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The rapid growth of location-based services has motivated the development of continuous range queries in networks. Existing query algorithms usually adopt an expansion tree to reuse the previous query results to get better efficiency. However, the high maintenance costs of the traditional expansion tree lead to a sharp efficiency decrease. In this...
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Context 1
... the relationship meet denotes the case in which segments seg i and seg j are adjacent to each other and equal denotes the case in which two segments are the same. Figure 2 illustrates an example of a line graph that corresponds to the network in Figure 1 A moving object is defined as follows: ...
Context 2
... it locates the object node mo that needs to be updated according to the identifier mid. Simultaneously, the old road segment node old lg could be easily retrieved using the bridgeable edges E mo→lg (Lines 1-2). Then, Lines 3-4 update the object node mo value with a new location and time from the location updates. ...
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Cypher is a query language for property graphs. It was originally designed and implemented as part of the Neo4j graph database, and it is currently used in a growing number of commercial systems, industrial applications and research projects. In this work, we provide denotational semantics of the core fragment of the read-only part of Cypher, which...
Citations
... More importantly, these efforts show that having context information common in movement statements readily accessible can provide valuable knowledge if those movement statements were made more accessible through the thorough characterization of their differences we have done. These frameworks have been used to organize research analysis into traffic interchange patterns (Zeng et al., 2013), detection of anomalies in traffic (Orellana et al., 2009), identifying key segments of trajectories (Ferrero et al., 2018), and construct database queries specific to moving objects (Zhang et al., 2016), to name just a few. Although these frameworks are for trajectories, they overlap with movement statements in that they are about geographic movement. ...
Understanding movement described in text documents is important since text descriptions of movement contain a wealth of geographic and contextual information about the movement of people, wildlife, goods, and much more. Our research makes several contributions to improve our understanding of movement descriptions in text. First, we show how interpreting geographic movement described in text is challenging because of general spatial terms, linguistic constructions that make the thing(s) moving unclear, and many types of temporal references and groupings, among others. Next, as a step to overcome these challenges, we report on an experiment with human subjects through which we identify multiple important characteristics of movement descriptions (found in text) that humans use to differentiate one movement description from another. Based on our empirical results, we provide recommendations for computational analysis using movement described in text documents. Our findings contribute towards an improved understanding of the important characteristics of the underused information about geographic movement that is in the form of text descriptions.
... More importantly, these efforts show that having context information common in movement statements readily accessible can provide valuable knowledge if those movement statements were made more accessible through the thorough characterization of their differences we have done. These frameworks have been used to organize research analysis into traffic interchange patterns (Zeng et al., 2013), detection of anomalies in traffic (Orellana et al., 2009), identifying key segments of trajectories (Ferrero et al., 2018), and construct database queries specific to moving objects (Zhang et al., 2016), to name just a few. Although these frameworks are for trajectories, they overlap with movement statements in that they are about geographic movement. ...
Understanding movement described in text documents is important since text descriptions of movement contain a wealth of geographic and contextual information about the movement of people, wildlife, goods, and much more. Our research makes several contributions to improve our understanding of movement descriptions in text. First, we show how interpreting geographic movement described in text is challenging because of general spatial terms, linguistic constructions that make the thing(s) moving unclear, and many types of temporal references and groupings, among others. Next, as a step to overcome these challenges, we report on an experiment with human subjects through which we identify multiple important characteristics of movement descriptions (found in text) that humans use to differentiate one movement description from another. Based on our empirical results, we provide recommendations for computational analysis using movement described in text documents. Our findings contribute towards an improved understanding of the important characteristics of the underused information about geographic movement that is in the form of text descriptions.
... Here, Incremental Euclidean Restriction (IER) [18] uses an R-tree to process objects on networks and the experimental results demonstrate that the performance is worse than incremental network expansion (INE) [18] and Voronoi-based range search algorithm (VRS) [17] . To address the dead space problem, a number of works address spatial queries on road networks, i.e., range queries [18,28], continuous range queries [29], k-nearest neighbor [19,28], continuous k-nearest neighbor [30], and shortest path queries [31,32]. These works improve the performance for specific queries, but these methods do not consider the data distribution. ...
... These works improve the performance for specific queries, but these methods do not consider the data distribution. Specifically, works such as [29,30] view each edge as a unit, and a road network is partitioned into an equal number of edges. An example here is the G-tree [19] and G*-tree [28], which is a hierarchical tree structure. ...
... Those algorithms partition vertices of the graph, thus loosing ("cutting") edges connecting vertices in different partitions. To achieve lossless graph partitioning, we leverage the concept of a line graph [29], which represents edges of the original graph as vertices that are connected by edges if they (the edges in the original graph) share a common vertex. In the line graph, we can then use traditional graph partitioning algorithms [37,38] for lossless partitioning. ...
The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are not computed in unrestricted Euclidean space, but on a network. While the majority of access methods cannot trivially be extended to network space, existing network index structures partition the network space without considering the data distribution. This potentially results in inefficiency due to a very skewed node distribution. To improve range query processing on networks, this paper proposes a balanced Hierarchical Network index (HN-tree) to query spatial objects on networks. The main idea is to recursively partition the data on the network such that each partition has a similar number of spatial objects. Leveraging the HN-tree, we present an efficient range query algorithm, which is empirically evaluated using three different road networks and several baselines and state-of-the-art network indices. The experimental evaluation shows that the HN-tree substantially outperforms existing methods.
... Furthermore, geospatial indexing is vital for geospatial queries in NoSQL databases. For better query performance, some researchers have extended current indexing methods for different NoSQL databases, including R-Tree for MongoDB [44,46], geohash extensions for MongoDB [45,47], a graph-based expansion tree (GET) for Neo4j [95], and a new hybrid indexing scheme called HB+-trie for key-value storage [96]. ...
Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages.
... Their experiment showed that the proposed algorithm significantly reduced the query processing time when compared with the period solution. Zhang, Lu, and Chen [13] addressed the problem of continuous range queries of moving objects in networks. They presented a line-graph-based algorithm, which was characterized by a novel graph-based expansion tree structure to monitor queries in an incremental way. ...
Location-based services (LBS) are a growing area of research. This editorial paper introduces the key research areas within the scientific field of LBS, which consist of positioning, modelling, communication, applications, evaluation, analysis of LBS data, and privacy and ethical issues. After that, 18 original papers are presented, which provide a general picture of recent research activities on LBS, especially related to the research areas of positioning, modelling, applications, and LBS data analysis. This Special Issue together with other recent events and publications concerning LBS show that the scientific field of LBS is rapidly evolving, and that LBS applications have become smarter and more ubiquitous in many aspects of our daily life.
