Introduction

Multi-dimensional models and their measurements are powerful tools for decision making according to different dimensions. Ontologies, in their basic expression as a Resource Description Framework [RDF], represent realities based on relations between classes and constitute the basis of the linked data of the semantic Web. Ontologies allow to represent a set of organized concepts of a specific topic, as well as make inferences (Pinilla & Barón, 2015).

The increase in the application of information and communication technologies means that little by little, cities are approaching the standards that define them as intelligent cities (Wiseli, Tanusetiawan, & Purnomo, 2017). The importance of this type of cities lies in providing an infrastructure that guarantees, among others, sustainable development. Based on the above, this study presents a proposal in which, through the application of technologies, expects to contribute to the achievement of the Sustainable Development Goals [SDG] - 2030, approved by the United Nations [UN] in 2015.

This paper provides a simple methodology for, starting from a multi-dimensional Data Warehouse model, to obtain an RDF ontology that is easily linked to other ontologies of the ODS-2030, more specifically to the Sustainable Development Objectives Interface Ontology [SDGIO] that is emerging through a global effort driven by the United Nations Environment Program [UNEP].

The proposal focuses on analyzing households that have safe drinking water and sanitation in the world. At the same time, it is interesting to observe the evolution, through the years, of the quality of water in homes, their availability, etc. Although this objective is somewhat ambitious, this study proposes considering as a starting point, a country as a case study so that later this analysis can be extended to the rest of the world.

The rest of the document is organized as follows: section 2 presents the related works; in section 3 the DW2RDF4SDG methodological proposal is presented, where the applied methodology and a case study are shown; and finally, in section 4, the conclusions and future work are presented.

II. Related Studies

In this section, some studies, projects, and policies of States that address the SDGs and that present water as a resource of interest, are discussed. Some works do not focus exactly on SDG 6, the goal studied in this proposal. However, they are considered relevant to it by their objectives or conclusions. In addition, an analysis of the works that address the possibility of generating an ontology from Multi-Dimensional Models [MMD] is presented.

A. Sustainable Development Goals

The UN presents 17 goals to transform the world (UN, 2015). They are grouped into three categories: social, economic, and environmental. These SDGs were established as an international agenda to address poverty, global environmental change, and social transformations towards sustainability (Wiegleb, 2017).

Although all the SDGs have a great importance, this work considers the goal number six [SDG6], whose priority is to guarantee the availability of water and its sustainable management, and sanitation for everybody (UN, 2015). In particular, according to Wiegleb (2017), this goal represents a global water agenda and an opportunity to guide the trajectories of development towards a world of water security, through water governance. In addition to the analysis of the SDGs, Wiegleb defines the governance of water as the institutions, activities, and formal and informal processes among the different actors, through which the collective interests on water are articulated, and the differences and joint actions are established.

On the other hand, SDG6 describes several goals. The development of this work considers the goal in which it seeks to achieve, by 2030, universal and equitable access to drinking water, at a price accessible to everyone.

Hoekstra, Chapagain, and van Oel (2017) present an analysis of progress towards SDG6, in which they study a set of papers in the Water Footprint Assessment field [WFA]. According to the researchers, the WFA is an emerging research area, focused on the analysis of the use of fresh water, scarcity, and pollution concerning to consumption, production, and trade. It is not easy to find a constructive critique of the SDGs. By analyzing research related to SDG6, Hoekstra et al. (2017) conclude that SDG6 lacks a goal for the use of more efficient rainwater, as well as an objective on the equitable distribution of water.

Beyond the shortcomings of the SDG6, identified by Hoekstra et al., (2017), it is undeniable that the SDGs have obtained great relevance, which is demonstrated in the work of Spijkers (2016), who analyzes whether they can serve as additional support for a “sustainable” interpretation of the general principles of international water law. Spijkers (2016) consider that the SDGs, mainly SDG6, can be used to motivate States to: interpret and apply international water law in a sustainable way; promote the development of the ecosystem approach in international water legislation; and use the legal framework of international water law to facilitate public participation at all levels of water governance.

Biermann, Kanie, and Kim (2017), on the other hand, provide an analysis and evaluation of the evolution, the foundations and the future prospects of the SDGs. In particular, they present how the SDGs exemplify a new type of global governance. According to the authors, it is a type of new governance, called “governance through the objectives”, created as a new mechanism of world politics, which uses global objectives or goals established by the member states of the UN.

Ait-Kadi (2016) highlights that SDG6 offers a promising future if countries affirm their leadership role in the direction of development and management of water resources, collaborating with the most vulnerable sectors. In addition, in order to carry out the vision of the SDGs, it is necessary to launch new strategies that make the way we all live and interact with our environment. The above to ensure that there is enough water to support development and inclusive well-being.

According to this vision, Giupponi and Gain (2017) present a tool to monitor progress, compare different geographical areas, highlight synergies, and conflicts within three dimensions: water, energy and food; to provide support for more effective strategies that meet the objectives. On the other hand, spatial data are crucial, because they contain the physical and socioeconomic phenomena of the areas of greatest interest for the development of the SDGs. Jha and Chowdary (2007) also consider that innovative technologies, such as the data obtained remotely and geographical information is a very important role.

Cope and Pincetl (2014) examine the state of spatial data on water management in the urban area of the city of Los Angeles (CA). The authors mention that the management of water resources is decentralized. Therefore, this decentralization generates an unequal quality of the geospatial data related to water management produced by the organizations available to the public. In this research, it is really important to have a central place to manage geospatial data, which determines an improvement, according to the authors, in the management of the resource in question, at all levels. Meanwhile, the United States Geological Survey [USGS] (2018) presents the water resources of that country, through the use of maps and geographic information that show different data referring to different water uses.

It is indisputable that the use of technology provides advantages in obtaining and processing data that allows different follow-ups. The SDGs have expanded the options for collecting such data. However, they have also highlighted the fundamental role of data sources, such as, for example, household surveys, which allow tracking progress and monitoring inequalities. The above is demonstrated in the study of Khan et al., (2017), where they confirm the feasibility of collecting information on the availability and quality of drinking water.

As mentioned, it is of great interest to support the highly vulnerable countries and it is mainly on the basis of their needs that the development of the SDGs is sought. For example, in Wayerworld they mention that millions of people do not have access to clean water and also with the growing demand for this resource, better management of water supply is necessary. The above is demonstrated in the study carried out in (Khan et al, 2017), where the authors conclude that drinking water consumed in households is more likely to be more contaminated than water collected from the sources. According to this and through a case study in South Africa, in (Cumming et al., 2017) present the need for ecological infrastructure in numerous development and sustainability issues, including food security, water supply, and reduction of poverty. On the other hand, in Mugagga and Nabaasa (2016) based on the SDGs, they discuss the centrality of water resources throughout Africa. Also in Jha and Chowdary (2007) they present a case study, in which they analyze the different problems that India has due to lack of water resources.

All these works, in different ways, support the need of a proposal that resolves how to deal with the water necessity as a finite resource. In particular, the goal of this research is vital: to achieve access to drinking water for all the people of the world. On the other hand, it also highlights the need for the use of sensors and technologies that support geographic information.

B. From a Multi-Dimensional Model to an Ontology

Looking to answer a series of questions about water as a resource, this work plans a proposal that becomes a more general instrument for any SDG that is supported by MMD (Malinowski & Zimnyi, 2008) and ontologies (Ashraf, Chang, Hussain, & Hussain, 2015). Therefore, it is interesting to analyze different works that obtain ontologies from MMD.

Given the use of ontologies in a wide range of applications, many researchers have explored the automation of the development of ontology models by reusing and inferring information from existing data models (Albarrak & Sibley, 2011). Based on the above, Albarrak and Sibley compare different methods that translate data models into ontology models. The present work focuses on multi-dimensional data models. Mainly, we rely on Data Warehouse [DW] to the development of this proposal.

A DW is a collection of data-oriented to topics, integrated, non-volatile, and variable over time, organized in such a way as to facilitate the process of decision-making (Inmon, 2005). On the other hand, the data offered by a DW are extracted from integrated, cleaned, and transformed sources to finally be used at the time of decision making (Inmon, 2005; Kimball & Ross, 2002; Golfarelli, & Rizzi, 2009). One of the challenges of this type of systems is, precisely, the dynamics of integration of the different data sources.

The DW is based on an MMD (Malinowski & Zimnyi, 2008). This model allows a better understanding of the data and a better performance in complex queries. Into the MMDs, the data is presented in an n-dimensional space, generally called a data cube or hypercube. A data cube is defined by dimensions (composed by hierarchies) and facts. The dimensions represent the perspectives that are used to analyze the data.

Based on the DW characteristics, Nebot, Berlanga, Pérez, Aramburu, and Pedersen (2009) consider that the concepts in ontologies can be described with the facts and dimensions of this type of systems, so they take the DW as a repository of ontologies. On the other hand, Kurze, Gluchowski, and Bohringer (2010) introduce an ontology of multi-dimensional data and consider that said ontology is capable of improving the conceptual modeling of this type of systems. Also, Hoekstra et al., (2017) consider that ontologies can be used to represent the analytical and domain concepts stored in a DW. However, they mention that extracting these concepts from a DW in production is not a trivial task since there are often many facts and dimensions. Taking into account these difficulties, a set of mapping rules is defined in its work, which allows an ontology to be extracted from the constructions of a DW. Prat, Akoka, and Comyn-Wattiau (2012) also define a series of transformations that map the MMD into an ontology.

Otherwise, Moreira, Cordeiro, Campos, and Borges (2014) start from an ontology to obtain an MMD. The authors consider that the conceptualization of real-world phenomena in multi-dimensional design continues to be a challenge. Therefore, they propose to obtain multi-dimensional concepts from an ontology, through a set of derivation rules. Although this approach is contrary to that presented in our proposal, which refers to an MMD in order to obtain an ontology, it is interesting to highlight the compatibility between the MMD and the ontologies.

C. Sustainable Development Goals Interface Ontology

The Sustainable Development Goals Interface Ontology [SDGIO] (Jensen, 2016) is being developed under the coordination of UNEP (2016) with the aim of providing a semantic bridge between the SDGs, their goals and indicators, and the groups of entities that they refer. In order to get a robust enough ontology, it is proposed to import classes from numerous existing ontologies and to include vocabularies universally accepted in the SDG-2030 domain, and in order to promote interoperability too. When there are no external classes to be added to SDGIO, new ontologies will be built to achieve that all the indicators and targets of the SDGs are taken into account. This is considered a relatively new initiative with nearly two years of work, but its open conception gives it an advantage for the proposal of this study, although it requires more effort to validate all its contribution.

Considering that many entities have their information management systems based on structured and operational DW, translating their multi-dimensional models into knowledge constructs in the field of ontologies to link them to factors monitored by the 17 SDGs could result in a contribution to follow-up at the national, regional and global levels of the SDGs-2030.

III. Proposal

This paper proposes a series of steps to obtain an ontology, from the data stored in a DW. Hence, we use the tools of Business Intelligence [BI] (Zimányi & Abelló, 2015), applying different Online Analytical Processing [OLAP] operations (Berson & Smith, 1997) that allows studying the measures or the facts obtained in the DW.

A. DW2RDF4SDG Methodological Approach

The following steps are considered, in order to obtain an ontology from the data stored in a DW.

- Questions approach: the set of questions raised allows defining the domain of interest.

- Multi-dimensional cube definition: taking into account the questions planned, it is necessary to identify the dimensions with the corresponding hierarchies and the facts that define the multi-dimensional cube. The model used for the representation of dimensions and dimensional dimensions is Conceptual Multi-Dimensional Model [CMDM], which, as the name implies, is a conceptual model for the specification of multi-dimensional bases (Carpani, 2000).

- Derivation of the domain ontology from the multi-dimensional cube: for each of the dimensions of the MDM cube the equivalent domain ontology is generated, obtaining an ontology for each dimension (partial ontologies). In other words, for each concept of the DW dimension, its equivalent is selected in one of the existing ontologies (SpatialObject, TemporalEntity, etc.)

- Integration of partial ontologies: each partial ontology, which represents the dimensions of the DW, is integrated with the others, thus obtaining the domain ontology.

- Elimination of duplicated elements of the resulting ontology.

- Contextualization of the ontology with SDGIO proposed by the United Nations (Jensen, 2016): in this last stage, the aim is to align the proposed ontology with SDGIO to offer greater interoperability with the de facto standards that are assuming a global level.

B. Case Study: Proposal Implementation for SDG6

Since water is a resource that should be accessible to everyone in the world, it is important to analyze the households that have potable water and sanitation. It is also important to carry out this analysis due to the evolution that has occurred in the world regarding this need over the years. Although a global approach, this study could begin to be carried out in a country and then add the analysis of different countries until a global vision can be achieved.

In order to validate this proposal, it is analyzed a case study that implements the methodology described in section III.B, for SDG6. For this purpose, the following questions are raised first:

- What is the number of households that do not have drinking water or sanitation in the world?

- How many people are affected by not having clean water or sanitation?

- How many children / adults / women / men are affected by not having clean water or sanitation?

- What is the availability of drinking water in the homes of a country at any given time?

- How has the availability of drinking water in a region or country evolved over time?

The answers based on the use of the tools available in the area of BI would help to locate the homes where is necessary to bring sanitation and safe water, which would allow the relevant authorities to take the necessary actions.

In addition, based on the prediction of the data, these technologies could contribute to take actions and make decisions that allow preventing future situations.

Multi-Dimensional Cube Definition

This section presents the multi-dimensional cube that is defined for the analysis of the dimensions and measures that participate in the households that have potable water and sanitation in the world.

Figure 1 and Figure 2 correspond to the graphic representation of each of the defined dimensions with their corresponding hierarchies.

Figure 1.
geographicLocation and time dimensions

Figure 2.
Figure 2. home dimension

The dimensions emerge from the questions described above. They are: home, geographicLocation and time.

As seen in Figure 1, the Time dimension has a hierarchy of three levels: Date, Month and Year. Each level has an identifier and a name.

On the other hand, Figure 3 presents the graphic representation of the dimensional relationship, in which all the dimensions defined are crossed to obtain all the measures that participate in that relationship.

Figure 3.
Affected dimensional relationship

The measures are the following: nmHome, nbPersons, nbChild, nbAdult, nbWomen, nbMen.

Derivation of the Domain Ontology from the Multi-Dimensional Cube

A partial ontology is constructed for each dimension of the multi-dimensional cube. In Figure 4, Figure 5, and Figure 6 the partial ontologies for each of the previously defined dimensions are shown below.

Figure 4
Ontology corresponding to the time dimension

Figure 5
Ontology corresponding to the geographicLocation dimension

Figure 6
Ontology corresponding to the home dimension

Finally, after eliminating duplicated elements, the resulting ontology is presented in Figure 7, obtained from the integration of the partial ontologies.

Figure 7
Ontology corresponding to the multi-dimensional cube

Figure 8
Linking of indicators, goals and objectives in the SDGIO framework

As shown in Figure 7, based on the ontological framework built for ODS6, the competency questions posed in step 1 of the methodology (questions responding to multidimensional cube analytic queries) could be complemented with other extensions of semantic queries. For example, questions would be raised regarding the geographic location of the analysis site, thanks to the link with the GEO ontological vocabulary, through the SpatialObject class. As the links of the classes, obtained from the dimensions of the cube, increase with other existing ontologies, the extension capacity of the queries would increase, as well as the contextualization of the origin data, also increasing its value for the decision maker.

Contextualization of the Ontology Proposed in the Framework of the SDGIO

In order to provide greater interoperability to the proposed ontology in the scope of the SDGs at the global level, it is proposed to align it to SDGIO as the last step of the DW2RDF4SDG Methodology (Jensen, 2016). There are very few studies, as far as could be obtained from the literature, which implement the SDGIO. Jensen (2016) and UNEP (2016) offer sketches of the SDGIO implementation. Following these good practices, it is possible to align the follow-up ontology of goal 6.1 of the SDGs, related to “achieving universal and equitable access to drinking water ...”, which was obtained with our methodology, considering the universal interfaces proposed by United Nations to monitor the SDG-2030.

Jensen (2016) shows how the objectives are linked to their goals and indicators, specifically in the area of SDG6, as shown in Figure 8.

Figure 8.
Linking of indicators, goals and objectives in the SDGIO framework

For the specific use of this work, starting from Figure 8, it would be necessary to model the alignment, for example, in the following way:

<Drinking water> IsEquivalentTo <PotableWater>

Considering that the SDGIO project is still under development, this stage is being addressed by the research group to offer a valid guide for its future generalization.

Advantages and Limitations of the DW2RDF4SDG Methodology

Starting from a multi-dimensional model offers the built ontology the guarantee of having dimensions and hierarchies of dimensions that contribute to relevant facts. Therefore, the resulting ontology could be effective for making decisions in the given domain, as the multi-dimensional cubes have shown, for decades, in the intelligence area of BI.

Another advantage of this proposal is the implicit concept of linked data of the Semantic Web, since the construction of the ontology maximize the reuse of other open vocabularies on the Web that are linked to the obtained ontology. The above allows the built ontology to be ready to be aligned with others that represent knowledge in the scope of the SDGs and that facilitate the information management about the indicators and the goals that lead to its fulfillment.

The proposed methodology is aimed to transforming data models, from multi-dimensional cubes to RDF ontology schemes; that is, it remains in the TBox terminological dimension, and does not include in its scope offering methodological guides or rules to convert data stored in the Data Warehouses into individuals of the ontologies (ABox). However, this research intends to continue working so that, in addition to an ontology modeling instrument, it is a useful semantic query tool using the data that comes from the BI, through the advantage that it would offer to extend (semantically) Data Warehouse queries based on the link with other open data on the Web.

The DW2RDF4SDG methodology complements (no replace) to the traditional BI model. While BI is a powerful tool for decision making in any organization, linking the data models of a DW with open semantic data models, would allow it to extend static queries stored in a data warehouse and contextualize the data of the organization with other data sets available through, for example, the Linked Open Data project.

IV. Conclusions and Future Work

This paper presents a proposal that contributes to the objective 6 of the SDGs proposed by the United Nations, from a monitoring perspective. This objective describes several goals; for the development of this work is considered the goal in which it seeks to achieve, by 2030, universal and equitable access to drinking water, an affordable price for everyone. Additionally, a group of works that address the SDGs, in particular the SDG6, are discussed and analyzed.

In this search, the DW2RDF4ODS Methodology that, from a Data Warehouse, based on a MMD, returns an ontology, is the main report of this work. The bibliography allows us to affirm that it is feasible to get an ontology starting from the data stored in a DW. Although the bibliography consulted is not enough, it shows that it is an area of research in which it is necessary to go in depth; it is based on this need that our work is addressed. Finally, we present a case study that implements the proposed methodology for SDG6.

As future work, it is proposed to go into detail about the development of the Methodology and then apply it in a larger DW, which allows obtaining an ontology that subsequently be published on the Web. Likewise, we will continue working on the methodology to exploit the advantages of the open-bound data paradigm, in order to enrich the organization’s data and offer the decision-maker the ability to expand queries by considering other context data.

On the other hand, there is research gap for future studies that are oriented to integrate the resulting ontologies of the DW2RDF4ODS Methodology with SDGIO, into various areas, and offer greater interoperability globally.

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Author notes