Recent advances on the Internet of Things (IoT) have posed great challenges to the search engine community. IoT systems manage huge numbers of heterogeneous sensors and/or monitoring devices, which continuously monito...
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Recent advances on the Internet of Things (IoT) have posed great challenges to the search engine community. IoT systems manage huge numbers of heterogeneous sensors and/or monitoring devices, which continuously monitor the states of real-world objects, and most data are generated automatically through sampling. The sampling data are dynamically changing so that the IoT search engine should support real-time retrieval. Additionally, the IoT search involves not only keyword matches but also spatial-temporal searches and value-based approximate searches, as IoT sampling data are generally from spatial-temporal scenario. To meet these challenges, we propose a Hybrid Real-time Search Engine Framework for the Internet of Things based on spatial-temporal, Value-based, and Keyword-based Conditions' (IoT-SVK Search Engine' or simply IoT-SVKSearch' for short) in this paper. The experiments show that the IoT-SVK search engine has satisfactory performances in supporting real-time, multi-modal retrieval of massive sensor sampling data in the IoT. Copyright (c) 2013 John Wiley & Sons, Ltd.
Recent advances in wireless sensor networks and positioning technologies have boosted new applications that manage moving objects. In such applications, a dynamic index is often built to expedite evaluation of spatial...
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Recent advances in wireless sensor networks and positioning technologies have boosted new applications that manage moving objects. In such applications, a dynamic index is often built to expedite evaluation of spatial queries. However, the development of efficient indexes is a challenge due to frequent object movement. In this paper, we propose a new update-efficient index method for moving objects in road networks. We introduce a dynamic data structure, called adaptive unit, to group neighboring objects with similar movement patterns. To reduce updates, an adaptive unit captures the movement bounds of the objects based on a prediction method, which considers road-network constraints and the stochastic traffic behavior. A spatial index (e.g., R-tree) for the road network is then built over the adaptive unit structures. Simulation experiments, carried on two different datasets, show that an adaptive-unit based index is efficient for both updating and querying performances.
Advances in wireless networks and positioning technologies (e.g., CPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering ana...
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ISBN:
(纸本)9783540717027
Advances in wireless networks and positioning technologies (e.g., CPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering analysis is becoming one of the most important requirements. In this paper, we present the problem of clustering moving objects in spatial networks and propose a unified framework to address this problem. Due to the innate feature of continuously changing positions of moving objects, the clustering results dynamically change. By exploiting the unique features of road networks, our framework first introduces a notion of cluster block (CB) as the underlying clustering unit. We then divide the clustering process into the continuous maintenance of CBs and periodical construction of clusters with different criteria based on CBs. The algorithms for efficiently maintaining and organizing the CBs to construct clusters are proposed. Extensive experimental results show that our clustering framework achieves high efficiency for clustering moving objects in real road networks.
In recent years, pavement engineering has gradually moved from new construction work to maintenance and management. However, effective real-time management by road regulatory authorities of all kinds of situations and...
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ISBN:
(纸本)9783037857403
In recent years, pavement engineering has gradually moved from new construction work to maintenance and management. However, effective real-time management by road regulatory authorities of all kinds of situations and distress that can affect pavement is a problem. At present, road regulatory authorities are subject to self-management mechanisms, however there are differences in resources for all unit levels and scales. This study utilizes a pavement management database in which statistical data has been stored that may change due to timing, environment, etc. As a result, spatial processing and the time factor are taken into account to develop a database of spatiotemporal objects and 3D Geo-Information. Finally we can provide useful pavement information and ways to improve system diversity.
Today, most commercial database systems provide some support for the management of temporal data, but the index support for efficiently accessing such data is rather limited. Existing access paths neglect the fact tha...
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ISBN:
(纸本)9781450362801
Today, most commercial database systems provide some support for the management of temporal data, but the index support for efficiently accessing such data is rather limited. Existing access paths neglect the fact that time intervals are located on the timeline and have a duration, two important pieces of information for querying temporal data. In this paper, we tackle this problem and introduce a novel index structure, termed Period Index, for efficiently accessing temporal data based on these two pieces of information. The index supports temporal queries that constrain the position of an interval on the timeline (range queries), its interval duration (duration queries), or both (range-duration queries). The key idea of the new index is to split the timeline into fixedlength buckets, each of which is divided into a set of cells that are organized in levels. The cells encode the position of intervals on the timeline, whereas the levels encode their duration. This grid-based index is well-suited for parallelization and non-uniform memory access (NUMA) architectures as it is common for modern hardware with large main-memories and multi-core servers. The Period Index is independent of the physical order of the data and has predictable performance due to the underlying hashing approach. We also propose an enhanced version of our index structure, termed Period Index., which continuously adapts the optimal bucket length to the distribution of the data. Our experiments show that Period Index. significantly beats other indexes for the class of queries that constrain both the position and the length of the time intervals, and it is competitive for queries that involve solely one temporal dimension.
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