Due to the power of learning representations from unlabeled graphs, graph contrastive learning (GCL) has shown excellent performance in community detection tasks. Existing GCL-based methods on the community detection usually focused on learning attribute representations of individual nodes, which, however, ignores structure semantics of communities (e.g., nodes in the same community should be structurally cohesive). Therefore, in this paper, we consider the community detection under the community structure semantics and propose an effective framework for graph contrastive learning under structure semantics (GCLS2) to detect communities. To seamlessly integrate interior dense and exterior sparse characteristics of communities with our contrastive learning strategy, we employ classic community structures to extract high-level structural views and design a structure semantic expression module to augment the original structural feature representation. Moreover, we formulate the structure contrastive loss to optimize the feature representation of nodes, which can better capture the topology of communities. To adapt to large-scale networks, we design a high-level graph partitioning (HGP) algorithm that minimizes the community detection loss for GCLS2 online training. It is worth noting that we prove a lower bound on the training of GCLS2 from the perspective of the information theory, explaining why GCLS2 can learn a more accurate representation of the structure. Extensive experiments have been conducted on various real-world graph datasets and confirmed that GCLS2 outperforms nine state-of-the-art methods, in terms of the accuracy, modularity, and efficiency of detecting communities.

Estimating spatial distributions is important in data analysis, such as traffic flow forecasting and epidemic prevention. To achieve accurate spatial distribution estimation, the analysis needs to collect sufficient user data. However, collecting data directly from individuals could compromise their privacy. Most previous works focused on private distribution estimation for one-dimensional data, which does not consider spatial data relation and leads to poor accuracy for spatial distribution estimation. In this paper, we address the problem of private spatial distribution estimation, where we collect spatial data from individuals and aim to minimize the distance between the actual distribution and estimated one under Local Differential Privacy (LDP). To leverage the numerical nature of the domain, we project spatial data and its relationships onto a one-dimensional distribution. We then use this projection to estimate the overall spatial distribution. Specifically, we propose a reporting mechanism called Disk Area Mechanism (DAM), which projects the spatial domain onto a line and optimizes the estimation using the sliced Wasserstein distance. Through extensive experiments, we show the effectiveness of our DAM approach on both real and synthetic data sets, compared with the state-of-the-art methods, such as Multi-dimensional Square Wave Mechanism (MDSW) and Subset Exponential Mechanism with Geo-I (SEM-Geo-I). Our results show that our DAM always performs better than MDSW and is better than SEM-Geo-I when the data granularity is fine enough.

The volume of graph data is increasing substantially, exerting significant pressure on graph analytics, especially when computing resources are limited. To address this challenge, we investigate the problem of developing compact representations that directly support widely used graph analytics. Leveraging interval encoding, we introduce two compact graph representations: the unified interval (UI) representation and the hybrid vertex-interval (HVI) representation. To minimize the sizes of these representations, we mathematically formulate two graph reordering problems, MUIP and MHVIP, and provide an NPhardness analysis. To solve these problems, we propose a spaceefficient edge-dropping framework, which, powered by a weightpriority approach, offers approximation ratio guarantees. We also develop a sampling method based on random walks to accelerate the edge-dropping process. Extensive experiments on 15 graph datasets demonstrate that the UI and HVI representations achieve an average compactness of 34.54% and 26.99%, respectively. Moreover, the HVI representation significantly speeds up various graph analytics, such as edge existence determination, triangle counting, and PageRank.

In many real-world applications such as social network analysis, knowledge graph discovery, biological network analytics, and so on, graph data management has become increasingly important and has drawn much attention from the database community. While many graphs (e.g., Twitter, Wikipedia, etc.) are usually evolving over time, it is of great importance to study the continuous subgraph matching (CSM) problem, a fundamental, yet challenging, graph operator, which continuously monitors subgraph matching results over dynamic graphs with a stream of edge updates. To efficiently tackle the CSM problem, we carefully design a general CSM processing framework, based on novel DynamIc Vertex DomINance Embedding (DIVINE), which maps vertex neighborhoods into an embedding space to enable efficient subgraph matching and incremental maintenance under dynamic updates. Inspired by low pruning power for high-degree vertices, we propose a new degree grouping technique to decompose high-degree star patterns into groups of lower-degree star substructures, and devise degree-aware star substructure synopses (DAS 3 ) over embeddings of star substructure groups. We develop efficient algorithms to incrementally maintain dynamic graphs and answer CSM queries by traversing DAS 3 synopses and applying our designed vertex dominance and range pruning strategies. Through extensive experiments, we confirm the efficiency of our proposed DIVINE approach over both real and synthetic graphs.

For the past decades, the subgraph similarity search over a large-scale data graph has become increasingly important and crucial in many real-world applications, such as social network analysis, bioinformatics network analytics, knowledge graph discovery, and many others. While previous works on subgraph similarity search used various graph similarity metrics such as the graph isomorphism, graph edit distance, and so on, in this paper, we propose a novel problem, namely subgraph similarity search under aggregated neighbor difference semantics (S 3 AND), which identifies subgraphs g in a data graph G that are similar to a given query graph q by considering both keywords and graph structures (under new keyword/structural matching semantics). To efficiently tackle the S 3 AND problem, we design two effective pruning methods, keyword set and aggregated neighbor difference lower bound pruning , which rule out false alarms of candidate vertices/subgraphs to reduce the S 3 AND search space. Furthermore, we construct an effective indexing mechanism to facilitate our proposed efficient S 3 AND query answering algorithm. Through extensive experiments, we demonstrate the effectiveness and efficiency of our S 3 AND approach over both real and synthetic graphs under various parameter settings.

As a promising distributed machine learning paradigm, Federated Learning (FL) has attracted increasing attention to deal with data silo problems without compromising user privacy. By adopting the classic one-to-multi training scheme (i.e., FedAvg), where the cloud server dispatches one single global model to multiple involved clients, conventional FL methods can achieve collaborative model training without data sharing. However, since only one global model cannot always accommodate all the incompatible convergence directions of local models, existing FL approaches greatly suffer from inferior classification accuracy. To address this issue, we present an efficient FL framework named FedCross, which uses a novel multi-to-multi FL training scheme based on our proposed multi-model cross-aggregation approach. Unlike traditional FL methods, in each round of FL training, FedCross uses multiple middleware models to conduct weighted fusion individually. Since the middleware models used by FedCross can quickly converge into the same flat valley in terms of loss landscapes, the generated global model can achieve a well-generalization. Experimental results on various well-known datasets show that, compared with state-of-the-art FL methods, Fed Cross can significantly improve FL accuracy within both IID and non-IID scenarios without causing additional communication overhead.

In many real-world applications such as social network analysis and online marketing/advertising, community detection is a fundamental task to identify communities (subgraphs) in social networks with high structural cohesiveness. While previous works focus on detecting communities alone, they do not consider the collective influences of users in these communities on other user nodes in social networks. Inspired by this, in this paper, we investigate the influence propagation from some seed communities and their influential effects that result in the influenced communities. We propose a novel problem, named Top-L most Influential Community DEtection ($\text{Top}L$-ICDE) over social networks, which aims to retrieve top-$L$seed communities with the highest influences, having high structural cohesiveness, and containing user-specified query keywords. To efficiently tackle the$\text{Top}L$-ICDE problem, we design effective pruning strategies to filter out false alarms of seed communities and propose an effective index mechanism to facilitate efficient Top-$L$community retrieval. We develop an efficient$\text{Top}L$-ICDE answering algorithm by traversing the index and applying our proposed pruning strategies. We also formulate and tackle a variant of$\text{Top}L$-ICDE, named diversified top-L most influential community detection ($\text{Top}L$-ICDE), which returns a set of$L$diversified communities with the highest diversity score (i.e., collaborative influences by$L$communities). We prove that$\text{DTop}L$-ICDE is NP-hard, and propose an efficient greedy algorithm with our designed diversity score pruning. Through extensive experiments, we verify the efficiency and effectiveness of our proposed$\text{Top}L$-ICDE and$\text{DTop}L$-ICDE approaches over real/synthetic social networks under various parameter settings.

Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the un- evenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregat- ing local models usually results in a low-generalized global model, which performs worse on most of the clients. To address the above issue, inspired by the observation from a geometric perspective that a well-generalized solution is located in a flat area rather than a sharp area, we propose a novel and heuristic FL paradigm named FedMR (Federated Model Recombination). The goal of FedMR is to guide the recombined models to be trained towards a flat area. Unlike conventional FedAvg-based methods, in FedMR, the cloud server recombines collected local models by shuffling each layer of them to generate multiple recombined models for local training on clients rather than an aggregated global model. Since the area of the flat area is larger than the sharp area, when local models are located in different areas, recombined models have a higher probability of locating in a flat area. When all recombined models are located in the same flat area, they are optimized towards the same direction. We theoretically analyze the convergence of model recombination. Experimental results show that, compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without exposing the privacy of each client.

As one of the most primitive operators in graph algorithms, such as the triangle counting, maximal clique enumeration, and subgraph listing, a set intersection operator returns common vertices between any two given sets of vertices in data graphs. It is therefore very important to accelerate the set intersection, which will benefit a bunch of tasks that take it as a built-in block. Existing works on the set intersection usually followed the merge intersection or galloping-search framework, and most optimization research focused on how to leverage the SIMD hardware instructions. In this paper, we propose a novel multi-level set intersection framework, namely hierarchical set partitioning and join (HERO), by using our well-designed set intersection bitmap tree (SIB-tree) index, which is independent of SIMD instructions and completely orthogonal to the merge intersection framework. We recursively decompose the set intersection task into small-sized subtasks and solve each subtask using bitmap and boolean AND operations. To sufficiently achieve the acceleration brought by our proposed intersection approach, we formulate a graph reordering problem, prove its NP-hardness, and then develop a heuristic algorithm to tackle this problem. Extensive experiments on real-world graphs have been conducted to confirm the efficiency and effectiveness of our HERO approach. The speedup over classic merge intersection achieves up to 188x and 176x for triangle counting and maximal clique enumeration, respectively.

The classic problem of exact subgraph matching returns those subgraphs in a large-scale data graph that are isomorphic to a given query graph, which has gained increasing importance in many real-world applications such as social network analysis, knowledge graph discovery in the Semantic Web, bibliographical network mining, and so on. In this paper, we propose a novel and effective graph neural network (GNN)-based path embedding framework (GNN-PE), which allows efficient exact subgraph matching without introducing false dismissals. Unlike traditional GNN-based graph embeddings that only produce approximate subgraph matching results, in this paper, we carefully devise GNN-based embeddings for paths, such that: if two paths (and 1-hop neighbors of vertices on them) have the subgraph relationship, their corresponding GNN-based embedding vectors will strictly follow the dominance relationship. With such a newly designed property of path dominance embeddings, we are able to propose effective pruning strategies based on path label/dominance embeddings and guarantee no false dismissals for subgraph matching. We build multidimensional indexes over path embedding vectors, and develop an efficient subgraph matching algorithm by traversing indexes over graph partitions in parallel and applying our pruning methods. We also propose a cost-model-based query plan that obtains query paths from the query graph with low query cost. Through extensive experiments, we confirm the efficiency and effectiveness of our proposed GNN-PE approach for exact subgraph matching on both real and synthetic graph data.

Recently, location-based social networks, that involve both social and spatial information, have received much attention in many real-world applications such as location-based services (LBS), map utilities, business planning, and so on. In this paper, we seamlessly integrate both social networks and spatial road networks, resulting in a so-called spatial-social network, and study an important and novel query type, named group planning query over spatial-social networks (GP-SSN), which is very useful for applications such as trip recommendations. In particular, a GP-SSN query retrieves a group of friends with common interests on social networks and a number of spatially close points of interest (POIs) on spatial road networks that best match group’s preferences and have the smallest traveling distances to the group. In order to tackle the GP-SSN problem, we design effective pruning methods, matching score pruning, user pruning, and distance pruning, to rule out false alarms of GP-SSN query answers and reduce the problem search space. We also propose effective indexing mechanisms to facilitate the GP-SSN query processing and develop efficient GP-SSN query answering algorithms via index traversals. Extensive experiments have been conducted to evaluate the efficiency and effectiveness of our proposed GP-SSN query processing approaches.

In many real-world applications such as social network analysis and online advertising/marketing, one of the most important and popular problems is calledinfluence maximization(IM), which finds a set ofkseed users that maximize the expected number of influenced user nodes. In practice, however, maximizing the number of influenced nodes may be far from satisfactory for real applications such as opinion promotion and collective buying. In this paper, we explore the importance ofstabilityandtrianglesin social networks, and formulate a novel problem in the influence spread scenario, namedtriangular stability maximization, over social networks, and generalize it to ageneral triangle influence maximizationproblem, which is proved to be NP-hard. We develop an efficientreverse influence sampling(RIS) based framework for the triangle IM with theoretical guarantees. To enable unbiased estimators, it demands probabilistic sampling of triangles, that is, sampling triangles according to their probabilities. We propose anedge-based triple samplingapproach, which is exactly equivalent to probabilistic sampling and avoids costly triangle enumeration and materialization. We also design several pruning and reduction techniques, as well as a cost-model-guided heuristic algorithm. Extensive experiments and a case study over real-world graphs confirm the effectiveness of our proposed algorithms and the superiority oftriangular stability maximizationand triangle influence maximization.

With the urbanization and development of infrastructure, the community search over road networks has become increasingly important in many real applications such as urban/city planning, social study on local communities, and community recommendations by real estate agencies. In this article, we propose a novel problem, namelytop-$k$kcommunity similarity search($Top\text{-}kCS^{2}$) over road networks, which efficiently and effectively obtains$k$spatial communities that are the most similar to a given query community in road-network graphs. In order to efficiently and effectively tackle the$Top\text{-}kCS^{2}$problem, in this paper, we will design an effective similarity measure between spatial communities, and propose a framework for retrieving$Top\text{-}kCS^{2}$query answers, which integrates offline pre-processing and online computation phases. Moreover, we also consider a variant, namelycontinuous top-$k$kcommunity similarity search($CTop\text{-}kCS^{2}$), where the query community continuously moves along a query line segment. We develop an efficient algorithm to split query line segment into intervals, incrementally obtain similar candidate communities for each interval, and refine actual$CTop\text{-}kCS^{2}$query answers. Extensive experiments have been conducted on real and synthetic data sets to confirm the efficiency and effectiveness of our proposed$Top\text{-}kCS^{2}$and$CTop\text{-}kCS^{2}$approaches under various parameter settings.

In a wide spectrum of real-world applications, it is very important to analyze and mine graph data such as social networks, communication networks, citation networks, and so on. However, the release of such graph data often raises privacy issue, and the graph privacy preservation has recently drawn much attention from the database community. While prior works on graph privacy preservation mainly focused on protecting the privacy of either the graph structure only or vertex attributes only, in this paper, we propose a novel mechanism for graph privacy preservation by considering attacks from both graph structures and vertex attributes, which transforms the original graph to a so-called$kt$-safe graph, via$k$-anonymity and$t$-closeness. We prove that the generation of a$kt$-safe graph is NP-hard, therefore, we propose a feasible framework for effectively and efficiently anonymizing a graph with low anonymization cost. In particular, we design a cost-model-based graph partitioning approach to enable our proposed divide-and-conquer strategy for the graph anonymization, and propose effective optimization techniques such as pruning method and a tree synopsis to improve the anonymization efficiency over large-scale graphs. Extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed$kt$-safe graph generation approach on both real and synthetic data sets.

Although Reinforcement Learning (RL) has achieved significant success in the Traffic Signal Control (TSC), most of them focus on the design of RL elements while the impact of the phase duration is neglected. Due to the lack of exploring dynamic phase duration, the overall performance and convergence rate of RL-based TSC approaches cannot be guaranteed, which may result in poor adaptability of RL methods to different traffic conditions. To address these issues, in this paper, we formulate a novel phase-duration-aware TSC (PDA-TSC) problem and propose an effective RL-based TSC approach, named MonitorLight. Our approach adopts a new traffic indicator, mixed pressure, which enables RL agents to simultaneously analyze the impacts of stationary and moving vehicles on intersections. Based on the observed mixed pressure of intersections, RL agents can autonomously determine whether or not to change the current signals in real-time. In addition, MonitorLight can adjust the control method for scenarios with different real-time requirements and achieve excellent results in different situations. Extensive experiments on both real-world and synthetic datasets demonstrate that MonitorLight outperforms the current state-of-the-art IPDALight by up to 2.84% and 5.71% in average vehicle travel time, respectively. Moreover, our method significantly speeds up the convergence, leading IPDALight by 36.87% and 34.58% in the start to converge episode and jumpstart performance, respectively.

Recently, location-based social networks, that involve both social and spatial information, have received much attention in many real-world applications such as location-based services (LBS), map utilities, business planning, and so on. In this paper, we seamlessly integrate both social networks and spatial road networks, resulting in a so-calledspatial-social network, and study an important and novel query type, namedgroup planning query over spatial-social networks(GP-SSN), which is very useful for applications such as trip recommendations. In particular, a GP-SSN query retrieves a group of friends with common interests on social networks and a number of spatially closepoints of interest(POIs) on spatial road networks that best match group’s preferences and have the smallest traveling distances to the group. In order to tackle the GP-SSN problem, we design effective pruning methods, matching score pruning, user pruning, and distance pruning, to rule out false alarms of GP-SSN query answers and reduce the problem search space. We also propose effective indexing mechanisms to facilitate the GP-SSN query processing, and develop efficient GP-SSN query answering algorithms via index traversals. Extensive experiments have been conducted to evaluate the efficiency and effectiveness of our proposed GP-SSN query processing approaches.

With the rapid development of mobile technology, nowadays, people spend a large amount of time on mobile devices. The locations and contexts of users are easily accessed by mobile advertising brokers, and the brokers can send customers related location-based advertisements. In this paper, we consider an important location-based advertising problem, namely maximum utility advertisement assignment (MUAA) problem, with the estimation of the interests of customers and the contexts of the vendors, we want to maximize the overall utility of ads by determining the ads sent to each customer subject to the constraints of the capacities of customers, the distance ranges and the budgets of vendors. We prove that the MUAA problem is NP-hard and intractable. Thus, we propose one offline approach, namely the${\sf reconciliation\ approach}$, which has an approximation ratio of$(1-\epsilon)\cdot \theta$. In addition, we also address the online scenario, in which customers arrive in a streaming fashion, with one novel online algorithm, namely the${\sf online\ adaptive\ factor-aware\ approach}$, which has a competitive ratio (compared to the optimal solution of the offline scenario) of$\frac{\ln (g)+1}{\theta }$,$g>e$, where$e$is the base of the natural logarithm. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets.

Multimodal path queries over transportation networks are receiving increasing attention due to their widespread applications. A multimodal path query consists of finding multimodal journeys from source to destination in transportation networks, including unrestricted walking, driving, cycling, and schedule-based public transportation. Transportation networks are generally continent-sized. This characteristic highlights the need for parallel computing to accelerate multimodal path queries. Meanwhile, transportation networks are often fragmented and distributively stored on different machines. This situation calls for exploiting parallel computing power for these distributed systems. Therefore, in this paper, we studydistributed multimodal path (DMP) queriesover large transportation networks. We develop algorithms to explore parallel computation. When evaluating a DMP query$Q$on a distributed multimodal graph$Gmult$, we show that the algorithms possess the following performance guarantees, irrespective of how$Gmult$is fragmented and distributed: (1) each machine is visited only once; (2) the total network traffic is determined by the size of$Q$and the fragmentation of$Gmult$; (3) the response time is decided by the largest fragment of$Gmult$; and (4) the algorithm is parallel scalable. Using real-life and synthetic data, we experimentally verify that the algorithms are scalable on large graphs.

With the urbanization and development of infrastructure, the community search over road networks has become increasingly important in many real applications such as urban/city planning, social study on local communities, and community recommendations by real estate agencies. In this paper, we propose a novel problem, namely top-k community similarity search (Top-kCS2), which efficiently and effectively obtains spatial communities that are the most similar to a given query community over road-network graphs. In order to efficiently and effectively tackle the Top-kCS2problem, in this paper, we will design an effective similarity measure between communities, and propose a framework for retrieving Top-kCS2query answers. Extensive experiments have been conducted on real and synthetic data sets to confirm the efficiency and effectiveness of our proposed Top-kCS2approach under various parameter settings.

In many real applications such as the data integration, social network analysis, and the Semantic Web, the entity resolution (ER) is an important and fundamental problem, which identifies and links the same real-world entities from various data sources. While prior works usually consider ER over static and complete data, in practice, application data are usually collected in a streaming fashion, and often incur missing attributes (due to the inaccuracy of data extraction techniques). Therefore, in this paper, we will formulate and tackle a novel problem, topic-aware entity resolution over incomplete data streams (TER-iDS), which online imputes incomplete tuples and detects pairs of topic-related matching entities from incomplete data streams. In order to effectively and efficiently tackle the TER-iDS problem, we propose an effective imputation strategy, carefully design effective pruning strategies, as well as indexes/synopsis, and develop an efficient TER-iDS algorithm via index joins. Extensive experiments have been conducted to evaluate the effectiveness and efficiency of our proposed TER-iDS approach over real data sets.

The group nearest neighbor (GNN) search on a road network$G_r$, i.e., finding the spatial objects as activity assembly points with the smallest sum of distances to query users on$G_r$, has been extensively studied; however, previous works neglected the fact that social relationships among query users, which ensure the maximally favorable atmosphere in the activity, can play an important role in GNN queries. Meanwhile, the ratings of spatial objects can also be used as recommended guidelines. Many real-world applications, such as location-based social networking services, require such queries. In this paper, we study two new problems: (1) a GNN search on a road network that incorporates cohesive social relationships (CGNN) and (2) a CGNN query under multi-criteria (MCGNN). Specifically, both the query users of highest closeness and the corresponding top-$j$objects are retrieved. To address critical challenges on the effectiveness of results and the efficiency of computation over large road-social networks: (1) for CGNN, we propose a filtering-and-verification framework. During filtering, we prune substantial unpromising users and objects using social and geospatial constraints. During verification, we obtain the object candidates, among which the top$j$are selected, with respect to the qualified users; (2) for MCGNN, we propose threshold-based selection and expansion strategies, where different strict boundaries are proposed to ensure that correct top-$j$objects are found early. Moreover, we further optimize search strategies to improve query performance. Finally, experimental results on real social and road networks significantly demonstrate the efficiency and efficacy of our solutions.

In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Specifically, it is necessary for IMDBs to have efficient snapshot performance to support certain special applications (e.g., consistent checkpoint, HTAP). Formally, the in-memory consistent snapshot problem refers to taking an in-memory consistent time-in-point snapshot with the constraints that 1) clients can read the latest data items and 2) any data item in the snapshot should not be overwritten. Various snapshot algorithms have been proposed in academia to trade off throughput and latency, but industrial IMDBs such as Redis adhere to the simple fork algorithm. To understand this phenomenon, we conduct comprehensive performance evaluations on mainstream snapshot algorithms. Surprisingly, we observe that the simple fork algorithm indeed outperforms the state-of-the-arts in update-intensive workload scenarios. On this basis, we identify the drawbacks of existing research and propose two lightweight improvements. Extensive evaluations on synthetic data and Redis show that our lightweight improvements yield better performance than fork, the current industrial standard, and the representative snapshot algorithms from academia. Finally, we have opensourced the implementation of all the above snapshot algorithms so that practitioners are able to benchmark the performance of each algorithm and select proper methods for different application scenarios.

Measuring similarity among data objects is important in data analysis and mining. SimRank is a popular link-based similarity measurement among nodes in a graph. To compute the all-pairs SimRank matrix accurately, iterative methods are usually used. For static graphs, current iterative solutions are not efficient enough, both in time and space, due to the unnecessary cost and storage by the nature of iterative updating. For dynamic graphs, all current incremental solutions for updating the SimRank matrix are based on an approximated SimRank definition, and thus have no accuracy guarantee. In this paper, we propose a novel local push based algorithm for computing and tracking all-pairs SimRank. Furthermore, we develop an iterative parallel two-step framework for local push to take advantage of modern hardwares with multicore CPUs. We show that our algorithms outperform the state-of-the-art methods.

For decades, the join operator over fast data streams has always drawn much attention from the database community, due to its wide spectrum of real-world applications, such as online clustering, intrusion detection, sensor data monitoring, and so on. Existing works usually assume that the underlying streams to be joined are complete (without any missing values). However, this assumption may not always hold, since objects from streams may contain some missing attributes, due to various reasons such as packet losses, network congestion/failure, and so on. In this paper, we formalize an important problem, namely join over incomplete data streams (Join-iDS), which retrieves joining object pairs from incomplete data streams with high confidences. We tackle the Join-iDS problem in the style of "data imputation and query processing at the same time". To enable this style, we design an effective and efficient cost-model-based imputation method via deferential dependency (DD), devise effective pruning strategies to reduce the Join-iDS search space, and propose efficient algorithms via our proposed cost-model-based data synopsis/indexes. Extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed Join-iDS approach on both real and synthetic data sets.

Maximum Range-Sum (MaxRS) query is an important operator in spatial database for retrieving regions of interest (ROIs). Given a rectangular query size a × b and a set of spatial objects associated with positive weights, MaxRS retrieves rectangular regions Q of size a × b, such that the sum of object weights covered by Q (i.e., range-sum) is maximized. Due to the inaccuracy of the location acquisition, the collected locations of spatial objects are inherently uncertain and imprecise, which can be modeled by uncertain objects. In this paper, we propose a Probabilistic Maximum Range-Sum (PMaxRS) query over uncertain spatial objects, which obtains a set γ* of rectangles such that the probability that each region Q ϵ γ* has the maximum range-sum exceeds a user-specified threshold Pt. We show that determining whether a given region Q is #P-complete. To tackle the hardness, we introduce the PMaxRS_Framework based on pruning and refinement strategies. In the pruning step, we propose a candidate generation technique to reduce the search space. In the refinement step, we design an efficient sampling-based approximation algorithm to verify the remaining candidate regions. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of our algorithms.

Nowadays, the locations and contexts of users are easily accessed by mobile advertising brokers, and the brokers can send customers related location-based advertisement. In this paper, we consider a location-based advertising problem, namely maximum utility advertisement assignment (MUAA) problem, with the estimation of the interests of customers and the contexts of the vendors, we want to maximize the overall utility of ads by determining the ads sent to each customer subject to the constraints of the capacities of customers, the distance ranges and the budgets of vendors. We prove that the MUAA problem is NP-hard and intractable. Thus, we propose one offline approach, namely the reconciliation approach, which has an approximation ratio of (1 - ε) · θ, where θ = min(a1/2n1c, a2/n2c, ⋯,am/nmc), and nz is the larger value between the number of valid vendors and the capacity aiof customer ui. Experiments on real data sets confirm the efficiency and effectiveness of our proposed approach.

The group nearest neighbor (GNN) search on a road network Gr, i.e., finding the spatial objects as activity assembly points with the smallest sum of distances to query users on Gr, has been extensively studied; however, previous works have neglected the fact that social relationships among query users, which ensure the maximally favorable atmosphere in the activity, can play an important role in GNN queries. Many real-world applications, such as location-based social networking services, require such queries. In this paper, we study a new problem: a GNN search on a road network that incorporates cohesive social relationships (CGNN). Specifically, both the query users of highest closeness and the corresponding top-j objects are retrieved. One critical challenge is to speed up the computation of CGNN queries over large social and road networks. To address this challenge, we propose a filtering-and-verification framework for efficient query processing. During filtering, we prune substantial unpromising users and objects using social and geographically spatial constraints. During verification, we obtain the object candidates, among which the top j are selected, with respect to the qualified users. Moreover, we further optimize search strategies to improve query performance. Finally, experimental results on real social and road networks significantly demonstrate the efficiency and efficacy of our solutions.

Weight-constrained route planning (WRP) over static graphs has been extensively studied due to its wide application to transportation networks. However, real transportation networks often evolve over time and are thus modeled as time-dependent graphs. In this paper, we study the WRP problem over a large time-dependent graph by incorporating continuous time and weight functions into it. Most existing works regarding route planning over time-dependent graphs are based on the first-in-first-out (FIFO) property. Unfortunately, the FIFO property does not hold for our problem. To solve the problem, we propose two novel route planning algorithms, namely, a baseline algorithm and an advanced algorithm. Specifically, the advanced algorithm is even more efficient than the baseline algorithm, as the advanced algorithm incorporates a fast traversal scheme and tight bounds of time functions to terminate the traversal as early as possible. We confirm the effectiveness and efficiency of our algorithms by extensive experiments on real datasets.

The constrained shortest path (CSP) query over static graphs has been extensively studied, since it has wide applications in transportation networks, telecommunication networks and etc. Such networks are dynamic and evolve over time, being modeled as time-dependent graphs. Therefore, in this paper, we study the CSP query over a large time-dependent graph. Specifically, we study the point CSP (PCSP) query and interval CSP (ICSP) query. We formally prove that it is NP-complete to process a PCSP query and at least EXPSPACE to answer an ICSP query. We propose approximate sequential algorithms to answer the PCSP and ICSP queries efficiently. We also develop parallel algorithms for the queries that guarantee to scale with big time-dependent graphs. Using real-life graphs, we experimentally verify the efficiency and scalability of our algorithms.

For decades, the crowdsourcing has gained much attention from both academia and industry, which outsources a number of tasks to human workers. Typically, existing crowdsourcing platforms include CrowdFlower, Amazon Mechanical Turk (AMT), and so on, in which workers can autonomously select tasks to do. However, due to the unreliability of workers or the difficulties of tasks, workers may sometimes finish doing tasks either with incorrect/incomplete answers or with significant time delays. Existing studies considered improving the task accuracy through voting or learning methods, they usually did not fully take into account reducing the latency of the task completion. This is especially critical, when a task requester posts a group of tasks (e.g., sentiment analysis), and one can only obtain answers of all tasks after the last task is accomplished. As a consequence, the time delay of even one task in this group could delay the next step of the task requester's work from minutes to days, which is quite undesirable for the task requester. Inspired by the importance of the task accuracy and latency, in this paper, we will propose a novel crowdsourcing framework, namely Fast and Reliable crOwdsourcinG framework (FROG), which intelligently assigns tasks to workers, such that the latencies of tasks are reduced and the expected accuracies of tasks are met. Specifically, our FROG framework consists of two important components, task scheduler and notification modules. For the task scheduler module, we formalize a FROG task scheduling (FROG-TS) problem, in which the server actively assigns workers to tasks to achieve high task reliability and low task latency. We prove that the FROG-TS problem is NP-hard. Thus, we design two heuristic approaches, request-based and batch-based scheduling. For the notification module, we define an efficient worker notifying (EWN) problem, which only sends task invitations to those workers with high probabilities of accepting the tasks. To tackle the EWN problem, we propose a smooth kernel density estimation approach to estimate the probability that a worker accepts the task invitation. Through extensive experiments, we demonstrate the effectiveness and efficiency of our proposed FROG platform on both real and synthetic data sets.

Modern networks are of huge sizes as well as high dynamics, which challenges the efficiency of community detection algorithms. In this paper, we study the problem of overlapping community detection on distributed and dynamic graphs. Given a distributed, undirected and unweighted graph, the goal is to detect overlapping communities incrementally as the graph is dynamically changing. We propose an efficient algorithm, called randomized Speaker-Listener Label Propagation Algorithm (rSLPA), based on the Speaker-Listener Label Propagation Algorithm (SLPA) by relaxing the probability distribution of label propagation. Besides detecting high-quality communities, rSLPA can incrementally update the detected communities after a batch of edge insertion and deletion operations. To the best of our knowledge, rSLPA is the first algorithm that can incrementally capture the same communities as those obtained by applying the detection algorithm from the scratch on the updated graph. Extensive experiments are conducted on both synthetic and real-world datasets, and the results show that our algorithm can achieve high accuracy and efficiency at the same time.

Graph similarity search is a common and fundamental operation in graph databases. One of the most popular graph similarity measures is the Graph Edit Distance (GED) mainly because of its broad applicability and high interpretability. Despite its prevalence, exact GED computation is proved to be NP-hard, which could result in unsatisfactory computational efficiency on large graphs. However, exactly accurate search results are usually unnecessary for real-world applications especially when the responsiveness is far more important than the accuracy. Thus, in this paper, we propose a novel probabilistic approach to efficiently estimate GED, which is further leveraged for the graph similarity search. Specifically, we first take branches as elementary structures in graphs, and introduce a novel graph similarity measure by comparing branches between graphs, i.e., Graph Branch Distance (GBD), which can be efficiently calculated in polynomial time. Then, we formulate the relationship between GED and GBD by considering branch variations as the result ascribed to graph edit operations, and model this process by probabilistic approaches. By applying our model, the GED between any two graphs can be efficiently estimated by their GBD, and these estimations are finally utilized in the graph similarity search. Extensive experiments show that our approach has better accuracy, efficiency and scalability than other comparable methods in the graph similarity search over real and synthetic data sets.

SimRank is a popular link-based similarity measurement among nodes in a graph. To compute the all-pairs SimRank matrix accurately, iterative methods are usually used. For static graphs, current iterative solutions are not efficient enough, both in time and space, due to unnecessary cost and storage by the nature of iterative updating. For dynamic graphs, all current incremental solutions for updating the Sim-Rank matrix are based on an approximated SimRank definition, and thus have no accuracy guarantee. In this paper, we propose a novel local push based algorithm for computing all-pairs SimRank. We show that our algorithms outperform the state-of-the-art static and dynamic all-pairs SimRank algorithms.

Identifying events from different sources is essential to various business process applications such as provenance querying or process mining. Distinct features of heterogeneous events, including opaque names and dislocated traces, prevent existing data integration techniques from performing well. To address these issues, in this paper, (1) we propose an event similarity function by iteratively evaluating similar neighbors. (2) In addition to event nodes, we further employ the similarity of edges (indicating relationships among events) in event matching. We prove NP-hardness of finding the optimal event matching w.r.t. node and edge similarities, and propose an efficient heuristic for event matching. Experiments demonstrate that the proposed event matching approach can achieve significantly higher accuracy than state-of-the-art matching methods. In particular, by considering the event edge similarity, our heuristic matching algorithm further improves the matching accuracy without introducing much overhead.

With the rapid advancement of mobile devices and crowdsourcing platforms, spatial crowdsourcing has attracted much attention from various research communities. A spatial crowdsourcing system periodically matches a number of locationbased workers with nearby spatial tasks (e.g., taking photos or videos at some specific locations). Previous studies on spatial crowdsourcing focus on task assignment strategies that maximize an assignment score based solely on the available information about workers/tasks at the time of assignment. These strategies can only achieve local optimality by neglecting the workers/tasks that may join the system in a future time. In contrast, in this paper, we aim to improve the global assignment, by considering both present and future (via predictions) workers/tasks. In particular, we formalize a new optimization problem, namely maximum quality task assignment (MQA). The optimization objective of MQA is to maximize a global assignment quality score, under a traveling budget constraint. To tackle this problem, we design an effective grid-based prediction method to estimate the spatial distributions of workers/tasks in the future, and then utilize the predictions to assign workers to tasks at any given time instance. We prove that the MQA problem is NPhard, and thus intractable. Therefore, we propose efficient heuristics to tackle the MQA problem, including MQA greedy and MQA divide-and-conquer approaches, which can efficiently assign workers to spatial tasks with high quality scores and low budget consumptions. Through extensive experiments, we demonstrate the efficiency and effectiveness of our approaches on both real and synthetic datasets.

In many real applications such as bioinformatics and biological network analysis, it has always been an important, yet challenging, topic to accurately infer/reconstruct gene regulatory networks (GRNs) from microarray data, and efficiently identify those matching GRNs with similar interaction structures for potential disease analysis and treatment tasks. Motivated by this, in this paper, we formalize the problem of ad-hoc inference and matching over gene regulatory networks (IM-GRN), which deciphers ad-hoc GRN graph structures online from gene feature databases (without full GRN materializations), and retrieves the inferred GRNs that are subgraph-isomorphic to a query GRN graph with high confidences. Specifically, we propose a novel probabilistic score to measure the possible interaction between any two genes (inferred from gene feature vectors), and thus model GRNs by probabilistic graphs, containing edge existence probabilities. In order to efficiently process IM-GRN queries, we propose effective reduction, pruning, and embedding strategies to significantly reduce the search space of GRN inference and matching, without materializing all GRNs. We also present an effective indexing mechanism and an efficient IM-GRN query processing algorithm by the index traversal. Finally, extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed IM-GRN query answering approaches over real/synthetic GRN data sets.

Graph keyword search has drawn many research interests, since graph models can generally represent both structured and unstructured databases and keyword searches can extract valuable information for users without the knowledge of the underlying schema and query language. In practice, data graphs can be extremely large, e.g., a Web-scale graph containing billions of vertices. The state-of-the-art approaches employ centralized algorithms to process graph keyword searches, and thus they are infeasible for such large graphs, due to the limited computational power and storage space of a centralized server. To address this problem, we investigate keyword search for Web-scale graphs deployed in a distributed environment. We first give a naive search algorithm to answer the query efficiently. However, the naive search algorithm uses a flooding search strategy that incurs large time and network overhead. To remedy this shortcoming, we then propose a signature-based search algorithm. Specifically, we design a vertex signature that encodes the shortest-path distance from a vertex to any given keyword in the graph. As a result, we can find query answers by exploring fewer paths, so that the time and communication costs are low. Moreover, we reorganize the graph data in the cluster after its initial random partitioning so that the signature-based techniques are more effective. Finally, our experimental results demonstrate the feasibility of our proposed approach in performing keyword searches over Web-scale graph data.

With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namelymulti-skill spatial crowdsourcing(MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers’ benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy,$g$-divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.

Resource Description Framework (RDF) has been widely used in the Semantic Web to describe resources and their relationships. The RDF graph is one of the most commonly used representations for RDF data. However, in many real applications such as the data extraction/integration, RDF graphs integrated from different data sources may often contain uncertain and inconsistent information (e.g., uncertain labels or that violate facts/rules), due to the unreliability of data sources. In this paper, we formalize the RDF data by inconsistent probabilistic RDF graphs, which contain both inconsistencies and uncertainty. With such a probabilistic graph model, we focus on an important problem, quality-aware subgraph matching over inconsistent probabilistic RDF graphs (QA-gMatch), which retrieves subgraphs from inconsistent probabilistic RDF graphs that are isomorphic to a given query graph and with high quality scores (considering both consistency and uncertainty). In order to efficiently answer QA-gMatch queries, we provide two effective pruning methods, namely adaptive label pruning and quality score pruning, which can greatly filter out false alarms of subgraphs. We also design an effective index to facilitate our proposed pruning methods, and propose an efficient approach for processing QA-gMatch queries. Finally, we demonstrate the efficiency and effectiveness of our proposed approaches through extensive experiments.

Although$k$NN search on a road network$G_r$, i.e., finding$k$nearest objects to a query user$q$on$G_r$, has been extensively studied, existing works neglected the fact that the$q$'s social information can play an important role in this$k$NN query. Many real-world applications, such as location-based social networking services, require such a query. In this paper, we study a new problem:$k$NN search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-artIndependent Cascade(IC) model in social network is applied to define social influence. One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, we propose three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based), and hybrid indexing algorithms. In the RN-based algorithm, we employ a filtering-and-verification framework for tackling the hard problem of computing social influence. In the SN-based algorithm, we embed social cuts into the index, so that we speed up the query. In the hybrid algorithm, we propose an index, summarizing the road and social networks, based on which we can obtain query answers efficiently. Finally, we use real road and social network data to empirically verify the efficiency and efficacy of our solutions.

Subgraph search is very useful in many real-world applications. However, users may be overwhelmed by the masses of matches. In this paper, we propose a subgraph skyline analysis problem, denoted as S2A, to support more complicated analysis over graph data. Specifically, given a large graph G and a query graph q, we want to find all the subgraphs g in G, such that g is graph isomorphic to q and not dominated by any other subgraphs. In order to improve the efficiency, we devise a hybrid feature encoding incorporating both structural and numeric features based on a partitioning strategy, and discuss how to optimize the space partitioning. We also present a skylayer index to facilitate the dynamic subgraph skyline computation. Moreover, an attribute cluster-based method is proposed to deal with the curse of dimensionality. Extensive experiments over real datasets confirm the effectiveness and efficiency of our algorithm.

A challenging task in the natural language question answering (Q/A for short) over RDF knowledge graph is how to bridge the gap between unstructured natural language questions (NLQ) and graph-structured RDF data (GOne of the effective tools is the "template", which is often used in many existing RDF Q/A systems. However, few of them study how to generate templates automatically. To the best of our knowledge, we are the first to propose a join approach for template generation. Given a workload D of SPARQL queries and a set N of natural language questions, the goal is to find some pairs q, n, for q∈ D ∧ n ∈, N, where SPARQL query q is the best match for natural language question n. These pairs provide promising hints for automatic template generation. Due to the ambiguity of the natural languages, we model the problem above as an uncertain graph join task. We propose several structural and probability pruning techniques to speed up joining. Extensive experiments over real RDF Q/A benchmark datasets confirm both the effectiveness and efficiency of our approach.

With the rapid development of mobile devices and the crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community, specifically, spatial crowdsourcing refers to sending a location-based request to workers according to their positions. In this paper, we consider an important spatial crowdsourcing problem, namely reliable diversity-based spatial crowdsourcing (RDB-SC), in which spatial tasks (such as taking videos/photos of a landmark or firework shows, and checking whether or not parking spaces are available) are time-constrained, and workers are moving towards some directions. Our RDB-SC problem is to assign workers to spatial tasks such that the completion reliability and the spatial/temporal diversities of spatial tasks are maximized. We prove that the RDB-SC problem is NP-hard and intractable. Thus, we propose three effective approximation approaches, including greedy, sampling, and divide-and-conquer algorithms. In order to improve the efficiency, we also design an effective cost-model-based index, which can dynamically maintain moving workers and spatial tasks with low cost, and efficiently facilitate the retrieval of RDB-SC answers. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets.

In real-world graphs such as social networks, Semantic Web and biological networks, each vertex usually contains rich information, which can be modeled by a set of tokens or elements. In this paper, we study a subgraph matching with set similarity (SMS2) query over a large graph database, which retrieves subgraphs that are structurally isomorphic to the query graph, and meanwhile satisfy the condition of vertex pair matching with the (dynamic) weighted set similarity. To efficiently process the SMS2query, this paper designs a novel lattice-based index for data graph, and lightweight signatures for both query vertices and data vertices. Based on the index and signatures, we propose an efficient two-phase pruning strategy including set similarity pruning and structure-based pruning, which exploits the unique features of both (dynamic) weighted set similarity and graph topology. We also propose an efficient dominating-set-based subgraph matching algorithm guided by a dominating set selection algorithm to achieve better query performance. Extensive experiments on both real and synthetic datasets demonstrate that our method outperforms state-of-the-art methods by an order of magnitude.

In many real applications, RDF (Resource Description Framework) has been widely used as a W3C standard to describe data in the Semantic Web. In practice, RDF data may often suffer from the unreliability of their data sources, and exhibit errors or inconsistencies. In this paper, we model such unreliable RDF data by probabilistic RDF graphs, and study an important problem, keyword search query over probabilistic RDF graphs (namely, the pg-KWS query). To retrieve meaningful keyword search answers, we design the score rankings for subgraph answers specific for RDF data. Furthermore, we propose effective pruning methods (via offline pre-computed score bounds and probabilistic threshold) to quickly filter out false alarms. We construct an index over the pre-computed data for RDF, and present an efficient query answering approach through the index. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposed approaches.

Since many graph data are often noisy and incomplete in real applications, it has become increasingly important to retrieve graphs g in the graph database D that approximately match the query graph q, rather than exact graph matching. In this paper, we study the problem of graph similarity search, which retrieves graphs that are similar to a given query graph under the constraint of graph edit distance. We propose a systematic method for edit-distance based similarity search problem. Specifically, we derive two lower bounds, i.e., partition-based and branch-based bounds, from different perspectives. More importantly, a hybrid lower bound incorporating both ideas of the two lower bounds is proposed, which is theoretically proved to have higher (at least not lower) pruning power than using the two lower bounds together. We also present a uniform index structure, namely u-tree, to facilitate effective pruning and efficient query processing. Extensive experiments confirm that our proposed approach outperforms the existing approaches significantly, in terms of both the pruning power and query response time.

Subgraph search is very useful in many real-world applications. However, users may be overwhelmed by the masses of matches. In this paper, we propose subgraph skyline search problem, denoted as S3, to support more complicated analysis over graph data. Specifically, given a large graph G and a query graph q, we want to find all the subgraphs g in G, such that g is graph isomorphic to q and not dominated by any other subgraphs. In order to improve the efficiency, we devise a hybrid feature encoding incorporating both structural and numeric features. Moreover, we present some optimizations based on partitioning strategy. We also propose a skylayer index to facilitate the dynamic subgraph skyline computation. Extensive experiments over real dataset confirm the effectiveness and efficiency of our algorithm.

Identifying duplicate events are essential to various business process applications such as provenance querying or process mining. Distinct features of heterogeneous events including opaque names, dislocated traces and composite events, prevent existing data integration from techniques performing well. To address these issues, in this paper, we propose an event similarity function by iteratively evaluating similar neighbors. We prove the convergence of iterative similarity computation, and propose several pruning and estimation methods. To efficiently support matching composite events, we devise upper bounds of event similarities. Experiments on real and synthetic datasets demonstrate that the proposed event matching approaches can achieve significantly higher accuracy than the state-of-the-art matching methods.

Recently, the management of transportation systems has become increasingly important in many real applications such as location-based services, supply chain management, traffic control, and so on. These applications usually involve queries over spatial road networks with dynamically changing and complicated traffic conditions. In this paper, we model such a network by a probabilistic time-dependent graph (PT-Graph), whose edges are associated with uncertain delay functions. We propose a useful query in the PT-Graph, namely a trip planner query (TPQ), which retrieves trip plans that traverse a set of query points in PT-Graph, having the minimum traveling time with high confidence. To tackle the efficiency issue, we present the pruning methods time interval pruning and probabilistic pruning to effectively rule out false alarms of trip plans. Furthermore, we design a pre-computation technique based on the cost model and construct an index structure over the pre-computed data to enable the pruning via the index. We integrate our proposed pruning methods into an efficient query procedure to answer TPQs. Through extensive experiments, we demonstrate the efficiency and effectiveness of our TPQ query answering approach.

Recently, due to ubiquitous data uncertainty in many real-life applications, it has become increasingly important to study efficient and effective processing of various probabilistic queries over uncertain data, which usually retrieve uncertain objects that satisfy query predicates with high probabilities. However, one annoying, yet challenging, problem is that, some probabilistic queries are very sensitive to low-quality objects in uncertain databases, and the returned query answers might miss some important results (due to low data quality). To identify both accurate query answers and those potentially low-quality objects, in this paper, we investigate the causes of query answers/non-answers from a novel angle of causality and responsibility (CR), and propose a new interpretation of probabilistic queries. Particularly, we focus on the problem of CR-based probabilistic nearest neighbor (CR-PNN) query, and design a general framework for answering CR-based queries (including CR-PNN), which can return both query answers with high confidences and low-quality objects that may potentially affect query results (for data cleaning purposes). To efficiently process CR-PNN queries, we propose effective pruning strategies to quickly filter out false alarms, and design efficient algorithms to obtain CR-PNN answers. Extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed approaches.

Due to many real applications of graph databases, it has become increasingly important to retrieve graphs g (in graph database D) that approximately match with query graph q, rather than exact subgraph matches. In this paper, we study the problem of graph similarity search, which retrieves graphs that are similar to a given query graph under the constraint of the minimum edit distance. Specifically, we derive a lower bound, branch-based bound, which can greatly reduce the search space of the graph similarity search. We also propose a tree index structure, namely b-tree, to facilitate effective pruning and efficient query processing. Extensive experiments confirm that our proposed approach outperforms the existing approaches by orders of magnitude, in terms of both pruning power and query response time.

Similarity search has been widely used in many applications such as information retrieval, image data analysis, and time-series matching. Previous work on similarity search usually consider the search problem in the full space. In this paper, however, we tackle a problem, subspace similarity search, which finds all data objects that match with a query object in the subspace instead of the original full space. In particular, the query object can specify arbitrary subspace with arbitrary number of dimensions. Due to the exponential number of possible subspaces specified by users, we introduce an efficient and effective pruning technique, which assigns scores to data objects with respect to pivots and prunes candidates via scores. We propose an effective multipivot-based method to preprocess data objects by selecting appropriate pivots, where the entire procedure is guided by a formal cost model, such that the pruning power is maximized. Then, scores of each data object are organized in sorted lists to facilitate an efficient subspace similarity search. Furthermore, many real-world application data such as image databases, time-series data, and sensory data often contain noises, which can be modeled as uncertain objects. Different from certain data, efficient query processing on uncertain data is more challenging due to its intensive computation of probability confidences. Thus, it is also crucial to answer subspace queries efficiently and effectively over uncertain objects. Specifically, we define a novel query, namely probabilistic subspace range query (PSRQ) in the uncertain database, which finds objects within a distance from a query object in any subspace with high probability. To address this query, we extend our proposed pruning techniques for precise data to that of answering PSRQ in arbitrary subspaces. Extensive experiments demonstrated the performance of our proposed approaches.

Due to the ubiquitous data uncertainty in many emerging real applications, efficient management of probabilistic databases has become an increasingly important yet challenging problem. In particular, one fundamental task of data management is to identify those unreliable data in the probabilistic database that violate integrity constraints (e.g., functional dependencies), and then quickly resolve data inconsistencies. In this paper, we formulate and tackle an important problem of repairing inconsistent probabilistic databases efficiently by value modification. Specifically, we propose a repair semantic, namely possible-world-oriented repair (PW-repair), which partitions possible worlds into several disjoint groups, and repairs these groups individually with minimum repair costs. Due to the intractable result that finding such a PW-repair strategy is NP-complete, we carefully design a heuristic-based greedy approach for PW-repair, which can efficiently obtain an effective repair of the inconsistent probabilistic database. Through extensive experiments, we show that our approach can achieve the efficiency and effectiveness of the repair on inconsistent probabilistic data.

In this paper, we tackle the problem of efficiently answering queries on probabilistic RDF data graphs. Specifically, we model RDF data by probabilistic graphs, and an RDF query is equivalent to a search over subgraphs of probabilistic graphs that have high probabilities to match with a given query graph. To efficiently processqueries on probabilistic RDF graphs, we propose effective pruning mechanisms, structural and probabilistic pruning. For the structural pruning, we carefully design synopses for vertex/edge labels by considering their distributions and other structural information, in order to improve the pruning power. For the probabilistic pruning, we derive a cost model to guide the pre-computation of probability upper bounds such that the query cost is expected to be low. We construct an index structure that integrates synopses/statistics for structural and robabilistic pruning, and propose an efficient approach to answer queries on probabilistic RDF graph data. The efficiency of our solutions has been verified through extensive experiments.

Similarity join processing in the streaming environment has many practical applications such as sensor networks, object tracking and monitoring, and so on. Previous works usually assume that stream processing is conducted over precise data. In this paper, we study an important problem of similarity join processing on stream data that inherently contain uncertainty (or called uncertain data streams), where the incoming data at each time stamp are uncertain and imprecise. Specifically, we formalize this problem as join on uncertain data streams (USJ), which can guarantee the accuracy of USJ answers over uncertain data. To tackle the challenges with respect to efficiency and effectiveness such as limited memory and small response time, we propose effective pruning methods on both object and sample levels to filter out false alarms. We integrate the proposed pruning methods into an efficient query procedure that can incrementally maintain the USJ answers. Most importantly, we further design a novel strategy, namely, adaptive superset prejoin (ASP), to maintain a superset of USJ candidate pairs. ASP is in light of our proposed formal cost model such that the average USJ processing cost is minimized. We have conducted extensive experiments to demonstrate the efficiency and effectiveness of our proposed approaches.

With the advance of wireless communication technology, it is quite common for people to view maps or get related services from the handheld devices, such as mobile phones and PDAs. Range queries, as one of the most commonly used tools, are often posed by the users to retrieve needful information from a spatial database. However, due to the limits of communication bandwidth and hardware power of handheld devices, displaying all the results of a range query on a handheld device is neither communication-efficient nor informative to the users. This is simply because that there are often too many results returned from a range query. In view of this problem, we present a novel idea that a concise representation of a specified size for the range query results, while incurring minimal information loss, shall be computed and returned to the user. Such a concise range query not only reduces communication costs, but also offers better usability to the users, providing an opportunity for interactive exploration. The usefulness of the concise range queries is confirmed by comparing it with other possible alternatives, such as sampling and clustering. Unfortunately, we prove that finding the optimal representation with minimum information loss is an NP-hard problem. Therefore, we propose several effective and nontrivial algorithms to find a good approximate result. Extensive experiments on real-world data have demonstrated the effectiveness and efficiency of the proposed techniques.

Efficient and effective manipulation of probabilistic data has become increasingly important recently due to many real applications that involve the data uncertainty. This is especially crucial when probabilistic data collected from different sources disagree with each other and incur inconsistencies. In order to accommodate such inconsistencies and enable consistent query answering (CQA), in this paper, we propose the all-possible-repair semantics in the context of inconsistent probabilistic databases, which formalize the repairs on the database as repair worlds via a graph representation. In turn, the CQA problem can be converted into one in the so-called repaired possible worlds (w.r.t. both repair worlds and possible worlds). We investigate a series of consistent queries in inconsistent probabilistic databases, including consistent range queries, join, and top-k queries, which, however, need to deal with an exponential number of the repaired possible worlds at high cost. To tackle the efficiency problem of CQA, in this paper, we propose efficient approaches for retrieving consistent query answers, including effective pruning methods to filter out false positives. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approaches.

Set similarity join has played an important role in many real-world applications such as data cleaning, near duplication detection, data integration, and so on. In these applications, set data often contain noises and are thus uncertain and imprecise. In this paper, we model such probabilistic set data on two uncertainty levels, that is, set and element levels. Based on them, we investigate the problem of probabilistic set similarity join (PS 2 J) over two probabilistic set databases, under the possible worlds semantics. To efficiently process the PS 2 J operator, we first reduce our problem by condensing the possible worlds, and then propose effective pruning techniques, including Jaccard distance pruning, probability upper bound pruning, and aggregate pruning, which can filter out false alarms of probabilistic set pairs, with the help of indexes and our designed synopses. We demonstrate through extensive experiments the PS 2 J processing performance on both real and synthetic data.

Data uncertainty is ubiquitous in many real-world applications such as sensor/RFID data analysis. In this paper, we investigate uncertain data that exhibit local correlations, that is, each uncertain object is only locally correlated with a small subset of data, while being independent of others. We propose a generic framework for dealing with this kind of uncertain and locally correlated data, in which we investigate a classical spatial query, nearest neighbor query , on uncertain data with local correlations (namely LC-PNN). Most importantly, to enable fast LC-PNN query processing, we propose a novel filtering technique via offline pre-computations to reduce the query search space. We demonstrate through extensive experiments the efficiency and effectiveness of our approaches.

Recently, many new applications, such as sensor data monitoring and mobile device tracking, raise up the issue of uncertain data management. Compared to "certain¿ data, the data in the uncertain database are not exact points, which, instead, often reside within a region. In this paper, we study the ranked queries over uncertain data. In fact, ranked queries have been studied extensively in traditional database literature due to their popularity in many applications, such as decision making, recommendation raising, and data mining tasks. Many proposals have been made in order to improve the efficiency in answering ranked queries. However, the existing approaches are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods are designed only for ranked queries in certain databases and cannot be applied to uncertain case directly. Motivated by this, we propose novel solutions to speed up the probabilistic ranked query (PRank) with monotonic preference functions over the uncertain database. Specifically, we introduce two effective pruning methods, spatial and probabilistic pruning, to help reduce the PRank search space. A special case of PRank with linear preference functions is also studied. Then, we seamlessly integrate these pruning heuristics into the PRank query procedure. Furthermore, we propose and tackle the PRank query processing over the join of two distinct uncertain databases. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches in answering PRank queries, in terms of both wall clock time and the number of candidates to be refined.

Reverse skyline queries over uncertain databases have many important applications such as sensor data monitoring and business planning. Due to the wide existence of uncertainty in many real-world data, answering reverse skyline queries accurately and efficiently over uncertain data has become increasingly important. In this article, we formalize the probabilistic reverse skyline query over uncertain data, in both monochromatic and bichromatic cases, and propose effective pruning methods, namely spatial pruning and probabilistic pruning , to reduce the search space of the reverse skyline query processing. Moreover, efficient query procedures have been presented seamlessly integrating the proposed pruning methods. Furthermore, a novel query type, namely Probabilistic Reverse Furthest Skyline (PRFS) query, is proposed and tackled under “the larger, the better” dominance semantics of skyline. Variants of probabilistic reverse skyline have been proposed and tackled, including those that return objects with top- k highest probabilities and that retrieve top- k reverse skylines. Extensive experiments demonstrated the efficiency and effectiveness of our approaches with various experimental settings.

Join processing in the streaming environment has many practical applications such as data cleaning and outlier detection. Due to the inherent uncertainty in the real-world data, it has become an increasingly important problem to consider the join processing on uncertain data streams, where the incoming data at each timestamp are uncertain and imprecise. Different from the static databases, processing uncertain data streams has its own requirements such as the limited memory, small response time, and so on. To tackle the challenges with respect to efficiency and effectiveness, in this paper, we formalize the problem of join on uncertain data streams (USJ), which can guarantee the accuracy of USJ answers over uncertain data, and propose effective pruning methods to filter out false alarms. We integrate the pruning methods into an efficient query procedure for incrementally maintaining USJ answers. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approaches.

Due to the existence of uncertain data in a wide spectrum of real applications, uncertain query processing has become increasingly important, which dramatically differs from handling certain data in a traditional database. In this paper, we formulate and tackle an important query, namely probabilistic top-k dominating (PTD) query, in the uncertain database. In particular, a PTD query re-trieves k uncertain objects that are expected to dynamically domi-nate the largest number of uncertain objects. We propose an effec-tive pruning approach to reduce the PTD search space, and present an efficient query procedure to answer PTD queries. Furthermore, approximate PTD query processing and the case where the PTD query is issued from an uncertain query object are also discussed. Extensive experiments have demonstrated the efficiency and effec-tiveness of our proposed PTD query processing approaches. 1.

With the advance of wireless communication technology, it is quite common for people to view maps or get related services from the handheld devices, such as mobile phones and PDAs. Range queries, as one of the most commonly used tools, are often posed by the users to retrieve needful information from a spatial database. However, due to the limits of communication bandwidth and hardware power of handheld devices, displaying all the results of a range query on a handheld device is neither communication efficient nor informative to the users. This is simply because that there are often too many results returned from a range query. In view of this problem, we present a novel idea that a concise representation of a specified size for the range query results, while incurring minimal information loss, shall be computed and returned to the user. Such a concise range query not only reduces communication costs, but also offers better usability to the users, providing an opportunity for interactive exploration. The usefulness of the concise range queries is confirmed by comparing it with other possible alternatives, such as sampling and clustering. Then we propose algorithms to find a good concise representation.

Skyline query is of great importance in many applications, such as multi-criteria decision making and business planning. In particular, a skyline point is a data object in the database whose attribute vector is not dominated by that of any other objects. Previous methods to retrieve skyline points usually assume static data objects in the database (i.e. their attribute vectors are fixed), whereas several recent work focus on skyline queries with dynamic attributes. In this paper, we propose a novel variant of skyline queries, namely metric skyline, whose dynamic attributes are defined in the metric space (i.e. not limited to the Euclidean space). We illustrate an efficient and effective pruning mechanism to answer metric skyline queries through a metric index. Most importantly, we formalize the query performance of the metric skyline query in terms of the pruning power, by a cost model, in light of which we construct an optimized metric index aiming to maximize the pruning power of metric skyline queries. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed pruning techniques as well as the constructed index in answering metric skyline queries.

Similarity search usually encounters a serious problem in the high-dimensional space, known as the “curse of dimensionality.” In order to speed up the retrieval efficiency, most previous approaches reduce the dimensionality of the entire data set to a fixed lower value before building indexes (referred to as global dimensionality reduction (GDR)). More recent works focus on locally reducing the dimensionality of data to different values (called the local dimensionality reduction (LDR)). In addition, random projection is proposed as an approximate dimensionality reduction (ADR) technique to answer the approximate similarity search instead of the exact one. However, so far little work has formally evaluated the effectiveness and efficiency of GDR, LDR, and ADR for the range query. Motivated by this, in this paper, we propose general cost models for evaluating the query performance over the reduced data sets by GDR, LDR, and ADR, in light of which we introduce a novel (A)LDR method, Partitioning based on RANdomized Search (PRANS). It can achieve high retrieval efficiency with the guarantee of optimality given by the formal models. Finally, a {\rm B}^{+}-tree index is constructed over the reduced partitions for fast similarity search. Extensive experiments validate the correctness of our cost models on both real and synthetic data sets and demonstrate the efficiency and effectiveness of the proposed PRANS method.

Similarity join (SJ) in time-series databases has a wide spectrum of applications such as data cleaning and mining. Specifically, an SJ query retrieves all pairs of (sub)sequences from two time-series databases that \varepsilon-match with each other, where \varepsilon is the matching threshold. Previous work on this problem usually considers static time-series databases, where queries are performed either on disk-based multidimensional indexes built on static data or by nested loop join (NLJ) without indexes. SJ over multiple stream time series, which continuously outputs pairs of similar subsequences from stream time series, strongly requires low memory consumption, low processing cost, and query procedures that are themselves adaptive to time-varying stream data. These requirements invalidate the existing approaches in static databases. In this paper, we propose an efficient and effective approach to perform SJ among multiple stream time series incrementally. In particular, we present a novel method, Adaptive Radius-based Search (ARES), which can answer the similarity search without false dismissals and is seamlessly integrated into SJ processing. Most importantly, we provide a formal cost model for ARES, based on which ARES can be adaptive to data characteristics, achieving the minimum number of refined candidate pairs, and thus, suitable for stream processing. Furthermore, in light of the cost model, we utilize space-efficient synopses that are constructed for stream time series to further reduce the candidate set. Extensive experiments demonstrate the efficiency and effectiveness of our proposed approach.

Similarity-based time-series retrieval has been a subject of long-term study due to its wide usage in many applications, such as financial data analysis, weather data forecasting, and multimedia data retrieval. Its original task was to find those time series similar to a pattern (query) time-series data, where both the pattern and data time series are static. Recently, with an increasing demand on stream data management, similarity-based stream time-series retrieval has raised new research issues due to its unique requirements during the stream processing, such as one-pass search and fast response. In this paper, we address the problem of matching both static and dynamic patterns over stream time-series data. We will develop a novel multiscale representation, called multiscale segment mean, for stream time-series data, which can be incrementally computed and thus perfectly adapted to the stream characteristics. Most importantly, we propose a novel multistep filtering mechanism, step by step, over the multiscale representation. Analysis indicates that the mechanism can greatly prune the search space and thus offer fast response. Furthermore, batch processing optimization and the dynamic case where patterns are also from stream time series are discussed. Extensive experiments show the multiscale representation together with the multistep filtering scheme can efficiently filter out false candidates and detect patterns, compared to the multiscale wavelet.

Skyline query is of great importance in many applications, such as multi-criteria decision making and business planning. In particular, a skyline point is a data object in the database whose attribute vector is not dominated by that of any other objects. Previous methods to retrieve skyline points usually assume static data objects in the database (i.e. their attribute vectors are fixed), whereas several recent work focus on skyline queries with dynamic attributes. In this paper, we propose a novel variant of skyline queries, namely metric skyline, whose dynamic attributes are defined in the metric space (i.e. not limited to the Euclidean space). We illustrate an efficient and effective pruning mechanism to answer metric skyline queries through a metric index. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed pruning techniques over the metric index in answering metric skyline queries.

Recently, many new applications, such as sensor data monitoring and mobile device tracking, raise up the issue of uncertain data management. Compared to "certain" data, the data in the uncertain database are not exact points, which, instead, often locate within a region. In this paper, we study the ranked queries over uncertain data. In fact, ranked queries have been studied extensively in traditional database literature due to their popularity in many applications, such as decision making, recommendation raising, and data mining tasks. Many proposals have been made in order to improve the efficiency in answering ranked queries. However, the existing approaches are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods are designed only for ranked queries in certain databases and cannot be applied to uncertain case directly. Motivated by this, we propose novel solutions to speed up the probabilistic ranked query (PRank) over the uncertain database. Specifically, we introduce two effective pruning methods, spatial and probabilistic, to help reduce the PRank search space. Then, we seamlessly integrate these pruning heuristics into the PRank query procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach in answering PRank queries, in terms of both wall clock time and the number of candidates to be refined.

Similarity search has been widely used in many applications such as information retrieval, image data analysis, and time-series matching. Specifically, a similarity query retrieves all data objects in a data set that are similar to a given query object. Previous work on similarity search usually consider the search problem in the full space. In this paper, however, we propose a novel problem, subspace similarity search, which finds all data objects that match with a query object in the subspace instead of the original full space. In particular, the query object can specify arbitrary subspace with arbitrary number of dimensions. Since traditional approaches for similarity search cannot be applied to solve the proposed problem, we introduce an efficient and effective pruning technique, which assigns scores to data objects with respect to pivots and prunes candidates via scores. We propose an effective multipivot-based method to pre-process data objects by selecting appropriate pivots, where the entire procedure is guided by a formal cost model, such that the pruning power is maximized. Finally, scores of each data object are organized in sorted list to facilitate an efficient subspace similarity search. Extensive experiments have verified the correctness of our cost model and demonstrated the efficiency and effectiveness of our proposed approach for the subspace similarity search.

In many privacy preserving applications such as Location-Based Services (LBS), medical data analysis, and data sequence matching, users often deliberately disturb the original data in order to avoid the release of their private information. Although these disturbed cloaked data cannot reveal the privacy information of individual users, they can still help perform some data mining tasks such as data classification. In this paper, we study one important and fundamental query predicate, that is, to find the cloaked time series that are similar to a query pattern. In this paper, we formalize such similarity search problem over the cloaked time series, and propose a novel approach to index the cloaked series, which can facilitate the similarity query.

Reverse skyline queries over uncertain databases have many important applications such as sensor data monitoring and business planning. Due to the existence of uncertainty in many real-world data, answering reverse skyline queries accurately and efficiently over uncertain data has become increasingly important. In this paper, we model the probabilistic reverse skyline query on uncertain data, in both monochromatic and bichromatic cases, and propose effective pruning methods to reduce the search space of query processing. Moreover, efficient query procedures have been presented seamlessly integrating the proposed pruning methods. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach with various experimental settings.

Similarity-based search has been a key factor for many applications such as multimedia retrieval, data mining, Web search and retrieval, and so on. There are two important issues related to the similarity search, namely, the design of a distance function to measure the similarity and improving the search efficiency. Many distance functions have been proposed, which attempt to closely mimic human recognition. Unfortunately, some of these well-designed distance functions do not follow the triangle inequality and are therefore nonmetric. As a consequence, efficient retrieval by using these nonmetric distance functions becomes more challenging, since most existing index structures assume that the indexed distance functions are metric. In this paper, we address this challenging problem by proposing an efficient method, that is, local constant embedding (LCE), which divides the data set into disjoint groups so that the triangle inequality holds within each group by constant shifting. Furthermore, we design a pivot selection approach for the converted metric distance and create an index structure to speed up the retrieval efficiency. Moreover, we also propose a novel method to answer approximate similarity search in the nonmetric space with a guaranteed query accuracy. Extensive experiments show that our method works well on various nonmetric distance functions and improves the retrieval efficiency by an order of magnitude compared to the linear scan and existing retrieval approaches with no false dismissals.

With the advance of hardware and communication technologies, stream time series is gaining ever-increasing attention due to its importance in many applications such as financial data processing, network monitoring, Web click-stream analysis, sensor data mining, and anomaly detection. For all of these applications, an efficient and effective similarity search over stream data is essential. Because of the unique characteristics of the stream, for example, data are frequently updated and real-time response is required, the previous approaches proposed for searching through archived data may not work in the stream scenarios. Especially, in the cases where data often arrive periodically for various reasons (for example, the communication congestion or batch processing), queries on such incomplete time series or even future time series may result in inaccuracy using traditional approaches. Therefore, in this paper, we propose three approaches,polynomial, Discrete Fourier Transform (DFT), and probabilistic, to predict the unknown values that have not arrived at the system and answer similarity queries based on the predicted data. We also apply efficient indexes, that is, a multidimensional hash index and a B+-tree, to facilitate the prediction and similarity search on future time series, respectively. Extensive experiments demonstrate the efficiency and effectiveness of our methods for prediction and answering queries.

The importance of query processing over uncertain data has recently arisen due to its wide usage in many real-world applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query,top-kquery,skylinequery, andsimilarityjoin. In this paper, we focus on another important query, namely, probabilistic group nearest neighbor (PGNN) query, in the uncertain database, which also has many applications. Specifically, given a set,Q, of query points, a PGNN query retrieves data objects that minimize the aggregate distance (e.g.,sum,min, andmax) to query setQ. Due to the inherent uncertainty of data objects, previous techniques to answer group nearest neighbor (GNN) query cannot be directly applied to our PGNN problem. Motivated by this, we propose effective pruning methods, namely, spatial pruning and probabilistic pruning, to reduce the PGNN search space, which can be seamlessly integrated into our PGNN query procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach, in terms of the wall clock time and thespeed-upratioagainstlinearscan.

Similarity search usually encounters a serious problem in the high dimensional space, known as the "curse of dimensionality". In order to speed up the retrieval efficiency, previous approaches usually reduce the dimensionality of the entire data set to a fixed lower value before building indexes (referred to as global dimensionality reduction (GDR)). More recent works focus on locally reducing the dimensionality of data to different values (called the local dimensionality reduction (LDR)). However, so far little work has formally evaluated the effectiveness and efficiency of both GDR and LDR for range queries. Motivated by this, in this paper, we propose a general cost model for both GDR and LDR, in light of which we introduce a novel LDR method, PRANS. It can achieve high retrieval efficiency with the guarantee of optimality given by the formal model. Finally, a B+-tree index is constructed over the reduced partitions for fast similarity search. Extensive experiments validate the correctness of our cost model on both real and synthetic data sets, and demonstrate the efficiency and effectiveness of the proposed PRANS method.

Similarity-based time series retrieval has been a subject of long term study due to its wide usage in many applications, such as financial data analysis, weather data forecasting, and multimedia data retrieval. Its original task was to find those time series similar to a pattern (query) time series data, where both the pattern and data time series are static. Recently, with an increasing demand on stream data management, similarity-based stream time series retrieval has raised new research issues due to its unique requirements during the stream processing, such as one-pass search and fast response. In this paper, we address the problem of matching patterns over high-speed stream time series data. We will develop a novel representation, called multi-scaled segment mean (MSM), for stream time series data, which can be incrementally computed and thus perfectly adapted to the stream characteristics. Most importantly, we propose a novel multi-step filtering mechanism over the multi-scaled representation. Analysis indicates that the mechanism can greatly prune the search space and thus offer fast response. Extensive experiments show the multi-scaled representation together with the multi-step filtering scheme can efficiently filter out false candidates and detect patterns, compared to the multiscaled wavelet.

The existing random sampling methods have at least one of the following disadvantages: they 1) are applicable only to certain update patterns, 2) entail large space overhead, or 3) incur prohibitive maintenance cost. These drawbacks prevent their effective application in stream environments (where a relation is updated by a large volume of insertions and deletions that may arrive in any order), despite the considerable success of random sampling in conventional databases. Motivated by this, we develop several fully dynamic algorithms for obtaining random samples from individual relations, and from the join result of two tables. Our solutions can handle any update pattern with small space and computational overhead. We also present an in-depth analysis that provides valuable insight into the characteristics of alternative sampling strategies and leads to precision guarantees. Extensive experiments validate our theoretical findings and demonstrate the efficiency of our techniques in practice

Due to the wide usage of stream time series, an efficient and effective similarity search over stream data becomes essential for many applications. Although many approaches have been proposed for searching through archived data, because of the unique characteristics of the stream, for example, data are frequently updated, traditional methods may not work for the stream time series. Especially, for the cases where the arrival of data is often delayed for various reasons, for example, the communication congestion or batch processing and so on, queries on such incomplete time series or even future time series may result in inaccuracy. Therefore, in this paper we propose two approaches, polynomial and probabilistic, to predict the unknown values that have not arrived at the system. We also present efficient indexes, that is, a multidimensional hash index and B+-tree, to facilitate the prediction and similarity search on future time series, respectively. Extensive experiments demonstrate the efficiency and effectiveness of our methods in terms of I/O, prediction and query accuracy