Cruncher: Distributed in-memory processing for location-based services

Abdelhamid, Ahmed S.; Tang, Mingjie; Aly, A. A.; Mahmood, Ahmed; Qadah, Thamir M.; Aref, Walid G.; Basalamah, Saleh

doi:10.1109/icde.2016.7498356

Cited by 10 publications

(15 citation statements)

References 10 publications

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“…Query Type Description Publish/subscribe systems [10,30,31,42,72,85,107,108,138,140,155,156] Publish/subscribe systems allow users to submit subscriptions that specify spatial and textual matching conditions. Such systems will notify the users in real-time when incoming data satisfies the matching conditions.…”

Section: Publish/subscribe Systemsmentioning

confidence: 99%

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Location- and keyword-based querying of geo-textual data: a survey

2021

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With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and

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Section: Publish/subscribe Systemsmentioning

confidence: 99%

“…We classify spatial matching conditions into range matching and distance matching. Range matching is the dominant spatial matching condition in existing studies [10,30,42,63,85,107,108,136,140,155,156]. The spatial component in these studies is a region.…”

Section: Publish/subscribe Systemsmentioning

confidence: 99%

Location- and keyword-based querying of geo-textual data: a survey

2021

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“…HadoopGIS [7], SATO [39], and SpatialHadoop [16] are big spatial processing systems on top of Hadoop. LocationSpark [37], Cruncher [6], Simba [41], SparkGIS [11] are spatial extensions to Spark. All these systems do not offer real-time big spatial data processing.…”

Section: Related Workmentioning

confidence: 99%

“…This cannot work for processing spatial streams in real-time. Cruncher [6] is a proposal adaptive spatial stream processing on top of Spark. However, Cruncher works only on micro-batch stream processing that has relatively high latency, i.e., seconds.…”

Section: Related Workmentioning

confidence: 99%

“…Spatial applications require extending the capabilities of general distributed data streaming systems to support spatial operations and spatial query processing. In particular, spatial partitioning and indexing techniques are needed to support efficient processing of spatial data [6,15,22,35,43,40,23,24,14]. Motivation A key challenge to improve the performance of a distributed system is to ensure workload balancing across its machines.…”

Section: Introductionmentioning

confidence: 99%

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SWARM: Adaptive Load Balancing in Distributed Streaming Systems for Big Spatial Data

Daghistani¹,

Aref²,

Ghafoor³

et al. 2020

Preprint

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The proliferation of GPS-enabled devices has led to the development of numerous locationbased services. These services need to process massive amounts of spatial data in real-time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. Existing systems are using static spatial partitioning to distribute the workload. In contrast, the real-time streamed spatial data follows non-uniform spatial distributions that are continuously changing over time. Distributed spatial streaming systems need to react to the changes in the distribution of spatial data and queries. This paper introduces SWARM, a light-weight adaptivity protocol that continuously monitors the data and query workloads across the distributed processes of the spatial data streaming system, and redistribute and rebalance the workloads soon as performance bottlenecks get detected. SWARM is able to handle multiple query-execution and data-persistence models. A distributed streaming system can directly use SWARM to adaptively rebalance the system's workload among its machines with minimal changes to the original code of the underlying spatial application. Extensive experimental evaluation using real and synthetic datasets illustrate that, on average, SWARM achieves 200% improvement over a static grid partitioning that is determined based on observing a limited history of the data and query workloads. Moreover, SWARM reduces execution latency on average 4x compared with the other technique.

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Streaming Big Spatial Data

Mahmood¹,

Aref²

2018

Encyclopedia of Big Data Technologies

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Cruncher: Distributed in-memory processing for location-based services

Cited by 10 publications

References 10 publications

Location- and keyword-based querying of geo-textual data: a survey

Location- and keyword-based querying of geo-textual data: a survey

SWARM: Adaptive Load Balancing in Distributed Streaming Systems for Big Spatial Data

Streaming Big Spatial Data

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