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224 Sharing of Distributed Geospatial Data through Grid Technology national climate data center (Ramapriyan et al., 2006). The data volume will increase signicantly if similar models of ner spatial resolutions, such as 1 km, are used. The models are being changed and rened from time to time and new geospatial data, the NASA EOS data and NOAA climate data, are being collected by satellites continu- ously. A xed computing environment that con- tains only static data sources will not fulll such kind of geospatial applications. Consequently, a capability of seamless and dynamic accessing to large quantities of distributed geospatial data is the key to the success of today’s and tomorrow’s geospatial applications. Although much progress in high performance computing has been made in recent years, there still lacks a mechanism to enable global-scale integration and sharing of large quantities of data, such as geospatial data, from large-scale, heterogeneous, and distributed storage systems. Fortunately, the emerging Grid technology might be able to solve this problem. Grid technology is a form of distributed computational technology that involves the coordination and sharing of computing, application, data, storage, and network resources across dynamic and geographically dis- persed organizations (Foster et al., 2001). Resource sharing in a Grid is highly controlled. Resource providers and consumers dene clearly and care- fully what is shared, who is allowed to share, and the conditions under which the sharing occurs. Individuals and/or institutions agreeing to follow such sharing rules form a virtual organization (VO). The resource sharing across multiple VOs is enabled by the Grid technology. The intrinsic advantages of the Grid technology t the problems of the sharing of distributed geospatial data very well (Di, 2005). The Globus Toolkit, currently at version 4, is an open source toolkit for building Grids provided by the Globus Alliance. It provides many useful components and services that make the use of Grid technology easier. sh Ar Ing of geosp At IAL dAt A through gr Id techno Logy To enable the sharing of distributed geospatial data, a large-scale infrastructure that can integrate the currently dispersed data together and enable the efcient sharing of those huge amounts of geospatial data in a secure and controllable man- ner is crucial. But because geospatial data are huge in quantity and geographically distributed across heterogeneous environments, there are still a lot of problems need to be faced with and solved in order to create such an infrastructure. Those major problems and how they can be ad- dressed by Grid technology are discussed in the following section. System heterogeneity. There are hundreds of large geospatial data centers and countless small or personal data centers around the world. Plat- forms and systems used to store and manage the geospatial data in each center may vary greatly. There are many types of high performance stor- age systems used, such as the Distributed Parallel Storage System (DPSS), the High Performance Storage System (HPSS), and the Storage Resource Broker (SRB). Unfortunately, these storage sys- tems typically use incompatible protocols for data access (Allcock et al., 2002). Also, the diversity of platforms and systems on which geospatial applications are running greatly increase the data sharing difculty. Thus, geospatial applications should be presented with a uniform view of data and uniform mechanisms for accessing the data independent from the platforms and systems used. Grid technology addresses this problem by providing storage system abstraction and uniform API for data accessing. Several components and tools have been provided in the Globus Toolkit, including GridFTP and OGSA-DAI, to integrate heterogeneous systems and make the geospatial data accessible throughout the Internet. Uniform mechanism to publish and discover geospatial data. Usually geospatial data are pub- 225 Sharing of Distributed Geospatial Data through Grid Technology lished by extracting their attributes – geospatial metadata, storing and managing them within catalogues, and making the metadata queryable. Heterogeneity exists in this process because, currently, different models are used to describe geospatial metadata and different methods are used to query geospatial metadata. For example, Earth Observation System (EOS) ClearingHOuse (ECHO) and EOS Data Gateway (EDG) both provide the capabilities to publish and discover NASA EOS data, each with a different model to describe NASA EOS metadata and a different approach for users to search NASA EOS data. To solve this problem, two issues need to be ad- dressed. One issue is the need for a widely accepted domain metadata schema to eliminate semantic heterogeneity of different metadata models. There are domain standards for geospatial metadata schemas available to address this issue, such as ISO 19115 – Geographic Information Metadata (ISO, 2003a) and ISO 19115 part 2 – extensions for imagery and gridded data (ISO, 2003b). The other issue is the need for uniform interfaces for publishing and discovering geospatial data from different metadata catalogues. An example of such uniform interfaces is the Catalogue Service – Web Prole (CSW) developed by the Open Geospatial Consortium (OGC) (Nebert and Whiteside, 2005; Wei et al., 2005). The intrinsic Service Oriented Architecture (SOA) characteristic of the Grid technology enables the cooperation of differ- ent catalogues. With Grid technology, legacy catalogues can be wrapped and exposed as Web services which provide uniform publishing and discovering interfaces, while leaving the internal mechanisms of the catalogues untouched. Grid technology also provides a mechanism for creat- ing federations of distributed catalogue services. Queries to any single accessing point of such a federation can be delivered to all the catalogue services throughout the federation. Thus the discovery of geospatial data can be much more efcient. Performance. Geospatial data are not only large in quantity but also huge in size. Although the computing capability and network bandwidth are increasing rapidly, accessing and transfer- ring large amounts of geospatial data are still huge burdens. Grid technology provides several mechanisms that can improve availability and accessing performance of geospatial data, one of which is an important component within a data- intensive Grid environment – Data Replication System (DRS) provided by Globus Toolkit. A data replica is a full or partial copy of the original data (Chervenak et al., 2001). With the help of DRS, multiple replicas of the original geospatial data can be created, distributed, and managed across different storage systems and data centers. DRS monitors the storage systems, computing plat- forms, and networks within a Grid environment in real time. If a user wants to access a specic geospatial data, DRS will choose one replica which provides the best accessing performance for the user. DRS can even choose more than one replica for the user and provide the user with a stripped-style data accessing mechanism which enables the user to retrieve different parts of the original geospatial data from different replicas simultaneously and combine those different parts into a complete data after retrieving. Multiple replicas are created to increase the availability of geospatial data; otherwise a single failure will make those geospatial data unavailable. The accessing performance is also improved by choosing optimized replicas. Other mechanisms are also provided by Grid technology to improve the accessing performance and reliability for geospatial data, like GridFTP, which provides much more improved data transfer performance than the traditional FTP protocol. Security. Security is a critical issue associ- ated with the sharing of geospatial data. Many of the geospatial data are sensitive and restricted to be accessed by only some special persons or organizations. Some of the geospatial data are to be shared for commercial purposes and are 226 Sharing of Distributed Geospatial Data through Grid Technology associated with an accessing fee. Currently dif- ferent organizations and communities are using diverse mechanisms to handle security related issues, such as authentication, authorization, and access control. Consequently, there is a need for a uniform security mechanism to coordinate the sharing of geospatial data across those naturally untrusted organizations and user communities while keeping the diverse local security mecha- nism intact. The Grid Security Infrastructure (GSI) provided by the Grid technology can be used to address this problem. Based on GSI, each geospatial organization or user community can form a VO. Each individual user, machine, stor- age system, application, or a VO will have one or more certicates as its identity. Certain trust relationships can be set up among different VOs (Welch et al. 2003). As a consequence, a larger VO is formed. Thus, ne-grain access control policies on geospatial data can be issued to any individual user, application, or VO that has one or more certicates through Community Autho- rization Service (CAS) provided by the Globus Toolkit. Currently, the X.509 certicates based on Public Key Infrastructure (PKI) are used by Grid technology and to provide high-level au- thentication, authorization, and single sign-on functionality (Welch 2005). Today, efforts have been taken by some geosci- ence communities to leverage Grid technology for the sharing of geospatial data. For example, Earth System Grid (ESG) is a research project sponsored by the U.S. Department of Energy (DOE) Ofce of Science to address the formidable challenges as- sociated with enabling analysis of and knowledge development from global earth system models. The goal of ESG is to provide a seamless and powerful environment that enables next genera- tion climate research by integrating distributed federations of supercomputers and large-scale data & analysis servers through a combination of Grid technology and emerging community technologies. The Center for Spatial Information Science and System (CSISS) in George Mason University also developed a prototype system for efcient sharing, customization, and acquisition of distributed NASA EOS data by integrating the Grid technology and Open Geospatial Consortium (OGC) Web Services technologies. This prototype system involves three partners distributed across the United States: George Mason University, NASA Ames Research Center, and Lawrence Livermore National Lab. Each partner forms a VO and trust relationships are set up among those three VOs to create an integrated Grid environment. About 20TB of remote sensing and climate simulation data are shared among this prototype. Grid-enabled Catalogue Service for Web (CSW) was implemented to provide uniform mechanism for data publication and discovery. Data Replication System and Resource Selection components were also implemented to improve the performance of data sharing. The customization of data was achieved by leveraging OGC Web Services, such as Web Coverage Service (WCS) and Web Map Service (WMS), to provide more options for geospatial data accessing. future trends The goal of the Grid technology is to create a computing and data management infrastructure that will provide the electronic underpinning for a global society in business, government, research, science, and entertainment (Berman et al., 2003). As an essential information source for scientic research and even people’s everyday life, distrib- uted geospatial data all over the world are also doomed to be integrated to form a global-scale warehouse to promote the sharing of geospatial data. Grid technology is still young and there are many open issues to be addressed and missing functionalities to be developed. New computing and network technologies are also emerging and advancing, such as the wireless and mobile computing technologies, which greatly extend the boundary for the sharing of geospatial informa- 227 Sharing of Distributed Geospatial Data through Grid Technology tion. With the maturation of Grid technology and the advancement of computing and network tech- nologies, this will not only be a dream: wherever the geospatial data are, they can be shared and accessed from almost anywhere at anytime. conc Lus Ion With the rapid accumulation of geospatial data and the advancement of geoscience, there is a critical requirement for an infrastructure that can integrate large-scale, heterogeneous, and distributed storage systems for the sharing of geospatial data within multiple user communities. The emerging Grid technology can address the problems associated with the sharing of distrib- uted geospatial data, including the heterogeneity of computing platforms and storage systems, uniform mechanism to publish and discover geospatial data, performance issues, and security and access control issues. Some efforts within the geospatial society have been taken to leverage the Grid technology for the sharing of distributed data. With the maturation of Grid technology, the integration and sharing of distributed geospatial data will be easier and more efcient. references Allcock, B., Bester, J., Bresnahan, J., Cherve- nak, L. A., Foster, I., Kesselman, C., Meder, S., Nefedova, V., Quesnel, D., & Tuecke, S. (2002, May). Data Management and Transfer in High Performance Computational Grid Environments. Parallel Computing Journal, 28(5), 749-771. Berman, F., Fox, G., & Hey, T., (2003). The Grid: past, present, future. In Berman, F., Fox, G., and Hey, A. eds, Grid Computing: Making the Global Infrastructure a Reality, 9-50. Wiley, New York, NY, USA. Chervenak, A., Foster, I., Kesselman, C., Salis- bury, C., & Tuecke, S. (2001). The Data Grid: Towards an Architecture for the Distributed Management and Analysis of Large Scientic Datasets. Journal of Network and Computer Ap- plications, 23, 187-200. Di, L. (2005). The Geospatial Grid. In Rana, S. and Sharma, J. (eds.), Frontiers of Geographic Information Technology. Springer-Verlag. Foster, I., Kesselman, C., & Tuecke, S., (2001). The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International Journal Supercomputer Applications, 15(3). ISO (2003a). Geographic Information – Metadata, ISO 19115:2003. May 08, 2003, 140pp. ISO (2003b). Geographic Information – Metadata – Part 2: Extensions for imagery and gridded data, ISO/WD 19115-2.2. Oct. 13, 2003, 41pp. Karimi, A. H. & Peachavanish, R., (2005). In- teroperability in Geospatial Information Systems. In Khosrow-Pour, M. (eds.), Encyclopedia of Information Science and Technology. Hershey, PA: Idea Group Reference. Lamberti, F., & Beco, S., (2002). SpaceGRID - An international programme to ease access and dissemination of Earth Observation data/prod- ucts: How new technologies can support Earth Observation Users Community. 22nd EARSeL Symposium & General Assembly, Prague, Czech Republic, June 4-6, 2002. Lo, C. P., & Yeung, A. K. W., (2002). Concepts and techniques of geographic information systems. Upper Saddle River, NJ: Prentice Hall. Nebert, D., & Whiteside, A., 2005. OGC TM Cata- logue Services Specication (Version 2.0.0). OGC Document Number: 04-021r3, 187pp. Ramapriyan, H., Isaac, D., Yang, W., Bonnlander, B., & Danks, D., (2006). An Intelligent Archive Testbed Incorporating Data Mining – Lessons and 228 Sharing of Distributed Geospatial Data through Grid Technology Observations. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2006. July 3- August 4, 2006, Denver, Colorado. Wei, Y., Di, L., Zhao, B., Liao, G., Chen, A., Bai, Y., & Liu, Y. (2005). The design and implementa- tion of a Grid-enabled catalogue service. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2005 on July 25-29, 2005, Seoul, Korea. Welch, V. (2005). Globus Toolkit Version 4 Grid Security Infrastructure: A Standards Perspec- tive. Welch, V., Siebenlist, F., Foster, I., Bresnahan, J., Czajkowski, K., Gawor, J., Kesselman, C. Meder, S., Pearlman, L., & Tuecke, S. (2003). Security for Grid Services. Twelfth International Symposium on High Performance Distributed Computing (HPDC-12), IEEE Press. key ter Ms Certicate: A public key and information about the certicate owner bound together by the digital signature of a CA. In the case of a CA certicate the certicate is self signed, i.e., it was signed using its own private key. Data Replica: A complete or partial copy of original data. DPSS: The Distributed-Parallel Storage System (DPSS) is a scalable, high-performance, distributed-parallel data storage system orginally developed as part of the DARPA -funded MAGIC Testbed, with additional support from the U.S. Dept. of Energy, Energy Research Division, Mathematical, Information, and Computational Sciences Ofce. Grid Technology: Grid technology is an emerging computing model that provides the ability to perform higher throughput computing by taking advantage of many networked comput- ers to model a virtual computer architecture that is able to distribute process execution across a parallel infrastructure. GridFTP: Extension of traditional FTP pro- tocol. It is a uniform, secure, high-performance interface to le-based storage systems on the Grid. HPSS: High Performance Storage System (HPSS) is hierarchical storage system software that manages and accesses terabytes to petabytes of data on disk and robotic tape libraries. OGSA-DAI: Open Grid Services Architecture – Data Accessing Interface. It is a middleware product which supports the exposure of data resources, such as relational or XML databases, on to Grids. SRB: The Storage Resource Broker (SRB) is a Data Grid Management System (DGMS) or simply a logical distributed le system based on a client-server architecture which presents the user with a single global logical namespace or le hierarchy. Virtual Organization: A Virtual Organiza- tion is a group of individuals or institutions who share the computing resources of a “Grid” for a common goal. X.509: In cryptography, X.509 is an ITU-T standard for public key infrastructure (PKI). X.509 species, amongst other things, standard formats for public key certicates and a certica- tion path validation algorithm. Section VI Location-Based Services 230 Chapter XXIX Cognitively Ergonomic Route Directions Alexander Klippel University of Melbourne, Australia Kai-Florian Richter Universität Bremen, Germany Stefan Hansen Spatial/Information Systems Ltd./LISAsoft, Australia Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. Abstr Act This contribution provides an overview of elements of cognitively ergonomic route directions. Cognitive ergonomics, in general, seeks to identify characteristics of cognitive information processing and to formal- ize these characteristics such that they can be used to improve information systems. For route directions, an increasing number of behavioral studies have, for example, pointed to the following characteristics: the use of landmarks, changing levels of granularity, the qualitative description of spatial relations. The authors detail these aspects and additionally introduce formal approaches that incorporate them to automatically provide route directions that adhere to principles of cognitive ergonomics. cogn It Ive Aspects of r oute dIrect Ions Route directions fascinate researchers in several elds. Since the 70s linguists and cognitive scien- tists have used verbal route directions as a window to cognition to learn about cognitive processes that reect structuring principles of environmen- tal knowledge (e.g., Klein, 1978). Over the last decade, the number of publications on various aspects of route directions has increased. Next to the general aspects of how to provide route direc- 231 Cognitively Ergonomic Route Directions tions and how to identify principles that allow us to dene what makes route directions cognitively ergonomic, technical aspects of navigation support systems have become an additional focus. The question required from the latter perspective is part of a broader approach that aims to formally characterize the meaning (semantics) of spatial relations. In other words, if we want to bridge the gap between information systems and behavioral analysis we have to answer how we perform the transition from data to knowledge. Several key elements can be identied based on psychological and linguistic literature on route directions that are pertinent for cognitively ergo- nomic route directions (Denis, 1997; Lovelace, Hegarty, & Montello, 1999; Tversky & Lee, 1999). These comprise the conceptualization of directions at decision points, the spatial chunking of route direction elements to obtain hierarchies and to change the level of granularity, the role of landmarks, the communication in different modalities, the traveling in different modes, and aspects of personalization (see Table 1). Most research on routes and route directions deals with navigation in urban structures such as street networks. The results discussed in this article focus on this domain. Appro Aches t o r epresent Ing r oute know Ledge Behavioral studies have substantiated key ele- ments of cognitively ergonomic route directions. To implement these aspects in information systems detailed formal characterizations of route knowl- edge are required. The approaches discussed below are a representative vocabulary that allows for the characterization of mental conceptualiza- tion processes reecting the results from behav- ioral studies (see Table 1). In this sense we can refer to them as Ontologies of Route Knowledge (Chandrasekaran, Josephson, & Benjamins, 1999; Gruber, 1993). In Guarino’s terminology these approaches would most likely be called domain ontologies (Guarino, 1998). One of the earliest approaches is the TOUR model by Kuipers (Kuipers, 1978) that later devel- oped into the Spatial Semantic Hierarchy (SSH) (Kuipers, 2000). Kuipers and his collaborators developed this approach to add the qualitative- ness that can be found in the organization of a cognitive agent’s spatial knowledge to approaches in robotics. The latter classically relied more on q uan titative spatial descriptions. The SSH al- lows for modeling cognitive representations of space as well as for building a framework for robot navigation, i.e. qualitative and quantita- Table 1. Cognitive ergonomics of route directions Cognitively ergonomic route directions • are qualitative, not quantitative, • allow for different levels of granularity and organize spatial knowledge hierarchically, • reect cognitive conceptualizations of directions at decision points, • chunk route direction elements into larger units to reduce cognitive load, • use landmarks to: ° disambiguate spatial situations, ° anchor turning actions, ° and to conrm that the right actions have been taken, • present information in multimodal communication systems allowing for an interplay of language and graphics, but respecting for the underlying conceptual structure, • allow for an adaptation to the user’s familiarity with an environment, as well as personal styles and different languages. 232 Cognitively Ergonomic Route Directions tive aspects are combined. The SSH especially reects the aspect of hierarchical organization of spatial knowledge by providing different levels of information representation: the sensory, control, causal, topological, and metrical level. Ontological characterizations are developed for each level to match human cognitive processes. The Route Graph model (Werner, Krieg- Brückner, & Herrmann, 2000) describes key elements for route based navigation. Similar to the SSH, it allows representing knowledge on different levels of granularity. However, it is much more abstract and does not provide any processes for acquiring this knowledge. It is intended to provide a formalism expressing key notions of route knowledge independent of a particular implementation, agent, or domain. Its focus is on a sound formal specication of basic elements and operations, like the transition from route knowledge to survey knowledge by merging routes into a graph-like structure. A linguistically grounded approach with the aim to generate verbal route directions is the CORAL project by Dale and coworkers (e.g., Dale, Geldof, & Prost, 2005). One of the central aspects of their approach is the organization of parts of a route into meaningful units, a process they call segmentation. Instead of providing turn-by-turn directions, this approach allows for a small number of instructions that capture the most important aspects of a route. The employed modeling language is called Route Planning Markup Language (RPML). Formalisms that model route knowledge on the conceptual level can be found in the theory of waynding choremes (Klippel, Tappe, Kulik, & Lee, 2005) and context-specic route directions (Richter & Klippel, 2005). These approaches model route knowledge modality-independent on the conceptual level. The waynding choreme theory employs conceptual primitives—as the result of conceptualization processes of a cogni- tive agent incorporating functional as well as geometrical environmental aspects—to dene basic as well as super-ordinate valid expressions on different levels of granularity. The approach to context-specic route directions builds on this theory. A systematics of route direction ele- ments determines which, and how, entities may be referred to in route directions. Accordingly, abstract relational specications are inferred by optimization processes that adapt route directions to environmental characteristics and inherent route properties. Human waynding, however, may not be restricted to a single mode of transportation. A typical example is public transport, where travelers frequently switch between pedestrian movement and passive transportation (trains, buses, etc.). Timpf (2002) analyzed route direc- tions for multi-modal waynding and developed two different ontologies of route knowledge: one representing knowledge from the perspective of the traveler and one taking the perspective of the transportation system. The former focuses on movement along a single route, i.e., actions to perform to reach the destination, while the latter provides concepts referring to the complete transportation network. An industry approach for formalizing route knowledge can be found in Part 6: Navigation Service of the OpenLS specication. The Open- GIS Location Services (OpenLS) Implementation Specication (Mabrouk, 2005) describes an open platform for location-based application services, the so called GeoMobility Server (GMS) proposed by the Open Geospatial Consortium (OGC). It offers a framework for the interoperable use of mobile devices, services and location-related data. The Navigation Service described in Part 6 of the OpenLS specication provides the access- ing client, amongst other services, with prepro- cessed data that is required for the generation of route directions. Based on XML specications, it denes a data structure that allows clients to generate their own route directions which may accord more to a user’s preferences. The used data model structures the route in maneuvers 233 Cognitively Ergonomic Route Directions (descriptions combining a turn at a decision point and proceeding on the following route segment) and enhances them with additional information about route elements. core Aspects of cogn It Ive Ly ergono MIc r oute dIrect Ions In the following, three aspects that are at the core of cognitively ergonomic route directions will be discussed in greater detail: cognitively adequate direction concepts, the use of landmarks, and spatial chunking to obtain hierarchies and change the level of granularity. Conceptualization of Directions at decision points The specication of direction changes is the most pertinent information in route directions. While current route information systems heavily rely on street names to identify the proper direction to take, behavioral research (Tom & Denis, 2003) has shown that from a cognitive perspective, street names are not the preferred means to re- orient oneself. People rather rely on landmarks (as discussed in the next section) and appropriate direction concepts. On the most basic level we have to specify the correspondence between a direction change (in terms of the angle) and a direction concept. For example, which sector is applicable to a concept like “turn right”? On a more elaborate level, we have to specify alterna- tive direction concepts and detail their scope of application. Figure 1 shows some examples of how the same direction change can result in different direction concepts (and corresponding verbalizations) depending, among other things, on the spatial structure in which the change occurs. We need this level of specicity for two reasons. First, a qualitative but precise direction model allows for verbally instantiating a situation model (Zwaan & Radvansky, 1998) of the encountered intersections. Second, intersections can function as landmarks. Just like classical examples of land- marks, such as the Eiffel Tower, in the context of a specic route, a salient intersection can be Figure 1. A change of a direction is associated with different conceptualizations according to the inter- section at which it takes place. The ‘pure’ change may be linguistically characterized as take the second exit at the roundabout (a). At intersection (b) it might change to the second right; at intersection (c) it may change to fork right, and at (d) it becomes veer right. [...]... Optimization Approach to Urban School Bus Routing: Formulation and Solution Method Transportation Research Part A - Policy and Practice, 29(2), 10 7-1 23 Ceder, A (2001) Operational Objective Functions in Designing Public Transport Routes Journal of Advanced Transportation, 35(2), 12 5-1 44 Ceder, A., & Wilson, N (19 86) Bus Network Design Transportation Research Part B - Methodological, 20B(4), 33 1-3 44 Chakroborty,... computational model defining acquisition and representation of spatial knowledge on different levels of abstraction ranging from sensory information to topological knowledge Personalization: Adaptation of information presentation and interaction with a device / software to the needs and preferences of a specific, individual user Wayfinding: The cognitive conceptual activity of planning and finding ones... Article 12 of the UN Universal Declaration of Human Rights (1948) PR IVACY IN LOC AT ION -AW ARE ENV IRON MENTS Location privacy has become an especially important issue in geoinformatics because of the emergence of location-aware computing Location-aware computing environments combine high-power mobile computer platforms, like personal digital assistants (PDAs) or cellular phones; location-sensing technology,... Location-Based Services In Proceedings of the 38th Hawaii International Conference on System Sciences (9 pages) IEEE Ko, Y-B., & Vaidya, N H (2002) Flooding-Based Geocasting Protocols for Mobile Ad Hoc Networks Proceeding of the Mobile Networks and Applications Kluwer Academic, 7 (6) , 47 1-4 80 Multicast Over Location-Based Services LocatioNet, (20 06) LocatioNet and Ericsson Enter Into Global Distribution... or electronic forms without written permission of IGI Global is prohibited Multicast Over Location-Based Services location-related communication has not been solved completely Traditional Location-Based Services (LBSs) determine the current location of a given person or a given group of people in order to process location-dependent information This use does not cover the full range that is conceivable... current solutions by pursuing a service integrated approach that encompasses pre-trip and on- trip services where on- trip services could be split into in-car and last-mile services (Bocci, 2005) The pre-trip service means the 3D navigation of the users in a city environment, and the on- trip service means the in-car and the last-mile services together The in-car service is a composition of an LBS and... services Examples of increasingly common location-based services include navigation and emergency response systems People using location-based services must reveal information about their location to a service provider One of the first location-based services, the AT&T “Find Friends” service, enabled friends and family to share information about their location Figure 1 Classification of types of privacy,... location privacy, three types of location-sensing technique can be distinguished (Kaasinen, 2003): Client-based positioning systems use a person’s mobile device to compute that person’s location Client-based position systems may be used without revealing location information to a third party However, most location-based services still require such information be disclosed GPS is an example of a client-based... environments Traditional LBSs map targets to locations (e.g., Where is person X located?), i.e., they find the position of a specific person or group of people This type of LBS is denoted as Tracking Services There are a lot of location positioning methods and technologies, such as the satellite-based Global Positioning System (GPS) that is widely applied (Hofmann-Wellenhof et al., 1997) The location... Implementations, and Numerical Results (No SWUTC/04/ 167 24 4-1 ) Austin, TX: Center for Transportation Research Flood, M M (19 56) The Traveling-Salesman Problem Journal of the Operations Research Society, 4, 6 1-7 5 Murray, A T (2003) A Coverage Model for Improving Public Transit System Accessibility and Expanding Access Annals of Operations Research, 123, 14 3-1 56 Murray, A T., & Wu, X (2003) Accessibility tradefoffs . several mechanisms that can improve availability and accessing performance of geospatial data, one of which is an important component within a data- intensive Grid environment – Data Replication. performance computing has been made in recent years, there still lacks a mechanism to enable global-scale integration and sharing of large quantities of data, such as geospatial data, from large-scale,. sharing of geospatial data. Many of the geospatial data are sensitive and restricted to be accessed by only some special persons or organizations. Some of the geospatial data are to be shared
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