Christian S. Jensen

Aalborg University, Ålborg, North Denmark, Denmark

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Publications (479)108.34 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Eco-routing is a simple yet effective approach to substantially reducing the environmental impact, e.g., fuel consumption and greenhouse gas (GHG) emissions, of vehicular transportation. Eco-routing relies on the ability to reliably quantify the environmental impact of vehicles as they travel in a spatial network. The procedure of quantifying such vehicular impact for road segments of a spatial network is called eco-weight assignment. EcoMark 2.0 proposes a general framework for eco-weight assignment to enable eco-routing. It studies the abilities of six instantaneous and five aggregated models to estimating vehicular environmental impact. In doing so, it utilizes travel information derived from GPS trajectories (i.e., velocities and accelerations) and actual fuel consumption data obtained from vehicles. The framework covers analyses of actual fuel consumption, impact model calibration, and experiments for assessing the utility of the impact models in assigning eco-weights. The application of EcoMark 2.0 indicates that the instantaneous model EMIT and the aggregated model SIDRA-Running are suitable for assigning eco-weights under varying circumstances. In contrast, other instantaneous models should not be used for assigning eco-weights, and other aggregated models can be used for assigning eco-weights under certain circumstances.
    GeoInformatica 07/2015; 19(3). DOI:10.1007/s10707-014-0221-7 · 1.29 Impact Factor
  • Dingming Wu, Byron Choi, Jianliang Xu, Christian S. Jensen
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    ABSTRACT: A moving top-$k$ spatial keyword (M $k$ SK) query, which takes into account a continuously moving query location, enables a mobile client to be continuously aware of the top-$k$ spatial web objects that best match a query with respect to location and text relevance. The increasing mobile use of the web and the proliferation of geo-positioning render it of interest to consider a scenario where spatial keyword search is outsourced to a separate service provider capable at handling the voluminous spatial web objects available from various sources. A key challenge is that the service provider may return inaccurate or incorrect query results (intentionally or not), e.g., due to cost considerations or invasion of hackers. Therefore, it is attractive to be able to authenticate the query results at the client side. Existing authentication techniques are either inefficient or inapplicable for the kind of query we consider. We propose new authentication data structures, the MIR-tree and MIR $^*$ -tree, that enable the authentication of MkSK queries at low computation and communication costs. We design a verification object for authenticating MkSK queries, and we provide algorithms for constructing verification objects and using these for verifying query results. A thorough experimental study on real data s- ows that the proposed techniques are capable of outperforming two baseline algorithms by orders of magnitude.
    IEEE Transactions on Knowledge and Data Engineering 04/2015; 27(4):922-935. DOI:10.1109/TKDE.2014.2350252 · 1.82 Impact Factor
  • Bin Yang, Chenjuan Guo, Yu Ma, Christian S. Jensen
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    ABSTRACT: A driver’s choice of a route to a destination may depend on the route’s length and travel time, but a multitude of other, possibly hard-to-formalize aspects, may also factor into the driver’s decision. There is evidence that a driver’s choice of route is context dependent, e.g., varies across time, and that route choice also varies from driver to driver. In contrast, conventional routing services support little in the way of context dependence, and they deliver the same routes to all drivers. We study how to identify context-aware driving preferences for individual drivers from historical trajectories, and thus how to provide foundations for personalized navigation, but also professional driver education and traffic planning. We provide techniques that are able to capture time-dependent and uncertain properties of dynamic travel costs, such as travel time and fuel consumption, from trajectories, and we provide techniques capable of capturing the driving behaviors of different drivers in terms of multiple dynamic travel costs. Further, we propose techniques that are able to identify a driver’s contexts and then to identify driving preferences for each context using historical trajectories from the driver. Empirical studies with a large trajectory data set offer insight into the design properties of the proposed techniques and suggest that they are effective.
    The VLDB Journal 02/2015; 24(2). DOI:10.1007/s00778-015-0378-1 · 1.70 Impact Factor
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    ABSTRACT: The ability to timely process significant amounts of continuously updated spatial data is mandatory for an increasing number of applications. Parallelism enables such applications to face this data-intensive challenge and allows the devised systems to feature low latency and high scalability. In this paper we focus on a specific data-intensive problem, concerning the repeated processing of huge amounts of range queries over massive sets of moving objects, where the spatial extents of queries and objects are continuously modified over time. To tackle this problem and significantly accelerate query processing we devise a hybrid CPU/GPU pipeline that compresses data output and save query processing work. The devised system relies on an ad-hoc spatial index leading to a problem decomposition that results in a set of independent data-parallel tasks. The index is based on a point-region quadtree space decomposition and allows to tackle effectively a broad range of spatial object distributions, even those very skewed. Also, to deal with the architectural peculiarities and limitations of the GPUs, we adopt non-trivial GPU data structures that avoid the need of locked memory accesses and favour coalesced memory accesses, thus enhancing the overall memory throughput. To the best of our knowledge this is the first work that exploits GPUs to efficiently solve repeated range queries over massive sets of continuously moving objects, characterized by highly skewed spatial distributions. In comparison with state-of-the-art CPU-based implementations, our method highlights significant speedups in the order of 14x-20x, depending on the datasets, even when considering very cheap GPUs.
  • Darius Šidlauskas, Simonas Šaltenis, Christian S. Jensen
    The VLDB Journal 10/2014; 23(5):817-841. DOI:10.1007/s00778-014-0353-2 · 1.70 Impact Factor
  • Darius Šidlauskas, Christian S. Jensen
  • Source
    Quan Z. Sheng, Jing He, Guoren Wang, Christian S. Jensen
    World Wide Web 07/2014; 17(4). DOI:10.1007/s11280-014-0280-6 · 1.62 Impact Factor
  • Qiang Qu, Siyuan Liu, Bin Yang, Christian S. Jensen
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    ABSTRACT: Increasing volumes of geo-referenced data are becoming available. This data includes so-called points of interest that describe businesses, tourist attractions, etc. by means of a geo-location and properties such as a textual description or ratings. We propose and study the efficient implementation of a new kind of query on points of interest that takes into account both the locations and properties of the points of interest. The query takes a result cardinality, a spatial range, and property-related preferences as parameters, and it returns a compact set of points of interest with the given cardinality and in the given range that satisfies the preferences. Specifically, the points of interest in the result set cover so-called allying preferences and are located far from points of interest that possess so-called alienating preferences. A unified result rating function integrates the two kinds of preferences with spatial distance to achieve this functionality. We provide efficient exact algorithms for this kind of query. To enable queries on large datasets, we also provide an approximate algorithm that utilizes a nearest-neighbor property to achieve scalable performance. We develop and apply lower and upper bounds that enable search-space pruning and thus improve performance. Finally, we provide a generalization of the above query and also extend the algorithms to support the generalization. We report on an experimental evaluation of the proposed algorithms using real point of interest data from Google Places for Business that offers insight into the performance of the proposed solutions.
  • Yu Ma, Bin Yang, Christian S. Jensen
  • Source
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    ABSTRACT: With the increasing availability of moving-object tracking data, trajectory search and matching is increasingly important. We propose and investigate a novel problem called personalized trajectory matching (PTM). In contrast to conventional trajectory similarity search by spatial distance only, PTM takes into account the significance of each sample point in a query trajectory. A PTM query takes a trajectory with user-specified weights for each sample point in the trajectory as its argument. It returns the trajectory in an argument data set with the highest similarity to the query trajectory. We believe that this type of query may bring significant benefits to users in many popular applications such as route planning, carpooling, friend recommendation, traffic analysis, urban computing, and location-based services in general. PTM query processing faces two challenges: how to prune the search space during the query processing and how to schedule multiple so-called expansion centers effectively. To address these challenges, a novel two-phase search algorithm is proposed that carefully selects a set of expansion centers from the query trajectory and exploits upper and lower bounds to prune the search space in the spatial and temporal domains. An efficiency study reveals that the algorithm explores the minimum search space in both domains. Second, a heuristic search strategy based on priority ranking is developed to schedule the multiple expansion centers, which can further prune the search space and enhance the query efficiency. The performance of the PTM query is studied in extensive experiments based on real and synthetic trajectory data sets.
    The VLDB Journal 06/2014; 23(3). DOI:10.1007/s00778-013-0331-0 · 1.70 Impact Factor
  • Xin Cao, Gao Cong, Christian S. Jensen, Man Lung Yiu
    05/2014; 7(9):733-744. DOI:10.14778/2732939.2732946
  • Anders Skovsgaard, Darius Sidlauskas, Christian S. Jensen
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    ABSTRACT: With the rapidly increasing deployment of Internet-connected, location-aware mobile devices, very large and increasing amounts of geo-tagged and timestamped user-generated content, such as microblog posts, are being generated. We present indexing, update, and query processing techniques that are capable of providing the top-k terms seen in posts in a user-specified spatio-temporal range. The techniques enable interactive response times in the millisecond range in a realistic setting where the arrival rate of posts exceeds today's average tweet arrival rate by a factor of 4-10. The techniques adaptively maintain the most frequent items at various spatial and temporal granularities. They extend existing frequent item counting techniques to maintain exact counts rather than approximations. An extensive empirical study with a large collection of geo-tagged tweets shows that the proposed techniques enable online aggregation and query processing at scale in realistic settings.
    2014 IEEE 30th International Conference on Data Engineering (ICDE); 03/2014
  • Bin Yang, Chenjuan Guo, Christian S. Jensen, Manohar Kaul, Shuo Shang
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    ABSTRACT: Different uses of a road network call for the consideration of different travel costs: in route planning, travel time and distance are typically considered, and green house gas (GHG) emissions are increasingly being considered. Further, travel costs such as travel time and GHG emissions are time-dependent and uncertain. To support such uses, we propose techniques that enable the construction of a multi-cost, time-dependent, uncertain graph (MTUG) model of a road network based on GPS data from vehicles that traversed the road network. Based on the MTUG, we define stochastic skyline routes that consider multiple costs and time-dependent uncertainty, and we propose efficient algorithms to retrieve stochastic skyline routes for a given source-destination pair and a start time. Empirical studies with three road networks in Denmark and a substantial GPS data set offer insight into the design properties of the MTUG and the efficiency of the stochastic skyline routing algorithms.
    2014 IEEE 30th International Conference on Data Engineering (ICDE); 03/2014
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    ABSTRACT: In this paper we investigate the use of GPUs to solve a data-intensive problem that involves huge amounts of moving objects. The scenario which we focus on regards objects that continuously move in a 2D space, where a large percentage of them also issues range queries. The processing of these queries entails a large quantity of objects falling into the range queries to be returned. In order to solve this problem by maintaining a suitable throughput, we partition the time into ticks, and defer the parallel processing of all the objects events (location updates and range queries) occurring in a given tick to the next tick, thus slightly delaying the overall computation. We process in parallel all the events of each tick by adopting an hybrid approach, based on the combined use of CPU and GPU, and show the suitability of the method by discussing performance results. The exploitation of a GPU allow us to achieve a speedup of more than 20× on several datasets with respect to the best sequential algorithm solving the same problem. More importantly, we show that the adoption of new bitmap-based intermediate data structure we propose to avoid memory access contention entails a 10× speedup with respect to naive GPU based solutions.
    Proceedings of the 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing; 02/2014
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    ABSTRACT: The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects (e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route , if a cluster has high spatial-object density and is spatially close to , it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scan- ed from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data.
    IEEE Transactions on Knowledge and Data Engineering 01/2014; 27(6):1-1. DOI:10.1109/TKDE.2014.2382583 · 1.82 Impact Factor
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    ABSTRACT: GPS-enabled devices are pervasive nowadays. Finding movement patterns in trajectory data stream is gaining in importance. We propose a group discovery framework that aims to efficiently support the online discovery of moving objects that travel together. The framework adopts a sampling-independent approach that makes no assumptions about when positions are sampled, gives no special importance to sampling points, and naturally supports the use of approximate trajectories. The framework's algorithms exploit state-of-the-art, density-based clustering (DBScan) to identify groups. The groups are scored based on their cardinality and duration, and the top-k groups are returned. To avoid returning similar subgroups in a result, notions of domination and similarity are introduced that enable the pruning of low-interest groups. Empirical studies on real and synthetic data sets offer insight into the effectiveness and efficiency of the proposed framework.
    IEEE Transactions on Knowledge and Data Engineering 12/2013; 25(12):2752-2766. DOI:10.1109/TKDE.2012.193 · 1.82 Impact Factor
  • Laura Radaelli, Christian S. Jensen
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    ABSTRACT: Indoor positioning systems based on fingerprinting techniques generally require costly initialization and maintenance by trained surveyors. Organic positioning systems aim to eliminate these deficiencies by managing their own accuracy and obtaining input from users and other sources. Such systems introduce new challenges, e.g., detection and filtering of erroneous user input, estimation of the positioning accuracy, and means of obtaining user input when necessary. We envision a fully organic indoor positioning system, where all available sources of information are exploited in order to provide room-level accuracy with no active intervention of users. For example, such systems can exploit pre-installed cameras to associate a user's location with a Wi-Fi fingerprint from the user's phone; and it can use a calendar to determine whether a user is in the room reported by the positioning system. Numerous possibilities for integration exist that may provide better indoor positioning.
    Proceedings of the Fifth ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness; 11/2013
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    ABSTRACT: This paper introduces a new class of temporal expression – named temporal expressions – and methods for recognising and interpreting its members. The commonest temporal expressions typically contain date and time words, like April or hours. Research into recognising and interpreting these typical expressions is mature in many languages. However, there is a class of expressions that are less typical, very varied, and difficult to automatically interpret. These indicate dates and times, but are harder to detect because they often do not contain time words and are not used frequently enough to appear in conventional temporally-annotated corpora – for example Michaelmas or Vasant Panchami. Using Wikipedia and linked data, we automatically construct a resource of English named temporal expressions, and use it to extract training examples from a large corpus. These examples are then used to train and evaluate a named temporal expression recogniser. We also introduce and evaluate rules for automatically interpreting these expressions, and we observe that use of the rules improves temporal annotation performance over existing corpora.
    Recent Advances in Natural Language Processing, RANLP 2013, Hissar, Bulgaria; 09/2013
  • Christian S. Jensen
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    ABSTRACT: The web is increasingly being used by mobile users, and it is increasingly possible to accurately geo-position mobile users. In addition, increasing volumes of geo-tagged web content are becoming available. Further, indications are that a substantial fraction of web keyword queries target local content. When combined, these observations suggest that spatial keyword querying is important and indeed gaining in importance. A prototypical spatial keyword query takes a user location and user-supplied keywords as parameters and returns web content that is spatially and textually relevant to these parameters. The paper reviews key concepts related to spatial keyword querying and reviews recent proposals by the author and his colleagues for spatial keyword querying functionality that is easy to use, relevant to users, and can be supported efficiently.
    Proceedings of the 7th International Workshop on Ranking in Databases; 08/2013
  • Kenneth S. Bøgh, Anders Skovsgaard, Christian S. Jensen
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    ABSTRACT: The notion of point-of-interest (PoI) has existed since paper road maps began to include markings of useful places such as gas stations, hotels, and tourist attractions. With the introduction of geopositioned mobile devices such as smartphones and mapping services such as Google Maps, the retrieval of PoIs relevant to a user's intent has became a problem of automated spatio-textual information retrieval. Over the last several years, substantial research has gone into the invention of functionality and efficient implementations for retrieving nearby PoIs. However, with a couple of exceptions existing proposals retrieve results at single-PoI granularity. We assume that a mobile device user issues queries consisting of keywords and an automatically supplied geo-position, and we target the common case where the user wishes to find nearby groups of PoIs that are relevant to the keywords. Such groups are relevant to users who wish to conveniently explore several options before making a decision such as to purchase a specific product. Specifically, we demonstrate a practical proposal for finding top-k PoI groups in response to a query. We show how problem parameter settings can be mapped to options that are meaningful to users. Further, although this kind of functionality is prone to combinatorial explosion, we will demonstrate that the functionality can be supported efficiently in practical settings.

Publication Stats

10k Citations
108.34 Total Impact Points

Institutions

  • 1970–2015
    • Aalborg University
      • • Department of Computer Science
      • • Department of Mathematical Sciences
      Ålborg, North Denmark, Denmark
  • 2010–2014
    • Aarhus University
      • Department of Computer Science
      Aarhus, Central Jutland, Denmark
  • 2012
    • The University of Hong Kong
      Hong Kong, Hong Kong
  • 2006
    • National University of Singapore
      • Department of Computer Science
      Singapore, Singapore
  • 1992–2004
    • University of Maryland, College Park
      • Department of Computer Science
      Maryland, United States
  • 1992–2000
    • The University of Arizona
      • Department of Computer Science
      Tucson, AZ, United States
  • 1999
    • National Technical University of Athens
      • School of Electrical and Computer Engineering
      Athens, Attiki, Greece