Christian Jensen

Christian Jensen
Aalborg University · Department of Computer Science

About

721
Publications
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Publications

Publications (721)
Article
With the continued digitization of societal processes, we are seeing an explosion in available data. This is referred to as big data. In a research setting, three aspects of the data are often viewed as the main sources of challenges when attempting to enable value creation from big data: volume, velocity, and variety. Many studies address volume o...
Preprint
Geo-social networks offer opportunities for the marketing and promotion of geo-located services. In this setting,we explore a new problem, called Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors (MaxInfBRkNN). The objective is to find a set of points of interest (POIs), which are geo-textually and socially attractive to social in...
Preprint
Full-text available
Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerable to outliers and exhibit low explainability. To address these two limitations, we propose robust a...
Article
Travel time or speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or data aggregation and represent different trade-offs between generalizability and accuracy. Function-fitting approaches learn functions that map feature vectors of, e.g., routes, to travel time o...
Preprint
Full-text available
In step with the digitalization of transportation, we are witnessing a growing range of path-based smart-city applications, e.g., travel-time estimation and travel path ranking. A temporal path~(TP) that includes temporal information, e.g., departure time, into the path is of fundamental to enable such applications. In this setting, it is essential...
Article
Full-text available
Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential buildin...
Article
Full-text available
Similarity search in metric spaces is used widely in areas such as multimedia retrieval, data mining, data integration, to name but a few. To accelerate metric similarity search, pivot-based indexing is often employed. Pivot-based indexing first computes the distances between data objects and pivots and then exploits filtering techniques that use t...
Article
Full-text available
Joins are essential and potentially expensive operations in database management systems. When data is associated with time periods, joins commonly include predicates that require pairs of argument tuples to overlap in order to qualify for the result. Our goal is to enable built-in systems support for such joins. In particular, we present an approac...
Preprint
Full-text available
Correlated time series (CTS) forecasting plays an essential role in many cyber-physical systems, where multiple sensors emit time series that capture interconnected processes. Solutions based on deep learning that deliver state-of-the-art CTS forecasting performance employ a variety of spatio-temporal (ST) blocks that are able to model temporal dep...
Preprint
Full-text available
People and vehicle trajectories embody important information of transportation infrastructures, and trajectory similarity computation is functionality in many real-world applications involving trajectory data analysis. Recently, deep-learning based trajectory similarity techniques hold the potential to offer improved efficiency and adaptability ove...
Article
Full-text available
Correlated time series (CTS) forecasting plays an essential role in many cyber-physical systems, where multiple sensors emit time series that capture interconnected processes. Solutions based on deep learning that deliver state-of-the-art CTS forecasting performance employ a variety of spatio-temporal (ST) blocks that are able to model temporal dep...
Preprint
Full-text available
With the sweeping digitalization of societal, medical, industrial, and scientific processes, sensing technologies are being deployed that produce increasing volumes of time series data, thus fueling a plethora of new or improved applications. In this setting, outlier detection is frequently important, and while solutions based on neural networks ex...
Preprint
The widespread deployment of smartphones and location-enabled, networked in-vehicle devices renders it increasingly feasible to collect streaming trajectory data of moving objects. The continuous clustering of such data can enable a variety of real-time services, such as identifying representative paths or common moving trends among objects in real...
Article
We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an assignment authority is to assign agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis...
Preprint
Nearest neighbor (NN) search is inherently computationally expensive in high-dimensional spaces due to the curse of dimensionality. As a well-known solution, locality-sensitive hashing (LSH) is able to answer c-approximate NN (c-ANN) queries in sublinear time with constant probability. Existing LSH methods focus mainly on building hash bucket-based...
Article
Full-text available
Nearest neighbor (NN) search is inherently computationally expensive in high-dimensional spaces due to the curse of dimensionality. As a well-known solution, locality-sensitive hashing (LSH) is able to answer c-approximate NN (c-ANN) queries in sublinear time with constant probability. Existing LSH methods focus mainly on building hash bucket-based...
Article
Full-text available
With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples inclu...
Preprint
Travel time or speed estimation are part of many intelligent transportation applications. Existing estimation approaches rely on either function fitting or aggregation and represent different trade-offs between generalizability and accuracy. Function-fitting approaches learn functions that map feature vectors of, e.g., routes, to travel time or spe...
Chapter
The original version of the book was inadvertently published with incorrect acknowledgements in chapters 28 and 31. The acknowledgements have been corrected and read as follows: Acknowledgement: “This paper is supported by the National Key Research and Development Program of China (Grant No. 2018YFB1403400), the National Natural Science Foundation...
Article
Full-text available
With the explosive use of GPS-enabled devices, increasingly massive volumes of trajectory data capturing the movements of people and vehicles are becoming available, which is useful in many application areas, such as transportation, traffic management, and location-based services. As a result, many trajectory data management and analytic systems ha...
Article
The deployment of vehicle location services generates increasingly massive vehicle trajectory data, which incurs high storage and transmission costs. A range of studies target offline compression to reduce the storage cost. However, to enable online services such as real-time traffic monitoring, it is attractive to also reduce transmission costs by...
Preprint
Trajectory similarity computation is a fundamental component in a variety of real-world applications, such as ridesharing, road planning, and transportation optimization. Recent advances in mobile devices have enabled an unprecedented increase in the amount of available trajectory data such that efficient query processing can no longer be supported...
Article
So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the...
Book
This volume constitutes the papers of several workshops which were held in conjunction with the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021, held in Taipei, Taiwan, in April 2021. The 29 revised full papers presented in this book were carefully reviewed and selected from 84 submissions. DASFAA 2021 pres...
Book
The three-volume set LNCS 12681-12683 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021, held in Taipei, Taiwan, in April 2021. The total of 156 papers presented in this three-volume set was carefully reviewed and selected from 490 submissions. The topic areas for the selecte...
Book
The three-volume set LNCS 12681-12683 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021, held in Taipei, Taiwan, in April 2021. The total of 156 papers presented in this three-volume set was carefully reviewed and selected from 490 submissions. The topic areas for the selecte...
Book
The three-volume set LNCS 12681-12683 constitutes the proceedings of the 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021, held in Taipei, Taiwan, in April 2021. The total of 156 papers presented in this three-volume set was carefully reviewed and selected from 490 submissions. The topic areas for the selecte...
Article
Clustering graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be considered, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attribute...
Preprint
We consider a setting with an evolving set of requests for transportation from an origin to a destination before a deadline and a set of agents capable of servicing the requests. In this setting, an assignment authority is to assign agents to requests such that the average idle time of the agents is minimized. An example is the scheduling of taxis...
Chapter
The top-k most relevant Semantic Place retrieval (kSP) query on spatial RDF data combines keyword-based and location-based retrieval. The query returns semantic places that are subgraphs rooted at a place entity with an associated location. The relevance to the query keywords of a semantic place is measured by a looseness score that aggregates the...
Article
Full-text available
Massive amounts of data that contain spatial, textual, and temporal information are being generated at a rapid pace. With streams of such data, which includes check-ins and geo-tagged tweets, available, users may be interested in being kept up-to-date on which terms are popular in the streams in a particular region of space. To enable this function...
Article
Full-text available
Vehicle routing is an important service that is used by both private individuals and commercial enterprises. Drivers may have different contexts that are characterized by different routing preferences. For example, during different times of day or weather conditions, drivers may make different routing decisions such as preferring or avoiding highwa...
Conference Paper
Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential buildin...
Chapter
Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential buildin...
Article
The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road ne...
Chapter
Full-text available
The chapter was originally published without open access. With the author(s)’ decision to opt for retrospective open access the copyright of the chapter changed to © The Author(s) 2020 and the chapter is now available under a CC BY 4.0 license at link.springer.com
Article
Full-text available
Data are increasingly available that enable detailed capture of travel costs associated with the movements of vehicles in road networks, notably travel time, and greenhouse gas emissions. In addition to varying across time, such costs are inherently uncertain, due to varying traffic volumes, weather conditions, different driving styles among driver...
Article
Full-text available
An online social network can be used for the diffusion of malicious information like derogatory rumors, disinformation, hate speech, revenge pornography, etc. This motivates the study of influence minimization that aim to prevent the spread of malicious information. Unlike previous influence minimization work, this study considers the influence min...
Preprint
Full-text available
The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road ne...
Preprint
With the continued digitalization of societal processes, we are seeing an explosion in available data. This is referred to as big data. In a research setting, three aspects of the data are often viewed as the main sources of challenges when attempting to enable value creation from big data: volume, velocity and variety. Many studies address volume...
Article
Increasingly massive volumes of vehicle trajectory data hold the potential to enable higher-resolution traffic services than hitherto possible. We use trajectory data to create a high-resolution, uncertain road-network graph, where edges are associated with travel-time distributions. In this setting, we study probabilistic budget routing that aims...
Article
Massive volumes of uncertain trajectory data are being generated by GPS devices. Due to the limitations of GPS data, these trajectories are generally uncertain. This state of affairs renders it is attractive to be able to compress uncertain trajectories and to be able to query the trajectories efficiently without the need for (full) decompression....
Article
Nearest neighbor (NN) search in high-dimensional spaces is inherently computationally expensive due to the curse of dimensionality. As a well-known solution to approximate NN search, locality-sensitive hashing (LSH) is able to answer c-approximate NN ( c -ANN) queries in sublinear time with constant probability. Existing LSH methods focus mainly on...
Article
Full-text available
Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks....
Preprint
Full-text available
Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often incomplete; for instance, OpenStreetMap only has speed limits for 13% of all Danish road segments. This is problematic for analysis tasks that rely on such information fo...
Conference Paper
The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road networks. We i...
Conference Paper
The increasingly massive volumes of vehicle trajectory data that are becoming available hold the potential to enable more accurate vehicle travel-time estimation than hitherto possible. To enable such uses, we present a multi-threaded, in-memory trajectory store that supports efficient and accurate travel-time estimation for road-network paths base...
Preprint
Full-text available
Machine learning techniques for road networks hold the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a road network by utilizing information of, e.g., adjacent road segments. While state-of-the-art GCNs target node classific...
Conference Paper
Large vehicle trajectory data sets can give detailed insight into traffic and congestion that is useful for routing as well as transportation planning. Making information from such data sets available to more users can enable applications that reduce travel time and fuel consumption. However, extracting such information efficiently requires deep kn...
Conference Paper
We propose two solutions to outlier detection in time series based on recurrent autoencoder ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent neural networks (S-RNNs). Such networks make it possible to generate multiple autoencoders with different neural network connection structures. The two solutions are ensem...
Article
Data cleaning is a prerequisite to subsequent data analysis, and is know to often be time-consuming and labor-intensive. We present IHCS, a hybrid data cleaning system that integrates error detection and repair to contend effectively with multiple error types. In a preprocessing step that precedes the data cleaning, IHCS formats an input dataset to...
Conference Paper
With the increasing availability of moving-object tracking data, use of this data for route search and recommendation is increasingly important. To this end, we propose a novel parallel split-and-combine approach to enable route search by locations (RSL-Psc). Given a set of routes, a set of places to visit O, and a threshold θ, we retrieve the rout...