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business data processing - data processing in accounting or business management data processing - (computer science) a series of operations on data by a computer in order to retrieve or transform or classify information.
Computer data processing is any process that uses a computer program to enter data and summarise, analyse or otherwise convert data into usable information. The process may be automated and run on a computer. It involves recording, analysing, sorting, summarising, calculating, disseminating and storing data. Because data is most useful when well-presented and actually informative, data-processing systems are often referred to as information systems. Nevertheless, the terms are roughly synonymous, performing similar conversions; dataprocessing systems typically manipulate raw data into information, and likewise information systems typically take raw data as input to produce information as output. Data processing may or may not be distinguished from data conversion, when the process is merely to convert data to another format, and does not involve any data manipulation.
Processing
Practically all naturally occurring processes can be viewed as examples of data processing systems where "observable" information in the form of pressure, light, etc. are converted by human observers into electrical signals in the nervous system as the senses we recognize as touch, sound, and vision. Even the interaction of non-living systems may be viewed in this way as rudimentary information processing systems. Conventional usage of the terms data processing and information systems restricts their use to refer to the algorithmic derivations, logical deductions, and statistical calculations that recur perennially in general business environments, rather than in the more expansive sense of all conversions of real-world measurements into realworld information in, say, an organic biological system or even a scientific or engineering system.
Elements of data processing
In order to be processed by a computer, data needs first be converted into a machine readable format. Once data is in digital format, various procedures can be applied on the data to get useful information. Data processing may involve various processes, including:
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Data acquisition Data entry Data cleaning Data coding Data transformation Data translation Data summarization Data aggregation Data validation
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Data tabulation Statistical analysis Computer graphics Data warehousing Data mining Data fusion
3.4 Methods of data processing
The first step of the data processing is to convert the raw (logged) data into a time series according to a particular averaging or summing period. The second step is to use these time series for further calculations. Conversion and calculation methods are summarised in this section. The average of a finite sequence of samples is commonly defined as:
(3.1)
The standard deviation of a finite sequence of samples is defined as:
(3.2)
The standard deviation can also be calculated from the sum of the squared sample values ( ):
(3.3)
The coefficient of determination relation to their measured values 464].
, or correlation coefficient, of estimated values
in
is calculated by eq. 3.4 [Montgomery and Runger 1999, p.
(3.4)
The averaging or summation period is denoted by , and called `clock period'. The start and end of each period are synchronised with clock and calendar. The first clock period of a day starts at 0h00.
3.4.1 Reference wind data
The output of the ultrasonic anemometers is directly given in a standard unit of velocity, for every component ( , , ) (figure 3.10). One-minute averages and sums of squared output values of the anemometer on the mast of the Auditorium are calculated and form the raw data. For a chosen period the one-minute data. , the mean wind speed components , and are calculated from
Next, these mean wind speed components are transformed into the global axis system (figure 2.4): , , . Furthermore, the quantities the definitions given in section 2.1.5. , , , , and can be calculated with
3.4.2 Façade wind data
The raw data of the ultrasonic anemometer mounted on the boom at façade position P4 consists of 1 second values ( , , , and , , ) (figure 3.10). From these, the -averaged quantities ,
are calculated.
3.4.3 Horizontal rain data
Horizontal rain data are obtained by three devices, two tipping-bucket rain gauges and a rain indicator. The horizontal rain intensity during a clock period tippings during this period: is calculated from the number of
(3.5)
with
= the (uncorrected) horizontal rain intensity [mm h
],
= the number of tips during
[s], = the effective volume [ml] of a bucket, and gauge.
= the catchment area [m ] of the rain
In fact the horizontal rain intensity
is an average during
.
If the sample rate of the rain indicator is denoted by is calculated by:
, the rain duration during a clock period
(3.6)
with
= the rain or precipitation duration [s] during , and = the number of samples at which the rain indicator senses rain or other forms of precipitation during the clock period.
Figure 3.20: Correction of rain intensity by combination of rain indicator data and tipping-bucket data. See section 3.4.3 for an explanation.
Corrected horizontal rain intensity The data of the rain indicator and one of the rain gauges can be combined to correct rain intensity data, especially during shorter averaging periods . A method is presented in this section. The upper two graphs of figure 3.20 are sketches of rain indicator and rain gauge readings as a function of time. The rain amount registered by the first tipping in a period , could have been collected during previous clock periods. The reading of the rain indicator can be of help to estimate how to `redistribute' this amount over the previous periods. To prevent that rain is distributed over too many previous periods, a minimum time is defined. This minimum time divides time into dry periods and periods with more or less continuous rain (i.e. rain spells). The result of the redistribution of rain amounts of the registered tippings is sketched in figure 3.20c. The redistribution is done in proportion to the precipitation times measured by the rain indicator. Summing up the redistributed rain amounts per clock period yields the so-called corrected rain intensity calculated with eq. 3.5 is
(figure 3.20d). For comparison, the uncorrected rain intensity sketched in figure 3.20e.
3.4.4 Raindrop spectrum data
Every 30 s, the disdrometer reports the mean particle velocity in every size class and the particle number concentration in every size class. We will denote these quantities with and 2.16):
(3.7)
[m s
]
[m [kg m
m
], respectively. The raindrop mass flux per drop size class per 30 s ( ]) can be calculated from these two quantities by (cf. eq. 2.15 and
(30 s)
with m
and ] of water.
= the centre and interval of the drop size class in m (table 3.4), and
= density [kg
The reported spectra are only taken into account if the disdrometer indicates that the precipitation is drizzle or rain. So, spectra of snow, hail and mixtures are discarded. The disdrometer output has two disadvantages for our purposes. Firstly, the disdrometer gives the raindrop number concentration spectrum, which is calculated from the quantity which we are interested in, namely the raindrop mass flux spectrum. We have to `recalculate' the raindrop mass flux spectrum by use of the given averaged particle velocities . Secondly, the interval of the size classes increases with the centre diameter, which results in larger absolute errors of the real mass flux at larger diameters. The two disadvantages can be solved by outputting every detected raindrop with its diameter and velocity, but it was not possible to us to adapt the software in the data processing unit of the disdrometer.
3.4.5 Driving rain data of the TUE-I and TUE-II gauges
In gauges TUE-I and TUE-II, the driving rain collected in a reservoir is measured by a balance, of which simply the weight values are logged. The (mean) driving rain intensity clock period mass: during a
is calculated from the difference between begin and end values of the logged
(3.8)
(3.9)
with
= a threshold value [kg] for the minimum detectable mass difference during a clock period, = the catchment area [m ] of the driving rain gauge, and = the clock period in h.
By the threshold value
one suppresses variations of the measured mass value due to e.g. = 0.105 g was found
temperature changes. It depends on the resolution of the balance ( suitable for the applied balances with a resolution of 0.1 g).
3.4.6 Driving rain data of the CTH and DTU gauges
The number of tippings of the CTH driving rain gauge is logged two times per second. The driving rain intensity can be calculated similar to , eq. 3.5.
The raw data of the DTU gauge consists of voltage values of the strain gauge. The data processing method was already mentioned in section 3.2.6. The resulting driving rain intensity is calculated from the difference of the 10-min averaged masses, of two subsequent 10-min clock periods, similar to eq. 3.9. Driving rain intensities for larger clock periods are calculated from the obtained 10-min driving rain intensities.