Existing core-set selection methods predominantly rely on heuristic scoring signals such as training dynamics or model uncertainty, lacking explicit modeling of data likelihood. This omission may hinder the constructed subset from capturing subtle yet critical distributional structures that underpin effective model training. In this work, we propose a novel, theoretically grounded approach that leverages diffusion models to estimate data likelihood via reconstruction deviation induced by partial reverse denoising. Specifically, we establish a formal connection between reconstruction error and data likelihood, grounded in the Evidence Lower Bound (ELBO) of Markovian diffusion processes, thereby enabling a principled, distribution-aware scoring criterion for data selection. Complementarily, we introduce an efficient information-theoretic method to identify the optimal reconstruction timestep, ensuring that the deviation provides a reliable signal indicative of underlying data likelihood. Extensive experiments on ImageNet demonstrate that reconstruction deviation offers an effective scoring criterion, consistently outperforming existing baselines across selection ratios, and closely matching full-data training using only 50% of the data. Further analysis shows that the likelihood-informed nature of our score reveals informative insights in data selection, shedding light on the interplay between data distributional characteristics and model learning preferences.

Bipartite graphs are widely used in many real-world applications, where discovering clusters is crucial for understanding their underlying structure. However, most existing clustering methods for bipartite graphs enforce the assignment ofallvertices to clusters, often neglecting the important roles of outliers and hubs. To address this limitation, we plan to extend the structural clustering model from unipartite to bipartite graphs. This extension is non-trivial due to the lack of common neighbors in bipartite graphs, which renders traditional similarity measures less effective. Recognizing that similarity is key to structural clustering, we resort to butterflies—the fundamental building blocks of bipartite graphs—to define a more effective similarity measure. Building on this, we further propose a novel structural clustering model, SBC, tailored for bipartite graphs. To enable clustering under this model, we develop efficient online and index-based methods, along with a dynamic maintenance method to accommodate graph updates over time. Extensive experiments on real-world bipartite graphs demonstrate that: (1) The SBC model greatly enhances clustering quality, achieving higher modularity while effectively identifying outliers and hubs. (2) Our proposed clustering methods are highly scalable, enabling the processing of graphs with up to 12.2 million edges within 2 seconds

Graph Neural Networks (GNNs) have achieved remarkable success in processing graph data. However, when graph contains node label noise (label error) and graph structural noise (edge error), the performance of GNNs will severely decrease. In the learning with noisy label, the methods based on sample selection and label generation reveal promising outlook. Unfortunately, most of these methods only focus on single node label nosie or graph edge noise, which cannot handle both two types of noise at the same time. Furthermore, these methods also lack measures for error accumulation during training. In this work, we propose a simple yet efficient method, named GNN Defender (GDF), to address challenges of both types of noises through cross-correction and two regularization technologies. Specifically, GDF consists of two networks (Nnet and Enet), which Nnet is responsible to predict node category and Enet is used to predict the connected probability of two nodes. For sample selection, the mean distribution distance between node category prediction and node original label is regard as the selecting indicator. Then, we consider node labels with distances less than the mean as clean labels. Similar to node, replacing indicator with the mean cosine similarity of connected to Adaptively complete clean edge selection. For cross correction, we use the clean probabilities of the edges between connected nodes as weights to generate pseudo labels for target nodes on the subgraph. The pseudo labels generated by edge information carry more credible supervision information, thereby completing the correction of node noise labels. Next, we compare the label consistency between connected node to achieve the noisy edge correction. It is worth noting that the clean probabilities of node labels are used to smooth graph structure, improving the edge correction quality. Finally, we propose category consistency regularization and subgraph clustering regularization to weaken the impact of error accumulation. Extensive experiments on single noise and mixed noise datasets show that our proposed framework far outperforms current baseline methods, with improvements ranging from 1.43% to 14.72%.

Supervised Fine-tuning has been pivotal in training autoregressive language models, yet it introduces exposure bias. To mitigate this, Post Fine-tuning, including on-policy and off-policy methods, has emerged as a solution to enhance models further. However, each has its limitations regarding performance enhancements and susceptibility to overfitting. In this paper, we introduce a novel on-policy approach called Evolution Strategy Optimization (ESO), which is designed by harnessing the principle of biological evolution, namely survival of the fittest. Particularly, we consider model tuning as an evolution process, and each output sentence generated by the model can provide a perturbation signal to the model parameter space. Then, the fitness of perturbation signals is quantified by the difference between its score and the averaged one offered by a reward function, which guides the optimization process. Empirically, the proposed method can achieve superior performance in various tasks and comparable performance in the human alignment task.

Adversarial training (AT) with projected gradient descent is most popular for improving model robustness under adversarial attacks. However, computational overheads become prohibitively large when AT is applied to large backbone models. It also suffers from robust overfitting that impairs model generalization. This paper contributes to solving both problems simultaneously towards building more trustworthy foundation models. We propose an adapter-based method to perform efficient AT directly in the feature space. The proposed approach improves the inner-loop convergence quality by eliminating robust overfitting, thus boosting robust generalization against unseen attacks and accelerating the training speed with fewer epochs. The proposed adapter can also enable adversarial detection by “plug-in” for testing at inference time. We evaluate the proposed approach on different backbone architectures and demonstrate its effectiveness for efficient AT at scale.

CAD drawing restoration is one of the most urgent needs in industrial manufacturing. The existing research focuses on the digitization of CAD drawings, However, there are actually many problems in digitized CAD drawings due to the upgrading of engineering drafting software, and it is difficult to repair reliably. In this paper, we use multi-modal large language models (MLLMs) to carry out digital CAD drawing restoration, and we use Retrieval-Augmented Generation (RAG) technology to inject engineering domain knowledge into MLLMs. In addition, a complete set of multiagent systems is constructed to realize restoration in accordance with the CAD drawing review process in the mechanical field. We collected 1,639 CAD drawings of bearing seats to evaluate our multi-agent system, verifying its reliability and robustness to various problematic drawings. Notably, ChatCAD also has a much simpler implementation than alternative methods trained on a huge dataset.

Approximate K Nearest Neighbor (AKNN) search in high-dimensional spaces is a critical yet challenging problem. In AKNN search, distance computation is the core task that dominates the runtime. Existing approaches typically use approx-imate distances to improve computational efficiency, often at the cost of reduced search accuracy. To address this issue, the state-of-the-art method, ADSampling, employs random projections to estimate approximate distances and introduces an additional distance correction process to mitigate accuracy loss. However, ADSampling has limitations in both effectiveness and generality, primarily due to its heavy reliance on random projections for distance approximation and correction. Motivated by this, we leverage data distribution to improve distance approximation via orthogonal projection, thereby ad-dressing the effectiveness limitation of ADSampling; we also adopt a data-driven approach to distance correction, decoupling the correction process from the distance approximation process, thereby overcoming the generality limitation of ADSampling. Ex-tensive experiments demonstrate the superiority and effectiveness of our method. In particular, compared to ADSampling, our method achieves a speedup of 1.6 to 2.1 times on real-world datasets while providing higher accuracy. In addition, our method shows superior performance in Ant Group image search scenarios and has been integrated into their search engine.

Dataset distillation aims to streamline the training process by creating a compact yet effective dataset for a much larger original dataset. However, existing methods often struggle with distilling large, high-resolution datasets due to prohibitive resource costs and limited performance, primarily stemming from sample-wise optimizations in the pixel space. Motivated by the remarkable capabilities of diffusion generative models in learning target dataset distributions and controllably sampling high-quality data tailored to user needs, we propose framing dataset distillation as a controlled diffusion generation task aimed at generating data specifically tailored for effective training purposes. By establishing a correlation between the overarching objective of dataset distillation and the trajectory influence function, we introduce the Influence-Guided Diffusion (IGD) sampling framework to generate training-effective data without the need to retrain diffusion models. An efficient guided function is designed by leveraging the trajectory influence function as an indicator to steer diffusions to produce data with influence promotion and diversity enhancement. Extensive experiments show that the training performance of distilled datasets generated by diffusions can be significantly improved by integrating with our IGD method and achieving state-of-the-art performance in distilling ImageNet datasets. Particularly, an exceptional result is achieved on the ImageNet-1K, reaching 60.3\% at IPC=50. Our code is available at https://github.com/mchen725/DD_IGD.

Direct preference optimization (DPO), a widely adopted offline preference optimization algorithm, aims to align large language models (LLMs) with human-desired behaviors using pairwise preference data. However, the generation of the winning response and the losing response within pairwise data are typically isolated, leading to weak correlations between them as well as suboptimal alignment performance. To address this issue, we propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC. Firstly, we increase the consistency and informativeness of the pairwise preference signals through targeted modifications, synthesizing a pseudo-winning response by improving the losing response with the winning response as a reference. Secondly, we identify that DPO alone is insufficient to model these correlations and capture nuanced variations. Therefore, we propose learning token-level correlations by dynamically leveraging the policy model's confidence during training. Comprehensive experiments on QA, math, and instruction-following tasks demonstrate the effectiveness of our approach, significantly surpassing competitive baselines, including DPO. Additionally, our in-depth quantitative analysis reveals the reasons behind our method's superior performance over DPO and showcases its versatility to other DPO variants.

Speech synthesis is crucial for human-computer interaction, enabling natural and intuitive communication. However, existing datasets involve high construction costs due to manual annotation and suffer from limited character diversity, contextual scenarios, and emotional expressiveness. To address these issues, we propose DialogueAgents, a novel hybrid agent-based speech synthesis framework, which integrates three specialized agents—a script writer, a speech synthesizer, and a dialogue critic—to collaboratively generate dialogues. Grounded in a diverse character pool, the framework iteratively refines dialogue scripts and synthesizes speech based on speech review, boosting emotional expressiveness and paralinguistic features of the synthesized dialogues. Using DialogueAgent, we contribute MultiTalk, a bilingual, multi-party, multi-turn speech dialogue dataset covering diverse topics. Extensive experiments demonstrate the effectiveness of our framework and the high quality of the MultiTalk dataset. We release the dataset and code1to facilitate future research on advanced speech synthesis models and customized data generation.

The Metric Nearness Problem involves restoring a non-metric matrix to its closest metric-compliant form, addressing issues such as noise, missing values, and data inconsistencies. Ensuring metric properties, particularly the $O(N^3)$ triangle inequality constraints, presents significant computational challenges, especially in large-scale scenarios where traditional methods suffer from high time and space complexity. We propose a novel solution based on the tropical inner product (max-plus operation), which we prove satisfies the triangle inequality for non-negative real matrices. By transforming the problem into a continuous optimization task, our method directly minimizes the distance to the target matrix. This approach not only restores metric properties but also generates metric-preserving embeddings, enabling real-time updates and reducing computational and storage overhead for downstream tasks. Experimental results demonstrate that our method achieves up to 60$\times$ speed improvements over state-of-the-art approaches, and efficiently scales from $1e4 \times 1e4$ to $1e5 \times 1e5$ matrices with significantly lower memory usage.

Partial Differential Equations (PDEs) underpin many scientific phenomena, yet traditional computational approaches often struggle with complex, nonlinear systems and irregular geometries. This paper introduces the AMG method, a Multi-Graph neural operator approach designed for efficiently solving PDEs on Arbitrary geometries. AMG leverages advanced graph-based techniques and dynamic attention mechanisms within a novel GraphFormer architecture, enabling precise management of diverse spatial domains and complex data interdependencies. By constructing multi-scale graphs to handle variable feature frequencies and a physics graph to encapsulate inherent physical properties, AMG significantly outperforms previous methods, which are typically limited to uniform grids. We present a comprehensive evaluation of AMG across six benchmarks, demonstrating its consistent superiority over existing state-of-the-art models. Our findings highlight the transformative potential of tailored graph neural operators in surmounting the challenges faced by conventional PDE solvers. Our code and datasets are available on https://github.com/lizhihao2022/AMG.

Signed graphs capture both positive and negative relationships between entities, with balance being a fundamental concept. In these graphs, a vertex is considered balanced if all cycles it belongs to contain an even number of negative edges. On the other hand, unbalanced vertices often experience cognitive dissonance and emotional disturbance, motivating efforts to modify the graph to achieve balance for these vertices. Yet, most existing research emphasizes global balance, focusing on lengthy cycles that represent distant interactions. In contrast, this paper shifts the focus to local balance, where a vertex is deemed balanced when the triangles (length-three cycles) it participates in are positive, reflecting more immediate relationships. Building on this, we introduce the Locally Balancing Signed Graph (LBS) problem, which aims to maximize the number of locally balanced vertices through graph modification. Despite the NP-hard nature of the LBS problem and the absence of properties such as monotonicity and submodularity, our novel greedy method effectively addresses these challenges. We further enhance our method with dynamic computation and pruning techniques. Extensive experiments show the efficacy of our greedy method in solving the LBS problem and underscore the substantial runtime reductions achieved through our optimization techniques.

We focus on the approximate nearest neighbor search (ANNS) in high dimensional space, which is a fundamental technique in computer vision and multimedia database. Among the ANNS solutions, graph-based approaches achieve excellent performance by executing a routing algorithm on a proximity graph to retrieve the nearest neighbors. However, most of their routing strategies are heuristic-based greedy routing, leading to suboptimal search results with large number of hops. In this paper, we propose a novel routing paradigm on graphs for ANNS problem by deep reinforcement learning. We design a reinforcement model to learn the routing policy by making use of both graph global and local topology information. A hops-optimized reward mechanism is devised to enable the model to be more efficient and effective. The final searching algorithm with the learned model is able to find the nearest neighbors without any backtracking in a small number of hops. Comprehensive experiments on real-world datasets demonstrate the superiorities of the proposed method over the state-of-the-art ANNS approaches.

Yuxin Jiang, Yufei Wang, Xingshan Zeng, Wanjun Zhong, Liangyou Li, Fei Mi, Lifeng Shang, Xin Jiang, Qun Liu, Wei Wang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Yuxin Jiang, Yufei Wang, Chuhan Wu, Wanjun Zhong, Xingshan Zeng, Jiahui Gao, Liangyou Li, Xin Jiang, Lifeng Shang, Ruiming Tang, Qun Liu, Wei Wang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Electric vehicles (EVs) have the potential to serve as energy storage solutions through bidirectional charging technology, which allows them to both draw power from and feed power back into the grid, homes, or other vehicles. This capability enables EVs to reduce emissions, optimize costs, and support the grid by storing energy during periods of high production and supplying it when demand is high. In this vision paper, we focus on unlocking the potential of EVs as energy storage solutions while ensuring they remain readily available for transportation, their primary purpose. A significant research gap exists in that most current studies prioritize energy management, often using simplistic approaches that inadequately address the travel needs of EV owners. We believe the database community can be instrumental in maximizing the dual role of EVs as transportation and energy storage. We present a non-exhaustive list of research directions for various EV stakeholders, including individual EV owners, groups of independent yet cooperative EVs, commercial EV fleets, and autonomous EVs, and hope to inspire the database community for further exploration.

In real-world road networks, edges possess additional numeric attributes, such as width and toll, in addition to length. Incorporating these additional attributes enhances the functionality of shortest-distance services. To this end, current research on shortest-distance computation requires that the numeric attributes of edges on a path exceed a lower bound$l$(within the interval [l,oo]). However, it often overlooks the benefits of setting an upper bound$r$(within the interval [l, r]). To bridge this gap, we introduce the numeric-constrained shortest-distance query problem, which enforces interval constraints [l, r] on the numeric attributes of edges on a path. There are two intuitive solutions: the Online-Search method, which excludes edges that violate the constraints during the search, leading to inefficient query responses, and the Full-Index method, which creates an index for each necessary interval constraint [l, r], resulting in excessive space consumption due to numerous potential intervals. Our novel index-based method, which uses the tree decomposition technique and exploits interval-distance relationships, aims to mitigate these issues. As confirmed by our experimental results, our method greatly improves query speed - over 20 times faster than Online-Search- and consumes less space than Full-Index.

Nonintrusive load monitoring (NILM) can monitor the operating state and energy consumption of electric appliances in a nonintrusive manner and provides a promising approach to improving electricity usage efficiency for residential and commercial buildings. Although machine learning (ML) methods are powerful and have significantly advanced the developments of NILM, they request a sizable amount of labeled data for model training. However, getting operational data of each electrical appliance in real life is challenging, so the requirements for labeled data limit the NLIM's practicality. To tackle this challenge, a novel multilayer momentum contrast (MLMoCo) learning mechanism is proposed for self-supervised feature representation learning. With only unlabeled aggregate load data, the proposed MLMoCo contrasts the augmented versions of the same sample (“positives”) with instances extracted from other samples (“negatives”). To maintain a dictionary with enough negative samples to be compared with the input, a momentum encoder is adopted to momentum update the parameters rather than by backpropagation during training. An event-based data augmentation method is also proposed to obtain the distinct but strongly related positive pairs for self-supervised feature learning. The experimental comparisons, including different state-of-the-art techniques and various downstream tasks with real-world datasets, demonstrate the remarkable performance gains of the proposed approach through learning from the unlabeled data, which could significantly increase the practicality of the NILM.

Learning with noisy labels is nontrivial for deep learning models. Sample selection is a widely investigated research topic for handling noisy labels. However, most existing methods face challenges such as imprecise selection, a lack of global selection capabilities, and the need for tedious hyperparameter tuning. In this paper, we propose CoSaR (Collaborative Selection and adversarial Regularization ), a twin-networks based model that performs globally adaptive sample selection to tackle label noise. Specifically, the collaborative selection estimates the average distribution distances between predictions and generation labels through the collaboration of two networks to address the bias of the average distribution distances and the manual tuning of hyperparameters. Adversarial regularization is integrated into CoSaR to restrict the network's tendency to fit and memorize noisy labels, thereby enhancing its collaborative selection capability. In addition, we employ a label smoothing regularization and two types of data augmentation to enhance the robustness of the model further. Extensive experiments on both synthetic and real-world noisy datasets demonstrate that the proposed model outperforms baseline methods remarkably, with an accuracy improvement ranging between +0.56% and +15.14%.

Distilled student models in teacher-student architectures are widely considered for computational-effective deployment in real-time applications and edge devices. However, there is a higher risk of student models to encounter adversarial attacks at the edge. Popular enhancing schemes such as adversarial training have limited performance on compressed networks. Thus, recent studies concern about adversarial distillation (AD) that aims to inherit not only prediction accuracy but also adversarial robustness of a robust teacher model under the paradigm of robust optimization. In the min-max framework of AD, existing AD methods generally use fixed supervision information from the teacher model to guide the inner optimization for knowledge distillation which often leads to an overcorrection towards model smoothness. In this paper, we propose an adaptive adversarial distillation (AdaAD) that involves the teacher model in the knowledge optimization process in a way interacting with the student model to adaptively search for the inner results. Comparing with state-of-the-art methods, the proposed AdaAD can significantly boost both the prediction accuracy and adversarial robustness of student models in most scenarios. In particular, the ResNet-18 model trained by AdaAD achieves top-rank performance (54.23% robust accuracy) on RobustBench under AutoAttack.

Masked Language Modeling (MLM) is widely used to pretrain language models. The standard random masking strategy in MLM causes the pre-trained language models (PLMs) to be biased towards high-frequency tokens. Representation learning of rare tokens is poor and PLMs have limited performance on downstream tasks. To alleviate this frequency bias issue, we propose two simple and effective Weighted Sampling strategies for masking tokens based on token frequency and training loss. We apply these two strategies to BERT and obtain Weighted-Sampled BERT (WSBERT). Experiments on the Semantic Textual Similarity benchmark (STS) show that WSBERT significantly improves sentence embeddings over BERT. Combining WSBERT with calibration methods and prompt learning further improves sentence embeddings. We also investigate fine-tuning WSBERT on the GLUE benchmark and show that Weighted Sampling also improves the transfer learning capability of the backbone PLM. We further analyze and provide insights into how WSBERT improves token embeddings.

We study the efficient regular expression (regex) matching problem. Existing algorithms are scanning-based algorithms that typically use an equivalent automaton compiled from the regex query to verify a document. Although some works propose various strategies to quickly jump to candidate locations in a document where a query result may appear, they still need to utilize the scanning-based method to verify these candidate locations. These methods become inefficient when there are still many candidate locations needed to be verified. In this paper, we propose a novel approach to efficiently compute all matching positions for a regex query purely based on a positional q-gram inverted index. We propose a gram-driven NFA to represent the language of a regex and show all regex matching locations can be obtained by finding positions on q-grams of GNFA that satisfy certain positional constraints. Then we propose several GNFA-based query plans to answer the query using the positional inverted index. In order to improve the query efficiency, we design the algorithm to build a tree-based query plan by carefully choosing a checking order for positional constraints. Experimental results on real-world datasets show that our method outperforms state-of-the-art methods by up to an order of magnitude in query efficiency.

Graph self-supervised learning aims to mine useful information from unlabeled graph data, and has been successfully applied to pre-train graph representations. Many existing approaches use contrastive learning to learn powerful embeddings by learning contrastively from two augmented graph views. However, none of these graph contrastive methods fully exploits the diversity of different augmentations, and hence is prone to overfitting and limited generalization ability of learned representations. In this paper, we propose a novel Graph Self-supervised Learning method with Augmentation-aware Contrastive Learning. Our method is based on the finding that the pre-trained model after adding augmentation diversity can achieve better generalization ability. To make full use of the information from the diverse augmentation method, this paper constructs new augmentation-aware prediction task which complementary with the contrastive learning task. Similar to how pre-training requires fast adaptation to different downstream tasks, we simulate train-test adaptation on the constructed tasks for further enhancing the learning ability; this strategy can be deemed as a form of meta-learning. Experimental results show that our method outperforms previous methods and learns better representations for a variety of downstream tasks.

Time series analysis is essential to many far-reaching applications of data science and statistics including economic and financial forecasting, surveillance, and automated business processing. Though being greatly successful of Transformer in computer vision and natural language processing, the potential of employing it as the general backbone in analyzing the ubiquitous times series data has not been fully released yet. Prior Transformer variants on time series highly rely on task-dependent designs and pre-assumed "pattern biases", revealing its insufficiency in representing nuanced seasonal, cyclic, and outlier patterns which are highly prevalent in time series. As a consequence, they can not generalize well to different time series analysis tasks. To tackle the challenges, we propose DifFormer, an effective and efficient Transformer architecture that can serve as a workhorse for a variety of time-series analysis tasks. DifFormer incorporates a novel multi-resolutional differencing mechanism, which is able to progressively and adaptively make nuanced yet meaningful changes prominent, meanwhile, the periodic or cyclic patterns can be dynamically captured with flexible lagging and dynamic ranging operations. Extensive experiments demonstrate DifFormer significantly outperforms state-of-the-art models on three essential time-series analysis tasks, including classification, regression, and forecasting. In addition to its superior performances, DifFormer also excels in efficiency - a linear time/memory complexity with empirically lower time consumption.

The recently proposed learned indexes have attracted much attention as they can adapt to the actual data and query distributions to attain better search efficiency. Based on this technique, several existing works build up indexes for multi-dimensional data and achieve improved query performance. A common paradigm of these works is to (i) map multi-dimensional data points to a one-dimensional space using a fixed space-filling curve (SFC) or its variant and (ii) then apply the learned indexing techniques. We notice that the first step typically uses a fixed SFC method, such as row-major order and z -order. It definitely limits the potential of learned multi-dimensional indexes to adapt variable data distributions via different query workloads. In this paper, we propose a novel idea of learning a space-filling curve that is carefully designed and actively optimized for efficient query processing. We also identify innovative offline and online optimization opportunities common to SFC-based learned indexes and offer optimal and/or heuristic solutions. Experimental results demonstrate that our proposed method, LMSFC, outperforms state-of-the-art non-learned or learned methods across three commonly used real-world datasets and diverse experimental settings.

In a graph, the reverse nearest neighbors (RNN) of vertex f refer to the set of vertices that consider f as their nearest neighbor. When f represents a facility like a subway station, its RNN comprises potential users who prefer the nearest facility. In practice, there may be underutilized facilities with small RNN sizes, and relocating these facilities to expand their service can be costly or infeasible. A more cost-effective approach involves selectively upgrading some edges (e.g., reducing their weights) to expand the RNN sizes of underutilized facilities. This motivates our research on the Expanding Reverse Nearest Neighbors (ERNN) problem, which aims to maximize the RNN size of a target facility by upgrading a limited number of edges. Solving the ERNN problem allows underutilized facilities to serve more users and alleviate the burden on other facilities. Despite numerous potential applications, ERNN is hard to solve: It can be proven to be NP-hard and APX-hard, and it exhibits non-monotonic and non-submodular properties. To overcome these challenges, we propose novel greedy algorithms that improve efficiency by minimizing the number of edges that need to be processed and the cost of processing each edge. Experimental results demonstrate that the proposed algorithms achieve orders of magnitude speedup compared to the standard greedy algorithm while greatly expanding the RNN.

Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential and fundamental operation in many applications from many domains such as multimedia database, information retrieval and computer vision. With the rapidly growing volume of data and the dramatically increasing demands of users, traditional heuristic-based ANNS solutions have been facing great challenges in terms of both efficiency and accuracy. Inspired by the recent successes of deep learning in many fields, substantial efforts have been devoted to applying deep learning techniques to ANNS for learning to index and learning to search, resulting in numerous algorithms that achieve state-of-the-art performance compared with conventional methods. In this survey paper, we comprehensively review the different types of deep learning-based ANNS methods according to two learning paradigms:learning to indexandlearning to search. We provide a comprehensive overview and analysis of these methods in a systematic manner. Based on the overview, we point out thatend-to-end learningwill be a new and promising research direction for deep learning-based ANNS, i.e., applying deep learning techniques to jointly learn the indexing and searching together, such that the underlying knowledge learned from data can directly contribute to the final searching performance. Finally, we conduct experiments and provide general performance analyses for the representative deep learning-based ANNS algorithms.

Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph$q$in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In recent years, many advanced techniques for query vertex ordering (i.e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. Instead of using the fixed heuristics to generate the matching order, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduces the number of redundant enumerations. With the help of the reinforcement learning framework, our model is able to consider the long-term benefits rather than only consider the local information at current ordering step. Extensive experiments on six real-life data graphs demonstrate that our proposed matching order generation technique could reduce up to two orders of magnitude of query processing time compared to the state-of-the-art algorithms.

On-boarding new devices into an existing SDN network is a pain for network operations (NetOps) teams, because much expert effort is required to bridge the gap between the configuration models of the new devices and the unified data model in the SDN controller. In this work, we present an assistant framework NAssim, to help NetOps accelerate the process of assimilating a new device into a SDN network. Our solution features a unified parser framework to parse diverse device user manuals into preliminary configuration models, a rigorous validator that confirm the correctness of the models via formal syntax analysis, model hierarchy validation and empirical data validation, and a deep-learning-based mapping algorithm that uses state-of-the-art neural language processing techniques to produce human-comprehensible recommended mapping between the validated configuration model and the one in the SDN controller. In all, NAssim liberates the NetOps from most tedious tasks by learning directly from devices' manuals to produce data models which are comprehensible by both the SDN controller and human experts. Our evaluation shows, NAssim can accelerate the assimilation process by 9.1x. In this process, we also identify and correct 243 errors in four mainstream vendors' device manuals, and release a validated and expert-curated dataset of parsed manual corpus for future research.

Subgraph counting is a fundamental graph analysis task which has been widely used in many applications. As the problem of subgraph counting is NP-complete and hence intractable, approximate solutions have been widely studied, which fail to work with large and complex query graphs. Alternatively, Machine Learning techniques have been recently applied for this problem, yet the existing ML approaches either only support very small data graphs or cannot make full use of the data graph information, which inherently limits their scalability, estimation accuracies and robustness.

A knowledge base is a large repository of facts usually represented as triples, each consisting of a subject, a predicate, and an object. The triples together form a graph, i.e., a knowledge graph. The triple representation in a knowledge graph offers a simple interface for applications to access the facts. However, this representation is not in a natural language form, which is difficult for humans to understand. We address this problem by proposing a system to translate a set of triples (i.e., a graph) into natural sentences. We take an encoder-decoder based approach. Specifically, we propose a Graph encoder with Content-Planning capability (GCP) to encode an input graph. GCP not only works as an encoder but also serves as a content-planner by using an entity-order aware topological traversal to encode a graph. This way, GCP can capture the relationships between entities in a knowledge graph as well as providing information regarding the proper entity order for the decoder. Hence, the decoder can generate sentences with a proper entity mention ordering. Experimental results show that GCP achieves improvements over state-of-the-art models by up to 3.6%, 4.1%, and 3.8% in three common metrics BLEU, METEOR, and TER, respectively. The code is available at (https://github.com/ruizhang-ai/GCP/).

Nonintrusive load monitoring (NILM) is a technique to disaggregate an appliance's load consumption from the aggregate load in a house. Monitoring the energy behavior has become increasingly important for home energy management. For many machine learning-based models, model training needs enough, and diverse appliance-level labeled data from different houses, which is very time-consuming, expensive, and unacceptable for users. In this article, we propose an algorithm based on the adversarial network and the joint adaptation network for energy disaggregation to decrease the distribution gaps of both the feature space and the label space between the source and target domains. With only very limited labeled data in the source domain and enough unlabeled data in the target domain, our proposed algorithm can obtain satisfactory accuracy results for NILM. Extensive experiments for intradomain and interdomain demonstrate that the proposed algorithm can significantly improve the domain adaptation. Comparing with the baseline method that without any domain adaptation, the improvement on mean absolute error with the proposed algorithm can reach 67.72%, 67.53%, and 66.56% for the washing machine (W.M), the dishwasher (D.W), and the microwave (M.V), respectively.

We study the efficient regular expression (regex) matching problem. Existing algorithms are the scanning-based algorithms which typically use an equivalent automaton compiled from the regex query to verify a document. Although some works propose various strategies to quickly jump tocandidate locationsin a document where a query result may appear, they still need to utilize the scanning-based method to verify these candidate locations. These methods become inefficient when there are still many candidate locations needed to be verified. In this article, we propose a novel approach to efficiently compute all matching positions for a regex query purely based on a positional$q$-gram inverted index. We propose a gram-driven NFA to represent the language of a regex and show all regex matching locations can be obtained by finding positions on$q$-grams of GNFA that satisfy certain positional constraints. Then we propose several GNFA-based query plans to answer the query using the positional inverted index. In order to improve the query efficiency, we design the algorithm to build a tree-based query plan by carefully choosing a checking order for positional constraints. Experimental results on real-world datasets show that our method outperforms state-of-the-art methods by up to an order of magnitude in query efficiency.

Entity alignment (EA) finds equivalent entities that are located in different knowledge graphs (KGs), which is an essential step to enhance the quality of KGs, and hence of significance to downstream applications (e.g., question answering and recommendation). Recent years have witnessed a rapid increase of EA approaches, yet the relative performance of them remains unclear, partly due to the incomplete empirical evaluations, as well as the fact that comparisons were carried out under different settings (i.e., datasets, information used as input, etc.). In this paper, we fill in the gap by conducting a comprehensive evaluation and detailed analysis of state-of-the-art EA approaches. We first propose a general EA framework that encompasses all the current methods, and then group existing methods into three major categories. Next, we judiciously evaluate these solutions on a wide range of use cases, based on their effectiveness, efficiency and robustness. Finally, we construct a new EA dataset to mirror the real-life challenges of alignment, which were largely overlooked by existing literature. This study strives to provide a clear picture of the strengths and weaknesses of current EA approaches, so as to inspire quality follow-up research.

Learning-based classifiers are susceptible to adversarial examples. Existing defence methods are mostly devised on individual classifiers. Recent studies showed that it is viable to increase adversarial robustness by promoting diversity over an ensemble of models. In this paper, we propose adversarial defence by encouraging ensemble diversity on learning high-level feature representations and gradient dispersion in simultaneous training of deep ensemble networks. We perform extensive evaluations under white-box and black-box attacks including transferred examples and adaptive attacks. Our approach achieves a significant gain of up to 52% in adversarial robustness, compared with the baseline and the state-of-the-art method on image benchmarks with complex data scenes. The proposed approach complements the defence paradigm of adversarial training, and can further boost the performance. The source code is available at https://github.com/ALIS-Lab/AAAI2021-PDD.

Recognition of human activities is an important task due to its far-reaching applications such as healthcare system, context-aware applications, and security monitoring. Recently, WiFi based human activity recognition (HAR) is becoming ubiquitous due to its non-invasiveness. Existing WiFi-based HAR methods regard WiFi signals as a temporal sequence of channel state information (CSI), and employ deep sequential models (e.g., RNN, LSTM) to automatically capture channel-over-time features. Although being remarkably effective, they suffer from two major drawbacks. Firstly, the granularity of a single temporal point is blindly elementary for representing meaningful CSI patterns. Secondly, the time-over-channel features are also important, and could be a natural data augmentation. To address the drawbacks, we propose a novel Two-stream Convolution Augmented Human Activity Transformer (THAT) model. Our model proposes to utilize a two-stream structure to capture both time-over-channel and channel-over-time features, and use the multi-scale convolution augmented transformer to capture range-based patterns. Extensive experiments on four real experiment datasets demonstrate that our model outperforms state-of-the-art models in terms of both effectiveness and efficiency.

BERT has set a new state-of-the-art performance on entity resolution (ER) task, largely owed to fine-tuning pre-trained language models and the deep pair-wise interaction. Albeit being remarkably effective, it comes with a steep increase in computational cost, as the deep-interaction requires to exhaustively compute every tuple pair to search for co-references. For ER task, it is often prohibitively expensive due to the large cardinality to be matched. To tackle this, we introduce a siamese network structure that independently encodes tuples using BERT but delays the pair-wise interaction via an enhanced alignment network. This siamese structure enables a dedicated blocking module to quickly filter out obviously dissimilar tuple pairs, and thus drastically reduces the cardinality of fine-grained matching. Further, the blocking and entity matching are integrated into a multi-task learning framework for facilitating both tasks. Extensive experiments on multiple datasets demonstrate that our model significantly outperforms state-of-the-art models (including BERT) in both efficiency and effectiveness.

Relation prediction is a fundamental task in network analysis which aims to predict the relationship between two nodes. Thus, this differes from the traditional link prediction problem predicting whether a link exists between a pair of nodes, which can be viewed as a binary classification task. However, in the heterogeneous information network (HIN) which contains multiple types of nodes and multiple relations between nodes, the relation prediction task is more challenging. In addition, the HIN might have missing relation types on some edges and missing node types on some nodes, which makes the problem even harder.

Similarity query (a.k.a. nearest neighbor query) processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications (e.g., classification & regression, deduplication, image retrieval, and recommender systems). Recently, representation learning and auto-encoding methods as well as pre-trained models have gained popularity. They basically deal with dense high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically.

Deep hashing based retrieval models have been widely used in large-scale image retrieval systems. Recently, there has been a surging interest in studying the adversarial attack problem in deep hashing based retrieval models. However, the effectiveness of existing adversarial attacks is limited by their poor perturbation management, unawareness of ranking weight, and only laser-focusing on the attack image. These shortages lead to high perturbation costs yet low AP reductions. To overcome these shortages, we propose a novel adversarial attack framework to improve the effectiveness of adversarial attacks. Our attack designs a dimension-wise surrogate Hamming distance function to help with wiser perturbation management. Further, in generating adversarial examples, instead of focusing on a single image, we propose to collectively incorporate relevant images combined with an AP-oriented (average precision) weight function. In addition, our attack can deal with both untargeted and targeted adversarial attacks in a flexible manner. Extensive experiments demonstrate that, with the same attack performance, our model significantly outperforms state-of-the-art models in perturbation cost on both untargeted and targeted attack tasks.

Keyword search is a fundamental task to retrieve information that is the most relevant to the query keywords. Keyword search over graphs aims to find subtrees or subgraphs containing all query keywords ranked according to some criteria. Existing studies all assume that the graphs have complete information. However, real-world graphs may contain some missing information (such as edges or keywords), thus making the problem much more challenging. To solve the problem of keyword search over incomplete graphs, we propose a novel model named KS-GNN based on the graph neural network and the auto-encoder. By considering the latent relationships and the frequency of different keywords, the proposed KS-GNN aims to alleviate the effect of missing information and is able to learn low-dimensional representative node embeddings that preserve both graph structure and keyword features. Our model can effectively answer keyword search queries with linear time complexity over incomplete graphs. The experiments on four real-world datasets show that our model consistently achieves better performance than state-of-the-art baseline methods in graphs having missing information.

There is an increasing interest in studying adversarial attacks on image retrieval systems. However, most of the existing attack methods are based on the white-box setting, where the attackers have access to all the model and database details, which is a strong assumption for practical attacks. The generic transfer-based attack also requires substantial resources yet the effect was shown to be unreliable. In this paper, we make the first attempt in proposing a query-efficient decision-based attack framework for the image retrieval (DAIR) to completely subvert the top-K retrieval results with human imperceptible perturbations. We propose an optimization-based method with a smoothed utility function to overcome the challenging discrete nature of the problem. To further improve the query efficiency, we propose a novel sampling method that can achieve the transferability between the surrogate and the target model efficiently. Our comprehensive experimental evaluation on the benchmark datasets shows that our DAIR method outperforms significantly the state-of-the-art decision-based methods. We also demonstrate that real image retrieval engines (Bing Visual Search and Face++ engines) can be attacked successfully with only several hundreds of queries.

Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion. Answering this problem accurately and efficiently is essential to many applications, such as density estimation, outlier detection, query optimization, and data integration. The estimation problem is especially challenging for large-scale high-dimensional data due to the curse of dimensionality, the large variance of selectivity across different queries, and the need to make the estimator consistent (i.e., the selectivity is non-decreasing in the threshold). We propose a new deep learning-based model that learns a query-dependent piecewise linear function as selectivity estimator, which is flexible to fit the selectivity curve of any distance function and query object, while guaranteeing that the output is non-decreasing in the threshold. To improve the accuracy for large datasets, we propose to partition the dataset into multiple disjoint subsets and build a local model on each of them. We perform experiments on real datasets and show that the proposed model consistently outperforms state-of-the-art models in accuracy in an efficient way and is useful for real applications.

A distance search in Hamming space finds binary vectors whose Hamming distances are no more than a threshold from a query vector. It is a fundamental problem in many applications, such as image retrieval, near-duplicate Web page detection, and scientific databases. State-of-the-art approaches to Hamming distance search are mainly based on the pigeonhole principle to generate a set of candidates and then verify them. We observe that the constraint by the pigeonhole principle is not always tight and may bring about unnecessary candidates. We also observe that the distribution in real data is often skewed, but most existing solutions adopt a simple equi-width partitioning and allocate the same threshold to all the parts, hence failing to exploit the data skewness to optimize query processing. In this paper, we propose a new form of the pigeonhole principle which allows variable partitioning and threshold allocation. Based on the new principle, we develop a tight constraint of candidates and devise cost-aware methods for partitioning and threshold allocation to optimize query processing. In addition, we extend our methods to answer Hamming distance join queries. We also discuss the application of the pigeonhole principle in set similarity search, a problem that can be converted to Hamming distance search equivalently. Our evaluation on datasets with various data distributions shows the robustness of our solution and its superior query processing performance to the state-of-the-art methods.

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FG-NET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention's sentence-specific context. This is inadequate for highly overlapping and/or noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro-f1 and micro-f1 respectively.

Entity resolution (ER) aims to identify entity records that refer to the same real-world entity, which is a critical problem in data cleaning and integration. Most of the existing models are attribute-centric, that is, matching entity pairs by comparing similarities of pre-aligned attributes, which require the schemas of records to be identical and are too coarse-grained to capture subtle key information within a single attribute. In this paper, we propose a novel graph-based ER model GraphER. Our model is token-centric: the final matching results are generated by directly aggregating token-level comparison features, in which both the semantic and structural information has been softly embedded into token embeddings by training an Entity Record Graph Convolutional Network (ER-GCN). To the best of our knowledge, our work is the first effort to do token-centric entity resolution with the help of GCN in entity resolution task. Extensive experiments on two real-world datasets demonstrate that our model stably outperforms state-of-the-art models.

To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming. On the contrary, distantly supervised methods acquire automatically annotated data using dictionaries to alleviate this requirement. Unfortunately, dictionaries hinder the effectiveness of distantly supervised methods for NER due to its limited coverage, especially in specific domains. In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. We generalize the distant supervision by extending the dictionary with headword based non-exact matching. We apply a function to better weight the matched entity mentions. We propose a span-level model, which classifies all the possible spans then infers the selected spans with a proposed dynamic programming algorithm. Experiments on all three benchmark datasets demonstrate that our method outperforms previous state-of-the-art distantly supervised methods.

The vast amount of web data enables us to build knowledge bases with unprecedented quality and coverage. Named Entity Disambiguation (NED) is an important task that automatically resolves ambiguous mentions in free text to correct target entries in the knowledge base. Traditional machine learning based methods for NED were outperformed and made obsolete by the state-of-the-art deep learning based models. However, deep learning models are more complex, requiring large amount of training data and lengthy training and parameter tuning time. In this paper, we revisit traditional machine learning techniques and propose a light-weight, tuneable and time-efficient method without using deep learning or deep learning generated features. We propose novel adaptive features that focus on extracting discriminative features to better model similarities between candidate entities and the mention's context. We learn a local ranking model based on traditional and the new adaptive features based on the learning-to-rank framework. While arriving at linking decisions individually via the local model, our method also takes into consideration the correlation between decisions by running multiple recurrent global models, which can be deemed as a learned local search method. Our method attains performances comparable to the state-of-the-art deep learning-based methods on NED benchmark datasets while being significantly faster to train.

Shortest-path distances on road networks have many applications such as finding nearest places of interest (POI) for travel recommendations. To compute a shortest-path distance, traditional approaches traverse the road network to find the shortest path and return the path length. When the distances are needed first (e.g., to rank POIs) while the shortest paths may be computed later (e.g., after a POI is chosen), one may precompute and store the distances, and answer distance queries by simple lookups. This approach, however, falls short in the worst-cast space cost – O(n2) for n vertices even with various optimizations. To address these limitations, we propose to learn an embedding for every vertex that preserves its distances to the other vertices. We then train a multi-layer perceptron (MLP) to predict the distance between two vertices given their embeddings. We thus achieve fast distance predictions without a high space cost. Experimental results on real road networks confirm these advantages. Meanwhile, our approach is up to 97% more accurate than the state-of-the-art approaches for distance predictions.

Approximate nearest neighbour search (ANNS) in high dimensional space is a fundamental problem in many applications, such as multimedia database, computer vision and information retrieval. Among many solutions, data-sensitive hashing-based methods are effective to this problem, yet few of them are designed for external storage scenarios and hence do not optimized for I/O efficiency during the query processing. In this paper, we introduce a novel data-sensitive indexing and query processing framework for ANNS with an emphasis on optimizing the I/O efficiency, especially, the sequential I/Os. The proposed index consists of several lists of point IDs, ordered by values that are obtained by learned hashing (i.e., mapping) functions on each corresponding data point. The functions are learned from the data and approximately preserve the order in the high-dimensional space. We consider two instantiations of the functions (linear and non-linear), both learned from the data with novel objective functions. We also develop an I/O efficient ANNS framework based on the index. Comprehensive experiments on six benchmark datasets show that our proposed methods with learned index structure perform much better than the state-of-the-art external memory-based ANNS methods in terms of I/O efficiency and accuracy.

Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge. Existing EA solutions mainly rely on structural information to align entities, typically through KG embedding. Nonetheless, in real-life KGs, only a few entities are densely connected to others, and the rest majority possess rather sparse neighborhood structure. We refer to the latter as long-tail entities, and observe that such phenomenon arguably limits the use of structural information for EA.

In this paper, we investigate the possibilities of utilizing deep learning for cardinality estimation of similarity selection. Answering this problem accurately and efficiently is essential to many data management applications, especially for query optimization. Moreover, in some applications the estimated cardinality is supposed to be consistent and interpretable. Hence a monotonic estimation w.r.t. the query threshold is preferred. We propose a novel and generic method that can be applied to any data type and distance function. Our method consists of a feature extraction model and a regression model. The feature extraction model transforms original data and threshold to a Hamming space, in which a deep learning-based regression model is utilized to exploit the incremental property of cardinality w.r.t. the threshold for both accuracy and monotonicity. We develop a training strategy tailored to our model as well as techniques for fast estimation. We also discuss how to handle updates. We demonstrate the accuracy and the efficiency of our method through experiments, and show how it improves the performance of a query optimizer.

Locality sensitive hashing (LSH) is a widely practiced c -approximate nearest neighbor( c -ANN) search algorithm in high dimensional spaces. The state-of-the-art LSH based algorithm searches an unbounded and irregular space to identify candidates, which jeopardizes the efficiency. To address this issue, we introduce the concept of virtual hypersphere partitioning. The core idea is to impose a virtual hypersphere, centered at the query, in the original feature space and only examine points inside the hypersphere. The search space of a hypersphere is isotropic and bounded, and thus more efficient than the existing one. In practice, we use multiple physical hyperspheres with different radii in corresponding projection subspaces to emulate the single virtual hypersphere. We also developed a principled method to compute the hypersphere radii for given success probability. Based on virtual hypersphere partitioning, we propose a novel disk-based indexing and searching scheme VHP to answer c -ANN queries. In the indexing phase, VHP stores LSH projections with independent B + -trees. To process a query, VHP keeps increasing the radii of physical hyperspheres co-ordinately, which in effect amounts to enlarging the virtual hypersphere, to accommodate more candidates until the success probability is met. Rigorous theoretical analysis shows that the proposed algorithm supports c -ANN search for arbitrarily small c ≥ 1 with probability guarantee. Extensive experiments on a variety of datasets, including the billion-scale ones, demonstrate that VHP could achieve different tradeoffs between efficiency and accuracy, and achieves up to 2x speedup in running time over the state-of-the-art methods.

Similarity query processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications. Recently, embedding and auto-encoding methods as well as pre-trained models have gained popularity. They basically deal with high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing for high-dimensional data. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically. In this tutorial, we summarize existing solutions, especially recent advancements from both database (DB) and machine learning (ML) communities, and analyze their strengths and weaknesses. We review exact and approximate methods such as cover tree, locality sensitive hashing, product quantization, and proximity graphs. We also discuss the selectivity estimation problem and show how researchers are bringing in state-of-the-art ML techniques to address the problem. By highlighting the strong connections between DB and ML, we hope that this tutorial provides an impetus towards new ML for DB solutions and vice versa.

Nearest neighbor search is a fundamental and essential operation in applications from many domains, such as databases, machine learning, multimedia, and computer vision. Because exact searching results are not efficient for a high-dimensional space, a lot of efforts have turned to approximate nearest neighbor search. Although many algorithms have been continuously proposed in the literature each year, there is no comprehensive evaluation and analysis of their performance. In this paper, we conduct a comprehensive experimental evaluation of many state-of-the-art methods for approximate nearest neighbor search. Our study (1) is cross-disciplinary (i.e., including 19 algorithms in different domains, and from practitioners) and (2) has evaluated a diverse range of settings, including 20 datasets, several evaluation metrics, and different query workloads. The experimental results are carefully reported and analyzed to understand the performance results. Furthermore, we propose a new method that achieves both high query efficiency and high recall empirically on majority of the datasets under a wide range of settings.

Sharing a pre-trained machine learning model, particularly a deep neural network via prediction APIs, is becoming a common practice on machine learning as a service (MLaaS) platforms nowadays. Although deep neural networks (DNN) have shown remarkable successes in many tasks, they are also criticized for the lack of interpretability and transparency. Interpreting a shared DNN model faces two additional challenges compared with interpreting a general model. (1) Limited training data can be disclosed to users. (2) The internal structure of the models may not be available. These two challenges impede the application of most existing interpretability approaches, such as saliency maps or influence functions, for DNN models. Case-based reasoning methods have been used for interpreting decisions; however, how to select and organize the data points under the constraints of shared DNN models is not discussed. Moreover, simply providing cases as explanations may not be sufficient for supporting instance level interpretability. Meanwhile, existing interpretation methods for DNN models generally lack the means to evaluate the reliability of the interpretation. In this article, we propose a framework named Shared Model INTerpreter (SMINT) to address the above limitations. We propose a new data structure called a boundary graph to organize training points to mimic the predictions of DNN models. We integrate local features, such as saliency maps and interpretable input masks, into the data structure to help users to infer the model decision boundaries. We show that the boundary graph is able to address the reliability issues in many local interpretation methods. We further design an algorithm named hidden-layer aware p-test to measure the reliability of the interpretations. Our experiments show that SMINT is able to achieve above 99% fidelity to corresponding DNN models on both MNIST and ImageNet by sharing only a tiny fraction of training data to make these models interpretable. The human pilot study demonstrates that SMINT provides better interpretability compared with existing methods. Moreover, we demonstrate that SMINT is able to assist model tuning for better performance on different user data.

Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed from the distilled sub-spaces along with some word level features to distinguish antonyms from synonyms. Experimental results show that the proposed model outperforms existing research on antonym synonym distinction in both speed and performance.

Triplets extraction is an essential and pivotal step in automatic knowledge base construction, which captures structural information from unstructured text corpus. Conventional extraction models use a pipeline of named entity recognition and relation classification to extract entities and relations, respectively, which ignore the connection between the two tasks. Recently, several neural network-based models were proposed to tackle the problem, and achieved state-of-the-art performance. However, most of them are unable to extract multiple triplets from a single sentence, which are yet commonly seen in real-life scenarios. To close the gap, we propose in this paper a joint neural extraction model for multitriplets, namely, TME, which is capable of adaptively discovering multiple triplets simultaneously in a sentence via ranking with translation mechanism. In experiment, TME exhibits superior performance and achieves an improvement of 37.6% on F1 score over state-of-the-art competitors.

There are many different ways in which external information might be used in an NLP task.This paper investigates how external syntactic information can be used most effectively in the Semantic Role Labeling (SRL) task.We evaluate three different ways of encoding syntactic parses and three different ways of injecting them into a state-of-the-art neural ELMo-based SRL sequence labelling model.We show that using a constituency representation as input features improves performance the most, achieving a new state-of-the-art for non-ensemble SRL models on the in-domain CoNLL'05 and CoNLL'12 benchmarks. 1

With the increasing popularity and diversity of social media, users tend to join multiple social platforms to enjoy different types of services. User identity linkage, which aims to link identical identities across different social platforms, has attracted increasing research attentions recently. Existing methods usually focus on pairwise identity linkage between two platforms, which cannot piece up the information from multi-sources to depict the intrinsic figures of social users. In this paper, we propose a novel adversarial learning based framework MSUIL with partially shared generators to perform Semi-supervised User Identity Linkage across Multiple social networks. The isomorphism across multiple platforms is captured as the complementary to link identities. The insight is that we aim to learn the desirable projection functions (generators) to not only minimize the distance between the distributions of user identities in arbitrary pairs of platforms, but also incorporate the available annotations as the learning guidance. The projection functions of different platform pairs share partial parameters, which ensures MSUIL can capture the interdependencies among multiple platforms and improves the model efficiency. Empirically, we evaluate our proposal over multiple datasets. The experimental results demonstrate the superiority of the proposed MSUIL model.

Nearest Neighbor search has been well solved in low-dimensional space, but is challenging in high-dimensional space due to the curse of dimensionality. As a trade-off between efficiency and result accuracy, a variety of c-approximate nearest neighbor (c-ANN) algorithms have been proposed to return a c-approximate NN with confident at least δ. We observe that existing c-ANN search algorithms have some limitations on I/O efficiency when their indexes are resided on the external memory, which is critical for handling large scale high-dimensional data. In this paper, we introduce an incremental search based c-ANN search algorithm, named I-LSH. Unlike the previous LSH methods, which expand the bucket width in an exponential way, I-LSH adopts a more natural search strategy to incrementally access the hash values of the objects. We provide rigorous theoretical analysis to underpin our incremental search strategy. Our comprehensive experiment results show that, compared with state-of-the-art I/O efficient c-ANN techniques, our algorithm can achieve much better I/O efficiency under the same theoretical guarantee.

Probabilistic queries usually suffer from the noisy query result sets, due to data uncertainty. In this paper, we propose an efficient optimization framework, termed as QueryClean, for both probabilistic skyline computation and probabilistic similarity search. Its goal is to optimize query quality by selecting a group of uncertain objects to clean under limited resource available, where an entropy based quality function is leveraged. We develop an efficient index to organize the possible result sets of probabilistic queries, which is able to help avoid multiple probabilistic query evaluations over a large number of possible worlds for quality computation. Moreover, using two newly presented heuristics, we present exact and approximate algorithms for the optimization problem. Extensive experiments on both real and synthetic data sets demonstrate the efficiency and scalability of QueryClean.

Adversarial examples induce model classification errors on purpose, which has raised concerns on the security aspect of machine learning techniques. Many existing countermeasures are compromised by adaptive adversaries and transferred examples. We propose a model-agnostic approach to resolve the problem by analysing the model responses to an input under random perturbations, and study the robustness of detecting norm-bounded adversarial distortions in a theoretical framework. Extensive evaluations are performed on the MNIST, CIFAR-10 and ImageNet datasets. The results demonstrate that our detection method is effective and resilient against various attacks including black-box attacks and the powerful CW attack with four adversarial adaptations.

A knowledge base is a large repository of facts that are mainly represented as RDF triples, each of which consists of a subject, a predicate (relationship), and an object.The RDF triple representation offers a simple interface for applications to access the facts.However, this representation is not in a natural language form, which is difficult for humans to understand.We address this problem by proposing a system to translate a set of RDF triples into natural sentences based on an encoder-decoder framework.To preserve as much information from RDF triples as possible, we propose a novel graph-based triple encoder.The proposed encoder encodes not only the elements of the triples but also the relationships both within a triple and between the triples.Experimental results show that the proposed encoder achieves a consistent improvement over the baseline models by up to 17.6%, 6.0%, and 16.4% in three common metrics BLEU, METEOR, and TER, respectively.

A similarity search in Hamming space finds binary vectors whose Hamming distances are no more than a threshold from a query vector. It is a fundamental problem in many applications, including image retrieval, near-duplicate Web page detection, and machine learning. State-of-the-art approaches to answering such queries are mainly based on the pigeonhole principle to generate a set of candidates and then verify them. We observe that the constraint based on the pigeonhole principle is not always tight and hence may bring about unnecessary candidates. We also observe that the distribution in real data is often skew, but most existing solutions adopt a simple equiwidth partitioning and allocate the same threshold to all the partitions, and hence fail to exploit the data skewness to optimize the query processing. In this paper, we propose a new form of the pigeonhole principle which allows variable partition size and threshold. Based on the new principle, we first develop a tight constraint of candidates, and then devise cost-aware methods for dimension partitioning and threshold allocation to optimize query processing. Our evaluation on datasets with various data distributions shows the robustness of our solution and its superior query processing performance to the state-of-the-art methods.

Phrase embedding aims at representing phrases in a vector space and it is important for the performance of many NLP tasks. Existing models only regard a phrase as either full-compositional or non-compositional, while ignoring the hybrid-compositionality that widely exists, especially in long phrases. This drawback prevents them from having a deeper insight into the semantic structure for long phrases and as a consequence, weakens the accuracy of the embeddings. In this paper, we present a novel method for jointly learning compositionality and phrase embedding by adaptively weighting different compositions using an implicit hierarchical structure. Our model has the ability of adaptively adjusting among different compositions without entailing too much model complexity and time cost. To the best of our knowledge, our work is the first effort that considers hybrid-compositionality in phrase embedding. The experimental evaluation demonstrates that our model outperforms state-of-the-art methods in both similarity tasks and analogy tasks.

Probabilistic queries have been extensively explored to provide answers with confidence, in order to support the real-life applications struggling with uncertain data, such as sensor networks and data integration. However, the uncertainty of data may propagate, and thus, the results returned by probabilistic queries contain much noise, which degrades query quality significantly. In this paper, we propose an efficient optimization framework, termed as QueryClean, for both probabilistic skyline computation and probabilistic similarity search. The goal of QueryClean is to optimize query quality via selecting a group of uncertain objects to clean under limited resource available, where a joint-entropy based quality function is leveraged. We develop an efficient structure called ASI to index the possible result sets of probabilistic queries, which helps to avoid many types of probabilistic query evaluations over a large number of the possible worlds for quality computation. Moreover, we present exact and approximate algorithms for the optimization problem, using two newly presented heuristics. Considerable experimental results on both real and synthetic data sets demonstrate the efficiency and scalability of our proposed framework QueryClean.

Knowledge graphs are typical large-scale multi-relational structures, which comprise a large amount of fact triplets. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is widely used to embed fact triplets into low-dimensional spaces. Many existing knowledge graph embedding models either focus on learning rich features from entities but fail to extract good features of relations, or employ sophisticated models that have rather high time and memory-space complexities. In this paper, we propose a novel knowledge graph embedding model, CombinE. It exploits entity features from two complementary perspectives via the plus and minus combinations. We start with the plus combination, where we use shared features of entity pairs participating in a relation to convey its relation features. To also allow differences of each pairs of entities participating in a relation, we also use the minus combination, where we concentrate on individual entity features, and regard relations as a channel to offset the divergence and preserve the prominence between head and tail entities. Compared with the state-of-the-art models, our experimental results demonstrate that CombinE outperforms existing ones and has low time and memory-space complexities.

In modern search engines, Knowledge Graphs have become a key component for knowledge discovery. When a user searches for an entity, the existing systems usually provide a list of related entities, but they do not necessarily give explanations of how they are related. However, with the help of knowledge graphs, we can generate relatedness graphs between any pair of existing entities. Existing methods of this problem are either graph-based or list-based, but they all have some limitations when dealing with large complex relatedness graphs of two related entity. In this work, we investigate how to summarize the relatedness graphs and how to use the summarized graphs to assistant the users to retrieve target information. We also implemented our approach in an online query system and performed experiments and evaluations on it. The results show that our method produces much better result than previous work.

With the growing popularity of electronic documents, replication can occur for many reasons. People may copy text segments from various sources and make modifications. In this paper, we study the problem of local similarity search to find partially replicated text. Unlike existing studies on similarity search which find entirely duplicated documents, our target is to identify documents that approximately share a pair of sliding windows which differ by no more than τ tokens. Our problem is technically challenging because for sliding windows the tokens to be indexed are less selective than entire documents, rendering set similarity join-based algorithms less efficient. Our proposed method is based on enumerating token combinations to obtain signatures with high selectivity. In order to strike a balance between signature and candidate generation, we partition the token universe and for different partitions we generate combinations composed of different numbers of tokens. A cost-aware algorithm is devised to find a good partitioning of the token universe. We also propose to leverage the overlap between adjacent windows to share computation and thus speed up query processing. In addition, we develop the techniques to support the large thresholds. Experiments on real datasets demonstrate the efficiency of our method against alternative solutions.

Given a set of objects and a query q, a point p is q’s Reverse k Nearest Neighbour (RkNN) if q is one of p’s k-closest objects. RkNN queries have received significant research attention in the past few years. However, we realize that the state-of-the-art algorithm, SLICE, accesses many objects that do not contribute to its RkNN results when running the filtering phase, which deteriorates the query performance. In this paper, we propose a novel RkNN algorithm with pre-computation by partitioning the data space into disjoint rectangular regions and constructing the guardian set for each region R. We guarantee that, for each q that lies in R, its RkNN results are only affected by the objects in R’s guardian set. The advantage of this approach is that the results of a query $$q\in R$$ can be computed by using SLICE on only the objects in its guardian set instead of using the whole dataset. Besides, we raise two new useful variants of RkNN and propose algorithms. Our comprehensive experimental study on synthetic and real the proposed approaches are the most efficient algorithms for RkNN and its variants.

Efficiently answering XML keyword queries has attracted much research effort in the last decade. The key factors resulting in the inefficiency of existing methods are thecommon-ancestor-repetition(CAR) andvisiting-useless-nodes(VUN) problems. To address the CAR problem, we propose agenerictop-downprocessing strategy to answer a given keyword query w.r.t. LCA/SLCA/ELCA semantics. By “top-down”, we mean that we visit allcommon ancestor(CA) nodes in a depth-first, left-to-right order; by “generic”, we mean that our method is independent of the query semantics. To address the VUN problem, we propose to use child nodes, rather than descendant nodes to test the satisfiability of a node$v$w.r.t. the given semantics. We propose two algorithms that are based on either traditional inverted lists or our newly proposed LLists to improve the overall performance. We further propose several algorithms that are based on hash search to simplify the operation of finding CA nodes from all involved LLists. The experimental results verify the benefits of our methods according to various evaluation metrics.

Query autocompletion has become a standard feature in many search applications, especially for search engines. A recent trend is to support theerror-tolerant autocompletion, which increases the usability significantly by matching prefixes of database strings and allowing a small number of errors. In this article, we systematically study the query processing problem for error-tolerant autocompletion with a given edit distance threshold. We propose a general framework that encompasses existing methods and characterizes different classes of algorithms and the minimum amount of information they need to maintain under different constraints. We then propose a novel evaluation strategy that achieves the minimum active node size by eliminating ancestor-descendant relationships among active nodes entirely. In addition, we characterize the essence of edit distance computation by a novel data structure namededit vector automaton(EVA). It enables us to compute new active nodes and their associated states efficiently by table lookups. In order to support large distance thresholds, we devise a partitioning scheme to reduce the size and construction cost of the automaton, which results in theuniversal partitioned EVA(UPEVA) to handle arbitrarily large thresholds. Our extensive evaluation demonstrates that our proposed method outperforms existing approaches in both space and time efficiencies.

There exists considerable literature on estimating the cardinality of set intersection result. In this paper, we consider a generalized problem for integer sets where, given a gap parameter δ, two elements are deemed as matches if their numeric difference equals δ or is within δ. We call this problem the gapped set intersection size estimation (GSISE/), and it can be used to model applications in database systems, data mining, and information retrieval. We first distinguish two subtypes of the estimation problem: the point gap estimation and range gap estimation. We propose optimized sketches to tackle the two problems efficiently and effectively with theoretical guarantees. We demonstrate the usage of our proposed techniques in mining top-K related keywords efficiently, by integrating with an inverted index. Finally, substantial experiments based on a large subset of the ClueWed09 dataset demonstrate the efficiency and effectiveness of the proposed methods.

We investigate the problem of processing a large amount of continuous spatial-keyword queries over streaming data, which is essential in many applications such as location-based recommendation and advertising, thanks to the proliferation of geo-equipped devices and the ensuing location-based social media applications. For example, a location-based e-coupon system may allow potentially millions of users to register their continuous spatial-keyword queries (e.g., interests in nearby sales) by specifying a set of keywords and a spatial region; the system then delivers each incoming spatial-textual object (e.g., a geo-tagged e-coupon) to all the matched queries (i.e., users) whose spatial and textual requirements are satisfied. While there are several prior approaches aiming at providing efficient query processing techniques for the problem, their approaches belong to spatial-first indexing method which cannot well exploit the keyword distribution. In addition, their textual filtering techniques are built upon simple variants of traditional inverted indexes, which do not perform well for the textual constraint imposed by the problem. In this paper, we address the above limitations and provide a highly efficient solution based on a novel adaptive index, named AP-Tree. The AP-Tree adaptively groups registered queries using keyword and spatial partitions, guided by a cost model. The AP-Tree also naturally indexes ordered keyword combinations. We present index construction algorithm that seamlessly and effectively integrates keyword and spatial partitions. Consequently, our method adapts well to the underlying spatial and keyword distributions of the data. Our extensive experiments demonstrate that AP-Tree achieves up to an order of magnitude improvement on efficiency compared with prior state-of-the-art methods.

We study efficient query processing for approximate string queries, which find strings within a string collection whose edit distances to the query strings are within the given thresholds. Existing methods typically hinge on the property that globally similar strings must share at least certain number of identical substrings or subsequences. They become ineffective when there are burst errors or when the number of errors is large. In this paper, we explore the opposite paradigm focusing on finding out the differences of database strings to the query string. We propose a new filtering method, called local filtering, based on the idea that two strings exhibiting substantial local dissimilarities must be globally dissimilar. We propose the concept of (positional) local distance to quantify the minimum amount of errors a query fragment contributes to the edit distance between the query and a data string. It also leads to effective pruning rules and can speed up verification via early termination. We devise a family of indexing methods based on the idea of precomputing (positional) local distances for all possible combinations of query fragments and edit distance thresholds. Based on careful analyses of subtle relationships among local distances, novel techniques are proposed to drastically reduce the amount of enumeration with no or little impact on the pruning power. Efficient query processing methods exploiting the new index and bit-parallelism are also proposed. Experimental results on real datasets show that our local filtering-based methods can achieve substantial speedup compared with state-of-the-art methods, and they are robust against factors such as dataset characteristics and large edit distance thresholds.

Given a set of users, a set of facilities and a query facility q , a reverse k nearest neighbors (R k NN) query returns every user u for which the query is one of its k closest facilities. R k NN queries have been extensively studied under a variety of settings and many sophisticated algorithms have been proposed to answer these queries. However, the existing experimental studies suffer from a few limitations. For example, some studies estimate the I/O cost by charging a fixed penalty per I/O and we show that this may be misleading. Also, the existing studies either use an extremely small buffer or no buffer at all which puts some algorithms at serious disadvantage. We show that the performance of these algorithms is significantly improved even when a small buffer (containing 100 pages) is used. Finally, in each of the existing studies, the proposed algorithm is mainly compared only with its predecessor assuming that it was the best algorithm at the time which is not necessarily true as shown in our experimental study. Motivated by these limitations, we present a comprehensive experimental study that addresses these limitations and compares some of the most notable algorithms under a wide variety of settings. Furthermore, we also present a carefully developed filtering strategy that significantly improves TPL which is one of the most popular R k NN algorithms. Specifically, the optimized version is up to 20 times faster than the original version and reduces its I/O cost up to two times.

A string-similarity measure quantifies the similarity between two text strings for approximate string matching or comparison. For example, the strings “Sam” and “Samuel” can be considered to be similar. Most existing work that computes the similarity of two strings only considers syntactic similarities, for example, number of common words or q -grams. While this is indeed an indicator of similarity, there are many important cases where syntactically-different strings can represent the same real-world object. For example, “Bill” is a short form of “William,” and “Database Management Systems” can be abbreviated as “DBMS.” Given a collection of predefined synonyms, the purpose of this article is to explore such existing knowledge to effectively evaluate the similarity between two strings and efficiently perform similarity searches and joins, thereby boosting the quality of approximate string matching. In particular, we first present an expansion-based framework to measure string similarities efficiently while considering synonyms. We then study efficient algorithms for similarity searches and joins by proposing two novel indexes, called SI-trees and QP-trees, which combine signature-filtering and length-filtering strategies. In order to improve the efficiency of our algorithms, we develop an estimator to estimate the size of candidates to enable an online selection of signature filters. This estimator provides strong low-error, high-confidence guarantees while requiring only logarithmic space and time costs, thus making our method attractive both in theory and in practice. Finally, the experimental results from a comprehensive study of the algorithms with three real datasets verify the effectiveness and efficiency of our approaches.

Nearest neighbor searches in high-dimensional space have many important applications in domains such as data mining, and multimedia databases. The problem is challenging due to the phenomenon called "curse of dimensionality". An alternative solution is to consider algorithms that returns a c -approximate nearest neighbor ( c -ANN) with guaranteed probabilities. Locality Sensitive Hashing (LSH) is among the most widely adopted method, and it achieves high efficiency both in theory and practice. However, it is known to require an extremely high amount of space for indexing, hence limiting its scalability. In this paper, we propose several surprisingly simple methods to answer c -ANN queries with theoretical guarantees requiring only a single tiny index. Our methods are highly flexible and support a variety of functionalities, such as finding the exact nearest neighbor with any given probability. In the experiment, our methods demonstrate superior performance against the state-of-the-art LSH-based methods, and scale up well to 1 billion high-dimensional points on a single commodity PC.

In this paper, we investigate the selectivity estimation problem for streaming spatio-textual data, which arises in many social network and geo-location applications. Specifically, given a set of continuously and rapidly arriving spatio-textual objects, each of which is described by a geo-location and a short text, we aim to accurately estimate the cardinality of a spatial keyword query on objects seen so far, where a spatial keyword query consists of a search region and a set of query keywords. To the best of our knowledge, this is the first work to address this important problem. We first extend two existing techniques to solve this problem, and show their limitations. Inspired by two key observations on the "locality" of the correlations among query keywords, we propose a local correlation based method by utilizing an augmented adaptive space partition tree ( A 2 SP -tree for short) to approximately learn a local Bayesian network on-the-fly for a given query and estimate its selectivity. A novel local boosting approach is presented to further enhance the learning accuracy of local Bayesian networks. Our comprehensive experiments on real-life datasets demonstrate the superior performance of the local correlation based algorithm in terms of estimation accuracy compared to other competitors.

Efficiently answering XML keyword queries has attracted much research effort in the last decade. One key factors resulting in the inefficiency of existing methods are the common-ancestor-repetition (CAR) and visiting-useless-nodes (VUN) problems. In this paper, we propose a generic top-down processing strategy to answer a given keyword query w.r.t. LCA/SLCA/ELCA semantics. By top-down, we mean that we visit all common ancestor (CA) nodes in a depth-first, left-to-right order, thus avoid the CAR problem; by generic, we mean that our method is independent of the labeling schemes and query semantics. We show that the satisfiability of a node v w.r.t. the given semantics can be determined by v's child nodes, based on which our methods avoid the VUN problem. We propose two algorithms that are based on either traditional inverted lists or our newly proposed LLists to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.

Evaluating similarity between sets is a fundamental task in computer science. However, there are many applications in which elements in a set may be uncertain due to various reasons. Existing work on modeling such probabilistic sets and computing their similarities suffers from huge model sizes or significant similarity evaluation cost, and hence is only applicable to small probabilistic sets. In this paper, we propose a simple yet expressive model that supports many applications where one probabilistic set may have thousands of elements. We define two types of similarities between two probabilistic sets using the possible world semantics; they complement each other in capturing the similarity distributions in the cross product of possible worlds. We design efficient dynamic programming-based algorithms to calculate both types of similarities. Novel individual and batch pruning techniques based on upper bounding the similarity values are also proposed. To accommodate extremely large probabilistic sets, we also design sampling-based approximate query processing methods with strong probabilistic guarantees. We have conducted extensive experiments using both synthetic and real datasets, and demonstrated the effectiveness and efficiency of our proposed methods.

A string similarity measure quantifies the similarity between two text strings for approximate string matching or comparison. For example, the strings "Sam" and "Samuel" can be considered similar. Most existing work that computes the similarity of two strings only considers syntactic similarities, e.g., number of common words or q-grams. While these are indeed indicators of similarity, there are many important cases where syntactically different strings can represent the same real-world object. For example, "Bill" is a short form of "William". Given a collection of predefined synonyms, the purpose of the paper is to explore such existing knowledge to evaluate string similarity measures more effectively and efficiently, thereby boosting the quality of string matching.

Hamming distance measures the number of dimensions where two vectors have different values. In applications such as pattern recognition, information retrieval, and databases, we often need to efficiently process Hamming distance query, which retrieves vectors in a database that have no more than k Hamming distance from a given query vector. Existing work on efficient Hamming distance query processing has some of the following limitations, such as only applicable to tiny error threshold values, unable to deal with vectors where the value domain is large, or unable to attain robust performance in the presence of data skew.

Query autocompletion is an important feature saving users many keystrokes from typing the entire query. In this paper we study the problem of query autocompletion that tolerates errors in users' input using edit distance constraints. Previous approaches index data strings in a trie, and continuously maintain all the prefixes of data strings whose edit distance from the query are within the threshold. The major inherent problem is that the number of such prefixes is huge for the first few characters of the query and is exponential in the alphabet size. This results in slow query response even if the entire query approximately matches only few prefixes. In this paper, we propose a novel neighborhood generation-based algorithm, IncNGTrie, which can achieve up to two orders of magnitude speedup over existing methods for the error-tolerant query autocompletion problem. Our proposed algorithm only maintains a small set of active nodes, thus saving both space and time to process the query. We also study efficient duplicate removal which is a core problem in fetching query answers. In addition, we propose optimization techniques to reduce our index size, as well as discussions on several extensions to our method. The efficiency of our method is demonstrated against existing methods through extensive experiments on real datasets.

Similarity joins play an important role in many application areas, such as data integration and cleaning, record linkage, and pattern recognition. In this paper, we study efficient algorithms for similarity joins with an edit distance constraint. Currently, the most prevalent approach is based on extracting overlapping grams from strings and considering only strings that share a certain number of grams as candidates. Unlike these existing approaches, we propose a novel approach to edit similarity join based on extracting nonoverlapping substrings, or chunks, from strings. We propose a class of chunking schemes based on the notion of tail-restricted chunk boundary dictionary. A new algorithm, VChunkJoin, is designed by integrating existing filtering methods and several new filters unique to our chunk-based method. We also design a greedy algorithm to automatically select a good chunking scheme for a given data set. We demonstrate experimentally that the new algorithm is faster than alternative methods yet occupies less space.

Given a query string Q , an edit similarity search finds all strings in a database whose edit distance with Q is no more than a given threshold τ. Most existing methods answering edit similarity queries employ schemes to generate string subsequences as signatures and generate candidates by set overlap queries on query and data signatures. In this article, we show that for any such signature scheme, the lower bound of the minimum number of signatures is τ + 1, which is lower than what is achieved by existing methods. We then propose several asymmetric signature schemes, that is, extracting different numbers of signatures for the data and query strings, which achieve this lower bound. A basic asymmetric scheme is first established on the basis of matching q -chunks and q -grams between two strings. Two efficient query processing algorithms (IndexGram and IndexChunk) are developed on top of this scheme. We also propose novel candidate pruning methods to further improve the efficiency. We then generalize the basic scheme by incorporating novel ideas of floating q -chunks, optimal selection of q -chunks, and reducing the number of signatures using global ordering. As a result, the Super and Turbo families of schemes are developed together with their corresponding query processing algorithms. We have conducted a comprehensive experimental study using the six asymmetric algorithms and nine previous state-of-the-art algorithms. The experiment results clearly showcase the efficiency of our methods and demonstrate space and time characteristics of our proposed algorithms.

Graphs are widely used to model complicated data semantics in many applications in bioinformatics, chemistry, social networks, pattern recognition, etc. A recent trend is to tolerate noise arising from various sources, such as erroneous data entry, and find similarity matches. In this paper, we study the graph similarity join problem that returns pairs of graphs such that their edit distances are no larger than a threshold. Inspired by the q-gram idea for string similarity problem, our solution extracts paths from graphs as features for indexing. We establish a lower bound of common features to generate candidates. An efficient algorithm is proposed to exploit both matching and mismatching features to improve the filtering and verification on candidates. We demonstrate the proposed algorithm significantly outperforms existing approaches with extensive experiments on publicly available datasets.

In this paper, we focus on efficient keyword query processing for XML data based on the SLCA and ELCA semantics. We propose a novel form of inverted lists for keywords which include IDs of nodes that directly or indirectly contain a given keyword. We propose a family of efficient algorithms that are based on the set intersection operation for both semantics. We show that the problem of SLCA/ELCA computation becomes finding a set of nodes that appear in all involved inverted lists and satisfy certain conditions. We also propose several optimization techniques to further improve the query processing performance. We have conducted extensive experiments with many alternative methods. The results demonstrate that our proposed methods outperform previous methods by up to two orders of magnitude in many cases.

The performances of supervised learning techniques on image classification problems heavily rely on the quality of their training images. But the acquisition of high quality training images requires significant efforts from human annotators. In this paper, we propose a novel multi-label batch model active learning (MLBAL) approach that allows the learning algorithm to actively select a batch of informative example-label pairs from which it learns at each learning iteration, so as to learn accurate classifiers with less annotation efforts. Unlike existing methods, the proposed approach fines the active selection granularity from example to example-label pair, and takes into account the informative label correlations for active learning. And the empirical studies demonstrate its effectiveness.

Multiple-Instance learning (MIL), which relaxes training annotation granularity from instance level to instance collection (bag) level by applying bag concept, obtains increasing attentions from computer vision community. Due to its flexible annotation mechanism, MIL has been naturally utilized on a variety of computer vision problems. And numerous models have been proposed, each of which is ingeniously designed to catch certain characteristics of MIL. However different models only perform well on certain tasks, and further improvement can hardly be achieved. In this paper, we propose a framework that combines multiple complementary models for solving MIL. Multiple-kernel learning as well as boosting based ensemble learning are utilized to achieve optimal combination. Moreover, the framework is extended to integrate active learning, so as to further reduce the annotation costs on acquiring an accurate image classifier. Experimental studies demonstrate the effectiveness of the proposed methods.

The problem of monitoring the correlations of discrete streams is to continuously monitor the temporal correlations among massive discrete streams. A temporal correlation of two streams is defined as a tracking behavior, i.e., the most recent pattern of one stream is very similar to a historical pattern of another stream. The challenge is that both the tracking stream and the tracked stream are evolving, which causes the frequent updates of the correlation-ships. The straightforward way of monitoring correlations by brute-force subsequence matching will be very expensive for massive streams. We propose techniques that are able to significantly reduce the number of expensive subsequence matching calls, by continuously pruning and refining the correlated streams. Extensive experiments on the streaming trajectories show the significant performance improvement achieved by the proposed algorithms.

Empowering users to access databases using simple keywords can relieve users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast-evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword-based search and exploration on databases. Several topics will be discussed, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, result comparison, query cleaning and suggestion, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. Finally we identify the challenges and opportunities for future research to advance the field.

Despite the proliferation of work on XML keyword query, it remains open to support keyword query over probabilistic XML data. Compared with traditional keyword search, it is far more expensive to answer a keyword query over probabilistic XML data due to the consideration of possible world semantics. In this paper, we firstly define the new problem of studying top-k keyword search over probabilistic XML data, which is to retrieve k SLCA results with the k highest probabilities of existence. And then we propose two efficient algorithms. The first algorithm PrStack can find k SLCA results with the k highest probabilities by scanning the relevant keyword nodes only once. To further improve the efficiency, we propose a second algorithm EagerTopK based on a set of pruning properties which can quickly prune unsatisfied SLCA candidates. Finally, we implement the two algorithms and compare their performance with analysis of extensive experimental results.

An important facility to aid keyword search on XML data is suggesting alternative queries when user queries contain typographical errors. Query suggestion thus can improve users' search experience by avoiding returning empty result or results of poor qualities. In this paper, we study the problem of effectively and efficiently providing quality query suggestions for keyword queries on an XML document. We illustrate certain biases in previous work and propose a principled and general framework, XClean, based on the state-of-the-art language model. Compared with previous methods, XClean can accommodate different error models and XML keyword query semantics without losing rigor. Algorithms have been developed that compute the top-k suggestions efficiently. We performed an extensive experiment study using two large-scale real datasets. The experiment results demonstrate the effectiveness and efficiency of the proposed methods.

Given a query string Q, an edit similarity search finds all strings in a database whose edit distance with Q is no more than a given threshold τ. Most existing method answering edit similarity queries rely on a signature scheme to generate candidates given the query string. We observe that the number of signatures generated by existing methods is far greater than the lower bound, and this results in high query time and index space complexities. In this paper, we show that the minimum signature size lower bound is τ+1. We then propose asymmetric signature schemes that achieve this lower bound. We develop efficient query processing algorithms based on the new scheme. Several dynamic programming-based candidate pruning methods are also developed to further speed up the performance. We have conducted a comprehensive experimental study involving nine state-of-the-art algorithms. The experiment results clearly demonstrate the efficiency of our methods.

Given a positive value r, a distance-based range query returns the objects that lie within the distance r of the query location. In this paper, we focus on the distance-based range queries that continuously change their locations in a euclidean space. We present an efficient and effective monitoring technique based on the concept of a safe zone. The safe zone of a query is the area with a property that while the query remains inside it, the results of the query remain unchanged. Hence, the query does not need to be reevaluated unless it leaves the safe zone. Our contributions are as follows: 1) We propose a technique based on powerful pruning rules and a unique access order which efficiently computes the safe zone and minimizes the I/O cost. 2) We theoretically determine and experimentally verify the expected distance a query moves before leaving the safe zone and, for majority of queries, the expected number of guard objects. 3) Our experiments demonstrate that the proposed approach is close to optimal and is an order of magnitude faster than a naïve algorithm. 4) We also extend our technique to monitor the queries in a road network. Our algorithm is up to two order of magnitude faster than a naïve algorithm.

With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. We also propose several efficient query processing methods for the new ranking method. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimental results demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency.

With the increasing amount of data and the need to integrate data from multiple data sources, one of the challenging issues is to identify near-duplicate records efficiently. In this article, we focus on efficient algorithms to find a pair of records such that their similarities are no less than a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the token ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. We have also studied the implementation of our proposed algorithm in stand-alone and RDBMS-based settings. Experimental results show our proposed algorithms can outperform previous algorithms on several real datasets.

Although keyword query enables inexperienced users to easily search XML database with no specific knowledge of complex structured query languages or XML data schemas, the ambiguity of keyword query may result in generating a great number of results that may be classified into different types. For users, each result type implies a possible search intention. To improve the performance of keyword query, it is desirable to efficiently work out the most relevant result type from the data to be retrieved.

Given a positive value r, a circular range query returns the objects that lie within the distance r of the query location. In this paper, we study the circular range queries that continuously change their locations. We present an efficient and effective technique to monitor such moving range queries by utilising the concept of a safe zone. The safe zone of a query is the area with a property that while the query remains inside it, the results of the query remain unchanged. Hence, the query does not need to be re-evaluated unless it leaves the safe zone. The shape of the safe zone is defined by the so-called guard objects. The cost of checking whether a query lies in the safe zone takes k distance computations, where k is the number of the guard objects. Our contributions are as follows. 1) We propose a technique based on powerful pruning rules and a unique access order which efficiently computes the safe zone and minimizes the I/O cost. 2) To show the effectiveness of the safe zone, we theoretically evaluate the probability that a query leaves the safe zone within one time unit and the expected distance a query moves before it leaves the safe zone. Additionally, for the queries that have diameter of the safe zone less than its expected value multiplied by a constant, we also give an upper bound on the expected number of guard objects. This upper bound turns out to be a constant, that is, it does not depend either on the radius r of the query or the density of the objects. The theoretical analysis is verified by extensive experiments. 3) Our thorough experimental study demonstrates that our proposed approach is close to optimal and is an order of magnitude faster than a nai¿ve algorithm.

K Nearest Neighbor search has many applications including data mining, multi-media, image processing, and monitoring moving objects. In this paper, we study the problem of KNN over multi-valued objects. We aim to provide effective and efficient techniques to identify KNN sensitive to relative distributions of objects.We propose to use quantiles to summarize relative-distribution-sensitive K nearest neighbors. Given a query Q and a quantile ¿ ¿ (0, 1), we firstly study the problem of efficiently computing K nearest objects based on a ¿-quantile distance e.g. median distance from each object to Q. The second problem is to retrieve the K nearest objects to Q based on overall distances in the ¿best population¿ with a given size specified by ¿-quantile for each object. While the first problem can be solved in polynomial time, we show that the 2nd problem is NP-hard. A set of efficient, novel algorithms have been proposed to give an exact solution for the first problem and an approximate solution for the second problem with the approximation ratio. Extensive experiment demonstrates that our techniques are very efficient and effective.

In this paper, we propose a novel multi-class graph boosting algorithm to recognize different visual objects. The proposed method treats subgraph as feature to construct base classifier, and utilizes popular error correcting output code scheme to solve multi-class problem. Both factors, base classifier and error-correcting coding matrix are considered simultaneously. And subgragphs, which are shareable by different classes, are wisely used to improve the classification performance. The experimental results on multi-class object recognition show the effectiveness of the proposed algorithm.

Substructure similarity search is to retrieve graphs that approximately contain a given query graph. It has many applications, e.g., detecting similar functions among chemical compounds. The problem is challenging as even testing subgraph containment between two graphs is NP-complete. Hence, existing techniques adopt the filtering-and-verification framework with the focus on developing effective and efficient techniques to remove non-promising graphs.

Near duplicate detection benefits many applications, e.g., on-line news selection over the Web by keyword search. The purpose of this demo is to show the design and implementation of MapDupReducer, a MapReduce based system capable of detecting near duplicates over massive datasets efficiently.

Uncertain data are inherent in various important applications and reverse nearest neighbor (RNN) query is an important query type for many applications. While many different types of queries have been studied on uncertain data, there is no previous work on answering RNN queries on uncertain data. In this paper, we formalize probabilistic reverse nearest neighbor query that is to retrieve the objects from the uncertain data that have higher probability than a given threshold to be the RNN of an uncertain query object. We develop an efficient algorithm based on various novel pruning approaches that solves the probabilistic RNN queries on multidimensional uncertain data. The experimental results demonstrate that our algorithm is even more efficient than a sampling-based approximate algorithm for most of the cases and is highly scalable.

Similarity join is a useful primitive operation underlying many applications, such as near duplicate Web page detection, data integration, and pattern recognition. Traditional similarity joins require a user to specify a similarity threshold. In this paper, we study a variant of the similarity join, termed top-k set similarity join. It returns the top-k pairs of records ranked by their similarities, thus eliminating the guess work users have to perform when the similarity threshold is unknown before hand. An algorithm, topk-join, is proposed to answer top-k similarity join efficiently. It is based on the prefix filtering principle and employs tight upper bounding of similarity values of unseen pairs. Experimental results demonstrate the efficiency of the proposed algorithm on large-scale real datasets.

Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of efficient processing of continuous skyline queries over sliding windows on uncertain data elements regarding given probability thresholds. We first characterize what kind of elements we need to keep in our query computation. Then we show the size of dynamically maintained candidate set and the size of skyline. We develop novel, efficient techniques to process a continuous, probabilistic skyline query. Finally, we extend our techniques to the applications where multiple probability thresholds are given or we want to retrieve "top-k" skyline data objects. Our extensive experiments demonstrate that the proposed techniques are very efficient and handle a high-speed data stream in real time.

In this paper, we propose a novel approach to automatically generating, instead of manually designing, discriminative visual features for face detection. The features are composed by multiple local features (e.g., Haar features), and such features can capture not only the local texture information but also their spatial configurations. Therefore, the proposed feature contains rich semantic information so that the classifier built on a set of such features can achieve high accuracy and high efficiency. Experimental results show that the proposed approach outperforms the techniques based on local features and the state-of-the-art discriminative features for face detection.

Empowering users to access databases using simple keywords can relieve the users from the steep learning curve of mastering a structured query language and understanding complex and possibly fast evolving data schemas. In this tutorial, we give an overview of the state-of-the-art techniques for supporting keyword search on structured and semi-structured data, including query result definition, ranking functions, result generation and top-k query processing, snippet generation, result clustering, query cleaning, performance optimization, and search quality evaluation. Various data models will be discussed, including relational data, XML data, graph-structured data, data streams, and workflows. We also discuss applications that are built upon keyword search, such as keyword based database selection, query generation, and analytical processing. Finally we identify the challenges and opportunities of future research to advance the field.

Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respect to a large dictionary of known entities, as the domain knowledge encoded in the dictionary helps to improve the extraction performance.

In this paper, we study the problem of continuous monitoring of reverse k nearest neighbor queries. Existing continuous reverse nearest neighbor monitoring techniques are sensitive towards objects and queries movement. For example, the results of a query are to be recomputed whenever the query changes its location. We present a framework for continuous reverse k nearest neighbor queries by assigning each object and query with a rectangular safe region such that the expensive recomputation is not required as long as the query and objects remain in their respective safe regions. This significantly improves the computation cost. As a by-product, our framework also reduces the communication cost in client-server architectures because an object does not report its location to the server unless it leaves its safe region or the server sends a location update request. We also conduct a rigid cost analysis to guide an effective selection of such rectangular safe regions. The extensive experiments demonstrate that our techniques outperform the existing techniques by an order of magnitude in terms of computation cost and communication cost.

Relational database is the most widely adopted and mature technology for information storage. As many services on the Web (e.g., blog and wiki sites) and advanced applications (e.g., customer relationship management systems and content management systems) are built on RDBMSs, increasing amount of text data is now stored in relational databases, accompanied by increasing demands of retrieving relevant information by free-style keyword search.

With the increasing amount of data and the need to integrate data from multiple data sources, a challenging issue is to find near duplicate records efficiently. In this paper, we focus on efficient algorithms to find pairs of records such that their similarities are above a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. Experimental results show that our proposed algorithms can achieve up to 2.6x - 5x speed-up over previous algorithms on several real datasets and provide alternative solutions to the near duplicate Web page detection problem.

There has been considerable interest in similarity join in the research community recently. Similarity join is a fundamental operation in many application areas, such as data integration and cleaning, bioinformatics, and pattern recognition. We focus on efficient algorithms for similarity join with edit distance constraints. Existing approaches are mainly based on converting the edit distance constraint to a weaker constraint on the number of matching q -grams between pair of strings. In this paper, we propose the novel perspective of investigating mismatching q -grams. Technically, we derive two new edit distance lower bounds by analyzing the locations and contents of mismatching q -grams. A new algorithm, Ed-Join, is proposed that exploits the new mismatch-based filtering methods; it achieves substantial reduction of the candidate sizes and hence saves computation time. We demonstrate experimentally that the new algorithm outperforms alternative methods on large-scale real datasets under a wide range of parameter settings.

With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly.

We consider the problem of continuously maintaining order sketches over data streams with a relative rank error guarantee ∊. Novel space-efficient and one-scan randomised techniques are developed. Our first randomised algorithm can guarantee such a relative error precision ∊ with confidence 1 - \delta using O( 1\_ \in \frac{1} {2}2 log 1d log ∊^2N) space, where N is the number of data elements seen so far in a data stream. Then, a new one-scan space compression technique is developed. Combined with the first randomised algorithm, the one-scan space compression technique yields another one-scan randomised algorithm that guarantees the space requirement is O( 1\frac{1} { \in } log(1\frac{1}{ \in } log 1\begin{gathered} \frac{1}{\delta } \hfill \\ \hfill \\ \end{gathered} )\frac{{\log ^{2 + \alpha } \in N}} {{1 - 1/2^\alpha }} (for\alpha \gt 0) on average while the worst case space remains O( \frac{1}{{ \in ^2 }}\log \frac{1} {\delta }\log \in ^2 N). These results are immediately applicable to approximately computing quantiles over data streams with a relative error guarantee \in and significantly improve the previous best space bound O( \frac{1} {{ \in ^3 }}\log \frac{1}{\delta }\log N). Our extensive experiment results demonstrate that both techniques can support an on-line computation against high speed data streams.

The skyline operator is important for multicriteria decision-making applications. Although many recent studies developed efficient methods to compute skyline objects in a given space, none of them considers skylines in multiple subspaces simultaneously. More importantly, the fundamental problem on the semantics of skylines remains open: Why and in which subspaces is (or is not) an object in the skyline? Practically, users may also be interested in the skylines in any subspaces. Then, what is the relationship between the skylines in the subspaces and those in the super-spaces? How can we effectively analyze the subspace skylines? Can we efficiently compute skylines in various subspaces and answer various analytical queries?In this article, we tackle the problem of multidimensional subspace skyline computation and analysis. We explore skylines in subspaces. First, we propose the concept of Skycube, which consists of skylines of all possible nonempty subspaces of a given full space. Once a Skycube is materialized, any subspace skyline queries can be answered online. However, Skycube cannot fully address the semantic concerns and may contain redundant information. To tackle the problem, we introduce a novel notion of skyline group which essentially is a group of objects that coincide in the skylines of some subspaces. We identify the decisive subspaces that qualify skyline groups in the subspace skylines. The new notions concisely capture the semantics and the structures of skylines in various subspaces. Multidimensional roll-up and drill-down analysis is introduced. We also develop efficient algorithms to compute Skycube, skyline groups and their decisive subspaces. A systematic performance study using both real data sets and synthetic data sets is reported to evaluate our approach.

We consider the problem of efficiently computing the skyline against the most recent N elements in a data stream seen so far. Specifically, we study the n-of-N skyline queries; that is, computing the skyline for the most recent n (/spl forall/n/spl les/N) elements. Firstly, we developed an effective pruning technique to minimize the number of elements to be kept. It can be shown that on average storing only O(log/sup d/ N) elements from the most recent N elements is sufficient to support the precise computation of all n-of-N skyline queries in a d-dimension space if the data distribution on each dimension is independent. Then, a novel encoding scheme is proposed, together with efficient update techniques, for the stored elements, so that computing an n-of-N skyline query in a d-dimension space takes O(log N+s) time that is reduced to O(d log log N+s) if the data distribution is independent, where s is the number of skyline points. Thirdly, a novel trigger based technique is provided to process continuous n-of-N skyline queries with O(/spl delta/) time to update the current result per new data element and O(log s) time to update the trigger list per result change, where /spl delta/ is the number of element changes from the current result to the new result. Finally, we extend our techniques to computing the skyline against an arbitrary window in the most recent N element. Besides theoretical performance guarantees, our extensive experiments demonstrated that the new techniques can support on-line skyline query computation over very rapid data streams.

An XML twig query, represented as a labeled tree, is essentially a complex selection predicate on both structure and content of an XML document. Twig query matching has been identified as a core operation in querying tree-structured XML data. A number of algorithms have been proposed recently to process a twig query holistically. Those algorithms, however, only deal with twig queries without OR-predicates. A straightforward approach that first decomposes a twig query with OR-predicates into multiple twig queries without OR-predicates and then combines their results is obviously not optimal in most cases. In this paper, we study novel holistic-processing algorithms for twig queries with OR-predicates without decomposition. In particular, we present a merge-based algorithm for sorted XML data and an index-based algorithm for indexed XML data. We show that holistic processing is much more efficient than the decomposition approach. Furthermore, we show that using indexes can significantly improve the performance for matching twig queries with OR-predicates, especially when the queries have large inputs but relatively small outputs.

XML documents are typically queried with a combination of value search and structure search. While querying by values can leverage traditional database technologies, evaluating structural relationship, specifically parent-child or ancestor-descendant relationship, between XML element sets has imposed a great challenge on efficient XML query processing. We propose XR-tree, namely, XML region tree, which is a dynamic external memory index structure specially designed for strictly nested XML data. The unique feature of XR-tree is that, for a given element, all its ancestors (or descendants) in an element set indexed by an XR-tree can be identified with optimal worst case I/O cost. We then propose a new structural join algorithm that can evaluate the structural relationship between two XR-tree indexed element sets by effectively skipping ancestors and descendants that do not participate in the join. Our extensive performance study shows that the XR-tree based join algorithm significantly outperforms previous algorithms.

We address issue related to containment join processing in tree-structured data such as XML documents. A containment join takes two sets of XML node elements as input and returns pairs of elements such that the containment relationship holds between them. While there are previous algorithms for processing containment joins, they require both element sets either sorted or indexed. We propose a novel and complete containment query processing framework based on a new coding scheme, PBiTree code. The PBiTree code allows us to determine the ancestor-descendant relationship between two elements from their PBiTree-based codes efficiently. We present algorithms in the framework that are optimized for various combinations of settings. In particular, the newly proposed partitioning based algorithms can process containment joins efficiently without sorting or indexes. Experimental results indicate that the containment join processing algorithms based on the proposed coding scheme outperform existing algorithms significantly.

Recent years witnessed an increasing interest in researches in XML, partly due to the fact that XML has now become the de facto standard for data interchange over the internet. A large amount of work has been reported on XML storage models and query processing techniques. However, few works have addressed issues of XML query optimization. In this paper, we report our study on one of the challenges in XML query optimization: containment join size estimation. Containment join is well accepted as an important operation in XML query processing. Estimating the size of its results is no doubt essential to generate efficient XML query processing plans. We propose two models, the interval model and the position model, and a set of estimation methods based on these two models. Comprehensive performance studies were conducted. The results not only demonstrate the advantages of our new algorithms over existing algorithms, but also provide valuable insights into the tradeoff among various parameters.

Presents, XParent, an XML document management system built on top of RDBMS. It is based on an efficient, model-mapping-based approach that uses a fixed database schema to store any XML documents without assistance of DTD. The visual query interface of XParent provides both expressive power for professionals and user friendliness for naive users. The proposed multi-level query translation scheme makes it possible to develop a generic XML application that supports multiple XML query languages and mapping schemas.

Pre-computed data cube facilitates OLAP (on-line analytical processing). It is well-known that data cube computation is an expensive operation. While most algorithms have been devoted to optimizing memory management and reducing computation costs, less work has addressed a fundamental issue: the size of a data cube is huge when a large base relation with a large number of attributes is involved. In this paper, we propose a new concept, called a condensed data cube. The condensed cube is of much smaller size than a complete non-condensed cube. More importantly, it is a fully pre-computed cube without compression, and, hence, it requires neither decompression nor further aggregation when answering queries. Several algorithms for computing a condensed cube are proposed. Results of experiments on the effectiveness of condensed data cube are presented, using both synthetic and real-world data. The results indicate that the proposed condensed cube can reduce both the cube size and therefore its computation time.