Management of Highly Dynamic Multidimensional Data in a Cluster of Workstations

Kriakov, Vassil; Delis, Alex; Kollios, George

doi:10.1007/978-3-540-24741-8_43

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…There were also proposals based on quadtrees like hQT* [10]. [11] describes an adaptive index method which offers dynamic load balancing of servers and distributed collaboration. However the structure requires a coordinator which maintains the load of each server.…”

Section: Related Workmentioning

confidence: 99%

Dynamic storage balancing in a distributed spatial index

Mouza

Litwin

Rigaux

2007

Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems

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We propose a general framework to index very large datasets of spatial data in a distributed system. Our proposal is built on the recently proposed Scalable Distributed Rtree (SD-Rtree) [4] and addresses specifically the server allocation problem. In SD-Rtree, a new server is assigned to the network whenever a split of a full node is required. We describe a more flexible allocation protocol which copes with a temporary shortage of storage resources. Our algorithm is especially based on k-NN query processing we introduce as well. We analyze the cost of this protocol, describe its features, and propose practical hints to use it. We also present experiments validating our approach.

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Section: Related Workmentioning

confidence: 99%

Dynamic storage balancing in a distributed spatial index

Mouza

Litwin

Rigaux

2007

Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems

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“…Hambrusch and Khokhar [6] present a distributed data structure based on orthogonal bisection trees (2-d KD trees). Kriakov et al [12] describe an adaptive index method which offers dynamic load balancing of servers and distributed collaboration. The structure requires a coordinator which maintains the load of each server.…”

Section: Related Workmentioning

confidence: 99%

Virtual and Consistent Hyperbolic Tree: A New Structure for Distributed Database Management

Magoni

2015

Networked Systems

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Abstract. This paper describes a new scalable, reliable and consistent structure for implementing a distributed database system. This structure is based on the hyperbolic geometry and we call it a Virtual and Consistent Hyperbolic tree (VCH-tree). This structure is intended for supporting queries over possibly large spatial datasets distributed over interconnected servers. The VCH-tree is comparable to the well-known R-tree structure, but it uses the hyperbolic geometry properties of the Poincaré disk model. It uses a distributed balanced Q-degree spatial tree that scales with data objects' insertions into potentially any number of storage servers through virtual hyperbolic coordinates. A user application manipulates the structure from a client node. The client can connect to the system through one of the database servers that is already in the VCH-tree. Messages are then routed towards the proper server by a greedy algorithm which is using the virtual hyperbolic coordinates attributed to each server. We have performed simulations to assess the efficiency and reliability of the VCH-tree. Results show that our VCHtree is consistent and has expected performances given the scalability and flexibility it provides to distributed database applications.

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“…The balancing needs to fully rebuild the tree using multicast from all the servers. [11] describes an adaptive index method which offers dynamic load balancing of servers and distributed collaboration. The structure requires a coordinator which maintains the load of each server.…”

Section: Related Workmentioning

confidence: 99%

SD-Rtree: A Scalable Distributed Rtree

Mouza

Litwin

Rigaux

2007

2007 IEEE 23rd International Conference on Data Engineering

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We propose a scalable distributed data structure (SDDS) called SD-Rtree. We intend our structure for large spatial datasets over clusters of interconnected servers. SD-Rtree generalizes the well-known Rtree structure. It uses a distributed balanced binary spatial tree that scales with insertions to potentially any number of storage servers through splits of the overloaded ones. A user/application manipulates the structure from a client node. The client addresses the tree through its image that the splits can make outdated. This may generate addressing errors, solved by the forwarding among the servers. Specific messages towards the clients incrementally correct the outdated images. We present the build-up of an SD-Rtree through insertions, focusing on the split and balancing algorithms. We follow with the point and window query algorithms. Experiments show additional aspects of SD-Rtree behavior. The results justify our various design choices and the overall utility of the structure.

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Management of Highly Dynamic Multidimensional Data in a Cluster of Workstations

Cited by 7 publications

References 24 publications

Dynamic storage balancing in a distributed spatial index

Dynamic storage balancing in a distributed spatial index

Virtual and Consistent Hyperbolic Tree: A New Structure for Distributed Database Management

SD-Rtree: A Scalable Distributed Rtree

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