A threshold-based algorithm for continuous monitoring of k nearest neighbors

Mouratidis, Kyriakos; Papadias, Dimitris; Bakiras, Spiridon; Tao, Yufei

doi:10.1109/tkde.2005.172

Cited by 94 publications

(62 citation statements)

References 24 publications

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“…All the nodes of the system are continuously organized into clusters computed through the k-means algorithm exclusively run by the management node, which is a clear impediment to the scalability of their approach. Other works aim at minimizing the processing cost for continuous monitoring [13], [9], [14] in the light of the theoretical results of [5], however similarly to [15], all these approaches suffer from a centralized handling of the clustering process. Recently, Choffnes et al [2] have proposed to leverage structured peer-to-peer architectures (i.e., Distributed Hashing Tables) to guarantee efficient and scalable monitoring management.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Characterization in Large Scale Networks

Anceaume

Busnel

Merrer

et al. 2014

2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks

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Abstract-The context of this work is the online characterization of anomalies in large scale systems. In particular, we address the following question: Given two successive configurations of the system, can we distinguish massive anomalies from isolated ones, the former ones impacting a large number of nodes while the second ones affect solely a small number of them, or even a single one? The rationale of this question is twofold. First, from a theoretical point of view, we characterize anomalies with respect to their neighborhood, and we show that there are anomaly scenarios for which isolated and massive anomalies are indistinguishable from an global observer point of view. We then relax the definition of this problem by introducing unresolved configurations, and exhibit necessary and sufficient conditions that allows any node to determine the type of anomaly it has been impacted by. This condition only depends on the close neighborhood of each node and thus is locally computable. We present an algorithm that implements this condition. We show through extensive simulations the performance of our algorithm. From a practical point of view, distinguishing isolated anomalies from massive ones is of utmost importance for networks providers. For instance, regarding Internet service providers that operate millions of home gateways, it would be very interesting to have procedures that allow gateways to self distinguish whether their dysfunction is caused by network-level anomalies or by their own hardware or software, and to notify the service provider only in the latter case.

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Section: Related Workmentioning

confidence: 99%

Anomaly Characterization in Large Scale Networks

Anceaume

Busnel

Merrer

et al. 2014

2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks

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“…A threshold-based algorithm is presented in [7] which assumes that moving objects have some computational capabilities and aims to minimize the network cost when handling c-kNN queries. A threshold is transmitted to each moving object and when its moving distance exceeds the threshold, the moving object issues an update.…”

Section: Threshold-based Updatesmentioning

confidence: 99%

“…Some prior work [6][7][8] has provided significant insight into these issues by assuming a set of computationally capable moving objects that cache query-aware information (e.g., thresholds or safe regions) and locally determine a mobile-initiated location update. In the simplest case, whenever an object moves it sends its new location to the server.…”

Section: Introductionmentioning

confidence: 99%

Efficient Location Updates for Continuous Queries over Moving Objects

Hsueh

Zimmermann

2010

J. Comput. Sci. Technol.

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The significant overhead related to frequent location updates from moving objects often results in poor performance. As most of the location updates do not affect the query results, the network bandwidth and the battery life of moving objects are wasted. Existing solutions propose lazy updates, but such techniques generally avoid only a small fraction of all unnecessary location updates because of their basic approach (e.g., safe regions, time or distance thresholds). Furthermore, most prior work focuses on a simplified scenario where queries are either static or rarely change their positions. In this study, two novel efficient location update strategies are proposed in a trajectory movement model and an arbitrary movement model, respectively. The first strategy for a trajectory movement environment is the Adaptive Safe Region (ASR) technique that retrieves an adjustable safe region which is continuously reconciled with the surrounding dynamic queries. The communication overhead is reduced in a highly dynamic environment where both queries and data objects change their positions frequently. In addition, we design a framework that supports multiple query types (e.g., range and c-kNN queries). In this framework, our query re-evaluation algorithms take advantage of ASRs and issue location probes only to the affected data objects, without flooding the system with many unnecessary location update requests. The second proposed strategy for an arbitrary movement environment is the Partition-based Lazy Update (PLU, for short) algorithm that elevates this idea further by adopting Location Information Tables (LITs) which (a) allow each moving object to estimate possible query movements and issue a location update only when it may affect any query results and (b) enable smart server probing that results in fewer messages. We first define the data structure of an LIT which is essentially packed with a set of surrounding query locations across the terrain and discuss the mobile-side and server-side processes in correspondence to the utilization of LITs. Simulation results confirm that both the ASR and PLU concepts improve scalability and efficiency over existing methods.

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“…Both inside queries [1,2] and nearest neighbor queries [15,16] have been studied in the literature of spatio-temporal and moving object databases (see [17] for a survey of different works in the field). However, existing works on locationdependent query processing implicitly assume GPS locations for the objects in a scenario.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Granule-Based Inside and Nearest Neighbor Queries

Ilarri

Corral

Bobed

et al. 2009

Advances in Databases and Information Systems

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Abstract. The development of location-based services and advances in the field of mobile computing have motivated an intensive research effort devoted to the efficient processing of location-dependent queries. In this context, it is usually assumed that location data are expressed at a fine geographic precision. Adding support for location granules means that the user is able to use his/her own terminology for locations (e.g., GPS, cities, states, provinces, etc.), which may have an impact in the semantics of the query, the way the results are presented, and the performance of the query processing. Along with its advantages, the management of the so-called location granules introduces new challenges for query processing. In this paper, we analyze two popular location-dependent constraints, inside and nearest neighbors, and enhance them with the possibility to specify location granules. In this context, we study the problem that arises when the locations of the objects are subject to some imprecision.

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A threshold-based algorithm for continuous monitoring of k nearest neighbors

Cited by 94 publications

References 24 publications

Anomaly Characterization in Large Scale Networks

Anomaly Characterization in Large Scale Networks

Efficient Location Updates for Continuous Queries over Moving Objects

Probabilistic Granule-Based Inside and Nearest Neighbor Queries

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