Results 281 to 290 of about 513,428 (333)

Data Readiness Level for Unstructured Data

Proceedings of the 2014 International Conference on Big Data Science and Computing, 2014
When time or computational resources is a constraint, dealing with large amount of data can be painful for many organizations. In this paper, we proposed a new concept called Data Readiness Level(DRL). It will measures the readiness of a file very quickly. We define readiness as ready for immediate analytical purposes. DRL is pair-wised measure, one is
Yang Lu, Xing Fang, Justin Zhan
openaire   +1 more source

Visualizing and modeling unstructured data

The Visual Computer, 1993
Scientific data are often sampled at unstructured spatial locations because of physical constraints, yet most visualization software applies only to gridded or regular data. We discuss several effective techniques for representing scalar and vector-valued functions that interpolate to irregularly located data.
T. A. Foley, H. Hagen, G. M. Nielson
openaire   +1 more source

Analytics for Noisy Unstructured Text Data I

2009
Accdrnig to rscheearch at Cmabrigde Uinervtisy, it deosn’t mttaer in what oredr the ltteers in a wrod are, the olny iprmoetnt tihng is that the frist and lsat ltteer be at the rghit pclae. Tihs is bcuseae the human mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe.1 Unfortunately computing systems are not yet as smart as the human mind.
Shourya Roy, L. Venkata Subramaniam
openaire   +1 more source

Knowledge extraction from unstructured data

2023
Data availability is becoming more essential, considering the current growth of web-based data. The data available on the web are represented as unstructured, semi-structured, or structured data. In order to make the web-based data available for several Natural Language Processing or Data Mining tasks, the data needs to be presented as machine-readable
openaire   +1 more source

Learning from unstructured multimedia data

Proceedings of the 23rd International Conference on World Wide Web, 2014
Information in today's world is highly heterogeneous and unstructured. Learning and inferring from such data is challenging and is an active research topic. In this paper, we present and investigate an approach to learning from heterogeneous and unstructured multimedia data.
Janani Kalyanam, Gert R.G. Lanckriet
openaire   +1 more source

Data Mining from Unstructured Documents

2023
Data Mining is the process of identifying and extracting valuable data by scanning through large volumes of structured and unstructured data, which would form the base for further processing using data analytics tools for cleansing, categorization and organization, etc.
openaire   +1 more source

Unstructured data treatment for big data solutions

2016 International Symposium on Semiconductor Manufacturing (ISSM), 2016
We constructed a system infrastructure capable of processing unstructured data, with the aim of practical application of the system for document data analysis in the manufacturing industry. Using past ISSM research paper data, papers were classified and verified.
Shintaro Sato, Akihiro Kayahara, Shin-ichi Imai   +2 more
openaire   +1 more source

The Power of Unstructured Data

International Journal of Strategic Information Technology and Applications, 2016
This study examined the incorporation of tacit knowledge into corporate business intelligence and its impact on business performance, specifically analyzing individual productivity. Business productivity in relation to the use of knowledge has been investigated but using macro-dimensions not specifically oriented to individual workers' productivity ...
Armando E. Paladino, Kathleen M. Hargiss, Caroline Howard   +2 more
openaire   +1 more source

Privacy Preserving Data Mining on Unstructured Data

2017
As Big Data is group of structured, unstructured and semi-structure data collected from various sources, it is important to mine and provide privacy to individual data. Differential Privacy is one the best measure which provides strong privacy guarantee. The chapter proposed differentially private frequent item set mining using map reduce requires less
Trupti Vishwambhar Kenekar, Ajay R. Dani
openaire   +1 more source

Knowledge Graph from Unstructure Data

International Journal of Innovative Science and Research Technology
This project presents a client-side, knowledge graph system that dynamically extracts and visualizes semantic relationships from unstructured natural language input. Unlike traditional keyword-based methods, this system uses lightweight Natural Language Processing (NLP) to interpret the contextual meaning of user queries.
G. Newton, M. Shajith Revanth, P. Minish, K. Tharuni Reddy   +3 more
openaire   +1 more source