Data redundancy

When data is collected from numerous sensors in a dense network, there is a high probability for redundancy. Data redundancy will result in unnecessary and replicated transmissions. Aggregation, based on correlated data of neighboring nodes, helps to reduce the total volume to be routed. 

Typically, the theory on the good design of relational databases has been looking at problems such as data redundancy in the tables, update and insertion anomaly issues. These problems are classically solved by transforming non optimal entity relationships models into their so-called normal forms (Third Normal Form, Boyce-Codd Normal Form). In our case though, the problem is slightly different and offers us the freedom not to... 

Data redundancy is minimized. Data redundancy is kept to a minimum by normalizing data into simple datasets which can then point to related datasets. This saves disk storage space and speeds up storage and modification operations. 

We note that this method can be used for any power law or sum thereof. It can even be mixed with a multiparameter fit. For instance, one could use it to fit y to a function such as o + ai 4 + 2 log x2 + 0 X3, or whatever suits your fancy. Keep in mind, though, that these various x- values are assumed to be free of experimental errors, and that a substantial data redundancy is needed when a large number of coefficients needs to be determined. Moreover, the parameters, though not necessarily mutually independent, should at least be distinguishable if you try to fit y= a ebx+c=a ec abx, no computer in the world will be able to find unique values for a and c, because there are infinitely many combinations of a and c that yield the same value for a ec. Finally, the larger the noise in the data, the less reliable the results will be. And, of course, there must be a good theoretical reason to use such a complicated model in the first place. 

In principle, only three detectors are needed to determine the tracer position. The availability of measured distances from 16 detectors resulted in data redundancy for location determination. To take advantage of this planned redundancy, a weighted least-square method based on an linearization scheme was used to determine the optimum tracer position. 

Here are given by Eq. (76), with the difference that j is fixed to the error variance in the first data set (k = 1). The assumed f should be close to the estimated (a) obtained by Eq. (77) from the residual. A value of "] (a) higher than assumed f indicates inconsistency in the assumptions made. One possibility is that the forward model needs corrections. Otherwise, adjustments are needed in assumptions about errors in measurements or a priori data. For example, in case the number of measurements in the first (k = 1) and second (k = 2) sets of observations are very different, the 2 can be adjusted by a factor N1/N2 in order to account for data redundancy in one of the sets (see Section 6.2). 

One of the most important functions that an information system must perform is data management (i.e., record keeping). Prior to the advent of true database software, computer data management was characterized by excessive data redundancy (or duplication), large data dependence (or coupling). 

We have identified the three major objectives for a DBMS as data independence, data redundancy reduction, and data resource control. These objectives are important for a single database. When there are multiple databases potentially located in a distributed geographic fashion, and potentially many users of one or more databases, additional objectives arise, including ... 

To reduce data redundancy and maintenance needs and increase flexibility of use of the data, by provision of independence between the data and the applications programs that use it... 

The information view organizes the information necessary to support the enterprise function ruid process using an information model. Data for a company are an important resource, so it is necessary to provide a method to describe or model the data, such as data structures, repository types, tuid locations, especially the relationships among different data. It is very important for the company to maintain the data resource consistently, eliminate possible data redundancy, and finally enable data integration. 

They produce redundancy and incompatibility. In addition to redundant and incompatible user interfaces, closed systems foster data redundancy, hardware and software incompatibility, duplication of programs and duplication of security schemes. In some cases, the user or programmer finds it easier to re-enter data that already reside in an existing database than to build a bridge to extract the information. Clearly, such duplication makes maintenance of consistency between data in various databases a major challenge. Each level of redundancy compounds the problem. Not only are direct costs increased unnecessarily, but the integrity, accuracy, and security of information are threatened. 

A database is a centralized repository for data storage that reduces data redundancy at different network nodes. Multiple databases can be accessed through the network, although some local databases may not be accessible. Central database server systems are set up based on equipment storage capabilities and cost. Detailed discussions of database and transaction processing may be found in Lewis et al. (2006) and Garcia-Molina et al. (2008). 

Data verification stores all critical data redundantly in memory and checks validity before use. 

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