Class-based Conditional MaxRS Query in Spatial Data Streams

Mostafiz, Mir Imtiaz; Mahmud, Farabi; Hussain, Muhammad; Ali, Mohammed Eunus; Trajcevski, Goce

doi:10.1145/3085504.3085517

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…Several variants of the MaxRS problem [10][11][12][13][14] also have been proposed. Some researchers proposed methods [16][17][18][19][20] for continuously monitoring the MaxRS problem over spatial data streams.…”

Section: Maxrs and Its Variants In 2d Spacementioning

confidence: 99%

Scalable Algorithms for Maximizing Spatiotemporal Range Sum and Range Sum Change in Spatiotemporal Datasets

et al. 2020

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In this paper, we introduce the three-dimensional Maximum Range-Sum (3D MaxRS) problem and the Maximum Spatiotemporal Range-Sum Change (MaxStRSC) problem. The 3D MaxRS problem tries to find the 3D range where the sum of weights across all objects inside is maximized, and the MaxStRSC problem tries to find the spatiotemporal range where the sum of weights across all objects inside is maximally increased. The goal of this paper is to provide efficient methods for data analysts to find interesting spatiotemporal regions in a large historical spatiotemporal dataset by addressing two problems. We provide a mathematical explanation for each problem and propose several algorithms for them. Existing methods tried to find the optimal region over two-dimensional datasets or to monitor a burst region over two-dimensional data streams. The majority of them cannot directly solve our problems. Although some existing methods can be used or modified to solve the 3D MaxRS problems, they have limited scalability. In addition, none of them can be used to solve the MaxStRS-RC problem (a type of MaxStRSC problem). Finally, we study the performance of the proposed algorithms experimentally. The experimental results show that the proposed algorithms are scalable and much more efficient than existing methods.

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Section: Maxrs and Its Variants In 2d Spacementioning

confidence: 99%

Scalable Algorithms for Maximizing Spatiotemporal Range Sum and Range Sum Change in Spatiotemporal Datasets

et al. 2020

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“…Finally, as mentioned in section 1, a preliminary version of this work has been presented in Mostafiz et al ( 2017 ). However, we note that the techniques for processing continuous monitoring queries over data streams (i.e., dynamic settings) must be adaptive, as data updates are often bursty and input characteristics may vary over time.…”

Section: Related Workmentioning

confidence: 99%

“…A preliminary version of this paper has appeared in Mostafiz et al ( 2017 ), where we focused on non-weighted version of the C-MaxRS problem, i.e., we only count the number of objects inside the query window. We proposed two algorithms, C-MaxRS + and C-MaxRS − to efficiently solve C-MaxRS for data updates appearing and disappearing one at a time.…”

Section: Introductionmentioning

confidence: 99%

“…We proposed two algorithms, C-MaxRS + and C-MaxRS − to efficiently solve C-MaxRS for data updates appearing and disappearing one at a time. The current article provides the following modifications and extensions to Mostafiz et al ( 2017 ): (1) we provide the modified version of our algorithms from Mostafiz et al ( 2017 ) to explicitly incorporate weighted version of the C-MaxRS problem, where each object and/or class can have different weights denoting its importance in the MaxRS computation. As it turns out (and demonstrated in the corresponding experiments) the weighted variant enables an increased pruning power; (2) we extend the work to consider novel settings of bulk updates handling of objects' appearance and disappearance and propose techniques for efficient computation of the C-MaxRS in those settings; (3) we conducted an extensive set of additional experiments to evaluate the benefits of our approaches.…”

Section: Introductionmentioning

confidence: 99%

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Conditional MaxRS Query for Evolving Spatial Data

Hussain¹,

Mostafiz²,

Mahmud³

et al. 2020

Front. Big Data

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We address the problem of maintaining the correct answer-sets to a novel query— Conditional Maximizing Range-Sum (C-MaxRS)—for spatial data. Given a set of 2D point objects, possibly with associated weights, the traditional MaxRS problem determines an optimal placement for an axes-parallel rectangle r so that the number—or, the weighted sum—of the objects in its interior is maximized. The peculiarities of C-MaxRS is that in many practical settings, the objects from a particular set—e.g., restaurants—can be of different types—e.g., fast-food, Asian, etc. The C-MaxRS problem deals with maximizing the overall sum—however, it also incorporates class-based constraints, i.e., placement of r such that a lower bound on the count/weighted-sum of objects of interests from particular classes is ensured. We first propose an efficient algorithm to handle the static C-MaxRS query and then extend the solution to handle dynamic settings, where new data may be inserted or some of the existing data deleted. Subsequently we focus on the specific case of bulk-updates, which is common in many applications—i.e., multiple data points being simultaneously inserted or deleted. We show that dealing with events one by one is not efficient when processing bulk updates and present a novel technique to cater to such scenarios, by creating an index over the bursty data on-the-fly and processing the collection of events in an aggregate manner. Our experiments over datasets of up to 100,000 objects show that the proposed solutions provide significant efficiency benefits over the naïve approaches.

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“…Next, Feng et al [11] study the best region search (BRS) problem that extends the aggregate function from SUM to support submodular monotone functions. Also, Mostafiz et al [20] extend the MaxRS problem by taking the types of the spatial objects into account. Specifically, they apply constraints to the types of the spatial objects while searching for the region with the maximum total weight.…”

Section: Related Workmentioning

confidence: 99%

Finding attribute-aware similar regions for data analysis

et al. 2019

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With the proliferation of mobile devices and location-based services, increasingly massive volumes of geo-tagged data are becoming available. This data typically also contains non-location information. We study how to use such information to characterize a region and then how to find a region of the same size and with the most similar characteristics. This functionality enables a user to identify regions that share characteristics with a user-supplied region that the user is familiar with and likes. More specifically, we formalize and study a new problem called the attribute-aware similar region search (ASRS) problem. We first define so-called composite aggregators that are able to express aspects of interest in terms of the information associated with a user-supplied region. When applied to a region, an aggregator captures the region's relevant characteristics. Next, given a query region and a composite aggregator, we propose a novel algorithm called DS-Search to find the most similar region of the same size. Unlike any previous work on region search, DS-Search repeatedly discretizes and splits regions until an split region either satisfies a drop condition or it is guaranteed to not contribute to the result. In addition, we extend DS-Search to solve the ASRS problem approximately. Finally, we report on extensive empirical studies that offer insight into the efficiency and effectiveness of the paper's proposals.

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Class-based Conditional MaxRS Query in Spatial Data Streams

Cited by 7 publications

References 18 publications

Scalable Algorithms for Maximizing Spatiotemporal Range Sum and Range Sum Change in Spatiotemporal Datasets

Scalable Algorithms for Maximizing Spatiotemporal Range Sum and Range Sum Change in Spatiotemporal Datasets

Conditional MaxRS Query for Evolving Spatial Data

Finding attribute-aware similar regions for data analysis

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