Efficient Bundled Spatial Range Queries

Zacharatou, Eleni Tzirita; Sidlauskas, Darius; Tauheed, Farhan; Heinis, Thomas; Ailamaki, Anastasia

doi:10.1145/3347146.3359077

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…(RN query [ 3 , 4 , 5 ]) . Given a positive integer r, a query point

, and a set of data points P, an RN query retrieves data points within query distance r to

such that dist

holds for

.…”

Section: Preliminariesmentioning

confidence: 99%

“…In this study, SP queries refer to a collection of nearest neighbor (NN) and range (RN) queries, which are basic query types in spatial databases. NN queries retrieve points of interest (POI), such as taxis and restaurants, closest to a query user [ 1 , 2 ], and RN queries retrieve POIs within a query distance [ 3 , 4 , 5 ]. Typically, location-based services (LBS), such as taxi-booking and ride-sharing services, use real-time spatial data to locate POIs close to the query user [ 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

Cho¹

2021

Sensors

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Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.

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“…(RN query [ 3 , 4 , 5 ]) . Given a positive integer r, a query point

, and a set of data points P, an RN query retrieves data points within query distance r to

such that dist

holds for

.…”

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

Cho¹

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Similarly, SATO [17] and AQWA [18] proposed by Vo et al and Aly et al, respectively, are two data-synopsis-based mechanisms aiming at finding the optimal partitioning scheme in order to lower the response time of spatial queries in distributed spatial datasets. However, these and other similar approaches dealing with spatial indexing and partitioning [19][20][21] overlook the temporal dimension of the data typical of smart city applications and, in consequence, might fall short in supporting requests intended to explore the historical behaviour from a given sequence of observations. This issue has also been addressed in the context of Wireless Sensor Networks (WSN).…”

Section: Spatiotemporal Data Managementmentioning

confidence: 99%

“…This is, for instance, if Ω k is set to hourly, then the dates are truncated to the exact hour (e.g., 2019-09-22T12:47:32.767Z → 2019-09-22T12:00:00.000Z). Once these time boundaries have been determined, the data summaries corresponding to the fragments in Φ q are retrieved from the view and aggregated for each of the temporal bins in the interval [τ m , τ n ] (lines [11][12][13][14][15][16][17][18][19][20][21]. Finally, the resulting aggregated summaries, along with their corresponding temporal bins, are paired together and incrementally appended to the result set to assemble the summary time series returned as output (R HS ) (lines 24-26).…”

Section: Query Processing: Historical-spatial Queriesmentioning

confidence: 99%

Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

Ordonez-Ante

Seghbroeck

Wauters

et al. 2020

Sensors

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Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving—on ingestion time—synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach.

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“…scale and completed 10 billion rides in 2018 [46]. The unprecedented rate of generation of location data has led to a considerable amount of research efforts that have been focused on, systems that scale out [1,2,8,9,14,39,40,41,48,50,51], databases [12,26,27,31,33], improving spatial query processing [11,18,19,20,35,42,43,44,53,34], or leveraging modern hardware and compiling techniques [6,7,37,36,38,52], to handle the increasing demands of applications today.…”

Section: Introductionmentioning

confidence: 99%

The Case for Learned Spatial Indexes

Pandey,

van Renen,

Kipf

et al. 2020

Preprint

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Spatial data is ubiquitous. Massive amounts of data are generated every day from billions of GPS-enabled devices such as cell phones, cars, sensors, and various consumerbased applications such as Uber, Tinder, location-tagged posts in Facebook, Twitter, Instagram, etc. This exponential growth in spatial data has led the research community to focus on building systems and applications that can process spatial data efficiently. In the meantime, recent research has introduced learned index structures. In this work, we use techniques proposed from a state-of-the art learned multi-dimensional index structure (namely, Flood) and apply them to five classical multi-dimensional indexes to be able to answer spatial range queries. By tuning each partitioning technique for optimal performance, we show that (i) machine learned search within a partition is faster by 11.79% to 39.51% than binary search when using filtering on one dimension, (ii) the bottleneck for tree structures is index lookup, which could potentially be improved by linearizing the indexed partitions (iii) filtering on one dimension and refining using machine learned indexes is 1.23x to 1.83x times faster than closest competitor which filters on two dimensions, and (iv) learned indexes can have a significant impact on the performance of low selectivity queries while being less effective under higher selectivities.

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Efficient Bundled Spatial Range Queries

Cited by 10 publications

References 8 publications

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

The Case for Learned Spatial Indexes

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