3,365 research outputs found

    Sketch-based Queries in Mobile GIS-Environments

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    Recent achievements in the field of mobile computing and wireless communication promise data retrieval anywhere and anytime. This development provided the basis to expand GIs technology to handheld devices, such as personal digital assistants (PDAs). Although traditional GIs technology is well suited for desktop workstations, it needs to be adapted in order to satisfy the requirements of users using handheld computing devices. This adaptation is necessary because the usability of traditional GISs depends on characteristics of desktop computers, such as their relatively large user interfaces (e.g., displays, keyboards, pointing devices), considerable computing resources (i.e., CPU, memory, storage, operating systems), and high bandwidth network connectivity. Small devices possess few of these characteristics, hence, requiring new and efficient methods for interaction with spatial databases. We propose a concept that supports sketch-based querying in mobile GIs environments. This concept combines newest techniques for spatial querying and mobile technologies. Such a combination is beneficial for users because it allows them to formulate queries by drawing the desired configuration with a pen on the touch-sensitive PDA screen, and consequently avoids typing complex statements in some SQL-like query language. Client-server architectures in mobile environments are characterized by low and fluctuating bandwidth, and by frequent disconnections. We discuss client-server strategies in mobile environments, suggest an adaptive client-server architecture for geomobile querying, and analyze the performance. It is shown that adaptation to the mobile environment is necessary in order to ensure efficiency of geo-mobile queries

    Collaborative Storage Management In Sensor Networks

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    In this paper, we consider a class of sensor networks where the data is not required in real-time by an observer; for example, a sensor network monitoring a scientific phenomenon for later play back and analysis. In such networks, the data must be stored in the network. Thus, in addition to battery power, storage is a primary resource: the useful lifetime of the network is constrained by its ability to store the generated data samples. We explore the use of collaborative storage technique to efficiently manage data in storage constrained sensor networks. The proposed collaborative storage technique takes advantage of spatial correlation among the data collected by nearby sensors to significantly reduce the size of the data near the data sources. We show that the proposed approach provides significant savings in the size of the stored data vs. local buffering, allowing the network to run for a longer time without running out of storage space and reducing the amount of data that will eventually be relayed to the observer. In addition, collaborative storage performs load balancing of the available storage space if data generation rates are not uniform across sensors (as would be the case in an event driven sensor network), or if the available storage varies across the network.Comment: 13 pages, 7 figure

    Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

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    In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time

    Energy-efficient query management scheme for a wireless sensor database system

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    Minimizing the communication overhead to reduce the energy consumption is an essential consideration in sensor network applications, and existing research has mostly concentrated on data aggregation and in-network processing. However, effective query management to optimize the query aggregation plan at the gateway side is also a significant approach to energy saving in practice. In this paper, we present a multiquery management framework to support historical and continuous queries, where the key idea is to reduce common tasks in a collection of queries through merging and aggregation, according to query region, attribute, time duration, and frequency, by executing the common subqueries only once. In this framework, we propose a query management scheme to support query partitioning, region aggregation and approximate processing, time partitioning and aggregation rules, multirate queries, and historical database. In order to validate the performance of our algorithm, a heuristic routing protocol is also described. The performance simulation results show that the overall energy consumption for forwarding and answering a collection of queries can be significantly reduced by applying our query management scheme. The advantages and disadvantages of the proposed scheme are discussed, together with open research issues

    Yellow Tree: A Distributed Main-memory Spatial Index Structure for Moving Objects

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    Mobile devices equipped with wireless technologies to communicate and positioning systems to locate objects of interest are common place today, providing the impetus to develop location-aware applications. At the heart of location-aware applications are moving objects or objects that continuously change location over time, such as cars in transportation networks or pedestrians or postal packages. Location-aware applications tend to support the tracking of very large numbers of such moving objects as well as many users that are interested in finding out about the locations of other moving objects. Such location-aware applications rely on support from database management systems to model, store, and query moving object data. The management of moving object data exposes the limitations of traditional (spatial) database management systems as well as their index structures designed to keep track of objects\u27 locations. Spatial index structures that have been designed for geographic objects in the past primarily assume data are foremost of static nature (e.g., land parcels, road networks, or airport locations), thus requiring a limited amount of index structure updates and reorganization over a period of time. While handling moving objects however, there is an incumbent need for continuous reorganization of spatial index structures to remain up to date with constantly and rapidly changing object locations. This research addresses some of the key issues surrounding the efficient database management of moving objects whose location update rate to the database system varies from 1 to 30 minutes. Furthermore, we address the design of a highly scaleable and efficient spatial index structure to support location tracking and querying of large amounts of moving objects. We explore the possible architectural and the data structure level changes that are required to handle large numbers of moving objects. We focus specifically on the index structures that are needed to process spatial range queries and object-based queries on constantly changing moving object data. We argue for the case of main memory spatial index structures that dynamically adapt to continuously changing moving object data and concurrently answer spatial range queries efficiently. A proof-of concept implementation called the yellow tree, which is a distributed main-memory index structure, and a simulated environment to generate moving objects is demonstrated. Using experiments conducted on simulated moving object data, we conclude that a distributed main-memory based spatial index structure is required to handle dynamic location updates and efficiently answer spatial range queries on moving objects. Future work on enhancing the query processing performance of yellow tree is also discussed

    ILARS: An Improved Empirical Analysis for Lars* Using Partitioning and Travel Penalty

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    In this paper we develop an improved web based location-aware recommender software system, ILARS, that uses location-based ratings to provide proper advice and counseling. Present recommender systems don’t consider about spatial attributes of users and also of items; But, ILARS*considers major classes regarding location such as spatial scores rate for the non-spatial things, non-spatial score rate for the spatial things, and spatial score rate for the spatial things. ILARS* deals with recommendation points for accomplishing user ranking locations with help of user partitioning methods, which that are spatially near querying users in an effective way that maximizes system computability by not reducing the systems quality. A style that supports recommendation successors nearer in travel distance to querying users is used by ILARS* to exploits item locations using travel penalty. For avoiding thorough access to any or all spatial things. ILARS* will apply these art singly, or based on the rating that is obtained. The experimental results show information from various location based social networks. Various social network tells that LARS* is magnified , most expanded ,inexpensive ,reasonable ,capable of showing recommendations which are accurate as compared to existing recommendation software systems. DOI: 10.17762/ijritcc2321-8169.15073
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