A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Types of missing data <s> There is now ample evidence of the ecological impacts of recent climate change, from polar terrestrial to tropical marine environments. The responses of both flora and fauna span an array of ecosystems and organizational hierarchies, from the species to the community levels. Despite continued uncertainty as to community and ecosystem trajectories under global change, our review exposes a coherent pattern of ecological change across systems. Although we are only at an early stage in the projected trends of global warming, ecological responses to recent climate change are already clearly visible. <s> BIB001 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Types of missing data <s> The forage-maturation hypothesis (FMH) states that herbivores migrate along a phenological gradient of plant development in order to maximize energy intake. Despite strong support for the FMH, the actual relationship between plant phenology and ungulate movement has remained enigmatic. We linked plant phenology (MODIS-normalized difference vegetation index [NDVI] data) and space use of 167 migratory and 78 resident red deer (Cervus elaphus), using a space-time-time matrix of "springness," defined as the instantaneous rate of green-up. Consistent with the FMH, migrants experienced substantially greater access to early plant phenology than did residents. Deer were also more likely to migrate in areas where migration led to greater gains in springness. Rather than "surfing the green wave" during migration, migratory red deer moved rapidly from the winter to the summer range, thereby "jumping the green wave." However, migrants and, to a lesser degree, residents did track phenological green-up through parts of the growing season by making smaller-scale adjustments in habitat use. Despite pronounced differences in their life histories, we found only marginal differences between male and female red deer in this study. Those differences that we did detect pointed toward additional constraints on female space-use tactics, such as those posed by calving and caring for dependent offspring. We conclude that whereas in some systems migration itself is a way to surf the green wave, in others it may simply be a means to reconnect with phenological spring at the summer range. In the light of ubiquitous anthropogenic environmental change, understanding the relationship between the green wave and ungulate space use has important consequences for the management and conservation of migratory ungulates and the phenomenon of migration itself. <s> BIB002 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Types of missing data <s> Biofilm development in drinking water distribution systems (DWDSs) is a real problem negatively affecting service and water quality, and, thus, the satisfaction of the final consumers. It is the direct and indirect responsible for many of the DWDSs’ problems, and a lot of resources are invested to mitigate its effects. Addressing this problem has been a concern of researchers and DWDS managers for years. However, it is only recently that both technology and data have been available to support the new approach presented in this work. Our proposal is based on the combination of various existing data sets from similar studies to conduct a meta-data analysis of biofilm development. The approach lies on an intensive data pre-processing. Having a complete and extensive database on biofilm development in DWDSs allows applying Machine Learning techniques to develop a practical model. It is based on a multidisciplinary research vision to formulate effective biofilm control strategies. This work presents the basis for the development of a useful decision-making tool to assist in DWDS management. The negative effects on service and consumers caused by biofilm would be mitigated maintaining it at the lowest level. The performance of the suggested models is tested with data coming from two different case-studies: the DWDSs of the city of Thessaloniki (Greece) and the Pennine Water Group experimental facility (UK). The results obtained validate this methodology as an excellent approach to studying biofilm development in DWDSs. <s> BIB003
Most of the references found in the literature classify missing data into missing completely at random, missing at random and missing not at random   . However, the origin of the missing data is analyzed in depth, so that the user can get criteria to understand. From the application point of view there are basically two main families of missing data: random (completely or not) and non-random, and it is crucial to distinguish between them. Gibert • Random missing data (include missing completely at random and missing at random, usually referred in the literature) are randomly produced and do not follow any particular pattern. This means that, even if unobserved, it is correct to assume that they follow the same distribution of observed data. Thus, observed data can provide useful information about them. They might appear by chance: * an employee is copying a set of manual measurements from paper to computer and incidentally skips one of them * a sensor is temporarily loosing connection with the server and one reading is lost * a forced missing value often is also random. It results of detecting a wrong observation. If it cannot be properly corrected, then it has to be transformed in a missing. • Non-random missing values are produced by identifiable causes; normally they come from a specific subpopulation; and there are differences in their behavior with respect to the observed data. In this case, observed data is not providing useful information about them. They might appear from many different reasons: * Because it is deliberately hidden: Too high concentration of nitrates in the outflow of a wastewater treatment plant in a concrete day can be deliberately substituted by a missing to avoid a fine. Some industries do not provide emissions of certain pollutants to avoid extra taxes. * Because data is explicitly not provided: In surveys on water consumption carried out by water companies, some users do not explicitly declare sensible variables to prevent eventual requalification of the household profile, often with consequences in the bill. Some of these variables are the existence of garden or pool on the property, the use of devices saving water, the number of occupants of the dwelling or the level of family income. * Because it corresponds to a special value. Presence of filamentous bacteria in a WWTP plant might indicate a further bulking and problems with the normal operation of the plant, providing effluent with too high concentration of organic matter, over the permitted limits. * It is not possible to obtain the measurement, by some reasons: * Because the technology is not available: in the case of Hurricane IKE, occurred in Cuba in 2008  , anemometers in Pinar del Río (western Cuba) ceased to measure wind speed over 250 km/h, since they simply broke, thus producing missing measurements after the moment in which wind reached this speed. In a volcano eruption, sensors are used to measure the toxics emitted by the eruption, but no measures are available in the middle of the crater, because the heat fudges the sensors. Also, water age in potable water distribution networks, relevant to predict biofilm development BIB003 . It is no available under conditions far from the steady state, since it depends on water quality and some consumption parameters that cannot be determined under conditions far from the steady state; * Because of a lack of privileges to get it: measurements of air quality in a grid of points of a certain mountain region including a military area will produce missing values in the points falling inside military zone. * Lost: The measurement has been taken correctly, and lost. This can happen when digital data bases corrupt after time, and past data disappears; in migration of software versions, sometimes this might also occur. Another example is the study of migration of birds, in which GPS trackers are used to mark the migratory birds. Some of the measures can be lost, just when visibility from the satellite is temporarily lost BIB002 BIB001 . * Structural: data unavailable for a certain part of the population: As an example, the variable form of the beak of a mammal, in a database on animal species leaving in a certain ecosystem, like a natural park or a forest. Number of legs of a fish is another example. These missing values often appear as a consequence of a wrong data encoding policy at data collection step. Meaningful values (like lackOfBeak) are a better option to represent structural missing values instead of missing values, this requiring a proper design of the data collection process.