spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, including climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data presents some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models on ST ocean data. To the best of our knowledge, a comprehensive survey of existing studies remains missing in the literature, which hinders not only computer scientists from identifying the research issues in ocean data mining but also ocean scientists to apply advanced STDM techniques. In this article, we provide a comprehensive survey of existing STDM studies for ocean science. Concretely, we first review the widely used ST ocean datasets and highlight their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean science into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are discussed. This survey can help scientists from both computer science and ocean science better understand the fundamental concepts, key techniques, and open challenges of STDM for ocean science.

Accurate spatial-temporal (ST) traffic prediction plays an essential role in intelligent transportation systems. Existing advanced traffic prediction methods typically utilize spatial-temporal graph neural networks (STGNNs) to capture the ST correlations and achieve excellent prediction performance. However, our experimental investigation reveals that existing static and dynamic graph-based STGNNs still incur excessive noise and redundancy, and fail to discover robust and reliable ST correlations in traffic networks. Moreover, most methods cannot explain the underlying reasons behind the ST correlations. To solve these problems, we propose a novel S patial- T emporal G raph M odeling framework via A daptive contrastive learning (ST-GMA). Firstly, we design a robust augmentation learning module to generate high-level and robust data augmentations via a self-supervised task for modeling reliable correlations. Then, we develop an adaptive contrastive learning module to update correlation graphs by effectively selecting positive and negative augmentations, reducing redundant calculations, and providing insights into the correlation changes. Finally, ST-GMA integrates the generated correlation graphs with ST convolution blocks to conduct traffic prediction tasks. Experimental results on five real-world datasets demonstrate that ST-GMA not only achieves significant prediction performance compared with state-of-the-art methods but also exhibits a new perspective on the interpretability of correlation changes.

Temporal causal discovery is a crucial task aimed at uncovering the causal relations within time series data. The latest temporal causal discovery methods usually train deep learning models on prediction tasks to uncover the causality between time series. They capture causal relations by analyzing the parameters of some components of the trained models, e.g., attention weights and convolution weights. However, this is an incomplete mapping process from the model parameters to the causality and fails to investigate the other components, e.g., fully connected layers and activation functions, that are also significant for causal discovery. To facilitate the utilization of the whole deep learning models in temporal causal discovery, we proposed an interpretable transformer-based causal discovery model termed CausalFormer, which consists of the causality-aware transformer and the decomposition-based causality detector. The causality-aware transformer learns the causal representation of time series data using a prediction task with the designed multi-kernel causal convolution which aggregates each input time series along the temporal dimension under the temporal priority constraint. Then, the decomposition-based causality detector interprets the global structure of the trained causality-aware transformer with the proposed regression relevance propagation to identify potential causal relations and finally construct the causal graph. Experiments on synthetic, simulated, and real datasets demonstrate the state-of-the-art performance of CausalFormer on discovering temporal causality.

Debt collection is utilized for risk control after credit card delinquency. The existing rule-based method tends to be myopic and non-adaptive due to the delayed feedback. Reinforcement learning (RL) has an inherent advantage in dealing with such task and can learn policies end-to-end. However, employing RL here remains difficult because of different interaction processes from standard RL and the notorious problem of optimistic estimations in the offline setting. To tackle these challenges, we first propose an Alternating Q-Learning (AQL) framework to adapt debt collection processes to comparable procedures in RL. Based on AQL, we further develop an Adversarial Conservative Alternating Q-Learning (ACAQL) to address the issue of overoptimistic estimations. Specifically, adversarial conservative value regularization is proposed to balance optimism and conservatism on Q-values of out-of-distribution actions. Furthermore, ACAQL utilizes the counterfactual action stitching to mitigate the overestimation by enhancing behavior data. Finally, we evaluate ACAQL on a real-world dataset created from Bank of Shanghai. Offline experimental results show that our approach outperforms state-of-the-art methods and effectively alleviates the optimistic estimation issue. Moreover, we conduct online A/B tests on the bank, and ACAQL achieves at least a$\emph {6\%}$improvement of the debt recovery rate, which yields tangible economic benefits.

Traditional graph classification requires large amounts of labeled data, which is expensive and time-consuming to acquire, especially in some special scenarios that domain knowledge is indispensable for labeling graphs. Observing that some key subgraphs can determine the properties of graphs (e.g. the toxicity of drug molecules depend on some toxic functional groups), in this paper we explore to classify graphs using unlabeled graphs plus a small number of key subgraphs for each class, which is called weakly-supervised graph classification. To this end, we develop the WeGraph method, where the graph classifier is trained with subgraph-based self-supervised learning and divergence- minimization based fine-tuning. Moreover, we design a key subgraph extraction algorithm to iteratively extract and update the key subgraphs, which makes the training process a closed loop. We conduct extensive experiments on different types of graph datasets to evaluate the effectiveness of WeGraph. Experimental results show that WeGraph can achieve high performance even when only one key subgraph is provided for each class.

Multivariate Time Series Anomaly Detection (MTS-AD) is crucial for the effective management and maintenance of devices in complex systems, such as server clusters, spacecrafts, and financial systems, and so on. However, upgrade or cross-platform deployment of these devices will introduce the issue of cross-domain distribution shift, which leads to the prototypical problem of domain adaptation for MTS-AD. Compared with general domain adaptation problems, MTS-AD domain adaptation presents two peculiar challenges: (1) the dimensions of data from the source domain and the target domain are usually different, so alignment without losing any information is necessary; and (2) the association between different variates plays a vital role in the MTS-AD task, which is overlooked by traditional domain adaptation approaches. Aiming at addressing the above issues, we propose a Variate Associated Domain Adaptation Method Combined with a Graph Deviation Network (VANDA) for MTS-AD, which includes two major contributions. First, we characterize the intra-domain variate associations of the source domain by a graph deviation network (GDN), which can share parameters across domains without dimension alignment. Second, we propose a sliding similarity to measure the inter-domain variate associations and perform joint training by minimizing the optimal transport distance between source and target data for transferring variate associations across domains. VANDA achieves domain adaptation by transferring both variate associations and GDN parameters from the source domain to the target domain. We construct two pairs of MTS-AD datasets from existing MTS-AD data and combine three domain adaptation strategies with six MTS-AD backbones as the benchmark methods for experimental evaluation and comparison. Extensive experiments demonstrate the effectiveness of our approach, which outperforms the benchmark methods, and significantly improves the AD performance of the target domain by effectively utilizing the source domain knowledge.

Incremental maximal clique enumeration (IMCE), which maintains maximal cliques in dynamic graphs, is a fundamental problem in graph analysis. A maximal clique has a solid descriptive power of dense structures in graphs. Real-world graph data is often large and dynamic. Studies on IMCE face significant challenges in the efficiency of incremental batch computation and hybrid edge changes. Moreover, with growing graph sizes, new requirements occur on indexing global maximal cliques and obtaining maximal cliques under specific vertex scope constraints. This work presents a new data structure SOMEi to maintain intermediate maximal cliques during construction. SOMEi serves as a space-efficient index to retrieve scope-constrained maximal cliques on the fly. Based on SOMEi, we design a procedure-oriented IMCE algorithm to deal with hybrid edge changes within a unified algorithm framework. In particular, the algorithm is able to process a large batch of edge changes and significantly improve the average processing time of a single edge change through an efficient pruning strategy. Experimental results on real and synthetic graph data demonstrate that the proposed algorithm outperforms all the baselines and achieves good efficiency through pruning.

Graph neural networks (GNNs) are prone to catastrophic forgetting of past experience in continuous learning scenarios. In this work, we propose a novel method for class-incremental graph learning (CGL) by class prototype construction and augmentation, which can effectively overcome catastrophic forgetting and requires no storage of exemplars (i.e., data-free). Concretely, on the one hand, we construct class prototypes in the embedding space that contain rich topological information of nodes or graphs to represent past data, which are then used for future learning. On the other hand, to boost the adaptability of the model to new classes, we employ class prototype augmentation (PA) to create virtual classes by combining current prototypes. Theoretically, we show that PA can promote the model's adaptation to new data and reduce the inconsistency of old prototypes in the embedding space, therefore further mitigate catastrophic forgetting. Extensive experiments on both node and graph classification datasets show that our method significantly outperforms the existing methods in reducing catastrophic forgetting, and beats the existing methods in most cases in terms of classification accuracy.

Discovering causal relationships from observational data is a challenging topic in artificial intelligence. Recent works formulate causal discovery as a continuous optimization problem with a differentiable acyclic constraint. Although these methods have achieved considerable performance improvement, they have two drawbacks: 1) they require a relatively large number of training samples; and 2) their performance will substantially deteriorate when facing heterogeneous noise. To address these problems, we first propose a low-order conditional independence (CI) constraint for the continuous optimization problem, and then design a soft version of the constraint by transforming it to a regularization term in the loss function of the continuous optimization problem. We show the convergence of continuous optimization with our constraint under some mild conditions, and the consistency of causal structure learning with the CI regularization. Extensive experiments on both synthetic and real-world datasets show that with our CI constraint or regularization, existing continuous optimization methods can achieve considerable performance improvement of causal discovery, especially when sample size is small.

In addition to relevance, diversity is an important yet less studied performance metric of cross-modal image retrieval systems, which is critical to user experience. Existing solutions for diversity-aware image retrieval either explicitly post-process the raw retrieval results from standard retrieval systems or try to learn multi-vector representations of images to represent their diverse semantics. However, neither of them is good enough to balance relevance and diversity. On the one hand, standard retrieval systems are usually biased to common semantics and seldom exploit diversity-aware regularization in training, which makes it difficult to promote diversity by post-processing. On the other hand, multi-vector representation methods are not guaranteed to learn robust multiple projections. As a result, irrelevant images and images of rare or unique semantics may be projected inappropriately, which degrades the relevance and diversity of the results generated by some typical algorithms like top-k. To cope with these problems, this paper presents a new method called CoLT that tries to generate much more representative and robust representations for accurately classifying images. Specifically, CoLT first extracts semantics-aware image features by enhancing the preliminary representations of an existing one-to-one cross-modal system with semantics-aware contrastive learning. Then, a transformer-based token classifier is developed to subsume all the features into their corresponding categories. Finally, a post-processing algorithm is designed to retrieve images from each category to form the final retrieval result. Extensive experiments on two real-world datasets Div400 and Div150Cred show that CoLT can effectively boost diversity, and outperforms the existing methods as a whole (with a higher F1 score).

The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans’ ability of learning to learn and intelligently transfers the learned generic object knowledge from the common heavy-tailed to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [ 58 , 59 , 74 , 78 ] that systematically study and compare them as the groups of fine-tuning/transfer learning and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented.

Sea surface temperature (SST) is one critical parameter of global climate change, and accurate SST prediction is important to various applications, e.g., weather forecasting, fishing directions, and disaster warnings. The global ocean system is unified and complex, and the SST patterns in different oceanic regions are highly diverse and correlated. However, existing data-driven SST prediction methods mainly consider the local patterns within a certain oceanic region, e.g., El Nino region and the Black sea. It is challenging but necessary to model the global SST correlations rather than that in a specific region to enhance the prediction accuracy of SST. In this work, we proposed a new method called Hierarchical Graph Recurrent Network (HiGRN) to address the issue. First, to learn the dynamic and diverse local SST patterns of specific locations, we design an adaptive node embedding with self-learned parameters to learn various SST patterns. Then we develop a hierarchical cluster generator to aggregate the locations with similar patterns into regional clusters and utilize a graph convolution network to learn the spatial correlations among these clusters. Finally, we introduce a multi-level attention mechanism to fuse the local patterns and regional correlations, and the output is fed into a recurrent network to achieve SST predictions. Extensive experiments on two real-world datasets show that our method largely outperforms the state-of-the-art SST prediction methods. The source code is available at https://github.com/Neoyanghc/HiGRN .

Recently, many regression-based conditional independence (CI) test methods have been proposed to solve the problem of causal discovery. These methods provide alternatives to test CI of x,y given Z by first removing the information of the controlling set Z from x and y , and then testing the independence between the two residuals R x,Z and R y,Z . When the residuals are linearly uncorrelated, the independence test between them is nontrivial. With the ability to calculate inner product in high-dimensional space, kernel-based methods are usually used to achieve this goal, but they are considerably time-consuming. In this paper, we test the independence between two linear combinations under linear structural equation model. We show that the dependence between the two residuals can be captured by the difference between the similarity of R x,Z and R y,Z and that of R x,Z and R r ( R r is an independent copy of R y,Z ) in high-dimensional space. With this result, we provide a new way to test CI based on the similarity between residuals, which is called SCIT — the abbreviation of Similarity-based CI Testing. Furthermore, we develop two versions of the proposal, called Kernel-SCIT and Neural-SCIT, respectively. Kernel-SCIT calculates the similarity by using kernel functions, while Neural-SCIT approximates the upper bound of the similarity by using deep neural networks. In both algorithms, random permutation tests are performed to control Type I error rate. The proposed tests are evaluated on (conditional) independence test and causal discovery with both synthetic and real datasets. Experimental results show that Kernel-SCIT is simpler yet more efficient and effective than the typical existing kernel-based methods HSIC and KCIT in the cases of small sample size, and Neural-SCIT can significantly boost the performance of CI testing when sufficient samples are available. The source code is available at https://github.com/xyw5vplus1/SCIT .

Cross-view geo-localization is to match scene images (e.g. ground-view images) with geo-tagged aerial images, which is crucial to a wide range of applications such as autonomous driving and street view navigation. Existing methods can neither address the perspective difference well nor effectively capture the scene information. In this work, we propose a Geometric and Scene Information Fusion (GSIF) model for more accurate cross-view geo-localization. GSIF first learns the geometric information of scene images and aerial images via log-polar transformation and spatial-attention aggregation to alleviate the perspective difference. Then, it mines the scene information of scene images via Sky View Factor (SVF) extraction. Finally, both geometric information and scene information are fused for image matching, and a balanced loss function is introduced to boost the matching accuracy. Experimental results on two real datasets show that our model can significantly outperforms the existing methods.

Nested Name Entity Recognition is to identify the entities with nested relationships from sentences, which has various applications ranging from relation extraction to semantic understanding. However, existing methods have two drawbacks, i.e., 1) error propagation when identifying entities at different nesting levels and 2) unable to uncover and utilize the complex correlations between the inner and outer entities. To address these two defects, we propose a bidirectional hierarchical(BidH) model for nested name entity recognition. BidH consists of a forward module and a backward module, where the former first extracts the inner entities and then extracts the outer ones, while the latter extracts the entities in the opposite direction. Furthermore, we design an entity masked self attention mechanism to combine the two modules by fusing their predictions and hidden states layer by layer. BidH can effectively deal with error propagation and exploit the correlations between entities at different nesting levels to improve the recognition accuracy. Experiments on the GENIA dataset show that BidH outperforms the state-of-the-art nested named entity recognition models in terms of F1 score.

Several visual graph query interfaces (a.k.a gui) expose a set of canned patterns (i.e., small subgraph patterns) to expedite subgraph query formulation by enabling pattern-at-a-time construction. Unfortunately, manual generation of canned patterns is not only labour intensive but also may lack diversity to support efficient visual formulation of a wide range of subgraph queries. Recent efforts have taken a data-driven approach to select high-quality canned patterns for a gui automatically from the underlying graph database. However, as the underlying database evolves, these selected patterns may become stale and adversely impact efficient query formulation. In this paper, we present a novel framework called Midas for efficient and effective maintenance of the canned patterns as the database evolves. Specifically, it adopts a selective maintenance strategy that guarantees progressive gain of coverage of the patterns without sacrificing their diversity and cognitive load. Experimental study with real-world datasets and visual graph interfaces demonstrates the effectiveness of Midas compared to static guis.

Privacy preserving is a paramount concern in publishing datasets that contain sensitive information. Preventing privacy disclosure and providing useful information to legitimate users for data analyzing/mining are conflicting goals. Randomized response is a class of techniques that perturbs each sensitive value in a certain way, so that personal privacy is protected while the large-trend of the entire dataset is still recoverable. However, existing randomized response techniques do not allow to flexibly configure the level of privacy protection, support only a few types of aggregate queries, and cannot achieve the best answer accuracy from perturbed data. These drawbacks impair the effectiveness of those techniques. This paper proposes a general framework based on randomized response techniques, which has good flexibility and extensibility, and can improve the effectiveness of randomized response methods. Our approach is validated by extensive experiments and comparison with existing randomized response and generalization methods.

In recent years, many methods have been developed for discovering causal relationships from observed data. However, as an important kind of causes existing in many causal systems, combined causes (e.g. multi-factor causes consisting of two or more component variables that individually might not be a cause) have not received enough attention. The existing approach includes both individual and combined variables in the causal discovery process using constraint-based methods, can neither distinguish a set of Markov equivalence classes nor identify a combined cause containing one (or more) individual cause(s), therefore can output only some combined causes, instead of all combined causes. In this paper, we first subsume all possible combined causes into three types and give them formal definitions, then extend the additive noise model (ANM) to infer combined causes. We show that if a candidate variable set X w.r.t. a target Y satisfies: (1) allowing ANM for only the forward direction X→Y, and (2) no disturbance variable is contained in X, i.e., removing any component of X will weaken the causal relationship between X and Y, then X forms a combined cause. Based on this finding, we develop an efficient method to discover combined causes. Furthermore, we also conduct extensive experiments to validate the proposed method on both synthetic and real-world data sets.

In the emerging business of food delivery, rider traffic accidents raise financial cost and social traffic burden. Although there has been much effort on traffic accident forecasting using temporal-spatial prediction models, none of the existing work studies the problem of detecting the takeaway rider accidents based on food delivery trajectory data. In this article, we aim to detect whether a takeaway rider meets an accident on a certain time period based on trajectories of food delivery and riders’ contextual information. The food delivery data has a heterogeneous information structure and carries contextual information such as weather and delivery history, and trajectory data are collected as a spatial-temporal sequence. In this article, we propose a TakeAway Rider Accident detection fusion network TARA-Net to jointly model these heterogeneous and spatial-temporal sequence data. We utilize the residual network to extract basic contextual information features and take advantage of a transformer encoder to capture trajectory features. These embedding features are concatenated into a pyramidal feed-forward neural network. We jointly train the above three components to combine the benefits of spatial-temporal trajectory data and sparse basic contextual data for early detecting traffic accidents. Furthermore, although traffic accidents rarely happen in food delivery, we propose a sampling mechanism to alleviate the imbalance of samples when training the model. We evaluate the model on a transportation mode classification dataset Geolife and a real-world Ele.me dataset with over 3 million riders. The experimental results show that the proposed model is superior to the state-of-the-art.

In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption. Homophily label propagation (i.e., two connected nodes share similar labels) in homogeneous graph (with same types of vertices and relations) has been extensively studied before. Unfortunately, real-life networks (e.g., social networks) are heterogeneous, they contain different types of vertices (e.g., users, images, and texts) and relations (e.g., friendships and co-tagging) and allow for each node to propagate both the same and opposite copy of labels to its neighbors. We propose a IC-partite label propagation model to handle the mystifying combination of heterogeneous nodes/relations and heterophily propagation. With this model, we develop a novel label inference algorithm framework with update rules in near-linear time complexity. Since real networks change overtime, we devise an incremental approach, which supports fast updates for both new data and evidence (e.g., ground truth labels) with guaranteed efficiency. We further provide a utility function to automatically determine whether an incremental or a re-modeling approach is favored. Extensive experiments on real datasets have verified the effectiveness and efficiency of our approach, and its superiority over the state-of-the-art label propagation methods.

Several commercial and academic frameworks for querying a large collection of small- or medium-sized data graphs (eg. chemical compounds) provide visual graph query interfaces (a.k.a GUI) to facilitate non-programmers to query these sources. However, construction of these visual interfaces is not data-driven. That is, it does not exploit the underlying data graphs to automatically generate the contents of various panels in a GUI. Such data-driven construction has several benefits such as facilitating efficient subgraph query formulation and portability of the interface across different application domains and sources. In this demonstration, we present a novel data-driven visual subgraph query interface construction engine called AURORA. Specifically, given a graph repository D containing a collection of small- or medium-sized data graphs, it automatically generates the GUI for D by populating various components of the interface. We demonstrate various innovative features of AURORA.

The paradigm of interleaving (i.e. blending) visual subgraph query formulation and processing by exploiting the latency offered by the GUI brings in several potential benefits such as superior system response time (SRT) and opportunities to enhance usability of graph databases. Recent efforts at implementing this paradigm are focused on subgraph isomorphism-based queries, which are often restrictive in many real-world graph applications. In this demonstration, we present a novel system called BOOMER to realize this paradigm on more generic but complex bounded 1-1 p-homomorphic(BPH) queries on large networks. Intuitively, a BPH query maps an edge of the query to bounded paths in the data graph. We demonstrate various innovative features of BOOMER, its flexibility, and its promising performance.

Novel road-network applications often recommend a moving object (e.g., a vehicle) about interesting services or tasks on its way to a destination. A taxi-sharing system, for instance, suggests a new passenger to a taxi while it is serving another one. The traveling cost is then shared among these passengers. A fundamental query is: given two nodes s and t, and an area A on road network graph , is there a “good” route (e.g., short enough path) P from s to t that crosses A in G? In a taxi-sharing system, s and t can be a taxi's current and destined locations, and A contains all the places to which a person waiting for a taxi is willing to walk. Answering this Route and Area Matching (ROAM) Query allows the application involved to recommend appropriate services to users efficiently. In this paper, we examine efficient ROAM query algorithms. Particularly, we develop solutions for finding a ρ-route, which is an s-t path that passes A, with a length of at most (1 + ρ) times the shortest distance between s and t. The existence of a ρ-route implies that a service or task located at A can be found for a given moving object m, and that m only deviates slightly from its current route. We present comprehensive studies on index-free and index-based algorithms for answering ROAM queries. Comprehensive experiments show that our algorithm runs up to 30 times faster than baseline algorithms.

Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. In this paper, we present two novel index structures called VACCINE and ADVISE to efficiently support exploratory subgraph search in a visual environment (VESS). VACCINE is an offline, feature-based index that stores rich information related to frequent and infrequent subgraphs in the underlying graph database and how they can be transformed from one subgraph to another. ADVISE, on the other hand, is an adaptive, compact, on-the-fly index instantiated during iterative visual formulation/reformulation of a subgraph query for exploratory search and records relevant information to efficiently support its repeated evaluation. These indexes engender more efficient and scalable visual exploratory subgraph search framework compared to a state-of-the-art technique.

Visual graph query interfaces (a.k.a gui ) widen the reach of graph querying frameworks across different users by enabling non-programmers to use them. Consequently, several commercial and academic frameworks for querying a large collection of small- or medium-sized data graphs (\textite.g., chemical compounds) provide such visual interfaces. Majority of these interfaces expose a fixed set ofcanned patterns (\textiti.e., small subgraph patterns) to expedite query formulation by enabling pattern-at-a-time in lieu of edge-at-a-time construction mode. Canned patterns to be displayed on a gui are typically selected manually based on domain knowledge. However, manual generation of canned patterns is labour intensive. Furthermore, these patterns may not sufficiently cover the underlying data graphs to expedite visual formulation of a wide range of subgraph queries. In this paper, we present a generic and extensible framework called Catapult to address these limitations. Catapult takes a data-driven approach toautomatically select canned patterns, thereby taking a concrete step towards the vision of data-driven construction of visual query interfaces. Specifically, it firstclusters the underlying data graphs based on their topological similarities and thensummarize each cluster to create acluster summary graph (csg ). The canned patterns within a user-specifiedpattern budget are then generated from these csg s by maximizingcoverage anddiversity, and minimizingcognitive load of the patterns. Experimental study with real-world datasets and visual graph interfaces demonstrates the superiority of Catapult compared to traditional techniques.

Privacy has received increasing concerns in publication of datasets that contain sensitive information. Preventing privacy disclosure and providing useful information to legitimate users for data mining are conflicting goals. Generalization and randomized response methods were proposed in database community to tackle this problem. However, both of them have postulated the same prior belief for all transactions, which might be wrong modeling and lead to privacy breach. Besides, generalization and randomized response methods usually require a privacy controlling parameter to control the tradeoff between privacy and data quality, which may put the data publishers in a dilemma. In this paper, a novel privacy preserving method for data publication is proposed based on conditional probability distribution and machine learning techniques, which can achieve different prior beliefs for different transactions. A basic cross sampling algorithm and a complete cross sampling algorithm are designed respectively for the settings of single sensitive attribute and multiple sensitive attributes, and an improved complete algorithm is developed by using Gibbs sampling, in order to enhance data utility when data are not sufficient. Our method can offer stronger privacy guarantee, while, as shown in the extensive experiments, retaining better data utility.

We investigate the relationship between conditional independence (CI) x ⫫ y | Z and the independence of two residuals x −E( x | Z )⫫ y −E( y | Z ), where x and y are two random variables and Z is a set of random variables. We show that if x , y , and Z are generated by following linear structural equation models and all external influences follow joint Gaussian distribution, then x ⫫ y | Z if and only if x −E( x | Z )⫫ y −E( y | Z ). That is, the test of x ⫫ y | Z can be relaxed to a simpler unconditional independence test of x −E( x | Z )⫫ y −E( y | Z ). Furthermore, testing x −E( x | Z )⫫ y −E( y | Z ) can be simplified by testing x −E( x | Z )⫫ y or y −E( y | Z )⫫ x . On the other side, if all these external influences follow non-Gaussian distributions and the model satisfies structural faithfulness condition, then we have x ⫫ y | Z ⇔ x −E( x | Z )⫫ y −E( y | Z ). We apply the results above to the causal discovery problem, where the causal directions are generally determined by a set of V -structures and their consistent propagations, so CI test-based methods can return a set of Markov equivalence classes. We show that in the linear non-Gaussian context, in many cases x −E( x | Z )⫫ z or y −E( y | Z )⫫ z (∀ z ∈ Z and Z is a minimal d -separator) is satisfied when x −E( x | Z )⫫ y −E( y | Z ), which implies z causes x (or y ) if z directly connects to x (or y ). Therefore, we conclude that CIs have useful information for distinguishing Markov equivalence classes. In summary, comparing with the existing discretization-based and kernel-based CI testing methods, the proposed method provides a simpler way to measure CI, which needs only one unconditional independence test and two regression operations. When being applied to causal discovery, it can find more causal relationships, which is extensively validated by experiments.

The widespread location-aware applications produce a vast amount of spatio-textual data that contains both spatial and textual attributes. To make use of this enriched information for users to describe their preferences for travel routes, we propose a Bounded-Cost Informative Route (BCIR) query to retrieve the routes that are the most textually relevant to the user-specified query keywords subject to a travel cost constraint. BCIR query is particularly helpful for tourists and city explorers to plan their travel routes. We will show that BCIR query is an NP-hard problem. To answer BCIR query efficiently, we propose an exact solution with effective pruning techniques and an approximate solution with performance guarantee. Extensive experiments over real data sets demonstrate that the proposed solutions achieve the expected performance.

Visual graph query interfaces (a.k.a GUI) make it easy for non-expert users to query graphs. Recent research has laid out and implemented a vision of a novel subgraph query processing paradigm where the latency offered by the GUI is exploited to blend visual query construction and processing by generating and refining candidate result matches iteratively during query formulation. This paradigm brings in several potential benefits such as superior system response time (srt) and opportunities to enhance usability of graph databases. However, these early efforts focused on subgraph isomorphism-based graph queries where blending is performed by iterative edge-to-edge mapping. In this paper, we explore how this vision can be realized for more generic but complex 1-1 p-homomorphic p-hom) queries introduced by Fan et al. A 1-1 p-hom query maps an edge of the query to paths in the data graph. We present a novel framework called BOOMER for blending bounded 1-1 p-hom (bph ) queries, a variant of 1-1 p-hom where the length of the path is bounded instead of arbitrary length. Our framework is based on a novel online , adaptive indexing scheme called cap index. We present two strategies for CAP index construction, immediate and deferment-based, and show how they can be utilized to facilitate judicious interleaving of visual bph query formulation and query processing. BOOMER is also amenable to modifications to a bph query during visual formulation. Experiments on real-world datasets demonstrate both efficiency and effectiveness of Boomer for realizing the visual querying paradigm on an important type of graph query.

Machine learning and artificial intelligence techniques have been applied to construct online portfolio selection strategies recently. A popular and state-of-the-art family of strategies is to explore the reversion phenomenon through online learning algorithms and statistical prediction models. Despite gaining promising results on some benchmark datasets, these strategies often adopt a single model based on a selection criterion (e.g., breakdown point) for predicting future price. However, such model selection is often unstable and may cause unnecessarily high variability in the final estimation, leading to poor prediction performance in real datasets and thus non-optimal portfolios. To overcome the drawbacks, in this article, we propose to exploit the reversion phenomenon by using combination forecasting estimators and design a novel online portfolio selection strategy, named Combination Forecasting Reversion (CFR), which outputs optimal portfolios based on the improved reversion estimator. We further present two efficient CFR implementations based on online Newton step (ONS) and online gradient descent (OGD) algorithms, respectively, and theoretically analyze their regret bounds, which guarantee that the online CFR model performs as well as the best CFR model in hindsight. We evaluate the proposed algorithms on various real markets with extensive experiments. Empirical results show that CFR can effectively overcome the drawbacks of existing reversion strategies and achieve the state-of-the-art performance.

Recently, exploratory search has received much attention in information retrieval and database fields. This search paradigm assists users who do not have a clear search intent and are unfamiliar with the underlying data space. Specifically, query formulation evolves iteratively as the user becomes more familiar with the content. Despite its growing importance, exploratory search on graph-structured data has received little attention in the literature. We demonstrate a system called picasso to realize exploratory sub-structure search on a graph database containing a set of small or medium-sized data graphs. picasso embodies several novel features such as progressive ( i.e. , iterative) formulation of queries visually and incremental processing, multi-stream results exploration wall to visualize, explore, and analyze search results to identify possible search directions.

The widespread location-aware applications produce a vast amount of spatio-textual data that contains both spatial and textual attributes. To make use of this enriched information for users to describe their preferences for travel routes, we propose a Bounded-Cost Informative Route (BCIR) query to retrieve the routes that are the most textually relevant to the user-specified query keywords subject to a travel cost constraint. BCIR query is particularly helpful for tourists and city explorers to plan their travel routes. We will show that BCIR query is an NP-hard problem. To answer BCIR query efficiently, we propose an exact solution with effective pruning techniques and two approximate solutions with performance guarantees. Extensive experiments over real data sets demonstrate that the proposed solutions achieve the expected performance.

Graph coloring is a fundamental NP-hard problem in graph theory. It has a wide range of real applications, such as Operations Research, Communication Network, Computational Biology and Compiler Optimization. Notable efforts have been spent on designing its approximation algorithms. Halldrsson proposed the algorithm (denoted as SampleIS) with the current best known approximation ratio. However, its time complexity is O(|G|3), where |G| is the number of vertices of a graph G. It is clear that SampleIS is not practical for large graphs. In this paper, we propose a practical vertex-cut based coloring technique (VColor) for coloring large graphs. First, we partition G into k connected components (CCs) of a small size s by removing a vertex-cut component (VCC). For each CC, we apply our novel coloring algorithm, based on maximal independent set enumeration. The approximation ratio and the time complexity for coloring the k CCs are log s + 1 and O(ks23s/3), respectively, whereas those of SampleIS are ks(log log ks)2/ log3 ks and O(k3s3). For the VCC, we simply apply SampleIS. To combine the colorings of the CCs and the VCC, we propose a maximum matching based algorithm. Second, in the context of a database of graphs, users may color many graphs. We propose an optimization technique, inspired by multi-query optimization, for coloring a set of graphs. We design a VP hierarchy (VPH) to represent the common subgraphs as the common CCs. Third, we propose techniques for determining the optimal values of the parameters of VColor. Our extensive experimental evaluation on real-world graphs confirms the efficiency and/or effectiveness of our proposed techniques. In particular, VColor is more than 500 times faster than SampleIS, and the number of colors used are comparable on real graphs Yeast and LS.

Many graphs in real-world applications, such as telecommunications networks, social-interaction graphs and co-authorship graphs, contain temporal information. However, existing graph mining algorithms fail to exploit these temporal information and the resulting subgraph patterns do not contain any temporal attribute. In this paper, we study the problem of mining a set of diversified temporal subgraph patterns from a temporal graph, where each subgraph is associated with the time interval that the pattern spans. This problem motivates important applications such as finding social trends in social networks, or detecting temporal hotspots in telecommunications networks. We propose a divide-and-conquer algorithm along with effective pruning techniques, and our approach runs 2 to 3 orders of magnitude faster than a baseline algorithm and obtains high-quality temporal subgraph patterns in real temporal graphs.

Online portfolio selection has attracted increasing attention from data mining and machine learning communities in recent years. An important theory in financial markets is mean reversion, which plays a critical role in some state-of-the-art portfolio selection strategies. Although existing mean reversion strategies have been shown to achieve good empirical performance on certain datasets, they seldom carefully deal with noise and outliers in the data, leading to suboptimal portfolios, and consequently yielding poor performance in practice. In this paper, we propose to exploit the reversion phenomenon by using robust$L_1$-median estimators, and design a novel online portfolio selection strategy named “Robust Median Reversion” (RMR), which constructs optimal portfolios based on the improved reversion estimator. We examine the performance of the proposed algorithms on various real markets with extensive experiments. Empirical results show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale real-time algorithmic trading applications.

Subgraph queries have been a fundamental query for retrieving patterns from graph data. Due to the well known NP hardness of subgraph queries, those queries may sometimes take a long time to complete. Our recent investigation on real- world datasets revealed that the performance of queries on graphs generally varies greatly. In other words, query clients may occasionally encounter “unexpectedly” long execution from a subgraph query processor. This paper aims to demonstrate a tool that alleviates the problem by monitoring subgraph query progress. Specifically, we present a novel subgraph query progress indicator called PIGEON that exploits query-time information to report to users accurate estimated query progress. In the demonstration, users may interact with PIGEON to gain insights on the query evaluation, which include the following: Users are enabled to (i) monitor query progress; (ii) analyze the causes of long query times; and (iii) abort queries that run abnormally long, which may sometimes contain human errors.

In dynamic distinct counting, we want to maintain a multi-set S of integers under insertions to answer efficiently the query: how many distinct elements are there in S? In external memory, the problem admits two standard solutions. The first one maintains $S$ in a hash structure, so that the distinct count can be incrementally updated after each insertion using O(1) expected I/Os. A query is answered for free. The second one stores S in a linked list, and thus supports an insertion in O(1/B) amortized I/Os. A query can be answered in O(N/B log_{M/B} (N/B)) I/Os by sorting, where N=|S|, B is the block size, and M is the memory size. In this paper, we show that the above two naive solutions are already optimal within a polylog factor. Specifically, for any Las Vegas structure using N^{O(1)} blocks, if its expected amortized insertion cost is o(1/log B}), then it must incur Omega(N/(B log B)) expected I/Os answering a query in the worst case, under the (realistic) condition that N is a polynomial of B. This means that the problem is repugnant to update buffering: the query cost jumps from 0 dramatically to almost linearity as soon as the insertion cost drops slightly below Omega(1).

A public transportation network can often be modeled as a timetable graph where (i) each node represents a station; and (ii) each directed edge (u,v) is associated with a timetable that records the departure (resp. arrival) time of each vehicle at station u (resp. v). Several techniques have been proposed for various types of route planning on timetable graphs, e.g., retrieving the route from a node to another with the shortest travel time. These techniques, however, either provide insufficient query efficiency or incur significant space overheads.

Spatial-keyword queries on road networks are receiving in-creasing attention with the prominence of location-based services. There is a growing need to handle queries on road networks in distributed environments because a large net-work is typically distributed over multiple machines and it will improve query throughput. However, all the existing work on spatial keyword queries is based on a centralized setting. In this paper, we develop a distributed solution to answering spatial keyword queries on road networks. Exam-ple queries include “find locations near a supermarket and a hospital, ” and “find Chinese restaurants within 500 meters from my current location. ” We define an operation for an-swering such queries and reduce the problem of answering a query into computing a function of such operations. We pro-pose a new distributed index that enables each machine to independently evaluate the operation on its network frag-ment in a distributed setting. We theoretically prove the space optimality of the proposed index technique. We con-duct experiments with a distributed setting. Experimen-tal results demonstrate the promising performance of our method.

Query processing that preserves both the query privacy at the client and the data privacy at the server is a new research problem. It has many practical applications, especially when the queries are about the sensitive attributes of records. However, most existing studies, including those originating from data outsourcing, address the data privacy and query privacy separately. Although secure multiparty computation (SMC) is a suitable computing paradigm for this problem, it has significant computation and communication overheads, thus unable to scale up to large datasets. Fortunately, recent advances in cryptography bring us two relevant tools - conditional oblivious transfer and homomorphic encryption. In this paper, we integrate database indexing techniques with these tools in the context of private search on key-value stores. We first present an oblivious index traversal framework, in which the server cannot trace the index traversal path of a query during evaluation. The framework is generic and can support a wide range of query types with a suitable homomorphic encryption algorithm in place. Based on this framework, we devise secure protocols for classic key search queries on B+-tree and R-tree indexes. Our approach is verified by both security analysis and performance study.

Existing differential privacy (DP) studies mainly consider aggregation on data sets where each entry corresponds to a particular participant to be protected. In many situations, a user may pose a relational algebra query on a database with sensitive data, and desire differentially private aggregation on the result of the query. However, no existing work is able to release such aggregation when the query contains unrestricted join operations. This severely limits the applications of existing DP techniques because many data analysis tasks require unrestricted joins. One example is subgraph counting on a graph. Furthermore, existing methods for differentially private subgraph counting support only edge DP and are subject to very simple subgraphs. Until recent, whether any nontrivial graph statistics can be released with reasonable accuracy for arbitrary kind of input graphs under node DP was still an open problem.

In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the GUI [4]. Our recent attempts at implementing this vision [4,6], show significant improvement in the system response time (SRT) for subgraph queries. However, these efforts are designed specifically for graph databases containing a large collection of small or medium-sized graphs. Consequently, its frequent fragment-based action-aware indexing schemes and query processing strategy are unsuitable for supporting subgraph queries on large networks containing thousands of nodes and edges. In this demonstration, we present a novel system called QUBLE (QUery Blender for Large nEtworks) to realize this novel paradigm on large networks. We demonstrate various innovative features of QUBLE and its promising performance.

Given two locations s and t in a road network, a distance query returns the minimum network distance from s to t, while a shortest path query computes the actual route that achieves the minimum distance. These two types of queries find important applications in practice, and a plethora of solutions have been proposed in past few decades. The existing solutions, however, are optimized for either practical or asymptotic performance, but not both. In particular, the techniques with enhanced practical efficiency are mostly heuristic-based, and they offer unattractive worst-case guarantees in terms of space and time. On the other hand, the methods that are worst-case efficient often entail prohibitive preprocessing or space overheads, which render them inapplicable for the large road networks (with millions of nodes) commonly used in modern map applications.

Differential privacy is a robust principle for privacy preserving data analysis tasks, and has been successfully applied to a variety of applications. However, the number of queries that can be answered is limited for preventing privacy disclosure. Once the privacy budget is exhausted, all succeeding queries must be rejected. Therefore, each of the historical query answers is valuable and it is important to exploit them together to learn more about the data. We propose to integrate all available linear query answers into a consistent form that embodies our knowledge learned from the noisy answers, obtaining more accurate answers to past queries and even new queries, improving the data utility. Two distinct approaches are developed for this purpose, one via principle component analysis, and another via maximum entropy method. The second approach also generates a synthetic database, which is useful for differentially private data publishing. One important goal of our work is to ensure that the running time of our approaches does not grow with the cardinality of the universe of a data tuple, so that high-dimensional data with very large domain can still be tackled efficiently.

In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the GUI to filter irrelevant matches and prefetch partial query results [8]. Our first attempt at implementing this vision, called GBLENDER [8], shows significant improvement in system response time (SRT) for sub graph containment queries. However, GBLENDER suffers from two key drawbacks, namely inability to handle visual sub graph similarity queries and inefficient support for visual query modification, limiting its usage in practical environment. In this paper, we propose a novel algorithm called PRAGUE (Practical visu Al Graph QUery Blender), that addresses these limitations by exploiting a novel data structure called spindle-shaped graphs (SPIG). A SPIG succinctly records various information related to the set of super graphs of a newly added edge in the visual query fragment. Specifically, PRAGUE realizes a unified visual framework to support SPIG-based processing of modification-efficient sub graph containment and similarity queries. Extensive experiments on real-world and synthetic datasets demonstrate effectiveness of PRAGUE.

Query (e.g., shortest path) on road networks has been extensively studied. Although most of the existing query processing approaches are designed for centralized environments, there is a growing need to handle queries on road networks in distributed environments due to the increasing query workload and the challenge of querying large networks. In this demonstration, we showcase a distributed system calledDISKs(DIstributedSpatialKeywordsearch) that is capable of efficiently supporting spatial group keyword search (S-GKS) on road networks. Given a group of keywordsXand a distancer, an SGKS returns locations on a road network, such that for each returned locationp, there exists a set of nodes (on the road network), which are located within a network distancerfrompand collectively containsX. We will demonstrate the innovative modules, performance and interactive user interfaces of DISKs.

Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial navigation products. The state-of-the-art solutions for the problem can be divided into two categories: spatial-coherence-based methods and vertex-importance-based approaches. The two categories of techniques, however, have not been compared systematically under the same experimental framework, as they were developed from two independent lines of research that do not refer to each other. This renders it difficult for a practitioner to decide which technique should be adopted for a specific application. Furthermore, the experimental evaluation of the existing techniques, as presented in previous work, falls short in several aspects. Some methods were tested only on small road networks with up to one hundred thousand vertices; some approaches were evaluated using distance queries (instead of shortest path queries), namely, queries that ask only for the length of the shortest path; a state-of-the-art technique was examined based on a faulty implementation that led to incorrect query results. To address the above issues, this paper presents a comprehensive comparison of the most advanced spatial-coherence-based and vertex-importance-based approaches. Using a variety of real road networks with up to twenty million vertices, we evaluated each technique in terms of its preprocessing time, space consumption, and query efficiency (for both shortest path and distance queries). Our experimental results reveal the characteristics of different techniques, based on which we provide guidelines on selecting appropriate methods for various scenarios.

Due to the complexity of graph query languages, the need for visual query interfaces that can reduce the burden of query formulation is fundamental to the spreading of graph data management tools to wider community. We present a novel HCI (human-computer interaction)-aware graph query processing paradigm, where instead of processing a query graph after its construction, it interleaves visual query construction and processing to improve system response time. We demonstrate a system called GBLENDER that exploits GUI latency to prune false results and prefetch candidate data graphs by employing a novel action-aware indexing scheme and a data structure called spindle-shaped graphs (SPIG). We demonstrate various innovative features of GBLENDER and its promising performance in evaluating subgraph containment and similarity queries.

DHT is a widely used building block for scalable P2P systems. However, as uniform hashing employed in DHTs destroys data locality, it is not a trivial task to support complex queries (e.g., range queries and k-nearest-neighbor queries) in DHT-based P2P systems. In order to support efficient processing of such complex queries, a popular solution is to build indexes on top of the DHT. Unfortunately, existing over-DHT indexing schemes suffer from either query inefficiency or high maintenance cost. In this paper, we propose LIGhtweight Hash Tree (LIGHT)—a query-efficient yet low-maintenance indexing scheme. LIGHT employs a novel naming mechanism and a tree summarization strategy for graceful distribution of its index structure. We show through analysis that it can support various complex queries with near-optimal performance. Extensive experimental results also demonstrate that, compared with state of the art over-DHT indexing schemes, LIGHT saves 50-75 percent of index maintenance cost and substantially improves query performance in terms of both response time and bandwidth consumption. In addition, LIGHT is designed over generic DHTs and hence can be easily implemented and deployed in any DHT-based P2P system.

Much work has focused on mining evolving data, and most approaches learn the latest model from the latest data. The problem with these approaches is that the learned model is always of low quality. In this paper, we propose a clustering approach to find hidden concepts that control data generation. Unlike traditional clustering methods that are based on data similarity (measured by Euclidean distance, e.g.), we devise a new similarity metric for concept similarity. We propose a two step algorithm, which uses dynamic programming and hierarchical clustering to find concepts in the data.

The multidimensional similarity query, an essential query for information processing in sensor networks, has not received sufficient attention in the research community of sensor networks. In this paper, we study the multidimensional similarity query for large-scale sensor networks and propose a new algorithm called DIC (dimension reduction by Chebyshev polynomials). In DIC algorithm, the normalized Chebyshv coefficients are adopted as indexing and theoretic storage location of multidimensional data, and the multidimensional data are stored in the sensor nodes close to the theoretic location. A query bounding is estimated by using DIC algorithm, and query is executed inside a small zone. Inside the small zone, a new method of the itinerary-based query propagation and data aggregation is presented. The DIC algorithm does not require to preserve any index structure in sensor nodes, and also do not reply on any infrastructure structures distributed among the sensor nodes. We provide extensive experiments to evaluate the performance of the algorithm. The experimental results demonstrate that DIC can indeed enable efficient similarity queries.

Generalization is a well-known method for privacy preserving data publication. Despite its vast popularity, it has several drawbacks such as heavy information loss, difficulty of supporting marginal publication, and so on. To overcome these drawbacks, we develop ANGEL,1 a new anonymization technique that is as effective as generalization in privacy protection, but is able to retain significantly more information in the microdata. ANGEL is applicable to any monotonic principles (e.g., l-diversity, t-closeness, etc.), with its superiority (in correlation preservation) especially obvious when tight privacy control must be enforced. We show that ANGEL lends itself elegantly to the hard problem of marginal publication. In particular, unlike generalization that can release only restricted marginals, our technique can be easily used to publish any marginals with strong privacy guarantees.

We consider skyline computation when the underlying data set is horizontally partitioned onto geographically distant servers that are connected to the Internet. The existing solutions are not suitable for our problem, because they have at least one of the following drawbacks: (1) applicable only to distributed systems adopting vertical partitioning or restricted horizontal partitioning, (2) effective only when each server has limited computing and communication abilities, and (3) optimized only for skyline search in subspaces but inefficient in the full space. This paper proposes an algorithm, called feedback-based distributed skyline (FDS), to support arbitrary horizontal partitioning. FDS aims at minimizing the network bandwidth, measured in the number of tuples transmitted over the network. The core of FDS is a novel feedback-driven mechanism, where the coordinator iteratively transmits certain feedback to each participant. Participants can leverage such information to prune a large amount of local data, which otherwise would need to be sent to the coordinator. Extensive experimentation confirms that FDS significantly outperforms alternative approaches in both effectiveness and progressiveness.

Many applications are driven by evolving data - patterns in Web traffic, program execution traces, network event logs, etc., are often non-stationary. Building prediction models for evolving data becomes an important and challenging task. Currently, most approaches work by "chasing trends", that is, they keep learning or updating models from the evolving data, and use these impromptu models for online prediction. In many cases, this proves to be both costly and ineffective - much time is wasted on re-learning recurring concepts, yet the classifier may remain one step behind the current trend all the time. In this paper, we propose to mine high-order models in evolving data. More often than not, there are a limited number of concepts, or stable distributions, in the data stream, and concepts switch between each other constantly. We mine all such concepts offline from a historical stream, and build high quality models for each of them. At run time, combining historical concept change patterns and cues provided by an online training stream, we find the most likely current concept and use its corresponding models to classify data in an unlabeled stream. The primary advantage of the high-order model approach is its high accuracy. Experiments show that in benchmark datasets, classification error of the high-order model is only a small fraction of that of the current best approaches. Another important benefit is that, unlike state-of-the-art approaches, our approach does not require users to tune any parameters to achieve a satisfying result on streams of different characteristics.

As Geography Markup Language (GML) becomes a standard for geographic information representation on the internet, more needs to efficiently store and retrieve GML data arise. Storing and retrieving GML data in object-relational database is a proper option considering its sophisticated technology and object-oriented features. The existing storage approaches for GML are not satisfactory for they consider only data aspects while ignoring semantic constraints. In the paper, we explore the structure and semantic characteristics existed in GML, and present a method to capture and map these constraints into object-relational databases during GML storage. Comparing with the existing methods, our approach preserves not only the data and structure, but also its semantics of GML document.

This demonstration presents a GML specific compressor that exploits the unique characteristics of GML documents to achieve better compression performance than the existing XML compressors. We call this compressor GPress, an abbreviation of "GML comPressor". To the best of our knowledge, GPress is the first compressor developed specifically for GML documents. Experimental results show that GPress outperforms the best existing XML compressor XMill evidently in compression ratio.

Graphs are widely used for modeling complicated data, including chemical compounds, protein interactions, XML documents, and multimedia. Information retrieval against such data can be formulated as a graph search problem, and finding an efficient solution to the problem is essential for many applications. A popular approach is to represent both graphs and queries on graphs by sequences, thus converting graph search to subsequence matching. State-of-the-art sequencing methods work at the finest granularity - each node (or edge) in the graph will appear as an element in the resulting sequence. Clearly, such methods are not semantic conscious, and the resulting sequences are not only bulky but also prone to complexities arising from graph isomorphism and other problems in searching. In this paper, we introduce a novel sequencing method to capture the semantics of the underlying graph data. We find meaningful components in graph structures and use them as the most basic units in sequencing. It not only reduces the size of resulting sequences, but also enables semantic-based searching. In this paper, we base our approach on chemical compound databases, although it can be applied to searching other complicated graphs, such as protein structures. Experiments demonstrate that our approach outperforms state-of-the-art graph search methods.

GML has become the de facto standard for electronic spatial data exchange among the applications of Web and distributed geographic information systems (GISs). As more and more geographical data is presented in GML, it is necessary to develop techniques for managing GML documents in databases. A possible solution is to store GML data into object-relational databases from which the users can retrieve the interested data. In this paper, we propose an approach to map GML schema to object-relational database schema by using GML schema graph, and algorithms for storing / querying valid GML documents into / from the relations generated by the corresponding object-relational schema. Spatial and non-spatial features embedded in GML document are stored in object-oriented relations, and the structures and constrains defined in GML schema are also well preserved. A prototype for GML documents storing and querying based on the proposed method is implemented on the basis of the Oracle/Spatial system. Preliminary experiment results shows that our method is feasible and efficient.

In this demonstration, we present a prototype system called SIPPER, which is the abbreviation for Selecting Informative Peers in Structured P2P Environment for Content-based Retrieval. SIPPER distinguishes itself from the existing P2P-IR systems by the following two features: First, to improve retrieval efficiency, SIPPER employs a novel peer selection method to direct the query to a small fraction of relevant peers in the network for searching globally relevant documents. Second, to reduce the bandwidth cost of meta data publishing, SIPPER uses a new publishing mechanism, the term-node publishing mechanism, which is different from the traditional term-document model [2].

Let R be a set of objects. An object o ∈ R is an outlier, if there exist less than k objects in R whose distances to o are at most r. The values of k, r, and the distance metric are provided by a user at the run time. The objective is to return all outliers with the smallest I/O cost.This paper considers a generic version of the problem, where no information is available for outlier computation, except for objects' mutual distances. We prove an upper bound for the memory consumption which permits the discovery of all outliers by scanning the dataset 3 times. The upper bound turns out to be extremely low in practice, e.g., less than 1% of R. Since the actual memory capacity of a realistic DBMS is typically larger, we develop a novel algorithm, which integrates our theoretical findings with carefully-designed heuristics that leverage the additional memory to improve I/O efficiency. Our technique reports all outliers by scanning the dataset at most twice (in some cases, even once), and significantly outperforms the existing solutions by a factor up to an order of magnitude.

No abstract available.

With the rapid growth of on-line information available, text classification is becoming more and more important. kNN is a widely used text classification method of high performance. However, this method is inefficient because it requires a large amount of computation for evaluating the similarity between a test document and each training document. In this paper, we propose a fast kNN text classification approach based on pruning the training corpus. By using this approach, the size of training corpus can be condensed sharply so that time-consuming on kNN searching can be cut off significantly, and consequently classification efficiency can be improved substantially while classification performance is preserved comparable to that of without pruning. Effective, algorithm for text corpus pruning is designed. Experiments over the Reuters corpus are carried out, which validate the practicability of the proposed approach. Our approach is especially suitable for on-line text classification applications.

Geography Markup Language (GML) has been adopted as de facto standard for geo-referenced information storing and exchanging, while Scalable Vector Graphics (SVG), also a W3C-recommended XML standard, is appearing as an ideal format for rendering maps. Usually, Extensible Stylesheet Language Transformations (XSLT) is used to transform GML documents toSVG documents. Considering the complexity and variety of GML documents, however, designing XSLT rules is not a easy task; even worse, such a method cannot guarantee that the generated SVG document is valid. In this paper, we present a novel method to transform GML documents to SVG documents, while guaranteeing that the result SVG documents conform to a certain DTD or Schema pre-specified by the users. With this new method, it is convenient for the users to construct transformation rules from GML to SVG guided by the pre-specified SVG DTD or schema. We propose three algorithms to implement the new method. We also build a prototype and conduct preliminary experiments, which validate the effectiveness of the new method.

This paper reports the techniques of dynamic tuning of XML storage schema in VXMLR, which is a XML management system based on RDBMS. With two different tuning strategies, VXMLR can dynamically adjust its storage schema based on the latest query records to improve its query processing efficiency. When a tuning event is triggered, VXMLR first derives from its history queries the initial mapping rules that map XML DTD to relational schemas; then by vertically partitioning the relational tables or redundantly storing the data relevant to history queries, some candidate storage schemas are generated; following that, the benefit and cost of each candidate schema is estimated; and finally a cost-driven approach is proposed to select the final storage schema from the candidate schemas under a certain space constraint. Experimental results validate the practicability and effectiveness of the proposed techniques.