Semantic Metadata Requirements for Data Warehousing from a

Dimensional Modeling Perspective

Susanna E. S. Campher

School of Computer Science and Information Systems, North-West University, Potchefstroom, South-Africa

Keywords: Data Warehousing, Semantic Data Management, Metadata, Dimensional Modeling.

Abstract: The era of big data has brought on new challenges to data warehousing. Emerging architectural paradigms

such as data fabric, data mesh, lakehouse and logical data warehouse are promoted as solutions to big data

analytics challenges. However, such hybrid environments, aimed at offering universal data platforms for

analytics, have schemas that tend to grow in size and complexity and become more dynamic and

decentralized, having a drastic impact on data management. Data integrity, consistency and clear meaning are

compromised in large architectures where traditional (relational) database principles do not apply. This paper

proposes an investigation into semantic metadata solutions in modern data warehousing from a (logical)

dimensional modeling perspective. The primary goal is to determine which metadata and types of semantics

are required to support automated dimensionalization as it is assumed to be a good approach to integrate data

with different modalities. A secondary goal is finding a suitable model to represent such metadata and

semantics for both human and computer interpretability and use. The proposal includes a description of the

research problem, an outline of the objectives, the state of the art, the methdology and assumptions, the

exepected outcome and current stage of the research.

1 INTRODUCTION

Since the first concepts of a data warehouse (DW) in

support of decision-making emerged in the late

1970s, these information systems have been heavily

reliant on relational data theories and architectures

appropriate for collecting and integrating mainly

structured data from internal operational information

systems in well-controlled “closed-world” scenarios

(Abelló et al., 2015, p. 571; Krishnan, 2013, p. 128).

The era of big data and distributed computing has

brought on new challenges to data warehousing as

well as data management in general. Decentralized

data generation, management, and polyglot

persistence result in large volumes of fast-moving,

dissociated, heterogeneous data. Big data use cases

generally involve collecting and integrating a variety

of data from various internal and external sources to

repurpose it for value-add analytics (Freitas & Curry,

2016, p. 90). Relational database management

systems and Structured Query Language (SQL) are

not suitable for storing and analyzing big data.

Conventional data warehousing approaches, from

https://orcid.org/0000-0002-3676-9327

data provisioning to schema design, have to be

reconsidered. In general, a paradigm shift regarding

data assets is required – where they are collected, how

they are analyzed, and how insights from analyses are

monetized (Kimball & Ross, 2013a, p. 527).

Emerging architectural paradigms such as data

fabric, data mesh, lakehouse and logical data

warehouse are promoted in industry as solutions to

big data analytics challenges (Gacitua et al., 2019, p.

15; IBM, 2021; Jägare, 2020; Welch, 2021).

However, such hybrid environments, aimed at

offering universal data platforms for analytics, have

schemas that tend to grow in size and complexity and

become more dynamic and decentralized, having a

drastic impact on data management (Freitas, 2015, p.

26). Data integrity, consistency and clear meaning are

compromised in large architectures where traditional

(relational) database principles do not apply (Helland,

2011, p. 40). Moreover, as data are typically

generated in formats for specific use cases at the

source, attempting to integrate and reuse it outside its

original context requires methodologies to better

describe and contextualize it for meaningful use

Campher, S.

Semantic Metadata Requirements for Data Warehousing from a Dimensional Modeling Perspective.

DOI: 10.5220/0011018200003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 129-136

ISBN: 978-989-758-569-2; ISSN: 2184-4992

129

elsewhere. In consequence, approaches to maximize

quality and usefulness within multiple contexts have

to be investigated (Freitas & Curry, 2016, p. 91). New

theory and taxonomy of data are also needed to

address data curation issues, such as data (object)

identification, versioning, lineage or provenance, and

accuracy measurement (Helland, 2011, p. 47),

typically of concern in the field of data warehousing.

Modern approaches to data warehousing include

the Kimball Lifecycle (Kimball et al., 2008), DW 2.0

(Inmon et al., 2010), and Data Vault 2.0 (Linstedt &

Olschimke, 2016). The Kimball Lifecycle is a well-

known industry standard prescribing a bus

architecture with a business process focus in

which individual logical dimensional models are

iteratively designed to fit into the overall

architecture. The methodology prescribes how

these models may be implemented in relational

database management systems (RDBMs) as a

queryable presentation layer that is easy to

understand and delivers fast query performance.

An extended relational DBMS architecture to

accommodate big data is briefly presented in

Kimball and Ross (2013a). DW 2.0 has a focus on

the lifecycle of data and information as well as

different types and structures of data and how they

relate, with metadata forming the backbone of the

infrastructure. The Data Vault 2.0 approach addresses

some of the issues associated with traditional data

warehousing, such as high rates of production

failures, business rule complexity and slow load times

over big data volumes. According to Linstedt (2019),

data acquisition into a raw data vault is faster and

highly automatable. However, the compromise is that

business rules are decoupled from a data vault, and

only about 60% of the process to create

dimensionalized information marts for consumption

can be automated, as raw data still need to be cleaned

and contextualized. Furthermore, the need to define

metadata for additional external data sources requires

human effort and places a growing demand on data

acquisition teams.

2 RESEARCH PROBLEM

Having more flexible and scalable data and DW

architectures and models to accommodate big data

does not address the challenges of data management,

per se. Issues that have to be addressed include

controlled data creation, integration, discovery and

retrieval; data contextualisation and resolution;

quality assurance, reliability and lineage

requirements; change data capture; and value-added

interpretability for both humans and machines.

Automating processes to address these issues is

necessary in order to scale with big data (Stonebraker

et al., 2013). As such, the inclusion of suitable

metadata about the meaning of the data is essential,

something that is often overlooked and

underspecified in source schemas (Krishnan, 2013, p.

181; Miller et al., 2001, p. 78). In that regard,

semantic data management and semantification of

(granular) data have to be considered.

In some of the Kimball Group’s last writings

before their retirement in 2015, best practices for

various aspects of big data were specified. The overall

recommendations include thinking dimensionally –

dividing the world into facts and dimensions – and

integrating data based on conformed dimensions. In

terms of architecture, Kimball and Ross (2013a, p.

533) advise building comprehensive ecosystems

around a logical data highway with various data

cashes of increased latency and data quality. In such

a highway, most of the data are kept in non-relational

form and big data analytics may be employed to

perform extract, transform and load (ETL) from one

cache to the next. ETL includes data filtering,

cleaning, dimensionalization and extracting value-

added information and business measures. From a

modeling perspective, emphasis is placed on

automated dimensionalization of data early on the

data highway as the best approach to integrate data

with different modalities in order to add value to data

as soon as possible. In one of their final design tips,

Kimball asserts that modern storage architecture

should be open to various types of tools and analytic

clients, with access to the data being provided through

a universal metadata layer. As such, metadata

descriptions of data of all types have to be extensible,

customizable and powerful enough to include enough

semantics for new complex data sources (Kimball,

2016, p. 848). Suitable semantic metadata

descriptions would enable seamless integration of

heterogeneous data into various presentational

formats, including dimensional models. For example,

social media sentiments such as “This product is

great!” with a link to a product page, may be

dimensionalized with information about the product,

customer, location, promotion, weather, etc. In a

healthcare environment, patient profiles may be

combined with image data such as sonograms and

electrocardiograms, and text data such as physician

reports or medical treatments.

In general, semantification of big data would

prove valuable to address data curation and

integration challenges in innovative ways, as

traditional schema matching methods (for instance-

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level data integration) do not work well with

heterogeneous and dynamic data schemas (Miller,

2014). Platforms where “specified (and unspecified)

schema information, constraints and relationships can

be learned, reasoned about and verified” are desirable

for integrating heterogeneous data (Miller et al.,

2001, p. 78). Machine-assisted data integration from

various data sources, including relational databases,

data lakes, distributed files and web data feeds, is

envisioned with active metadata management and

supporting technologies, such as semantic knowledge

graphs and embedded machine learning (Gupta,

2021). For this to realize, it is crucial that computer

systems and machines understand the semantics of

data and are able to connect technical, business,

operational and social types of metadata. Syntactic

representations of schemas, metadata and data need

to convey enough semantics on a fine-grained data

(attribute) level for the automatic creation of

mappings between data elements and other data

integration tasks. Incorporating Semantic Web (SW)

technologies, such as taxonomies, ontologies and the

Resource Description Framework (RDF) into data

solutions enables intelligent links to be created within

the data, resulting in a powerful semantic layer for

data integration and analysis (Krishnan, 2013, pp.

193, 204). Semantification should also be done in a

manner that is easy for business users to understand;

for example, with the use of knowledge graphs (Den

Hamer et al., 2021; Gupta, 2021).

The focus of this study will be on semantic

metadata solutions in modern data warehousing from

a (logical) dimensional modeling perspective, in

particular. The primary goal is to determine which

metadata and types of semantics are required to

enable automated dimensionalization while meeting

fundamental data warehousing principles and

practices (such as having subject-oriented, integrated,

time variant, and non-volatile collections of data).

Dimensionalization of data is assumed to be a good

approach to integrate data with different modalities.

A secondary goal is finding a suitable model to

represent such metadata and semantics for both

human and computer interpretability and use. The

following research questions are proposed:

Q1) “What are the metadata and semantic

requirements that will enable automated

dimensionalization for integration?”; and

Q2) “How can heterogeneous data be semantified

to enable both human and machine

interpretability in data warehousing?”

3 OUTLINE OF OBJECTIVES

This research will endeavor to answer the questions

by systematically studying design principles and best

practices (or patterns) in data warehousing,

specifically related to dimensionalization for the

purpose of data integration and management. The aim

is to analyze and understand these design patterns,

and to document their rationale and function into a

suitable knowledge base (KB) from which

requirements for semantic data enrichment can be

extracted. The KB will be used to derive a conceptual

model for metadata and semantified data

representation in support of machine-enabled data

warehousing.

The objectives of the study are as follows:

O1. Conduct a literature study regarding

Semantic Web technologies and the use of these

technologies in data architectures and data

warehousing.

O2. Conduct a literature study regarding

knowledge representation as well as data, metadata

and semantic modeling.

O3. Perform pattern mining on key data

warehousing practices and principles, specifically

related to dimensional modeling for integration and

data management in data warehousing. This objective

includes the creation of an ontology for representing

the design pattern concepts and categories. The

patterns are to capture and include the rationale

behind the specific practice as well as metadata and

semantic data requirements.

O4. Design and develop a KB to document the

design patterns (Artifact 1). The KB should represent

a theoretical model of the observed patterns in a

suitable representation.

O5. Evaluate the validity of the KB in terms of fit,

relevance, workability, and modifiability by applying

it within the context of the second research question

(Q2). A comparison to a “design theory nexus”, as

described by Pries-Heje and Baskerville (2008), may

be included.

O6. Derive a conceptual model (Artifact 2) to

semantically enrich heterogeneous data based on the

metadata and semantic data requirements embedded

in the design pattern KB.

O7. Demonstrate and evaluate the utility, quality,

and efficacy of the conceptual model using a real-

world problem scenario. Evaluation could also

include comparison to the Common Warehouse

Metamodel (CWM) multidimensional metamodel

(OMG, 2003), the RDF data cube vocabulary (W3C,

2014), and the HANDLE metadata model for data

lakes (Eichler et al., 2021).

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Although some of these objectives may seem

loosely formulated, a large aspect of this study will

involve determining the specific objectives of a

solution to the research problem. The research

problem is considered a “wicked problem” as

described by Rittel and Webber (1973) since the

solution objectives are not clear, the problem itself

can only be formulated in terms of a solution, and the

various consequences of a particular solution is

unbounded over application domain and time. (More

information regarding the characteristics of wicked

problems are provided in assumption A5 in section

5.3.) It is also the reason why a problem-centred

Design Science Research (DSR) approach is

considered appropriate. In a problem-centred DSR

cycle, determining specific solution objectives is an

essential part of the research process (Peffers et al.,

2007, p. 55). Furthermore, the search for suitable

metadata and semantics in support of automation in

data warehousing, in this study, will focus

specifically on those required for automated

dimensionalization as a means to both instance-level

and conceptual level data integration and

management. Considering all possible data

warehousing domains and purposes is beyond the

scope of this investigation. However, creating an

extensible KB for data warehousing design patters is

an effort towards assimilating a design theory nexus

that would be helpful in future when more design

patterns regarding alternative data warehousing

practices and approaches are added. A design theory

nexus is helpful to understand, evaluate and compare

available alternatives from many competing, and

often highly dissimilar approaches with different

underlying assumptions and theories; it assists in

selecting an ideal solution to a complex problem,

sometimes by combining dissimilar design theories

(Pries-Heje & Baskerville, 2008, p. 750).

4 STATE OF THE ART

Surveys regarding the use of SW technologies in data

warehousing include Abelló et al. (2015), and Gacitua

et al. (2019). Results indicate that these technologies

offer promising solutions to challenges associated

with data provisioning and schema management in

DWs. Specific areas mentioned as opportunities for

further research include automatic (or semi-

automatic) derivation of ontological mappings of

heterogeneous data, ways to enable semantic-aware

data integration and DW schema management,

automatic data transformation for semantically

traceable models, and semantically enriching and

annotating multidimensional models.

Ptiček et al. (2019) describe the potential of a

semantic paradigm in warehousing of big data. They

emphasise the need to address the integration of

(schemaless) big data into DWs on a higher level of

abstraction, i.e., the data modeling phase, and

describe various promising solutions, such as

ontology-based schema integration. Selma et al.

(2012) propose a methodology for designing an

ontology-based Web DW given ontology-based

databases as sources. Mami et al. (2016) developed a

method of big data semantic enrichment using RDF

vocabularies, but without considering an attached

source schema (which requires source schema

extraction). McCusker et al. (2009) demonstrated

how semantically well-annotated, distributed data can

be integrated using semantic extraction,

transformation and loading (SETL) with the Web

Ontology Language (OWL) into an ontologically-

driven data store or knowledge base. Bansal and

Kagemann (2015) also proposed a SETL framework

for integrating linked Web data which involves steps

of data preparation, creating a semantic data model,

and generating RDF triples.

Although all of these studies show promising

results and suggest further research into SW

technologies for data warehousing, these approaches

still require either sources that are already semantified

well enough, or big data schema extraction methods.

Schema extraction is not trivial and an

underdeveloped field that could prove to be

unsustainable as a semi-automatic process having to

be continually developed for new data representations

and having to scale proportionally with data size

(Mami et al., 2016, p. 389; Ptiček et al., 2019, p. 402).

Furthermore, McCusker et al. (2009) and Bansal and

Kagemann (2015) reported the need for manual

intervention even though their experimental data

were annotated with semantic metadata.

5 METHODOLOGY

5.1 Research Approach

The study will be conducted using a mix of methods

from the interpretive paradigm as well as DSR.

Firstly, the study is concerned with analyzing and

understanding (existing) knowledge about data

warehousing principles and practices from a

dimensional modeling perspective. The aim is not to

determine which practices are the best, but to

critically examine the context that led to different

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practices, and compare and summarize them in order

to incorporate such perspectives into a proposed

solution to the research problem. Secondly,

conceptualizations of this knowledge need to be

presented in a manner that is open, extensible and

interpretable by humans and machines.

Understanding how this can be done will be

investigated through the creation of artifacts – a KB

and derived conceptual model for semantification of

heterogeneous data.

The overall process of the study will follow a

DSR approach similar to the one conducted by Berndt

et al. (2003) and described by Peffers et al. (2007) as

an example of a problem-centered approach (as

opposed to an objective-centered solution for which

the specific solution objectives are clear from the

start). It is also acknowledged that DSR can take on

other types of methodologies, such as one to support

context-specific research; Peffers et al. (2007)

provide the example of employing custom methods

for requirements analysis in information systems.

The research plan is depicted in Figure 1. The

process flow on the left outlines the general activities

of DSR, while detailed steps are depicted on the right.

Figure 1: Research plan.

During activity 1, the research problem is

identified and the value of a solution is justified.

Some of the metarequirements (or objectives of the

solution) may also be obtained during this activity by

investigating the state of the problem and existing

knowledge (Peffers et al., 2007, p. 55). Consequently,

some general objectives for the solution can be

inferred in activity 2.a (defining the objectives of a

solution), but may require additional investigation.

More specific objectives, related to the first research

question (Q1), will be obtained through data

collection and analysis during activity 2.b, which will

involve pattern mining of data warehousing

principles and practices. (This activity is described in

more detail in the following section about data

collection and analysis.)

Knowledge of theory emerging from the previous

steps may then be applied during activity 3 to design

and develop DSR artifacts. The artifacts will be

demonstrated and evaluated during activities 4 and 5,

respectively. Both artifacts will be evaluated by

comparing the various design objectives to actual

observed results, and possibly to other industry

standards. Finally, the disciplinary knowledge

obtained from the process will be communicated in a

doctoral thesis (activity 6). The activities of design,

demonstration and evaluation are iterative, and may

also require revisiting the solution objectives.

5.2 Data Collection and Analysis

Data collection and analysis will involve model-

based qualitative methods similar to those described

by Hentrich et al. (2015) and also conducted by Zdun

et al. (2018). It is a combination of methods based on

Grounded Theory (GT), and those for collecting and

analyzing best practices, such as pattern mining. The

empirical data about data warehousing design

patterns will undergo coding and comparison,

resulting in concepts and categories (and a pattern

taxonomy) that can be organized and formulated into

a suitable KB (see Gorton et al. (2015) for similar

work). Memo-writing and diagramming will be

performed throughout to record comparisons and the

developing thinking during analysis.

The process of data collection and analysis (the

pattern mining) is depicted in the detail steps of

activity 2.b of the research plan Figure 1. Sampling

will begin with initial, purposive sampling, e.g., best

practices for big data in data warehousing provided

by Kimball and Ross (2013a), and the latest collection

of best practices and data warehousing design tips

provided by Kimball and Ross (2016). Theoretical

sampling will follow the analysis of data from this

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133

first source, informed by coding, comparison and

memo-writing, in an attempt to fill gaps, clarify

uncertainties, and test interpretations in the

developing theory. Data analysis followed by

theoretical sampling will be repeated until theoretical

saturation is reached, i.e., when the design patterns

(the theory) are “dense and logical and there are no

gaps in the explanations” (Corbin & Strauss, 2014, p.

139) or all the concepts in the developing theory can

be substantiated from the data and are well

understood (Sbaraini et al., 2011, p. 3). The design

and development of the DSR artifacts will be based

on theory obtained from this process.

5.3 Assumptions

The following assumptions are currently relevant

within the context of this study.

A1. A specific paradigm (and associated

methodology) is a construct of human thought – a

constellation of assumptions, theories and methods –

which remains a heuristic device that should not be

singled out as an absolute view to which the world

could actually conform (Mingers, 2001). Declaring

assumptions and critically selecting and using

suitable methods throughout (different phases of) a

project are more important than committing to a

single customary paradigm or methodology.

A2. Software design patterns can be considered

sociological phenomena – they represent successful

problem-solving behavior and involve significant

evolutionary and human aspects. Grounded Theory

methods are consequently suitable to discover such

patterns (Hentrich et al., 2015). The same reasoning

can be applied to design patterns in data warehousing.

A3. Dimensional models present data in a

predictable, intuitive framework, which simplifies

both human and computer processing of the schemas

(Kimball & Ross, 2016, pp. 149,153); “This process

of dimensionalization is a good application for big

data analytics” (Kimball & Ross, 2013b, p. 538).

Dimensionalization is therefore a good approach to

integrate data with different modalities.

A4. “We must be systematic, but we should keep

our systems open” (Whitehead, 1956, p. 8). It is

important to be sensitive to the limitations in specific

systems and to realize there is always more detail

beyond. Such an “open system” approach could be

applicable to this study in terms of two aspects.

Firstly, any model or mechanism to represent

semantics of data, ought to be void of global

consistency requirements. This is in line with the

thinking of Berners-Lee (1998) about a Semantic

Web language being “complete” in the sense that it

should be able to represent all kinds of data and

information about the world with arbitrary

complexity, including paradoxes and tautologies.

Berners-Lee (1998) further supposed that knowledge

representation systems will be “webized” (become

part of the Semantic Web) once “centralized concepts

of absolute truth, total knowledge, and total

provability” are eliminated. Secondly, in terms of

qualitative research conducted, a researcher ought to

remain open to theoretical frameworks.

A5. The problem of finding a model for semantic

content management is a “wicked problem”. March

and Hevner (2007, pp. 1036-1037) state that “data

integration has been studied extensively in the context

of heterogeneous databases; however, its solution

remains elusive”. Bizer et al. (2011) challenge the

scientific community to “demonstrate the benefit of

semantics for data integration”, and that “[w]e have

talked extensively about smarter databases but the

actual requirement remains vague”. According to

Rittel and Webber (1973), a wicket problem does not

have a definitive formulation; including sufficient

detail about the problem would require an exhaustive

list of all its conceivable solutions up front. A wicked

problem has neither clear criteria that determines

when a solution has been found, nor criteria to prove

that all solutions have been considered. It also has a

good-or-bad solution rather than a true-or-false one,

has no ultimate test for a solution, and no one solution

that fit all variants of the problem. A wicket problem

is often a symptom of another problem and the

resolution of the problem depends on how the

underlying problem is explained. For example, it is

possible to claim that effective data management is a

problem because (a) modern data architectures have

schemas that tend to grow in size and complexity, and

(b) data integrity, consistency and clear meaning are

compromised in large architectures where traditional

database principles do not apply. Because of

explanations like these, one might argue for stricter

schema management, or for finding ways to translate

and apply relational principles in big data

environments. Regardless, in literature there are

explanations of the data management problem in

terms of lack of suitable data semantics.

Consequently, it is considered worth investigating

ways in which adding suitable semantics may solve

the problem.

6 EXPECTED OUTCOME

The contribution of this study, in general, is towards

solving problems related to data warehousing in the

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era of big data. Specific contributions foreseen are

C1) a collection of design patterns regarding

modelling, management and data curation practices

and principles for dimensionalization and integration

in data warehousing; C2) a prototype of an extensible

KB for DW design patters; and C3) a conceptual

model for semantification of heterogeneous data in

support of automated dimensionalization and

integration in data warehousing.

7 STAGE OF THE RESEARCH

This study is currently in phase 2.a (define objectives

of a solution) of the research plan depicted in Figure

1. It involves a literature study and investigation into

existing knowledge and solutions regarding the

research problem and questions.

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