Probabilistic Similarity Search for Uncertain Time Series

Aßfalg, Johannes; Kriegel, Hans‐Peter; Kröger, Peer; Renz, Matthias

doi:10.1007/978-3-642-02279-1_31

Cited by 42 publications

(64 citation statements)

References 15 publications

(10 reference statements)

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“…In Sect. 4, we describe a model that allows for efficient processing of sliding windows with uncertain data. In Sect.…”

Section: Contributionsmentioning

confidence: 99%

“…Uncertain sliding windows can be used as building blocks for common streaming operators, such as joins, as we will show later in Sect. 5.…”

Section: From Value To Existential Uncertaintymentioning

confidence: 99%

“…We introduce uncertainty through perturbation, similar to prior work [5,13,33,41,49]. We introduce value uncertainty by considering uniform, normal, and exponential error distributions with zero mean and varying standard deviation within [0.1, 1.0].…”

Section: Datasetsmentioning

confidence: 99%

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Sliding windows over uncertain data streams

et al. 2014

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Uncertain data streams can have tuples with both value and existential uncertainty. A tuple has value uncertainty when it can assume multiple possible values. A tuple is existentially uncertain when the sum of the probabilities of its possible values is <1. A situation where existential uncertainty can arise is when applying relational operators to streams with value uncertainty. Several prior works have focused on querying and mining data streams with both value and existential uncertainty. However, none of them have studied, in depth, the implications of existential uncertainty on sliding window processing, even though it naturally arises when processing uncertain data. In this work, we study the challenges arising from existential uncertainty, more specifically the management of count-based sliding windows, which are a basic building block of stream processing applications. We extend the semantics of sliding window to define the novel concept of uncertain sliding windows and provide both exact and approximate algorithms for managing windows under existential uncertainty. We also show how current state-of-the-art techniques for answering similarity join queries can be easily adapted to be used with uncertain sliding windows. We evaluate our proposed techniques under a variety of configurations using real data. The results show that the algorithms used to maintain uncertain sliding windows can efficiently operate while providing a highquality approximation in query answering. In addition, we show that sort-based similarity join algorithms can perform better than index-based techniques (on 17 real datasets) when the number of possible values per tuple is low, as in many real-world applications.

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“…In Sect. 4, we describe a model that allows for efficient processing of sliding windows with uncertain data. In Sect.…”

Section: Contributionsmentioning

confidence: 99%

“…Uncertain sliding windows can be used as building blocks for common streaming operators, such as joins, as we will show later in Sect. 5.…”

Section: From Value To Existential Uncertaintymentioning

confidence: 99%

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Sliding windows over uncertain data streams

et al. 2014

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“…Let D be a dataset with N = 4 and m = 3. Let S1, S2, S3 and S4 be the instantiated distance partitions: S1 = {[2, 2] : 0.33, [4,4] : 0.33, [6,6] : 0.33}, S2 = { [4,8] : 1}, S3 = {[1, 1] : 0.33, [5,5] : 0.33, [9,9] : 0.33} and S4 = {[1, 1] : 0.33, [3,3] : 0.33, [7,7] : 0.33}. The PNN probability estimates determined using the Eq.4 and Eq.…”

Section: Lemma 2 (Dependencies In Distance Partitions)mentioning

confidence: 99%

“…Pioneering studies on modeling and querying uncertain data series include DUST [27], PROUD [31] and MUNICH [7], and have been experimentally and analytically compared in [14]. Figure 2 positions these studies and our proposal, the Holistic-PkNN algorithm, by their underlying uncertainty model on the x-axis and by their ability to represent correlation on the y-axis.…”

Section: Introductionmentioning

confidence: 99%

Top-k nearest neighbor search in uncertain data series

2014

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Many real applications consume data that is intrinsically uncertain, noisy and error-prone. In this study, we investigate the problem of finding the top-k nearest neighbors in uncertain data series, which occur in several different domains. We formalize the top-k nearest neighbor problem for uncertain data series, and describe a model for uncertain data series that captures both uncertainty and correlation. This distinguishes our approach from prior work that compromises the accuracy of the model by assuming independence of the value distribution at neighboring time-stamps. We introduce the Holistic-PkNN algorithm, which uses novel metric bounds for uncertain series and an efficient refinement strategy to reduce the overall number of required probability estimates. We evaluate our proposal under a variety of settings using a combination of synthetic and 45 real datasets from diverse domains. The results demonstrate the significant advantages of the proposed approach.

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Real-Time Data Analytics in Sensor Networks

Palpanas

2012

Managing and Mining Sensor Data

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Abstract. The proliferation of Wireless Sensor Networks (WSNS) in the past decade has provided the bridge between the physical and digital worlds, enabling the monitoring and study of physical phenomena at a granularity and level of detail that was never before possible. In this study, we review the efforts of the research community with respect to two important problems in the context of WSNS: real-time collection of the sensed data, and real-time processing of these data series.

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Probabilistic Similarity Search for Uncertain Time Series

Cited by 42 publications

References 15 publications

Sliding windows over uncertain data streams

Sliding windows over uncertain data streams

Top-k nearest neighbor search in uncertain data series

Real-Time Data Analytics in Sensor Networks

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