scholarly journals Reverse Skyline Computation over Sliding Windows

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Junchang Xin ◽  
Zhiqiong Wang ◽  
Mei Bai ◽  
Guoren Wang
Keyword(s):  
Real World ◽  
Data Streams ◽  
Experimental Studies ◽  
Market Analysis ◽  
Skyline Queries ◽  
Sliding Windows ◽  
Real World Applications ◽  
Data Points ◽  
Pruning Technique ◽  
Reverse Skyline

Reverse skyline queries have been used in many real-world applications such as business planning, market analysis, and environmental monitoring. In this paper, we investigated how to efficiently evaluate continuous reverse skyline queries over sliding windows. We first theoretically analyzed the inherent properties of reverse skyline on data streams and proposed a novel pruning technique to reduce the number of data points preserved for processing continuous reverse skyline queries. Then, an efficient approach, called Semidominance Based Reverse Skyline (SDRS), was proposed to process continuous reverse skyline queries. Moreover, an extension was also proposed to handlen-of-Nand(n1,n2)-of-Nreverse skyline queries. Our extensive experimental studies have demonstrated the efficiency as well as effectiveness of the proposed approach with various experimental settings.

scholarly journals Multimodal Linear Discriminant Analysis via Structural Sparsity

2017 ◽  
Author(s):  
Yu Zhang ◽  
Yuan Jiang
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Real World ◽  
Specific Class ◽  
Linear Discriminant ◽  
Class Separability ◽  
Dimensionality Reduction Technique ◽  
Real World Applications ◽  
Data Points ◽  
Real World Datasets

Linear discriminant analysis (LDA) is a widely used supervised dimensionality reduction technique. Even though the LDA method has many real-world applications, it has some limitations such as the single-modal problem that each class follows a normal distribution. To solve this problem, we propose a method called multimodal linear discriminant analysis (MLDA). By generalizing the between-class and within-class scatter matrices, the MLDA model can allow each data point to have its own class mean which is called the instance-specific class mean. Then in each class, data points which share the same or similar instance-specific class means are considered to form one cluster or modal. In order to learn the instance-specific class means, we use the ratio of the proposed generalized between-class scatter measure over the proposed generalized within-class scatter measure, which encourages the class separability, as a criterion. The observation that each class will have a limited number of clusters inspires us to use a structural sparse regularizor to control the number of unique instance-specific class means in each class. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed MLDA method.

scholarly journals Learning neural decoders without labels using multiple data streams

2021 ◽  
Author(s):  
Steven M. Peterson ◽  
Rajesh P. N. Rao ◽  
Bingni W. Brunton
Keyword(s):  
Real World ◽  
Data Streams ◽  
Video Data ◽  
Neural Decoding ◽  
Computer Interfaces ◽  
Multiple Data ◽  
Real World Applications ◽  
Recorded Data ◽  
Robust Adaptive ◽  
Decoding Accuracy

AbstractRecent advances in neural decoding have accelerated the development of brain-computer interfaces aimed at assisting users with everyday tasks such as speaking, walking, and manipulating objects. However, current approaches for training neural decoders commonly require large quantities of labeled data, which can be laborious or infeasible to obtain in real-world settings. One intriguing alternative uses self-supervised models that share self-generated pseudo-labels between two data streams; such models have shown exceptional performance on unlabeled audio and video data, but it remains unclear how well they extend to neural decoding. Here, we learn neural decoders without labels by leveraging multiple simultaneously recorded data streams, including neural, kinematic, and physiological signals. Specifically, we apply cross-modal, self-supervised deep clustering to decode movements from brain recordings; these decoders are compared to supervised and unimodal, self-supervised models. We find that sharing pseudo-labels between two data streams during training substantially increases decoding performance compared to unimodal, self-supervised models, with accuracies approaching those of supervised decoders trained on labeled data. Next, we develop decoders trained on three modalities that match or slightly exceed the performance of supervised models, achieving state-of-the-art neural decoding accuracy. Cross-modal decoding is a flexible, promising approach for robust, adaptive neural decoding in real-world applications without any labels.

Continuous Probabilistic Reverse Skyline Monitoring over Uncertain Data Streams

2013 ◽  
Vol 380-384 ◽  
pp. 2681-2686
Author(s):  
Yong Tao Yang ◽  
Yi Jie Wang ◽  
Min Guo ◽  
Xiao Yong Li
Keyword(s):  
Data Streams ◽  
Uncertain Data ◽  
Continuous Queries ◽  
Skyline Queries ◽  
Marketing Decision ◽  
Formal Definitions ◽  
Uncertain Data Streams ◽  
Data Elements ◽  
Pruning Techniques ◽  
Reverse Skyline

Reverse skyline is useful for supporting many applications, such as marketing decision,environmental monitoring. Since the uncertainty of data is inherent in many scenarios, there is a needfor processing probabilistic reverse skyline queries. In this paper, we study the problem of efficientlyprocessing these queries on uncertain data streams. We first show the formal definitions of reverseskyline probability and probabilistic reverse skyline. Then we propose a new algorithm called CPRSto maintain the most recent N uncertain data elements and to process continuous queries on them.CPRS is based on R-tree, and efficient pruning techniques, one of which is based on a new structurenamed Characteristic Rectangle, are incorporated into it to handling the extra computing complexityarising from the uncertainty of data. Finally, extensive experiments demonstrate that our techniquesare very efficient in handling uncertain data streams.

scholarly journals EMM-CLODS: An Effective Microcluster and Minimal Pruning CLustering-Based Technique for Detecting Outliers in Data Streams

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Mohamed Jaward Bah ◽  
Hongzhi Wang ◽  
Li-Hui Zhao ◽  
Ji Zhang ◽  
Jie Xiao
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Experimental Studies ◽  
Streaming Data ◽  
Detection Accuracy ◽  
Clustering Methods ◽  
Time Consumption ◽  
Data Points ◽  
Evolving Data ◽  
Real World Datasets

Detecting outliers in data streams is a challenging problem since, in a data stream scenario, scanning the data multiple times is unfeasible, and the incoming streaming data keep evolving. Over the years, a common approach to outlier detection is using clustering-based methods, but these methods have inherent challenges and drawbacks. These include to effectively cluster sparse data points which has to do with the quality of clustering methods, dealing with continuous fast-incoming data streams, high memory and time consumption, and lack of high outlier detection accuracy. This paper aims at proposing an effective clustering-based approach to detect outliers in evolving data streams. We propose a new method called Effective Microcluster and Minimal pruning CLustering-based method for Outlier detection in Data Streams (EMM-CLODS). It is a clustering-based outlier detection approach that detects outliers in evolving data streams by first applying microclustering technique to cluster dense data points and effectively handle objects within a sliding window according to the relevance of their status to their respective neighbors or position. The analysis from our experimental studies on both synthetic and real-world datasets shows that the technique performs well with minimal memory and time consumption when compared to the other baseline algorithms, making it a very promising technique in dealing with outlier detection problems in data streams.

Improved Macro-clusters generation using Top-k shared Micro-clusters in Data Streams

2017 ◽  
Vol 7 (10) ◽  
pp. 52
Author(s):  
LAKSHMI PRANEETHA
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Bloom Filter ◽  
Scientific Applications ◽  
Pruning Algorithm ◽  
Density Data ◽  
Data Points ◽  
Short Time ◽  
Information Streams

Now-a-days data streams or information streams are gigantic and quick changing. The usage of information streams can fluctuate from basic logical, scientific applications to vital business and money related ones. The useful information is abstracted from the stream and represented in the form of micro-clusters in the online phase. In offline phase micro-clusters are merged to form the macro clusters. DBSTREAM technique captures the density between micro-clusters by means of a shared density graph in the online phase. The density data in this graph is then used in reclustering for improving the formation of clusters but DBSTREAM takes more time in handling the corrupted data points In this paper an early pruning algorithm is used before pre-processing of information and a bloom filter is used for recognizing the corrupted information. Our experiments on real time datasets shows that using this approach improves the efficiency of macro-clusters by 90% and increases the generation of more number of micro-clusters within in a short time.

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