SD-Rtree: A Scalable Distributed Rtree

Mouza, Cédric Du; Litwin, Witold; Rigaux, Philippe

doi:10.1109/icde.2007.367875

Cited by 54 publications

(43 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most dynamic, tree-based distributed P2P data management systems (e. g., [3]) have the deficiency of only being able to address query hot spots by repartitioning data instead of replicating it. Figure 4(i) illustrates such a scenario with two query hot spots within a histogram region.…”

Section: Load Balancing In Hisbasementioning

confidence: 99%

See 1 more Smart Citation

Community-driven data grids

Scholl

Kemper

2008

Proc. VLDB Endow.

View full text Add to dashboard Cite

Beyond already existing huge data volumes, e-science communities face major challenges in managing the anticipated data deluge of forthcoming projects. Community-driven data grids target at domain-specific federations and provide a distributed, collaborative data management by employing dominant data characteristics (e. g., data skew) and query patterns to optimize the overall throughput. By combining well-established techniques for data partitioning and replication with Peer-to-Peer (P2P) technologies we can address several challenging problems: data load balancing, handling of query hot spots, and the adaption to short-term burst as well as long-term load redistributions.

show abstract

Section: Load Balancing In Hisbasementioning

confidence: 99%

“…SD-Rtrees [3] are a scalable distributed data structure, which is built especially for point-and range-queries for large-scale spatial data sets. Based on the R-tree, new servers are added to network in case of an overload and affected data objects are distributed evenly.…”

Section: Related Workmentioning

confidence: 99%

Community-driven data grids

Scholl

Kemper

2008

Proc. VLDB Endow.

View full text Add to dashboard Cite

show abstract

“…However, these proposals do not meet the requirements for data-intensive applications from e-science communities as they only apply data partitioning or do not deal with skewed data distributions. Other data structures [6] that are designed for large scale data sets currently do not address query hot spots (Section 5).…”

Section: Problem Statementmentioning

confidence: 99%

“…6 for performing the local query processing. After assigning each node a random identifier, we distributed the data according to the partitioning scheme.…”

mentioning

confidence: 99%

Workload-aware data partitioning in community-driven data grids

Scholl

Bauer

Müller

et al. 2009

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology

View full text Add to dashboard Cite

Collaborative research in various scientific disciplines requires support for scalable data management enabling the efficient correlation of globally distributed data sources. Motivated by the expected data rates of upcoming projects and a growing number of users, communities explore new data management techniques for achieving high throughput. Community-driven data grids deliver such highthroughput data distribution for scientific federations by partitioning data according to application-specific data and query characteristics. Query hot spots are an important and challenging problem in this environment. Existing approaches to load-balancing from Peer-to-Peer (P2P) data management and sensor networks do not directly meet the requirements of a data-intensive e-science environment. In this paper, our contributions are partitioning schemes based on multi-dimensional index structures enabling communities to trade off data load balancing and handling query hot spots via splitting and replication. We evaluate the partitioning schemes with two typical kinds of data sets from the astrophysics domain and workloads extracted from Sloan Digital Sky Survey (SDSS) query traces and perform throughput measurements in real and simulated networks. The experiments demonstrate the improved workload distribution capabilities and give promising directions for the development of future community grids.

show abstract

“…We can note the distinct difference between a MRI and a distributed multidimensional index [4]. In the latter case, the nodes of a single index are themselves distributed on different servers.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional routing indices for efficient distributed query processing

Doulkeridis

Vlachou

Nørvåg

et al. 2009

Proceedings of the 18th ACM Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Traditional routing indices in peer-to-peer (P2P) networks are mainly designed for document retrieval applications and maintain aggregated one-dimensional values representing the number of documents that can be obtained in a certain direction in the network. In this paper, we introduce the concept of multidimensional routing indices (MRIs), which are suitable for handling multidimensional data represented by minimum bounding regions (MBRs). Depending on data distribution on peers, the aggregation of the MBRs may lead to MRIs that exhibit extremely poor performance, which renders them ineffective. Thus, focusing on a hybrid unstructured P2P network, we analyze the parameters for building MRIs of high selectivity. We present techniques that boost the query routing performance by detecting similar peers and grouping and reassigning these peers to other parts of the hybrid network in a distributed and scalable way. We demonstrate the advantages of our approach using largescale simulations.

show abstract

SD-Rtree: A Scalable Distributed Rtree

Cited by 54 publications

References 13 publications

Community-driven data grids

Community-driven data grids

Workload-aware data partitioning in community-driven data grids

Multidimensional routing indices for efficient distributed query processing

Contact Info

Product

Resources

About