Providing Structure to Big Data

The current explosion of interest in ‘Big data’ is driven by the inclusion of unstructured data into the analyses. According to analyst consensus, 80 percent of data falls into the ‘unstructured’ category, which is fundamentally different from ‘structured’ data. Unstructured content is largely created by humans: inconsistent, emotional, careless, opinionated, lazy, driven, over-worked, always unique, humans. In a 1998 Reuter’s report, information overload is seen as a problem by 42% of respondents. In the United Kingdom 47% stated that information overload damaged their relationships and 42% thought it reduced their job satisfaction. The principal reason is due to the amount of electronic information and the plethora of storage structures/methods used in organising it. Appreciating the origins of the data that we seek to analyse is the first step into producing actionable insights through applying structure in the form of hierarchical ‘Taxonomies’, direct keyword ‘Tags’ or applying semantic data that exposes the business value.

 

Summary

Information overload has been a problem for organisations where there have been a number of attempts to ease this problem from studies of the different types of information which cause overload to a more visual approach, the latter being particularly popular since the advent of the graphical user interface (GUI). Organisations are placing great value on the end-user device and its importance as a portal into managing information assets with numerous end-user device strategies to enable the best, most appropriate, hardware/management interface for both private and public sector knowledge workers. In recent years, studies have concentrated on the management or structuring of this complex information, re-branding it as ‘Big Data’ and the ways in which it is visualised within the office workspace environment.
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BigData

Big data is commonly described as data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the structures of your database architectures. To gain value from this data, you must choose an alternative way to process it. While the term may seem to reference the volume of data, that isn’t always the case. The term big data – especially when used by vendors – may refer to the technology (which includes tools and processes) that an organisation requires to handle the large amounts of data and storage facilities. This thesis builds upon such work; in particular, the ‘Necklace’ and ‘CyberCity’ concepts are used in order to develop a new ontological model based on a hierarchical structure as a way of providing structural guidance when enabling large data set applications by organisations.

Approaches
The thesis shows how this model (GMM) has been implemented into a concept prototype, known as Virtual Gatekeeper, which incorporates a fourth dimensional representation of data which is typical of many organisational situations today.
GMM Early evaluation of this prototype revealed that this new system, based upon the  model, was usable and reduced cognitive load for experienced knowledge workers. The thesis therefore addresses five important issues concerned in the management and mining of large information datasets:
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  • Linking tasks and associated information,
  • Lack of current structure for ordering and accessing information,
  • Automation of indexing of information,
  • Persisted activity sessions,
  • Screen clutter of task documents.

Keywords:

Big data, Knowledge management, Information management, Information overload, Screen clutter, Visualisation, Fourth data dimension, Semantic desktop, Hyper-semantic data modelling, Generic Management Model,  Information Universe, Knowledge workers.

References:

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Last edited: by Dave Richardson on 03/02/2014 at 12:18

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