Papers by Christian S. Jensen
2012 IEEE 28th International Conference on Data Engineering, 2012
Indoor spaces accommodate large numbers of spatial objects, e.g., points of interest (POIs), and ... more Indoor spaces accommodate large numbers of spatial objects, e.g., points of interest (POIs), and moving populations. A variety of services, e.g., location-based services and security control, are relevant to indoor spaces. Such services can be improved substantially if they are capable of utilizing indoor distances. However, existing indoor space models do not account well for indoor distances. To address this shortcoming, we propose a data management infrastructure that captures indoor distance and facilitates distance-aware query processing. In particular, we propose a distance-aware indoor space model that integrates indoor distance seamlessly. To enable the use of the model as a foundation for query processing, we develop accompanying, efficient algorithms that compute indoor distances for different indoor entities like doors as well as locations. We also propose an indexing framework that accommodates indoor distances that are pre-computed using the proposed algorithms. On top of this foundation, we develop efficient algorithms for typical indoor, distance-aware queries. The results of an extensive experimental evaluation demonstrate the efficacy of the proposals.
Proceedings of the VLDB Endowment, 2011
With the growing use of location-based services, location privacy attracts increasing attention f... more With the growing use of location-based services, location privacy attracts increasing attention from users, industry, and the research community. While considerable effort has been devoted to inventing techniques that prevent service providers from knowing a user's exact location, relatively little attention has been paid to enabling so-called peer-wise privacy---the protection of a user's location from unauthorized peer users. This paper identifies an important efficiency problem in existing peer-privacy approaches that simply apply a filtering step to identify users that are located in a query range, but that do not want to disclose their location to the querying peer. To solve this problem, we propose a novel, privacy-policy enabled index called the PEB-tree that seamlessly integrates location proximity and policy compatibility. We propose efficient algorithms that use the PEB-tree for processing privacy-aware range and k NN queries. Extensive experiments suggest that the...
Proceedings of the 8th ACM international symposium on Advances in geographic information systems, 2000
Spatial applications must manage partwhole (PW) relationships between spatial objects, for exampl... more Spatial applications must manage partwhole (PW) relationships between spatial objects, for example, the division of an administrative region into zones based on land use. Support for conceptual modeling of relationships between parts and whole, suchasaggregationandmembership,hasbeenwellresearchedin the objectoriented (OO) community; however, spatial data has generally not been considered. We propose here a practical approachtointegratingsupport for spatial PWrelationshipsinto conceptual modeling languages. Three different types of relationships-spatial part, spatial membership, and spatial inclusion-that are of general utility in spatial applications are identified and formally defined using a consistent classification framework based on spatial derivation and constraint relationships. An extension of the Unified Modeling Language (UML) for spatiotemporaldata, namelyExtended Spatiotemporal UML, is used to demonstrate the feasibility of using such an approach to define modeling constructs supporting spatial PW relationships.
Advances in Spatial and Temporal Databases
Proceedings of the 11th ACM international symposium on Advances in geographic information systems, 2003
Lecture Notes in Computer Science, 2001
2011 IEEE 27th International Conference on Data Engineering, 2011
Web users and content are increasingly being geopositioned. This development gives prominence to ... more Web users and content are increasingly being geopositioned. This development gives prominence to spatial keyword queries, which involve both the locations and textual descriptions of content. We study the efficient processing of continuously moving topk spatial keyword (MkSK) queries over spatial keyword data. State-of-the-art solutions for moving queries employ safe zones that guarantee the validity of reported results as long as the user remains within a zone. However, existing safe zone methods focus solely on spatial locations and ignore text relevancy. We propose two algorithms for computing safe zones that guarantee correct results at any time and that aim to optimize the computation on the server as well as the communication between the server and the client. We exploit tight and conservative approximations of safe zones and aggressive computational space pruning. Empirical studies with real data suggest that our proposals are efficient.
Transactions in GIS, 2005
Multiple representation of geographic information occurs when a real‐world entity is represented ... more Multiple representation of geographic information occurs when a real‐world entity is represented more than once in the same or different databases. This occurs frequently in practice, and it invariably results in the occurrence of inconsistencies among the different representations of the same entity. In this paper, we propose an approach to the modeling of multiple represented entities, which is based on the relationships among the entities and their representations. Central to our approach is the Multiple Representation Schema Language that, by intuitive and declarative means, is used to specify rules that match objects representing the same entity, maintain consistency among these representations, and restore consistency if necessary. The rules configure a Multiple Representation Management System, the aim of which is to manage multiple representations over a number of autonomous federated databases. We present a graphical and a lexical binding to the schema language. The graphic...
SIGMOD Digital Review, 2000
Home Home. ...
Audio music is increasingly becoming available in digital form, and the digital music collections... more Audio music is increasingly becoming available in digital form, and the digital music collections of individuals continue to grow. Addressing the need for effective means of retrieving music from such collections, this paper proposes new techniques for content-based similarity search. Each music object is modeled as a time sequence of high-dimensional feature vectors, and dynamic time warping (DTW) is used as the similarity measure. To accomplish this, the paper extends techniques for time-series-length ...
Proceedings of the …, Aug 30, 2005
There has been a growing interest in improving the publication processes for database research pa... more There has been a growing interest in improving the publication processes for database research papers. This panel reports on recent changes in those processes and presents an initial cut at historical data for the VLDB Journal and ACM Transactions on Database Systems.
Encyclopedia of Database Systems, 2009
7th International Conference on Mobile Data Management (MDM'06), 2006
Personalization and recommendation technologies provide the basis for applications tailored to th... more Personalization and recommendation technologies provide the basis for applications tailored to the needs of individual users. These technologies play an increasingly important role for financial service providers, in addition to several firms of the digital economy. According to a recent paper (N. Leavitt, A Technology that Comes Highly Recommended, IEEE Computing now, April 8, 2013), recommender systems technology "is used by shopping websites such as Amazon, which receives about 35 percent of its revenue via product recommendations. It is also used by coupon sites like Groupon; by travel sites to suggest flights, hotels, and rental cars; by social-networking sites such as LinkedIn; by video sites like Netflix to recommend movies and TV shows, and by music, news, and food sites to suggest songs, news stories, and restaurants, respectively. Even financial-services firms recently began using recommender systems to provide alerts for investors about key market events in which they might be interested". According to a Bloomberg Business news appeared on March 2015, funds run by robots account for 400 Billion Dollars. The aim of this workshop was to bring together researchers and practitioners working in financial services related areas in order to: (1) understand and discuss open research challenges, (2) provide an overview of existing technologies, and (3) provide a basis for information exchange between industry and academia.
In Pickup-and-Delivery problems (PDP), mobile workers are employed to pick up and deliver items w... more In Pickup-and-Delivery problems (PDP), mobile workers are employed to pick up and deliver items with the goal of reducing travel and fuel consumption. Unlike most existing efforts that focus on finding a schedule that enables the delivery of as many items as possible at the lowest cost, we consider trichromatic (worker-item-task) utility that encompasses worker reliability, item quality, and task profitability. Moreover, we allow customers to specify keywords for desired items when they submit tasks, which may result in multiple pickup options, thus further increasing the difficulty of the problem. Specifically, we formulate the problem of Online Trichromatic Pickup and Delivery Scheduling (OTPD) that aims to find optimal delivery schedules with highest overall utility. In order to quickly respond to submitted tasks, we propose a greedy solution that finds the schedule with the highest utility-cost ratio. Next, we introduce a skyline kinetic tree-based solution that materializes intermediate results to improve the result quality. Finally, we propose a density-based grouping solution that partitions streaming tasks and efficiently assigns them to the workers with high overall utility. Extensive experiments with real and synthetic data offer evidence that the proposed solutions excel over baselines with respect to both effectiveness and efficiency.
The Vldb Journal, Mar 11, 2020
Vehicle routing is an important service that is used by both private individuals and commercial e... more 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 highways. The increasing availability of vehicle trajectory data yields an increasingly rich data foundation for context-aware, preference-based vehicle routing. We aim to improve routing quality by providing new, efficient routing techniques that identify and take contexts and their preferences into account. In particular, we first provide means of learning contexts and their preferences, and we apply these to enhance routing quality while ensuring efficiency. Our solution encompasses an off-line phase that exploits a contextual preference tensor to learn the relationships between contexts and routing preferences. Given a particular context for which trajectories exist, we learn a routing preference. Then, we transfer learned preferences from contexts with trajectories to similar contexts without trajectories. In the on-line phase, given a context, we identify the corresponding routing preference and use it for routing. To achieve efficiency, we propose preference-based contraction hierarchies that are capable of speeding up both off-line learning and on-line routing. Empirical studies with vehicle trajectory data offer insight into the properties of proposed solution, indicating that it is capable of improving quality and is efficient.
IEEE Internet Computing, May 1, 2011
Geo-Social Networks (GeoSNs) extend social networks by providing context-aware services that supp... more Geo-Social Networks (GeoSNs) extend social networks by providing context-aware services that support the association of location with users and content. We are witnessing a proliferation of GeoSNs, and indications are that these are rapidly attracting increasing numbers of users. The availability of user location yields new capabilities that provide benefits to users as well as service providers. GeoSNs currently offer different types of services, including photo sharing, friend tracking, and "check-ins." However, the introduction of location generates new privacy threats, which in turn calls for new means of affording user privacy in GeoSNs. This article categorizes GeoSNs according to the services they offer; it studies three privacy aspects that are central to GeoSNs, namely location, absence, and co-location privacy; and it discusses possible means of providing these kinds of privacy, as well as presents unresolved privacy-related challenges in GeoSNs.
Online car-hailing services have gained substantial popularity. An effective taxi fleet managemen... more Online car-hailing services have gained substantial popularity. An effective taxi fleet management strategy should not only increase taxi utilization by reducing taxi idle time, but should also improve passenger satisfaction by minimizing passenger waiting time. We demonstrate a fleet management system called SOUP that aims at minimizing taxi idle time and that monitors the fleet movement status. SOUP includes a passenger request prediction model called ST-GCSL that predicts the number of requests in the near future, and it includes a demand-aware route planning algorithm called DROP that provides idle taxis with search routes to serve potential requests. In addition, SOUP supports visualizing and analyzing historical passenger requests, simulating fleet movement, and computing evaluation metrics. We demonstrate how SOUP accurately predicts passenger demand and significantly reduces taxi idle time.
arXiv (Cornell University), Sep 23, 2021
The widespread deployment of smartphones and location-enabled, networked in-vehicle devices rende... more 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-time. However, little attention has so far been given to the quality of clusters-for example, it is beneficial to smooth short-term fluctuations in clusters to achieve robustness to exceptional data. We propose the notion of evolutionary clustering of streaming trajectories, abbreviated ECO, that enhances streaming-trajectory clustering quality by means of temporal smoothing that prevents abrupt changes in clusters across successive timestamps. Employing the notions of snapshot and historical trajectory costs, we formalize ECO and then formulate ECO as an optimization problem and prove that ECO can be performed approximately in linear time, thus eliminating the iterative processes employed in previous studies. Further, we propose a minimal-group structure and a seed point shifting strategy to facilitate temporal smoothing. Finally, we present all algorithms underlying ECO along with a set of optimization techniques. Extensive experiments with two real-life datasets offer insight into ECO and show that it outperforms state-of-the-art solutions in terms of both clustering quality and efficiency.
arXiv (Cornell University), Sep 24, 2020
We consider a setting with an evolving set of requests for transportation from an origin to a des... more 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 (agents) to meet incoming requests for trips while ensuring that the taxis are empty as little as possible. In this paper, we study the problem of spatial-temporal demand forecasting and competitive supply (SOUP). We address the problem in two steps. First, we build a granular model that provides spatial-temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) algorithm that predicts the service requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-aware route planning (DROP) algorithm that considers both the spatial-temporal predictions and the supplydemand state. We report on extensive experiments with realworld and synthetic data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals. Index Terms-Spatial-temporal request forecasting, graph convolutional networks, route planning
IEEE Transactions on Knowledge and Data Engineering, 2021
We consider a setting with an evolving set of requests for transportation from an origin to a des... more 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 (agents) to meet incoming passenger requests for trips while ensuring that the taxis are empty as little as possible. In this paper, we study the problem of spatial-temporal demand forecasting and competitive supply (SOUP). We address the problem in two steps. First, we build a granular model that provides spatial-temporal predictions of requests. Specifically, we propose a Spatial-Temporal Graph Convolutional Sequential Learning (ST-GCSL) model that predicts the requests across locations and time slots. Second, we provide means of routing agents to request origins while avoiding competition among the agents. In particular, we develop a demand-aware route planning (DROP) algorithm that considers both the spatial-temporal predictions and the supply-demand state. We report on extensive experiments with real-world data that offer insight into the performance of the solution and show that it is capable of outperforming the state-of-the-art proposals.
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Papers by Christian S. Jensen