Titlebook: Database Systems for Advanced Applications; 20th International C Matthias Renz,Cyrus Shahabi,Muhammad Aamir Cheema Conference proceedings 2

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Compatriot

https://doi.org/10.1007/978-3-642-46587-1t scalable to large data sets, making multidimensional outlier detection for big data still an open challenge. Existing approximate neighbor search methods are designed to preserve distances as well as possible. In this article, we present a highly scalable approach to compute the nearest neighbors

Entirety

Allure

Mehmet Huseyin Bilgin,Hakan Danis,Ugur Canireless sensor networks. In such applications, a large number of sensor devices are deployed to collect useful information such as temperature readings and vehicle positions. However, these distributed sensors usually have limited computational and communication power and thus the amount of sensor q

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Hrvoje Perčević,Mirjana Hladika,Marina Mićinlock independent disjoint (BID) probabilistic databases. This problem is fundamental to evaluate queries whose time complexity is PTIME. We first introduce two new probabilistic table models which are the correlated table and the correlated block table, and a hybrid project which executes a disjoint

moribund

Katarína Petrovčiková,František Sudzinafor new parallel algorithms for efficient query processing on large scale uncertain strings. In this paper, we proposed a MapReduce-based parallel algorithm, called MUSK, for answering top-. queries over large scale uncertain strings. We used the probabilistic .-grams to generate key-value pairs. To

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Ronald Voorn,Sabrina Hegner,Ad Pruynerrors in traditional databases, but they fall short of guiding us to find errors in probabilistic databases, especially for databases with complex correlations among data. In this paper, we propose a method for tracing errors in probabilistic databases by adopting Bayesian network (BN) as the frame