Environmental Engineering Reference
In-Depth Information
sample. If no database is available, we have to construct our problem-specific database
based on, e.g., measured analytical or toxicological data.
Data processing may refer to the refining of raw or source data (without any
manipulation), that has been collected from the database or the analytical pro-
gram/experiment, or to a step in the information processing cycle in which data is
acquired, entered, validated, processed, stored, organized and output in a form, which
is needed for the creation of information as a response to a query or as a routine report.
Raw data collected often contain too much data to make a sensible analysis. Output
data are the processed, summarized, categorized data.
Data processing is typically a computable step in the information cycle. Computa-
tional or electric data processing tended to proclaim itself “Information Technology''
and to function art for art's sake in the beginning of the development of information
technologies. Professionals of the computer technology still tend to forget the primary
reason, i.e., provision of processed data to be integrated into the information and
knowledge systems in aid of solving an environmental problem. It has become clear by
now that IT should serve the needs of researchers, constructors, managers and decision
makers. IT professionals and those who are working for the environment should work
together and understand each other. A programmer should function as an interpreter,
who knows both languages, the language of the IT tools and the language of the end
user, the manager or decision maker.
A database is an organized collection of data and their containing data structures
for one or several purposes. A traditional library is also a database, where the topics
are organized according to their topics, year of edition and authors. The computerized
world usually refers the word “database'' to those databases in digital form, using
a standard language such as the most widespread SQL (structured query language),
which has been developed for the relational data models. Databases need database
management systems (DBMSs) which enable the effective handling of databases and
the containing data.
Data quality means availability, interpretability, usability, including validity, accu-
racy, precision, latency, etc. of the data. Statistical data used to draw conclusions and
inferences should be accurate and consistent. This is important for ensuring the valid-
ity of all inferences drawn on the basis of the data. Statistical raw data processing
needs to eliminate (expunge) outlier data points in order to ensure accuracy of the
conclusions.
Data evaluation and interpretation is needed to obtain as much information
from the available data as possible. The relationship between the data contains more
information than the data themselves. Data evaluation has high importance in envi-
ronmental management, where uncertainties are significant in every risk management
step and influence the selection of the proper risk management tools. Associations
and interactions are determining factors in environmental risk and its assessment.
Heterogeneities influence the efficiency of risk reduction technologies and the validity
of monitoring data.
Information is an organized and pertinent answer to a certain query or question.
Information started to have a meaning, which made its placing into a wider con-
text possible, when Shannon & Weaver in 1949 published the Mathematical Theory
of Communications and, based on this, Brilloin (1962) interpreted information as
the opposite of entropy, giving the status of a general theory to information theory.
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