By Mor Naaman (auth.), Nikos Mamoulis, Thomas Seidl, Torben Bach Pedersen, Kristian Torp, Ira Assent (eds.)
This quantity constitutes the refereed complaints of the eleventh overseas Symposium on Spatial and Temporal Databases, SSTD 2009, held in Aalborg, Denmark, in July 2009.
The 20 revised complete papers provided including three keynotes, 7 brief papers, and 10 demonstration papers, have been completely reviewed and chosen from a complete of sixty two learn submissions and eleven demonstration submissions. The papers are prepared in topical sections on spatial and circulation networks, integrity and defense, doubtful info and new applied sciences, indexing and tracking relocating gadgets, complicated queries, in addition to on types and languages.
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Additional info for Advances in Spatial and Temporal Databases: 11th International Symposium, SSTD 2009 Aalborg, Denmark, July 8-10, 2009 Proceedings
Shahabi (a) Location mapping (b) Object location update (c) Query location update Fig. 5. Data and query object location update update which falls into cell c(6, 5) in Figure 5(a). Since neither the new nor the old location of p3 are mapped to the active-cells (marked cells in the Figure 5(a)) which overlap with the active-edges, the movement of p3 is ignored. If relevant updates are received, ER-CkNN updates the results of the queries by considering the update categories. Speciﬁcally, ER-CkNN considers two cases.
Edu Abstract. In this paper, we propose an eﬃcient method to answer continuous k nearest neighbor (CkNN) queries in spatial networks. Assuming a moving query object and a set of data objects that make frequent and arbitrary moves on a spatial network with dynamically changing edge weights, CkNN continuously monitors the nearest (in network distance) neighboring objects to the query. Previous CkNN methods are ineﬃcient and, hence, fail to scale in large networks with numerous data objects because: 1) they heavily rely on Dijkstra-based blind expansion for network distance computation that incurs excessively redundant cost particularly in large networks, and 2) they blindly map all object location updates to the network disregarding whether the updates are relevant to the CkNN query result.
O(logN )) as well as high frequency of location updates in large spatial networks with numerous objects, the overhead incurred due to blind object location mapping with IMA/GMA becomes intolerable with real-world applications. In this paper, we propose ER-CkNN, a Euclidean Restriction (ER) based method for eﬃcient CkNN query answering. ER-CkNN addresses the two shortcomings of IMA/GMA by leveraging ER to enable guided search and localized mapping, respectively. , ﬁltering by ER), which is then reﬁned by computing their network distance from q to identify the exact nearest neighbors.
Advances in Spatial and Temporal Databases: 11th International Symposium, SSTD 2009 Aalborg, Denmark, July 8-10, 2009 Proceedings by Mor Naaman (auth.), Nikos Mamoulis, Thomas Seidl, Torben Bach Pedersen, Kristian Torp, Ira Assent (eds.)