Theoretical Aspects of Data Compression Methods
Information, in a broad sense, is organized, processed and coded data. It provides context for other data and allows effective decision making. For instance, a single customer’s sale in a restaurant is multiple data this becomes information as soon as the company is able to discern the most preferred or least preferred dish.
But information today does not only refer to facts and figures. There are both unprocessed as well as processed information. Unprocessed information is data that remains in the memory of an individual machine. This may be due to poor design or to storage problems. Examples of unprocessed information include temperature readings, prices of particular items and historical weather conditions.
Examples of data visualization tools include Numenta’s Neurofuzz, MetaMind and WPTune. These tools help people to visualize data sets in order to make it easier to process and extract meaningful information. Also, there are many tools that help individuals to gather and analyze information in an easily manageable format. The concepts and principles of information theory are used by these tools such as the transactional model, the event model and the ordinal model. Each tool uses different methods and it all depends on what is required for the job.
Data compression methods refer to the process of reducing information systems to simpler structures. NLP (Neural Parsing and Language Processing) also plays an important role in information science. A network is made up of nodes and links and all these nodes should connect effectively via communication links.
This way, a system can be decompositionized into smaller components or elements and more complicated functions can be derived. For instance, a data compression analysis method could be used to decompose a system into the following constituents e.g. numbers, text, images etc. Also, these components can be decomposed further into smaller elements e.g. textual and graphical data, audio and video clips etc.
However, information technology has to deal with non computational aspects as well, such as business, legal, social and human issues. One example is information science which deals with problems arising due to non-computational issues. For instance, e-mail, news letters, faxes, internet browsing activities, cell phone location tracking etc are all non computational in nature and as such fall outside of the purview of this science. Also, the informational equivalent of pragmatics can be seen as being a subset of information science and hence it could also be said to constitute a subset of information science.