Synthetic trajectory generation is essential for addressing privacy concerns and data scarcity in mobility-related applications.However, existing methods in this area ignore co-movement patterns.In this paper, we propose CA-Gen, a Co-movement Aware trajectory Generation framework based on Generative Adversarial Networks (GANs).To enhance the realism of generated trajectories, CA-Gen integrates a waypoint-guided search strategy through frequent subsequence mining.Experiments on real-world datasets demonstrate that CA-Gen outperforms SOTA methods, generating trajectories that preserve both individual mobility behaviors and co-movement dynamics.Our implementation is available online 1 .

Abstract Synthetic trajectory generation is essential for addressing privacy concerns and data scarcity in mobility-related applications. Although existing solutions effectively capture general spatio-temporal features, they often overlook co-movement patterns among moving objects, which are crucial for applications such as traffic simulation, ride-sharing, and crowd modeling. Moreover, most approaches rely on road network representations, limiting generalization and failing to preserve fine-grained mobility trends. To tackle these challenges, we propose CA-Gen, a Co-movement Aware trajectory generation framework based on Generative Adversarial Networks (GANs). Instead of employing road vertex mapping, we introduce a hot grid-cell based trajectory representation to enhance robustness and generalization. To better simulate real-world co-movement patterns, we design a way-point guided search algorithm based on frequent subsequence mining. Extensive experiments on real-world datasets show that CA-Gen significantly outperforms existing SOTA methods, generating realistic trajectories that retain both individual mobility characteristics and co-movement trends, providing a privacy-preserving and high-fidelity solution for mobility analysis.

Point-to-hyperplane nearest neighbor search (P2HNNS) is a fundamental problem which has many applications in data mining and machine learning. In this paper, we propose a provable Locality-Sensitive-Hashing (LSH) scheme based on multi-level quantization errors to solve this problem. In the indexing phase, for each data point, we compute the hash values of its residual vectors generated by a stepwise quantization process. In the query phase, for each processed point, we first determine its suitable level for hashing and then determine the size of hash bucket based on its quantization error in that level. We theoretically show that this treatment not only yields a probability guarantee on query results, but also makes the generated hash functions much more efficient to prune those false points. Experimental results on five real datasets show that the proposed approach generally runs 2X-10X faster than the state-of-the-art LSH-based approaches.

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

A spatial skyline query is a query to find a set of data points that are not spatially dominated by other data points, given a set of data points P and query points Q in a multidimensional space. The query enumerates the skyline points based on distance in a multidimensional space. However, existing spatial skyline queries can lead to large errors with actual travel distances in geo-spaces because the query is based on the Euclidean distance.

Approximate nearest neighbor search (ANNS) is a fundamental problem that has a wide range of applications in information retrieval and data mining. Among state-of-the-art in-memory ANNS methods, graph-based methods have attracted particular interest owing to their superior efficiency and query accuracy. Most of these methods focus on the selection of edges to shorten the search path, but do not pay much attention to the computational cost at each hop. To reduce the cost, we propose a novel graph structure called HVS. HVS has a hierarchical structure of multiple layers that corresponds to a series of subspace divisions in a coarse-to-fine manner. In addition, we utilize a virtual Voronoi diagram in each layer to accelerate the search. By traversing Voronoi cells, HVS can reach the nearest neighbors of a given query efficiently, resulting in a reduction in the total search cost. Experiments confirm that HVS is superior to other state-of-the-art graph-based methods.

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

A key service in vehicular transportation is routing according to estimated travel times. With the availability of massive volumes of vehicle trajectory data, it has become increasingly feasible to estimate travel times, which are typically modeled as probability distributions in the form of histograms. An earlier study shows that use of a carefully selected, context-dependent subset of available trajectories when estimating a travel-time histogram along a user-specified path can significantly improve the accuracy of the estimates. This selection of trajectories cannot occur in a pre-processing step, but must occur online—it must be integrated into the routing itself. It is then a key challenge to be able to select very efficiently the "right" subset of trajectories that offer the best accuracy when the cost of a route is to be assessed. To address this challenge, we propose a solution that applies novel indexing to all available trajectories and that then is capable of selecting the most relevant trajectories and of computing a travel-time distribution based on these trajectories. Specifically, the solution utilizes an in-memory trajectory index and a greedy algorithm to identify and retrieve the relevant trajectories. The paper reports on an extensive empirical study with a large real-world GPS data set that offers insight into the accuracy and efficiency of the proposed solution. The study shows that the proposed online selection of trajectories can be performed efficiently and is able to provide highly accurate travel-time distributions.

Query autocompletion (QAC) is an important interactive feature that assists users in formulating queries and saving keystrokes. Due to the convenience it brings to users, QAC has been adopted in many applications, including Web search engines, integrated development environments (IDEs), and mobile devices. For existing QAC methods, users have to manually type delimiters to separate keywords in their inputs. In this paper, we propose a novel QAC paradigm through which users may abbreviate keywords by prefixes and do not have to explicitly separate them. Such paradigm is useful for applications where it is inconvenient to specify delimiters, such as desktop search, text editors, and input method editors. E.g., in an IDE, users may input getnev and we suggest GetNextValue. We show that the query processing method for traditional QAC, which utilizes a trie index, is inefficient under the new problem setting. A novel indexing and query processing scheme is hence proposed to efficiently complete queries. To suggest meaningful results, we devise a ranking method based on a Gaussian mixture model, taking into consideration the way in which users abbreviate keywords, as opposed to the traditional ranking method that merely considers popularity. Efficient top-k query processing techniques are developed on top of the new index structure. Experiments demonstrate the effectiveness of the new QAC paradigm and the efficiency of the proposed query processing method.

In this paper, we present a compressed data structure for moving object trajectories in a road network, which are represented as sequences of road edges. Unlike existing compression methods for trajectories in a network, our method supports pattern matching and decompression from an arbitrary position while retaining high compressibility with theoretical guarantees. Specifically, our method is based on FM-index, a fast and compact data structure for pattern matching. To further enhance the compression performance, we incorporate thesparsityof road networks. In particular, we present the novel concepts ofrelative movement labelingandPseudoRank, each contributing to significant reduction in data size and query processing time. Our theoretical analysis and experimental studies reveal the advantages of our proposed method as compared to existing trajectory compression methods and FM-index variants.

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

In this paper, we propose a method that fully combines top-k query processing with replication strategy in mobile ad hoc networks (MANETs). The goal is to acquire perfect accuracy of query results with a minimal overhead and delay. Currently, no replication strategy achieves efficient allocation of replicas for top-k queries, and no top-k query processing guarantees perfect accuracy of query results in MANETs. We propose a new replication strategy FReT (topology-Free Replication for Top-k query) and new top-k query processing methods. FReT advantages efficient top-k query processing from limited search area even if mobile nodes move. In our top-k query processing method, the search area gradually increases until receiving an exact answer. We demonstrate, through extensive simulations, that our approaches function well in terms of small delay and overhead.

Along with the popularization of location-based social networking (LBSN), semantic trajectories, which are trajectories with additional information such as photographs and texts, are increasing, and their utilization is required. We consider frequent pattern extraction as applicable to analysis of semantic trajectories and extraction of regions of interest (ROIs). In this research, we propose SimDBSCAN, which considers both spatial density and similarity of points, by extending DBSCAN, which uses density-based clustering, in order to capture users' interests. Since SimDBSCAN identifies points that are interested in the same object in the neighborhood as ROIs, it is possible to detect not only known ROIs such as tourist sites but also unknown ROIs. In this paper, we explain the algorithm of SimDBSCAN and present the experimental results using photographs collected from Flickr. The experiments show that useful ROIs and patterns can be extracted by the proposed method.

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

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

Data stream management systems (DSMSs) are useful for the management and processing of continuous data at a high input rate with low latency. In the automotive domain, embedded systems use a variety of sensor data and communications from outside the vehicle to promote autonomous and safe driving. Thus, the software developed for these systems must be capable of handling large volumes of data and complex processing. At present, we are developing a platform for the integration and management of data in an automotive embedded system using a DSMS. However, compared with conventional DSMS fields, we have encountered new challenges such as precompiling queries when designing automotive systems (which demands time predictability), distributed stream processing in in-vehicle networks, and real-time scheduling and sensor data fusion by stream processing. Therefore, we developed an automotive embedded DSMS (AEDSMS) to address these challenges. The main contributions of the present study are: (1) a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) the development of AEDSMS to tackle these challenges; and (3) an evaluation of AEDSMS during runtime using a driving assistance application.

Wikipedia is an Internet encyclopedia where any user can edit articles. Because editors act on their own judgments, editors' biases are reflected in edit actions. When editors' biases are reflected in articles, the articles should have low credibility. However, it is difficult for users to judge which parts in articles have biases. In this paper, we propose a method of clustering editors by editors' biases for the purpose that we distinguish texts' biases by using editors' biases and aid users to judge the credibility of each description. If each text is distinguished such as by colors, users can utilize it for the judgments of the text credibility. Our system makes use of the relationships between editors: agreement and disagreement. We assume that editors leave texts written by editors that they agree with, and delete texts written by editors that they disagree with. In addition, we can consider that editors who agree with each other have similar biases, and editors who disagree with each other have different biases. Hence, the relationships between editors enable to classify editors by biases. In experimental evaluation, we verify that our proposed method is useful in clustering editors by biases. Additionally, we validate that considering the dependency between editors improves the clustering performance.

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

Subsequence matching is a basic problem in the field of data stream mining. In recent years, there has been significant research effort spent on efficiently finding subsequences similar to a query sequence. Another challenging issue in relation to subsequence matching is how we identify common local patterns when both sequences are evolving. This problem arises in trend detection, clustering, and outlier detection. Dynamic time warping (DTW) is often used for subsequence matching and is a powerful similarity measure. However, the straightforward method using DTW incurs a high computation cost for this problem. In this paper, we propose a one-pass algorithm, CrossMatch, that achieves the above goal. CrossMatch addresses two important challenges: (1) how can we identify common local patterns efficiently without any omission? (2) how can we find common local patterns in data stream processing? To tackle these challenges, CrossMatch incorporates three ideas: (1) a scoring function, which computes the DTW distance indirectly to reduce the computation cost, (2) a position matrix, which stores starting positions to keep track of common local patterns in a streaming fashion, and (3) a streaming algorithm, which identifies common local patterns efficiently and outputs them on the fly. We provide a theoretical analysis and prove that our algorithm does not sacrifice accuracy. Our experimental evaluation and case studies show that CrossMatch can incrementally discover common local patterns in data streams within constant time (per update) and space.

In sensor environments and moving robot applications, the position of an object is often known imprecisely because of measurement error and/or movement of the object. In this paper, we present query processing methods for spatial databases in which the position of the query object is imprecisely specified by a probability density function based on a Gaussian distribution. We define the notion of a probabilistic range query by extending the traditional notion of a spatial range query and present three strategies for query processing. Since the qualification probability evaluation of target objects requires numerical integration by a method such as the Monte Carlo method, reduction of the number of candidate objects that should be evaluated has a large impact on query performance. We compare three strategies and their combinations in terms of the experiments and evaluate their effectiveness.

A nearest neighbor query is an important notion in spatial databases and moving object databases. In the emerging application fields of moving object technologies, such as mobile sensors and mobile robotics, the location of an object is often imprecise due to noise and estimation errors. We propose techniques for processing a nearest neighbor query when the location of the query object is specified by an imprecise Gaussian distribution. First, we consider two query processing strategies for pruning candidate objects, which can reduce the number of objects that require numerical integration for computing the qualification probabilities. In addition, we consider a hybrid approach that combines the two strategies. The performance of the proposed methods is evaluated using test data.

With the recent progress of spatial information technologies and communication technologies, it has become easier to track positions of a large number of moving objects in real-time. Mobility statistics plays an important role in the interactive analysis of a large collection of moving objects trajectories and its use of movement pattern prediction. The development of an effective mobility statistics measure and its efficient computation method are critical issues. Thus, we propose an approach for constructing a mobility histogram to summarize a number of moving object trajectories. The histogram is based on a mobility statistics model called the Markov chain model. To facilitate an interactive analysis performed by a user, we provide a mobility histogram data cube-like logical representation and support an OLAP-style analysis. Since trajectory data is often received continuously as a trajectory stream, we have to support dynamic histogram construction and maintenance. We introduce a tree structure as the physical representation of a histogram and present histogram construction and maintenance methods that work efficiently within the given upperbound size. We evaluate the performance and the precision of the proposed method by means of experiments.

Based on the recent progress of digital cartography, global positioning systems (GPSs), and hand-held devices, there are growing needs of technology that provides neighborhood information to moving objects according to their locations and trajectories. In this paper we propose spatial query generation models that take account of the current position and the past/future trajectories of a moving object to provide appropriate neighborhood information to it. For this purpose, we introduce an influence model of trajectory points and derive neighborhood query generation models using adaptive ellipsoid distances. We describe query processing strategies for these query generation models and show incremental query update procedures to support continual query facilities with low processing cost. Finally, we present experimental results to show the effectiveness of our approach.

With the increased popularity of XML, XML publishing of RDBs has been attracting a lot of research interest. One of the typical approaches is to use a middleware system to render XML views over RDBs and to allow users to access data with XML query languages such as XQuery. The query processing is done efficiently by making the best of the querying power of RDBMSs. Namely, XML queries are translated into SQL queries and tagging operations, which are processed by the RDBMSs and middleware, respectively. In some XML query languages including XQuery, use of user-defined foreign functions is enabled or planned as an extension feature to cope with domain dependent semantics. Foreign functions are defined for XML fragments, and their implementations are often given by codes in a general programming language. The existing query processing schemes on XML views do not consider cases where foreign functions are included in XML queries. We propose extended schemes to process XML queries in such cases. In the proposed schemes, the middleware takes care of processing foreign functions as well as tagging operations. Therefore, the proposed schemes are applicable to XML views on commonly available RDBMSs. Three types of query processing schemes are proposed, and their performance is studied with experiments.

Recent progress of digital cartography and Internet technologies enabled new types of services on the network such as search engines that provide information within some specific geographic areas and retrieval services which allow map-oriented query interfaces. We call such services spatial information sources. In this paper, we propose a framework to integrate heterogeneous spatial information sources to provide an integrated view to users. Our main focus is heterogeneity of spatial information sources-since existing spatial information sources differ in their contents and query capabilities, integration of such sources requires an appropriate framework to describe their contents and query capabilities. In this paper, we show such a description framework and illustrate query processing strategies that utilize source descriptions of spatial information sources.

Integration of heterogeneous information sources has been one of important data engineering research issues. Various type of information sources are available today. They include dissemination-based information sources, which actively and autonomously deliver information from server sites to users. We have been developing a mediator/wrapper-based information integration system, in which we employ ECA (event-condition-action) rules to enable users to define new information delivery services integrating multiple existing dissemination-based information sources. However, it is not easy for users to directly specify ECA rules and to verify them. In this paper, we propose a scheme to specify new information delivery services using the framework of the relational algebra. We discuss some important properties of the specification and show how we can derive ECA rules to implement the delivery services.

The integration of heterogeneous information sources is an important data engineering research issue. Various types of information sources are available today. They include dissemination-based information sources, which actively and autonomously deliver information from servers to users. We are developing a mediator/wrapper-based information integration system in which we employ ECA rules to define new information delivery channels, integrating multiple existing dissemination-based information sources. ECA rules in this system are derived from integration requirement specifications based on relational algebra provided by users. Dissemination-based information sources usually have data arrival properties, such as an information delivery schedule. Using the data arrival properties of underlying information sources, the system can derive more appropriate ECA rules and check the consistency of requirements more accurately. This paper proposes an extended scheme to process information integration requirements using source data arrival properties of dissemination-based information sources.

Object-oriented database systems (OODBs) need efficient support for manipulation of complex objects. In particular, support of queries involving evaluations of set predicates is often required in handling complex objects. In this paper, we propose a scheme to apply signature file techniques, which were originally invented for text retrieval, to the support of set value accesses, and quantitatively evaluate their potential capabilities. Two signature file organizations, the sequential signature file and the bit-sliced signature file, are considered and their performance is compared with that of the nested index for queries involving the set inclusion operator (⊆). We develop a detailed cost model and present analytical results clarifying their retrieval, storage, and update costs. Our analysis shows that the bit-sliced signature file is a very promising set access facility in OODBs.