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Computing and managing cardinal direction relations

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2005年第12期17卷 1610-1623页

作者： Skiadopoulos, S Giannoukos, C Sarkas, N Vassiliadis, P Sellis, T Koubarakis, M Natl Tech Univ Athens Sch Elect & Comp Engn Div Comp Sci GR-15773 Athens Hellas Greece Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece Tech Univ Crete Dept Elect & Comp Engn Khania 73100 Crete Greece

Qualitative spatial reasoning forms an important part of the commonsense reasoning required for building intelligent Geographical Information Systems (gis). Previous research has come up with models to capture cardinal direction relations for typical gis data. In this paper, we target the problem of efficiently computing the cardinal direction relations between regions that are composed of sets of polygons and present two algorithms for this task. The first of the proposed algorithms is purely qualitative and computes, in linear time, the cardinal direction relations between the input regions. The second has a quantitative aspect and computes, also in linear time, the cardinal direction relations with percentages between the input regions. Our experimental evaluation indicates that the proposed algorithms outperform existing methodologies. The algorithms have been implemented and embedded in an actual system, CARDIRECT, that allows the user to 1) specify and annotate regions of interest in an image or a map, 2) compute cardinal direction relations between them, and 3) pose queries in order to retrieve combinations of interesting regions.

关键词： spatial databases and gis cardinal direction relations computing spatial relations

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T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2013年第1期25卷 220-232页

作者： Yuan, Jing Zheng, Yu Xie, Xing Sun, Guangzhong Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China

This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm of a city and taxi drivers' intelligence in choosing driving directions in the physical world. We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the intelligence of experienced drivers so as to provide a user with the practically fastest route to a given destination at a given departure time. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest and customized route for end users. We build our system based on a real-world trajectory data set generated by over 33,000 taxis in a period of three months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70 percent of the routes suggested by our method are faster than the competing methods, and 20 percent of the routes share the same results. On average, 50 percent of our routes are at least 20 percent faster than the competing approaches.

关键词： spatial databases and gis data mining GPS trajectory driving directions driving behavior

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Topological operators: a relaxed query processing approach

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GEOINFORMATICA 2012年第1期16卷 67-110页

作者： Belussi, Alberto Catania, Barbara Podesta, Paola Univ Genoa Dipartimento Informat & Sci Informaz I-16146 Genoa Italy Univ Verona Dipartimento Informat I-37134 Verona Italy

Relaxation and approximation techniques have been proposed as approaches for improving the quality of query results, in terms of completeness and accuracy, in environments where the user may not be able to specify the query in a complete and exact way, since data are quite heterogeneous or she may not know all the characteristics of data at hand. This problem, mainly addressed for relational and XML data, is nowadays quite relevant also for geo-spatial data, due to their increasing usage in highly critical decisional processes. Among geo-spatial queries, those based on spatial and more precisely topological relations are currently used in an increasing number of applications. As far as we know, no approach has been proposed so far for relaxing queries based on topological predicates when they return an empty or insufficient answer, in order to improve result quality and user satisfaction. In this paper, we consider this problem and we present a general relaxation strategy for, possibly multi-domain, topological selection and join queries. Two specific semantics are also provided: the first applies the minimum amount of relaxation in order to get an acceptable answer;the second relaxes the given query of a certain fixed amount, depending on the considered topological predicate. Index-based processing algorithms, for efficiently executing relaxed queries based on the proposed semantics, are also presented and a specific topological similarity function, to be used for relaxation purposes, is proposed. Experimental results show that the overhead given by query relaxation is acceptable.

关键词： Information technology and systems Database management Database applications spatial databases and gis

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Managing Evolving Uncertainty in Trajectory databases

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2014年第7期26卷 1692-1705页

作者： Jeung, Hoyoung Lu, Hua Sathe, Saket Yiu, Man Lung SAP Res Brisbane Qld 4105 Australia Aalborg Univ Dept Comp Sci DK-9220 Aalborg Denmark Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

Modern positioning technologies enable collecting trajectories from moving objects across different locations over time, typically containing time-varying measurement errors of positioning systems. Unfortunately, current models on uncertain trajectories are incapable of capturing dynamically changing uncertainty in trajectory data, and lack the support of recent progress made in improving localization accuracy. In order to tackle these problems, we address three important issues centric to uncertain trajectory management. First, we propose a flexible trajectory modeling approach that takes into account model-inferred actual positions, time-varying uncertainty, and nondeterministic uncertainty ranges. Second, we develop three estimators that effectively infer evolving densities of trajectory data. Last, we present an efficient mechanism to evaluate probabilistic range queries on those evolving-density trajectories. Empirical results on two large-scale real datasets demonstrate the quality and efficiency of our approach.

关键词： Query processing spatial databases and gis probabilistic algorithms

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Trading Off Accuracy, Timeliness, and Uplink Usage in Online GPS Tracking

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2016年第8期15卷 2124-2136页

作者： Musa, A. B. M. Biagioni, James Eriksson, Jakob Univ Illinois Dept Comp Sci Chicago IL 60607 USA

In an online GPS tracking system, a fundamental trade-off exists between timeliness, the average time interval between a recorded change in location and a change in the reported location;accuracy, the average error between the actual location and the reported location;and uplink usage, the average amount of data used per second of tracking. While tracking efficiency has been addressed in the literature, our thrifty tracking system presents the first unified view of timeliness, accuracy, and uplink usage, allowing the user to specify desired targets for any two of these objectives, while optimizing the third. We also provide a closed-form characterization of this three-way trade-off, and demonstrate that our system converges to the predicted performance.

关键词： spatial databases and gis mobile communication systems mobile computing client/server data compaction and compression data communications data communications aspects system applications and experience

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A link-based storage scheme for efficient aggregate query processing on clustered road networks

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INFORMATION SYSTEMS 2010年第1期35卷 75-93页

作者： Demir, Engin Aykanat, Cevdet Cambazoglu, B. Barla Bilkent Univ Dept Comp Engn Ankara Turkey Yahoo Res Barcelona Spain

The need to have efficient storage schemes for spatial networks is apparent when the volume of query processing in some road networks (e.g., the navigation systems) is considered. Specifically, under the assumption that the road network is stored in a central server, the adjacent data elements in the network must be clustered on the disk in such a way that the number of disk page accesses is kept minimal during the processing of network queries. In this work, we introduce the link-based storage scheme for clustered road networks and compare it with the previously proposed junction-based storage scheme. in order to investigate the performance of aggregate network queries in clustered road networks, we extend our recently proposed clustering hypergraph model from junction-based storage to link-based storage. We propose techniques for additional storage savings in bidirectional networks that make the link-based storage scheme even more preferable in terms of the storage efficiency. We evaluate the performance of our link-based storage scheme against the junction-based storage scheme both theoretically and empirically. The results of the experiments conducted on a wide range of road network datasets show that the link-based storage scheme is preferable in terms of both storage and query processing efficiency. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Storage management spatial databases and gis Road networks Link-based storage Clustering Hypergraphs

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Redistricting Using Constrained Polygonal Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2012年第11期24卷 2065-2079页

作者： Joshi, Deepti Soh, Leen-Kiat Samal, Ashok Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

Redistricting is the process of dividing a geographic area consisting of spatial units-often represented as spatial polygons-into smaller districts that satisfy some properties. It can therefore be formulated as a set partitioning problem where the objective is to cluster the set of spatial polygons into groups such that a value function is maximized [ 1]. Widely used algorithms developed for point-based data sets are not readily applicable because polygons introduce the concepts of spatial contiguity and other topological properties that cannot be captured by representing polygons as points. Furthermore, when clustering polygons, constraints such as spatial contiguity and unit distributedness should be strategically addressed. Toward this, we have developed the Constrained Polygonal spatial Clustering (CPSC) algorithm based on the A* search algorithm that integrates cluster-level and instance-level constraints as heuristic functions. Using these heuristics, CPSC identifies the initial seeds, determines the best cluster to grow, and selects the best polygon to be added to the best cluster. We have devised two extensions of CPSC-CPSC* and CPSC*-PS-for problems where constraints can be soft or relaxed. Finally, we compare our algorithm with graph partitioning, simulated annealing, and genetic algorithm-based approaches in two applications-congressional redistricting and school districting.

关键词： spatial clustering polygonal clustering constraint-based processing data mining spatial databases and gis

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OpenStreetMap: User-Generated Street Maps

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IEEE PERVASIVE COMPUTING 2008年第4期7卷 12-18页

作者： Haklay, Mordechai (Muki) Weber, Patrick UCL Dept Civil Environm & Geomat Engn London WC1E 6BT England UCL Dept Comp Sci London WC1E 6BT England

The OpenStreetMap project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.

关键词： Web Sites Cartography Geographic Information Systems Groupware Software Tools Software Tool Open Street Map Project User Generated Street Map Peer Production Model Wikipedia Copyright Scheme Web Site Cartography Software Developer Global Positioning System Educational Institutions Earth Geologic Measurements Collaborative Software History Geology Mobile Handsets Standards Publication Standards Development Computer Supported Cooperative Work spatial databases and gis User Generated Content Collaborative Computing

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spatialSSJP: QoS-Aware Adaptive Approximate Stream-Static spatial Join Processor

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第1期35卷 73-88页

作者： Al Jawarneh, Isam Mashhour Bellavista, Paolo Corradi, Antonio Foschini, Luca Montanari, Rebecca Univ Bologna Dipartimento Informat Scienzae & Ingn DISI I-40136 Bologna Italy

The widespread adoption of Internet of Things (IoT) motivated the emergence of mixed workloads in smart cities, where fast arriving geo-referenced big data streams are joined with archive tables, aiming at enriching streams with descriptive attributes that enable insightful analytics. Applications are now relying on finding, in real-time, to which geographical regions data streaming tuples belong. This problem requires a computationally intensive stream-static join for joining a dynamic stream with a disk-resident static table. In addition, the time-varying nature of fluctuation in geospatial data arriving online calls for an approximate solution that can trade-off QoS constraints while ensuring that the system survives sudden spikes in data loads. In this paper, we present spatialSSJP, an adaptive spatial-aware approximate query processing system that specifically focuses on stream-static joins in a way that guarantees achieving an agreed set of Quality-of-Service goals and maintains geo-statistics of stateful online aggregations over stream-static join results. spatialSSJP employs a state-of-art stratified-like sampling design to select well-balanced representative geospatial data stream samples and serve them to a stream-static geospatial join operator downstream. We implemented a prototype atop Spark Structured Streaming. Our extensive evaluations on big real datasets show that our system can survive and mitigate harsh join workloads and outperform state-of-art baselines by significant magnitudes, without risking rigorous error bounds in terms of the accuracy of the output results. spatialSSJP achieves a relative accuracy gain against plain Spark joins of approximately 10% in worst cases but reaching up to 50% in best case scenarios.

关键词： Algorithms for data and knowledge management Data Architecture spatial databases and gis QoS Data Management spatial Join spatial Indexes Geospatial Analysis Apache Spark Query Processing Big Data Applications

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Dictionary-Based Compression for Long Time-Series Similarity

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2010年第11期22卷 1609-1622页

作者： Lang, Willis Morse, Michael Patel, Jignesh M. Univ Wisconsin Dept Comp Sci Madison WI 53706 USA MITRE Washington Mitre Corp Innovat Informat Engn & Biometr Mclean VA 22102 USA

Long time-series data sets are common in many domains, especially scientific domains. Applications in these fields often require comparing trajectories using similarity measures. Existing methods perform well for short time series but their evaluation cost degrades rapidly for longer time series. In this work, we develop a new time-series similarity measure called the Dictionary Compression Score (DCS) for determining time-series similarity. We also show that this method allows us to accurately and quickly calculate similarity for both short and long time series. We use the well-known Kolmogorov Complexity in information theory and the Lempel-Ziv compression framework as a basis to calculate similarity scores. We show that off-the-shelf compressors do not fair well for computing time-series similarity. To address this problem, we developed a novel dictionary-based compression technique to compute time-series similarity. We also develop heuristics to automatically identify suitable parameters for our method, thus, removing the task of parameter tuning found in other existing methods. We have extensively compared DCS with existing similarity methods for classification. Our experimental evaluation shows that for long time-series data sets, DCS is accurate, and it is also significantly faster than existing methods.

关键词： spatial databases and gis database management

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