Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

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This paper introduces Memory-limited Online Subspace Estimation Scheme (MOSES) for both estimating the principal components of streaming data and reducing its dimension. More specifically, in various applications such as sensor networks, the data vectors a ...

The constant flux of data and queries alike has been pushing the boundaries of data analysis systems. The increasing size of raw data files has made data loading an expensive operation that delays the data-to-insight time. To alleviate the loading cost, in ...

Testing mutual independence among several random vectors of arbitrary dimensions is a challenging problem in Statistics, and it has gained considerable interest in recent years. In this article, we propose some nonparametric tests based on different notion ...

Modern applications accumulate data at an exponentially increasing rate and traditional database systems struggle to keep up. Decision support systems used in industry, rely heavily on data analysis, and require real-time responses irrespective of data siz ...

Plastic pollution has reached alarming levels in recent years. While macro- and microplastic pollution are attested and studied since the 1970s, much less is known about the associated nanoscopic fragments. Due to their ability to cross biological barriers ...

Current Approximate Query Processing (AQP) engines are far from silver-bullet solutions, as they adopt several static design decisions that target specific workloads and deployment scenarios. Offline AQP engines target deployments with large storage budget ...

Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...

III-N family of materials has offered multiple groundbreaking technologies in the field of optoelectronics and high-power radio-frequency (RF) devices. Blue light-emitting diodes (LEDs) have revolutionized low-energy lighting. Gallium nitride (GaN) RF mark ...

Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by mor ...

Efficiently querying multiple spatial data sets is a growing challenge for scientists. Astronomers query data sets that contain different types of stars (e.g., dwarfs, giants, stragglers) while neuroscientists query different data sets that model different ...

Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., walking or running) the input motion (e.g., leg movement) belongs to. The bott ...

Motivation: Unbiased clustering methods are needed to analyze growing numbers of complex data sets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small data sets. ...

The proliferation of phasor measurement units (PMUs) presents new challenges in archiving and processing large amounts of synchrophasor data which necessitates advanced data compression methods. This paper proposes a singular value decomposition (SVD)-base ...

Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due to the curse of dimensionality. A flavor of approximation is, therefore, necessary to practically solve the problem of nearest neighbor search. In this pap ...

In this work, we study graph-based multi-arms bandit (MAB) problems aimed at optimizing actions on irregular and high-dimensional graphs. More formally, we consider a decision-maker that takes sequential actions over time and observes the experienced rewar ...

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

Chat with Graph Search

MOSES: A Streaming Algorithm for Linear Dimensionality Reduction

Adaptive partitioning and indexing for in situ query processing

Tests of mutual independence among several random vectors using univariate and multivariate ranks of nearest neighbours

Timely and cost-efficient data exploration through adaptive tuning

Detection and Quantification of Nonlabeled Polystyrene Nanoparticles Using a Fluorescent Molecular Rotor

Taster: Self-Tuning, Elastic and Online Approximate Query Processing

Landscape and training regimes in deep learning

Heterostructure design and field management in III-N high-electron mobility electronic devices

Time- and Space-Efficient Spatial Data Analytics

Efficient Bundled Spatial Range Queries

Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis

A Real-time Synchrophasor Data Compression Method Using Singular Value Decomposition

HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces

Spectral MAB for Unknown Graph Processes

Graph-based Methods for Visualization and Clustering