You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A survey on pre-processing techniques: Relevant issues in the context of environmental data mining 
Chapter Title:  Determining the data matrix columns 
1. Abstract of Cited Paper (BIB001):  The Millennium Development Goals set the target to “halve by 2015 the proportion of people without sustainable access to safe drinking water and basic sanitation”. The current international definition of indicators of access is insufficient for measuring this target. Furthermore, the lack of an internationally agreed definition and measurement methodology is causing confusion and uncertainty regarding the figures that are disseminated worldwide. Moreover, the current context, in which almost 70% of funds for the sector are channelled through national governments, emphasises the importance of a monitoring system for national water sectors in developing countries. From this, an improvement in investment efficiency is expected. The water point mapping methodology, promoted and widely developed by the international organization WaterAid, is presented as an alternative way of defining water access indicators. The present paper describes its potential for defining new indicators and making improvements. Our research is based on the results of a pilot study carried out by Ingenieria Sin Fronteras (Engineering without Borders, Spain) in the Same district, Tanzania. The paper concludes with a discussion of the challenges posed by the implementation of this methodology and proposes further steps for improvement.

Literature Review:
The second issue is to determine the subset of columns of the data matrix to be considered for the analysis. This is related to the goals of the analysis and the kind of questions that have to be answered with the analysis, or the decisions to be made afterwards. Datasets may contain irrelevant variables regarding to these goals, questions or decisions as well as redundant variables BIB001 . As previously stated, the quality of discovered knowledge usually depends on the quality of the data used. Also, the success of some learning schemes, in their attempts to construct models of data, often relies on the identification of a small set of highly predictive variables. The inclusion of irrelevant, redundant and noisy variables in the model can result in poor predictive performance and increased computation. This is the reason why a previous work for determining the variables to be kept is required. At this stage of the analysis an expert-guided variable selection process is to be conducted. This means that, according to the goals of the analysis, the existing knowledge on the targeted Environmental System and the meaning of the available variables, a first selection is performed. This first step is interactive with the experts. For example, given a database with information about water infrastructures in a set of villages BIB002 , with an interest in improving drinking water access to households, all variables describing the status of the sewer network infrastructures can be dismissed. In case of doubt, it is preferable to be maximalist and keep a variable that will be disregarded later along the analysis, than to find during the data mining step that some variables initially dismissed become required. The pre-processing can imply several transformations on the data matrix (like selection of some rows) that may complicate the addition of other variables in an intermediate point of the modeling process. Re-processing a certain data mining model without some variable has a much lower computational cost than re-pre-processing the whole database.