Distance join queries on spatial networks

Sankaranarayanan, Jagan; Alborzi, Houman; Samet, Hanan

doi:10.1145/1183471.1183506

Cited by 37 publications

(25 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RELATED WORK Several spatial join approaches have been developed in the past with the vast majority [14] focusing on the filtering phase and only few addressing the refinement phase [15]. Because distance join approaches [16] can be trivially implemented as a variation of a spatial join (by enlarging the objects by the distance predicate) we do not distinguish between the two but instead categorize related work according to its use of data-or space-oriented partitioning.…”

Section: Elements In Datasets (Millions)mentioning

confidence: 99%

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Pavlović

Heinis

Tauheed³

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

Abstract-Spatial joins are becoming increasingly ubiquitous in many applications, particularly in the scientific domain. While several approaches have been proposed for joining spatial datasets, each of them has a strength for a particular type of density ratio among the joined datasets. More generally, no single proposed method can efficiently join two spatial datasets in a robust manner with respect to their data distributions. Some approaches do well for datasets with contrasting densities while others do better with similar densities. None of them does well when the datasets have locally divergent data distributions.In this paper we develop TRANSFORMERS, an efficient and robust spatial join approach that is indifferent to such variations of distribution among the joined data. TRANSFORM-ERS achieves this feat by departing from the state-of-the-art through adapting the join strategy and data layout to local density variations among the joined data. It employs a join method based on data-oriented partitioning when joining areas of substantially different local densities, whereas it uses big partitions (as in space-oriented partitioning) when the densities are similar, while seamlessly switching among these two strategies at runtime. We experimentally demonstrate that TRANSFORMERS outperforms state-of-the-art approaches by a factor of between 2 and 8.

show abstract

Section: Elements In Datasets (Millions)mentioning

confidence: 99%

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Pavlović

Heinis

Tauheed³

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

View full text Add to dashboard Cite

show abstract

“…Various algorithms, such as exhaustive algorithm, recursive algorithm, Heap algorithm, and priority queue based algorithms are proposed. Many variation of join queries over multi-dimensional space have been studied in different contexts, including road networks [18] and moving objects [20]. Spatial queries such as nearest neighbor queries over fuzzy objects have been recently studied [22].…”

Section: Related Workmentioning

confidence: 99%

Top-k Similarity Join over Multi-valued Objects

Zhang

Liang

et al. 2012

Database Systems for Advanced Applications

View full text Add to dashboard Cite

Abstract. The top-k similarity joins have been extensively studied and used in a wide spectrum of applications such as information retrieval, decision making, spatial data analysis and data mining. Given two sets of objects U and V, a top-k similarity join returns k pairs of most similar objects from U ×V. In the conventional model of top-k similarity join processing, an object is usually regarded as a point in a multi-dimensional space and the similarity between two objects is usually measured by distance metrics such as Euclidean distance. However, in many applications an object may be described by multiple values (instances) and the conventional model is not applicable since it does not address the distributions of object instances. In this paper, we study top-k similarity join queries over multi-valued objects. We apply quantile based distance to explore the relative instance distribution among the multiple instances of objects. Efficient and effective techniques to process top-k similarity joins over multi-valued objects are developed following a filtering-refinement framework. Novel distance, statistic and weight based pruning techniques are proposed. Comprehensive experiments on both real and synthetic datasets demonstrate the efficiency and effectiveness of our techniques.

show abstract

“…However, besides these client applications, shortest path finding has been recently addressed with regards to the efficient encoding of path views. Samet et al [13][14][15] pre-compute the shortest paths between all possible vertices in the network. The path view is encoded by subdividing the shortest paths from a single vertex based on the first edges of each shortest path.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Shortest Path Algorithm for Intra-Regional Queries on Hierarchical Networks

Guerra-Filho

Samet

2012

Advances in Geographic Information Science

Self Cite

View full text Add to dashboard Cite

Abstract.A hierarchical approach to the single-pair shortest path problem subdivides a network with n vertices into r regions with the same number m of vertices (n = rm) and iteratively creates higher levels of a hierarchical network by merging a constant number c of adjacent regions. In a hierarchical approach, shortest paths are computed at higher levels and expanded towards lower levels through intra-regional queries. We introduce a hybrid shortest path algorithm to perform intra-regional queries. This strategy uses a subsequence of pre-processed vertices that belong to the shortest path while actually computing the whole shortest path. At the lowest level, the hybrid algorithm requires O(m) time and space assuming a uniform distribution of vertices. For higher levels, the path view approach takes O(1) time and requires O(c k m) space.

show abstract

Distance join queries on spatial networks

Cited by 37 publications

References 27 publications

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Top-k Similarity Join over Multi-valued Objects

A Hybrid Shortest Path Algorithm for Intra-Regional Queries on Hierarchical Networks

Contact Info

Product

Resources

About