Primer Experimento: Análisis de sensibilidad de las variables

The selection of patterns is based on the

Summary and Conclusion

4.9

This iteration of the design science, core design-build-evaluate cycle, represents the process

that is used to generate new knowledge. This iteration consists of two major stages. The first

stage is the design and build of the 4D-SETL framework itself (including the loading of the

foundational ontology to the graph database), the second stage consist of the design and

development activities that are conducted via the application of the 4D-SETL framework

itself. This second activity consisted of the extraction transformation and loading of the first

two experimental datasets. At this stage, the overall design science evaluation is weak, due to

the simplicity of the initial two datasets. However, the framework has been applied,

experience gained and lessons learned. The integration has also resulted in the framework,

locations and all temporal states (year, month and day) from 1862 to the present. The scope

of the calendar coverage was selected based on the needs of the Companies House integration

were the first company incorporation event in the dataset occurs during the year 1862.

Analysis of the geographic dataset has revealed that there are issues related to the postcodes.

Firstly, they not represent a non-geographic location, as is the case for large corporations.

Secondly, due to the fact that codes are reused – the code, over the course of time refers to a

range of different locations. This is a barrier to the integration of historic data if the

movement of companies over time – would not be absolutely reliable.

The 4D-SETL analysis stage, which considers not just structural elements of the dataset but

how the elements evolve over time has proved of value by providing insight into such issues

DESIGN SCIENCE SECOND ITERATION – 4D-SETL

CHAPTER 5.

IMPROVEMENT AND FURTHER APPLICATION

Introduction

5.1

This chapter describes the execution of the second iteration of the design science

methodology - the design-build-evaluate cycle. As with the first iteration the overall aim is to

discover new knowledge related to the exploitation of 4D perdurantist ontologies for

semantic integration through act of designing, building and evaluating artefacts. This aim is

met through applying the 4D-SETL framework to more complex and larger scale datasets.

Therefore, more complex ontological patterns are required for loading and integration of

larger scale instance level data with the number of graph nodes and edge connections grows

into the millions. An additional objective of the iteration is to improve the framework and

address the deficiencies identified in the previous iteration by redesigning the 4D-SETL and

software tool-chain.

This cycle builds on the experience and knowledge created during the previous iteration. The

4D-SETL framework is again applied to the integration of two experimental datasets with the

resultant integrated datasets stored within a graph database warehouse system. The evaluation

methods identified by and described by Hevner et al. (2004) that are applicable to the

artefacts resulting from this iteration, namely the technical experiment and the applicable

Figure 5.1: Research Design-Build-Evaluate Cycle Interaction Two 5.1.1 Aim and Objectives of Iteration Two

The overall aim of the second iteration is to continue the evaluation of the effectiveness of

employing a 4D ontology to facilitate the semantic interpretation and integration of datasets.

This aim is met through the set of objectives outlined in Table 5.1.

Stage Activity Objective Section

Reference

Design

1 Improve the design the of 4D-SETL framework. 5.

2 Improve the design the tool chain to support the framework 5.2.1

Build

3 The application of the 4D-SETL framework to develop and load the SIC 2007 domain ontology and instance level data.

5.3

4 The application of the 4D-SETL framework to develop and load the Companies House domain ontology and instance level ontology (bulk load).

5.4

Evaluation:

5 Technical experiment heuristic testing of the warehouse artefacts. 5.5.1 6 Illustrative scenario: describing the artefact’s effectiveness. 5.5.5

Table 5.1: Stages and Activities and Within this Iteration Iteration 1 Design: 4D-SETL framework and supporting toolchain Build: A solution and apply 4D-SETL

to Datasets 1 & 2 Evaluation: Resultant artefacts This Iteration - Iteration 2 Improve Design: 4D-SETL framework and supporting toolchain Build: Incorporate improvements and apply 4D-SETL to Datasets 2 & 3 Evaluation: Resultant artefacts Iteration 3 Improve Design: 4D-SETL framework and supporting toolchain Build: Incorporate improvements and apply 4D-SETL to Dataset 5 Evaluation: Resultant artefacts Knowledge and Artefact quality Time

Following the completion of the evaluation of the artefacts produced by the first iteration of

the design science cycle, the knowledge gained is again employed to inform the subsequent

and final iteration. Through this process, the quality of the artefacts produced by the

subsequent iteration of the design science cycle can be improved. Figure 5.2 depicts 4D-

SETL framework, activities and related artefacts

Figure 5.2: Overview of the Second DS Iteration and the Resultant Artefacts

ETL

2

4D-SETL Framework

Dataset Worksheet (Spreadsheet) 2.3D Interpretation of each dataset

element

3. 4D Interpretation of each dataset element including 4D

Patterns Identify Integration ‘points’

BORO UML Model

Graph Database Implementation 1. Compile natural language

models of each dataset

8. Evaluation 5. Application Coding ETL instance level data

7. Application Coding - Query Development

Conclusion, Lessons learned (input to next iteration) 4. Develop the domain ontology

in BORO UML and automatically generate the Graph Database instantiation of the ontology (ETL – of domain ontology)

4D-SETL Iteration Two (Repeated for each dataset)

ETL