Moving range k nearest neighbor queries with quality guarantee over uncertain moving objects

Lee, Eun-Young; Cho, Hyung‐Ju; Chung, Tae-Sun; Ryu, Ki-Yeol

doi:10.1016/j.ins.2015.07.034

Cited by 17 publications

(6 citation statements)

References 30 publications

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“…Management of uncertain data has been extendedly studied over the decades in a wide range of issues, e.g., data modeling [18], pre-processing [19], storing and indexing [20], [21], and learning or mining [1], [22]- [25]. In particular, the most important one is query processing in the presence of uncertainty such as top-k query [26], reverse ranking query [27], skyline query [28], and nearest neighbor query [29]. There have been many works devoting to supporting uncertainty processing in database systems [30] or developing prototype systems for managing uncertain data, e.g., Trio [31], MystiQ [32], Orion [33], BayesStore [49] and Avatar [34].…”

Section: Related Work a Uncertain Data Management And Analysismentioning

confidence: 99%

Online Computing Quantile Summaries Over Uncertain Data Streams

Liang

Liu

2019

IEEE Access

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Quantile summarization is a useful tool in data streams management and mining that can efficiently capture the distribution of the data. A quantile of a sequence of points is the point with a given rank in the sequence. Given a sequence of uncertain points S on the real line, each represented by one-dimensional probability density function (pdf), we study the problem of incrementally maintaining quantile summaries on S such that for any query with a given rank, the summaries can provide a point as the quantile within a given error. We define quantile on uncertain data with discrete or continuous pdf in terms of two error metrics under possible worlds semantics. For an answer to a quantile query on uncertain data, we give the methods for calculating the value of the error and thereby discussing the high-level features of the summaries that can answer approximate quantile query under the two error metrics. We propose an online, space efficient algorithm to compute such summary data on uncertain data streams. The experimental results show that our algorithm substantially outperforms other techniques, such as Monte Carlo and averaging methods, in terms of query error and space for storing the summary data. INDEX TERMS Data preprocessing, possible worlds, quantile summaries, uncertain data streams.

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Section: Related Work a Uncertain Data Management And Analysismentioning

confidence: 99%

Online Computing Quantile Summaries Over Uncertain Data Streams

Liang

Liu

2019

IEEE Access

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“…Initially, map step is applied to each of the data and the output of the data is stored in the temporal memory for reducing redundancy and fault tolerance. The mapping function arranges the entire data in the sorting order and picks the optimized data using the greedy approach (Lee et al 2015). The selected data are used for reshuffling and redistributing the data in the environment.…”

Section: Proposed Systemmentioning

confidence: 99%

Performance analysis of efficient data distribution in P2P environment using hybrid clustering techniques

Raju¹,

Chandrasekaran²

2019

Soft Comput

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In this paper, K-means algorithm has been applied for distributed large data using hybrid clustering techniques. K-means is a simple and scalable algorithm which can be applied on large datasets. It is one of the well-known unsupervised clustering algorithms that fail in providing structured to unstructured data to enable extraction of valuable information. Peer-to-peer (P2P) technologies divide the data or resources between the peers for managing the network bandwidth, network participants and processing powers. During the data distribution process in the P2P environments, accuracy, computation complexity and distributed clustering accuracy are the important issues as they reduce the entire system performance. So, the author in this paper considered the system for the distribution of data in P2P environment using mining techniques. The data have been distributed using the hybrid map reducing method which analyzes the large volume of data by performing filtering and sorting. The cluster approach analyzes and manages the neighboring relationship about the peer nodes that helps in the management of the cluster distribution in the dynamic environment. Determination of the efficiency of the cluster formed is done with the help of the hybrid clustering algorithm, and the related system architecture is proposed. The clustering efficiency has been enhanced in the P2P environment using the distributed data network. The efficiency of the formed cluster was evaluated in terms of Jaccard index, F-measures, mutual information and rand measure. The performance of the system was analyzed using the experimental results and discussions, namely, error rate, accuracy and time. The multi-objective system helps in easing the difficulties in the implementation of P2P environment sensitive to initial solutions. Keywords Peer-to-peer Á Distributed mining systems Á Map reduce Á Greedy-based local approximation fuzzy clustering approach Á Jaccard index Á F-measure Á Mutual information Á Rand measure Communicated by A

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“…There are several works [6,9,13,24,28,31,46] which consider different types of probabilistic queries (e.g., different variants of nearest neighbour queries and range queries within temporal ranges and trajectories). However, none of them, but for [6], consider that the uncertainty can also affect the queried position (e.g., the query could be expressed in terms also of a moving object, and thus, be subject of uncertainty).…”

Section: Lb -Indexed -Android -Bq M5mentioning

confidence: 99%

“…This type of queries can be considered a specialization for the spatial domain in R 2 of fuzzy probabilistic range queries [38] or uncertain range queries [45]; the concept of probabilistic similarity join defined in [25] is also similar as well. While there have been many works on the processing of different types of queries in the presence of uncertainty [6,9,13,24,28,31,46], there is a lack of an in-depth formal study of this type of queries, along with an efficient processing approach, where uncertainty is also considered for the queried position (as it can be the position of moving object) and which does not assume the availability of indexes of moving objects. Moreover, we provide an extensive experimental evaluation including simulated scenarios and mobile devices.…”

Section: Introductionmentioning

confidence: 99%

“…• We analyze a type of query, that we call probabilistic inside query, and provide a method to solve them efficiently when the underlying probability density function is the uniform distribution. Note that, in fact, the uniform distribution is the case where the least information we have about the position of the objects, and it has been used in many previous works [11,12,24,28].…”

Section: Introductionmentioning

confidence: 99%

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Handling location uncertainty in probabilistic location-dependent queries

Bernad

Bobed

Ilarri

et al. 2017

Information Sciences

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Location-based services have motivated intensive research in the field of mobile computing, and particularly on location-dependent queries. Existing approaches usually assume that the location data are expressed at a fine geographic precision (physical coordinates such as GPS). However, many positioning mechanisms are subject to an inherent imprecision (e.g., the cell-id mechanism used in cellular networks can only determine the cell where a certain moving object is located). Moreover, even a GPS location can be subject to an error or be obfuscated for privacy reasons. Thus, moving objects can be considered to be associated to an uncertainty area where they can be located. In this paper, we analyze the problem introduced by the imprecision of the location data available in the data sources by modelling them using uncertainty areas. To do so, we propose to use a higher-level representation of locations which includes uncertainty, formalizing the concept of uncertainty location granule. This allows us to consider probabilistic location-dependent queries, among which we will focus on probabilistic inside (range) constraints. The adopted model allows us to develop a systematic and efficient approach for processing this kind of queries. An experimental evaluation shows that these probabilistic queries can be supported efficiently.

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Moving range k nearest neighbor queries with quality guarantee over uncertain moving objects

Cited by 17 publications

References 30 publications

Online Computing Quantile Summaries Over Uncertain Data Streams

Online Computing Quantile Summaries Over Uncertain Data Streams

Performance analysis of efficient data distribution in P2P environment using hybrid clustering techniques

Handling location uncertainty in probabilistic location-dependent queries

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