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Contextualisation is then the process of identifying the data relevant to an entity based on the entity's contextual information. Contextualisation excludes irrelevant data from consideration and has the potential to reduce data from several aspects including volume, velocity, and variety in large-scale data intensive applications (Yavari et al.).
Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines, or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Vitaly Friedman (2008) the "main ...
Contextual inquiry is a field data collection technique used to capture detailed information about how users of a product interact with the product in their normal work environment. This information is captured by both observations of user behavior and conversations with the user while she or he works.
High-level and low-level, as technical terms, are used to classify, describe and point to specific goals of a systematic operation; and are applied in a wide range of contexts, such as, for instance, in domains as widely varied as computer science and business administration.
Context awareness is regarded as an enabling technology for ubiquitous computing systems. Context awareness is used to design innovative user interfaces, and is often used as a part of ubiquitous and wearable computing. It is also beginning to be felt in the internet with the advent of hybrid search engines.
Narrative is a powerful tool in the transfer, or sharing, of knowledge, one that is bound to cognitive issues of memory, constructed memory, and perceived memory. Jerome Bruner discusses this issue in his 1990 book, Acts of Meaning, where he considers the narrative form as a non-neutral rhetorical account that aims at "illocutionary intentions", or the desire to communicate meaning. [10]
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Overview of a data-modeling context: Data model is based on Data, Data relationship, Data semantic and Data constraint. A data model provides the details of information to be stored, and is of primary use when the final product is the generation of computer software code for an application or the preparation of a functional specification to aid a computer software make-or-buy decision.