Unstructured data

Blumberg R, Atre S. The problem with unstructured data. DM Rev 2003. Helfrich JP. Knowledge management systems coping with the data explosion. Biosilico 2004 2 8-11. 

Blumberg R, Atre S. Digging into the Web XML, Meta Data and Other Paths to Unstructured Data. DM Review Magazine May 2003. 

Unstructured data such as associated original Excel, Word, jpg, gif, tif, and pdf files... 

A very important aspect of mining in unstructured data sources is the presentation of the results to the user, typically an expert in a given domain. A steadily growing number of tools for the presentation and navigation of results coming from information extraction approaches is being made available in this review we will focus on two of them Cytoscape76 and AliBaba.77... 

There are several challenges in maintaining the ever increasing amount of data and documents that come under the umbrella of Part 58 for a nonclinical laboratory study. First, how does the laboratory organize, store, and retrieve these data and documents in a way that is GLP compliant and efficient especially when 80% of that data is unstructured data (i.e., free form notes, procedures, letters, reports, and so on) Second, how does the laboratory maintain control on the data and documents to ensure their integrity ... 

The raw data required to be retained for GLP is composed of both structured and unstructured data. Some of the raw data in a laboratory resides in relational databases (RDBMS),... 

A computational connection pattern that is formed in (simplified) analogy to biological (cortical) neural networks and used successfully for the classification of unstructured data. Neural networks have a broad range of applications in biology and chemistry (Schneider Wrede, 1998). 

The largest benefit of a demand-driven value network is assessing and building the value network to meet upcoming demand. The use of technologies to sense market insights from unstructured data has helped companies to sense potential supplier failures before the... 

Traditional demand management processes use structured transactional data (orders and shipments). Unstructured data sources, such as weather patterns and social sentiment data, are increasingly important sources of insight for market-driven value networks. Unfortunately, unstructured data cannot be used in traditional demand architectures. 

Take ownership of the data. As part of the building of horizontal processes, take ownership for the data. Design metadata and data standards, and invest in systems for data harmonization, pattern recognition, and the use of both structured and unstructured data. 

New forms of unstructured data are growing that enable quicker insights and less data latency. Traditional supply chain processes were based only on structured data. 

Ninety percent of the world s data was generated in the last two years, and 80 percent of that data is nnstructured—presentations, e-mails, audio files, and video files—and will not fit neatly into transactional systems. The use of unstructured data for early market sensing could have prevented 60 percent of the major supply chain disruptions outlined in Chapter 1. In Kgure 6.10, we share an overview of the changes with big data supply chains. 

Supply chain risk management. The combination of structured and unstructured data on suppliers can improve the time to sense a supplier risk. The translation of this data for early warning is a big data supply chain opportunity. 

Feldman, R. and Sanger, J. (2007) The Text Mining Handbook Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, New York. 

Apart from fast factoring, another potential application of swift quantum computation is the search of unstructured data bases. Thus Grover (1997) has recently proposed a quantum algorithm that can query such files much faster than classical algorithms. 

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