Top-k nearest neighbor search in uncertain data series

Dallachiesa, Michele; Palpanas, Themis; Ilyas, Ihab F.

doi:10.14778/2735461.2735463

Cited by 40 publications

(29 citation statements)

References 33 publications

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“…In addition, some efforts have been devoted to probabilistic range queries [16,24], similarity queries [19], k nearest neighbor (kNN) search [17,37], similarity joins [23,31], and reverse kNN (RkNN) retrieval [7] for specific metric uncertain data (e.g., sets, data series, graphs, etc.). Nevertheless, all these works utilize uncertain models and design special algorithms for the particularly metric uncertain data they aim at.…”

Section: Probabilistic Range Queriesmentioning

confidence: 99%

Indexing Metric Uncertain Data for Range Queries

Chen

Gao

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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Range queries in metric spaces have applications in many areas such as multimedia retrieval, computational biology, and locationbased services, where metric uncertain data exists in different forms, resulting from equipment limitations, high-throughput sequencing technologies, privacy preservation, or others. In this paper, we represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B + -tree (UPB-tree) and the uncertain pivot B + -forest (UPB-forest), are proposed accordingly in order to support probabilistic range queries w.r.t. a wide range of uncertain data types and similarity metrics. Both index structures use a small set of effective pivots chosen based on a newly defined criterion, and employ the B + -tree(s) as the underlying index. By design, they are easy to be integrated into any existing DBMS. In addition, we present efficient metric probabilistic range query algorithms, which utilize the validation and pruning techniques based on our derived probability lower and upper bounds. Extensive experiments with both real and synthetic data sets demonstrate that, compared against existing state-of-the-art indexes for metric uncertain data, the UPB-tree and UPB-forest incur much lower construction costs, consume smaller storage spaces, and can support more efficient metric probabilistic range queries.

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Section: Probabilistic Range Queriesmentioning

confidence: 99%

Indexing Metric Uncertain Data for Range Queries

Chen

Gao

et al. 2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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“…In addition to ranked queries, other types of uncertain database queries are handled such as nearest neighbor query (Kriegel, Kunath, & Renz, 2007), group nearest neighbor , reverse nearest neighbor ), Top-k nearest neighbor (Dallachiesa, 2014) and range query (Li, 2014).…”

Section: Query Processingmentioning

confidence: 99%

Handling Uncertainty in Database: An Introduction and Brief Survey

Othman¹,

Eldin²,

Zanfaly³

2015

CIS

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In the last years, uncertainty management became an important aspect as the presence of uncertain data increased rapidly. Due to the several advanced technologies that have been developed to record large quantity of data continuously, resulting is a data that contain errors or may be partially complete. Instead of dealing with data uncertainty by removing it, we must deal with it as a source of information. To deal with this data, database management system should have special features to handle uncertain data. The aim of this paper is twofold: on one hand, to introduce some main concepts of uncertainty in database by focusing on different data management issues in uncertain databases such as join and query processing, database integration, indexing uncertain data, security and information leakage and representation formalisms. On the other hand, to provide a survey of the current database management systems dealing with uncertain data, presenting their features and comparing them.

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“…In the last decade, several database and stream processing systems with support for uncertainty have been proposed [6,13,15,17,27,28,42,45,46], eventually leading to two emerging tuple models.…”

Section: Related Workmentioning

confidence: 99%

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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Top-k nearest neighbor search in uncertain data series

Cited by 40 publications

References 33 publications

Indexing Metric Uncertain Data for Range Queries

Indexing Metric Uncertain Data for Range Queries

Handling Uncertainty in Database: An Introduction and Brief Survey

Sliding windows over uncertain data streams

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