Titlebook: Data Warehousing and Knowledge Discovery; 12th International C Torben Bach Pedersen,Mukesh K. Mohania,A Min Tjoa Conference proceedings 201

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Lecture Notes in Computer Science context is theoretically investigated, and we present both lossless and lossy pruning techniques. An efficient and scalable algorithm is proposed, which exploits the inclusion-exclusion principle for fast entropy computation. This algorithm is empirically evaluated through experiments on synthetic

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Lecture Notes in Computer Sciencerevious results, we restrict to the simple, but appealing subclass of simple conjunctive queries. The proposed algorithm makes use of the functional dependencies of the database to optimise the generation of queries and prune redundant queries. Furthermore, our algorithm is capable of detecting prev

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Lecture Notes in Computer Scienceample-based correlation coefficient, the accuracy of the estimation is dependent on the quality and quantity of the sample. Like all statistical models, these correlation coefficients can suffer from overfitting, which results in the representation of random error instead of an underlying trend..In

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Computer Analysis of Images and Patternsh convergence has not been properly addressed so far. The current generation of stream processing systems isin general built separately from the data warehouse and query engine, which can causesignificant overhead in data access and data movement, and is unable to take advantage of the functionaliti

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Barter

https://doi.org/10.1007/978-3-030-89128-2ures across a given image set. This paper describes and compares two such approaches. The first is founded on a weighted graph mining technique whereby the ROI is represented using a tree structure which allows the application of a weighted graph mining technique to identify features of interest, wh

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characteristic

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/263200.jpg

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https://doi.org/10.1007/978-3-642-15105-7Business Intelligence; algorithms; business process intelligence; clustering; data cleansing; data mining