SCUBA: Scalable Cluster-Based Algorithm for Evaluating Continuous Spatio-temporal Queries on Moving Objects

Nehme, Rimma V.; Rundensteiner, Elke A.

doi:10.1007/11687238_58

Cited by 38 publications

(26 citation statements)

References 26 publications

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“…SCUBA [Nehme and Rundensteiner 2006] is a proposal to evaluate continuous queries on objects moving in road networks. Although this work focuses on moving range queries on moving objects, it is claimed that the approach is also applicable to other types of queries, such as kNN queries and aggregate queries.…”

Section: Scuba (Scalable Cluster-based Algorithm)mentioning

confidence: 99%

“…There is no explicit concern about retrieving the current locations of the objects in an answer, but just about obtaining the positive and negative updates (i.e., detect objects which enter/exit the answer). Finally, although it seems that the approach would work even in the absence of road networks, a road-network mobility model is assumed in [Nehme and Rundensteiner 2006], and the proposal in [Nehme and Rundensteiner 2007] is evaluated using a generator of network-based moving objects.…”

Section: Scuba (Scalable Cluster-based Algorithm)mentioning

confidence: 99%

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Location-dependent query processing

2010

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The continuous development of wireless networks and mobile devices has motivated an intense research in mobile data services. Some of these services provide the user with context-aware information. Specifically, location-based services and location-dependent queries have attracted a lot of interest.In this article, the existing literature in the field of location-dependent query processing is reviewed. The technological context (mobile computing) and support middleware (such as moving object databases and data stream technology) are described, location-based services and locationdependent queries are defined and classified, and different query processing approaches are reviewed and compared.

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Section: Scuba (Scalable Cluster-based Algorithm)mentioning

confidence: 99%

Section: Scuba (Scalable Cluster-based Algorithm)mentioning

confidence: 99%

Location-dependent query processing

2010

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“…A large body of research has been devoted to moving object databases [26,22,21,13,19]. Many efficient index- [26] to index the positions of moving objects whose moving pattern can be described by a function.…”

Section: Related Workmentioning

confidence: 99%

“…Indexing of moving objects with non-linear motion patterns is addressed in [24], where server-level coarse indexes and client-level refined indexes are combined to incorporate predictive queries over nonlinear motion patterns of the objects. Query processing and optimization algorithms for continuous query [21,22,20,19], mainly range queries and nearest neighbor queries, have been proposed. In SCUBA [22], moving micro-clusters are utilized for pre-filtering in order to reduce unnecessary spatial joins and perform intelligent load shedding.…”

Section: Related Workmentioning

confidence: 99%

“…Query processing and optimization algorithms for continuous query [21,22,20,19], mainly range queries and nearest neighbor queries, have been proposed. In SCUBA [22], moving micro-clusters are utilized for pre-filtering in order to reduce unnecessary spatial joins and perform intelligent load shedding. Our work is different for the following reasons: (1) Our data comes from static geo-sensor networks monitoring spatially and temporally continuous phenomena, not from moving objects; (2) in moving object databases, queries typically originate from a moving object and are mostly moving window queries or nearest neighbor queries.…”

Section: Related Workmentioning

confidence: 99%

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Querying streaming point clusters as regions

Zhang

Huang

2010

Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming

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This paper focuses on one important type of geo-streaming data -point geo-streams. Many interesting applications require selected discrete points with similar observations to be clustered according to spatial proximity and further elevated into higher-level spatial regions. Querying streaming point clusters as regions directly in a geo-stream database has many benefits, but is also very challenging. We propose two query optimization strategies, namely semanticsbased optimization and incremental optimization for answering queries involving both point geo-streams and static data set. The experimental results on a streaming meteorological data set demonstrate the effectiveness and the efficiency of the query processing techniques. Compared with the baseline methods, our optimization methods can reduce the total execution time by more than an order of magnitude.

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Prospective Data Model and Distributed Query Processing for Mobile Sensing Data Streams

Brahem

Zeitouni

Yeh

et al. 2020

Lecture Notes in Computer Science

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With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd-Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and analyzing this continuously growing data raise several challenges due not only to their volume, their velocity, and their complexity but also to the gap between raw data samples and the desired application view in terms of correlation between observations and in terms of granularity. In this paper, we put forward a proposal that offers an abstract view of any spatio-temporal data series as well as their manipulation. Our approach allows to support this high-level logical view and provides efficient processing by mapping both the representation and the manipulation to an internal physical model. We explore an implementation within a distributed framework and envision the adaptation of data organization methods combining aggressive indexing and partitioning over time and space. The mapping from the logical view and the actual data storage will lead to revisiting the traditional database query rewriting and optimization techniques. This proposal is a first step in the objective of coping with the complexity, the imperfection of large data sizes in the MCS context.

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SCUBA: Scalable Cluster-Based Algorithm for Evaluating Continuous Spatio-temporal Queries on Moving Objects

Cited by 38 publications

References 26 publications

Location-dependent query processing

Location-dependent query processing

Querying streaming point clusters as regions

Prospective Data Model and Distributed Query Processing for Mobile Sensing Data Streams

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