Results 61 to 70 of about 25,310,122 (328)
Statistical data reduction for streaming data [PDF]
2017 New York Scientific Data Summit (NYSDS), 2017 Bulk of the streaming data from scientific simulations and experiments consists of numerical values, and these values often change in unpredictable ways over a short time horizon. Such data values are known to be hard to compress, however, much of the random fluctuation is not essential to the scientific application and could therefore be removed ...
Wu, Kesheng +3 more
openaire +3 more sources
Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data
Int. J. Interact. Multim. Artif. Intell., 2020 . In this paper, incremental hierarchical clustering is deployed for unifying the streaming data in a hierarchical manner. SPARQL queries are used for extracting semantic annotations between the hierarchical clustered data.
Vijender Kumar Solanki +1 more
semanticscholar +1 more source
Low-Rank Tucker Approximation of a Tensor From Streaming Data [PDF]
SIAM Journal on Mathematics of Data Science, 2019 This paper describes a new algorithm for computing a low-Tucker-rank approximation of a tensor. The method applies a randomized linear map to the tensor to obtain a sketch that captures the important directions within each mode, as well as the ...
Yiming Sun +4 more
semanticscholar +1 more source
Digital twins to accelerate target identification and drug development for immune‐mediated disorders
FEBS Open Bio, EarlyView.Digital twins integrate patient‐derived molecular and clinical data into personalised computational models that simulate disease mechanisms. They enable rapid identification and validation of therapeutic targets, prediction of drug responses, and prioritisation of candidate interventions.
Anna Niarakis, Philippe Moingeon
wiley +1 more source
When is it Biased? Assessing the Representativeness of Twitter's Streaming API [PDF]
, 2014 Twitter has captured the interest of the scientific community not only for its massive user base and content, but also for its openness in sharing its data. Twitter shares a free 1% sample of its tweets through the "Streaming API", a service that returns
Liu, Huan +2 more
core +1 more source
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data [PDF]
The Web Conference, 2019 Kernel density estimation is a simple and effective method that lies at the heart of many important machine learning applications. Unfortunately, kernel methods scale poorly for large, high dimensional datasets.
Benjamin Coleman, Anshumali Shrivastava
semanticscholar +1 more source
Harnessing Fungal Biowelding for Constructing Mycelium‐Engineered Materials
Advanced Engineering Materials, EarlyView.Mycelium‐bound composites (MBCs) offer low‐carbon alternatives for construction, yet interfacial bonding remains a critical challenge. This review examines fungal biowelding as a biocompatible adhesive, elucidating mycelium‐mediated interfacial mechanisms and their role in material assembly. Strategies to optimize biowelding are discussed, highlighting
Xue Brenda Bai +2 more
wiley +1 more source
A mediation system for continuous spatial queries on a unified schema using Apache Spark
Big Earth DataRecent advances in big and streaming data systems have enabled real-time analysis of data generated by Internet of Things (IoT) systems and sensors in various domains. In this context, many applications require integrating data from several heterogeneous
Thi Thu Trang Ngo +3 more
doaj +1 more source
Research on a real-time receiving scheme of streaming data
Tongxin xuebao, 2022 Discussing the common scenarios in modern data warehouse systems that need to receive a large amount of streaming data, connect it with the existing data on the disk, and then store it in the warehouse.By rationally setting disk paging and applying cache
Xiaoyan ZHANG +3 more
doaj
Structure Selection from Streaming Relational Data [PDF]
, 2011 Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where
Mihalkova, Lilyana +1 more
core