Relational Databases versus Graph Databases: Gen X versus Gen Y


When developing databases it is known that developers have choices and preferences. For some the relational model is tried and tested, for others it is outdated and confining. So what is the best choice? Where do you hedge your bets? This post will define and explore the connection between the two in order to understand the crux of this question. While data modelling is continually evolving, and therefore continually complicated, for this discussion at least, we will still to the basics.

Relational Databases:

blog2Relational databases are not a “new thing”. While the technology narrative instills in us that the world wide web and all its glories are a very recent invention the relational database has been in existence since the 1970s. Conceived by Edgar Codd as a response to user demands this system is anything but new. This data model is built on a system of tables and the references or relationships between these tables are defined by “keys”. Therefore, to connect these tables, that is to display the relationship, a process called “joining” is required. These operations are usually considered quite server and memory intensive. Therefore, to produce the most efficient model the developer must choose to what level they will ‘normalise’ their data. This process is simply one of standardisation in which data is classified into more accurate categories. Ranging from first to fifth levels the amount of normalisation required is usually at the discretion of the developer. However, additional costs, server space and intensity all factor into this decision. While the process of normalisation can strengthen the capabilities of the database it has been argued that the need for classification can cause difficulties for non-quantitative fields of study, such as the humanities. Conversely, for simple data structures which require little probing the relational database can serve as a truly tested and reliable model.

Graph Databases:

As stated above the relational database is tried and tested to be both successful and useful for certain types of data structures, and arguably could be utilised for others if incorporated properly. However, it is possible that the success of relational databases lies on rocky foundations. A lack of real competition only serves as a highlight and praise to the strength of RDBs. However, graph bases present a new challenge to this longstanding hegemony and now that a viable alternative is on the market, a decision must be made.

It could be argued that graph databases simply build on the relationship model. Both of these databases rely on their ability to connect or “join” data in response to server demand. However, in the case of graph databases, tables and keys have been replaced by a system of nodes and edges. Essentially the nodes represent classes or entities. blog1These entities are “joined” by relationship records, or edges, which can be defined by type, direction or other additional attributes. Therefore, when performing the graph equivalent of a “joining” operation graph databases uses this list of edges, or predicates”, to find the connected nodes. The benefits of this nodes and edges system is the intuitive way in which it allows you to store and manipulate data. In graph databases your data is more flexible, while it should still be small and normalised to a degree the verb defined connection process allows for greater adaptability. This is particularly relevant for humanities databases as it allows expansion beyond the confines of the RDB when handling difficult data.


In conclusion, if you’re data is relatively neat and quantitative based there is no need to be pressured into incorporating it into a graph database. In these scenarios RDBs have been proven as an effective model for uncomplicated data storage. However, if you’re data is becoming more complex and requires more detail it may be necessary to upgrade to the newer graph database. Not only will this database allow for greater flexibility with you data but, it will also decrease the strain on “joining” operations thanks to the subject, object and predicate basis.

Leaflet, Framework7 and Beyond

When I began my MA in Digital Humanities in Maynooth I was a traditional historian, a historian with some experience with XML yes, but that’s as far as it went.  This course has introduced me to Javascript, TEI and all sorts of other technological challenges, from server issues to database nightmares. However, nothing on this course has presented a bigger challenge than developing my own app. As part of the course I am creating a walking tour mobile application of ‘Little Jerusalem’ for my internship. The aim is to construct a user-friendly, informative and immersive experience to bring to light the history of Dublin’s Jewish hub in the early twentieth century. So far my journey has encompassed numerous challenges, of all varieties, culminating in the determination that a historic walking tour app is most definitely a job for a digital humanist.

Up to this point my journey has largely been defined by technical triumphs and tribulations. However, before delving into the complicated world of map construction and Framework7, it is important to acknowledge the aspects of this project which I did not anticipate when beginning my project. Initially, I believed this project to have two strands: technology and history. Both these strands could have separate creation processes which would be merged together in the final stages to develop a finished product. What I failed to identify was the intermediate discipline, the skill of merging these two aspects in a flawless sequence. For example, I initially planned to have an overarching narrative guiding my tour. This plan was quickly scrapped when it became obvious that in order to do this I would face a myriad of questions, such as, how do you predict a user’s walking speed? Which direction will they take? Would they have to do the whole tour and if so how would this affect those whom may not be able to or may not want to? What if they get lost? And so I was forced to take a third aspect into consideration, best practice. Through research of existing apps and cultural heritage literature it became clear that while there is no right or wrong answer there are decisions to be made and that the best time to make these decisions is while designing your application’s structure.

LJ2Therefore, after constructing an idea of how the app should function, in line with best practice guidelines and keeping the historical narrative in mind, it was time to start app development. For the map itself I am using Leaflet, an open-source JavaScript library for mobile-friendly interactive maps. I chose leaflet purely because of its open-source nature, meaning that the original code is open to all. I found Leaflet incredibly easy to use and their documentation is really helpful, as well as the online community who answer all and any question asked. I would describe my experience with Leaflet as painless.

Unfortunately, Framework7 is a little more complicated. Framework7 was chosen, again because it is open-source, but also, because it provided templates with an existing navigational structure. Owing to this I did not need to create my app from scratch but ‘clean out’ the existing code and replace the features I did not need with my own. The clear out was easy but navigating Framework7’s structure has not. There was a crossroads point in my project where I could have chosen to programme using Javascript or to go down the line of Framework7, and while I occasionally curse my choice, Framework7 was designed for mobile experiences. I chose to work with it in the hope that my finished product would be specifically geared towards its intended use and would therefore, hopefully, produce an enjoyable and easy user experience.

While there is plenty documentation online to try and LJsupport Framework7 users it is pretty daunting for a novice coder. I still face many challenges in implementing my code and achieving the features I want – content popups are my latest struggle- but the greatest lesson I have learned is to have confidence in what you do know. The application is difficult, increasingly so as the app develops and features become more technical, and while the functions do not appear to resemble anything like those on my JQuery crash course, there is little to separate them. The moral of the story when approaching a mammoth task like this: you know more than you think you do.

I do not think my technological struggle is near its completion. I have no doubt there are many days of anguish ahead but I can confirm that the joy of perfecting that code and accomplishing a task is greater than any error Framework7 can throw at you – and trust me, there are many!

Data Visualisations: Knowledge or not?


As a visual learner, I can see the immediate appeal of data visualisations. Large quantities of information can be represented in succinct forms, highlighted by colour and emphasised through shapes. This kind of display is engaging and provides a nice alternative for what can sometimes be a monotonous world of stagnant text. However, as with any source these visualisations must be subjected to scrutiny. Without delving too far into the deep realm of epistemology it is important to assess what transforms data into knowledge and where do these visualisations fall on our triangle.

I am not an epistemologist, nor am I an expert in data analysis, but in the study of history we are trained to see that while we may never know the past in absolute certainty we can employ strategies to obtain as much knowledge as is within our power. My aim with this post is to highlight how humanities research techniques can be applied to data visualisations so that we can estimate the place of these visual products both on the triangle and within research.

To truly understand the risks in using data visualisations we need to look at the process behind their fncreation. The first is data collation.  During a recent lecture Dr Vinayak Das Gupta posited to our class that data was fact regardless of our state of knowing. Building on this, if we can accept that data is fact, and therefore non-negotiable, our emphasis must shift to the researcher who gathers this data. For any form of data collation, the researcher must set parameters to define their data by. These parameters decide which data is included and which is left behind. Coming from the humanities perspective this selection process is equal to the choice of selecting primary resources. A good researcher will aim to gather large quantities using clear and unbiased parameters in the same way that historians aim to use a wide variety of primary source material. Inherent in both disciplines is the possibility of biased selections. Therefore, when using data visualisations, as with secondary texts, it is essential to interrogate which how the data was collected and which parameters were applied.

Following on from data source selection is contextual support. In the same way that primary source material must be placed within its historical background so to must data visualisations be placed in context. In his TED talk on the subject David McCandless exhibits how visualisations without context can be dangerous and misleading (McCandless). McCandless begins with a visualisation comparing American military spending to other countries, as expected the visualisation returns a large red sector for America which dominates the screen. However, McCandless follows this nvisualisation with another placing American military spending within the context of American GDP. The new data context reveals a new side to the data in general and alters our view of military spending. Therefore, context is integral to understanding whether these visualisations are providing us with knowledge or a skewed reflection of manipulated data sets.

Therefore, data visualisations are very similar to a historian’s traditional realm of benefits. However, they come with added benefits. Thanks to data processing researchers can assess large quantities of data which would overwhelm if not outreach the traditional research. In addition to bringing a visually pleasing product to the reader visualisations can accomplish tasks that would be near impossible without the technology and allows us as researchers to see trends and patterns that we may have never noticed without this technology.

So where does this leave visualisations on the data-knowledge triangle. Despite their benefits there is no doubt that as with any other source visualisations need to be subjected to scrutiny and placed within context to be of true value. They, in themselves, are not enough to constitute knowledge and we cannot automatically assume that they are justified even though the data may be true. As a consequence, visualisations lie firmly in the information category. With added information and justification, they may assist us in our pursuit of knowledge but they in themselves are not enough to constitute it.

Further Reading:

‘Recorded Crime Offences by Type of Offence and Quarter’ Date accessed: 15 February 2017.

McCandless, David, ‘The Beauty of Data Visualisations’ TED Talks. Date accessed: 15 February 2017

Constructing Communities: The benefits of BIM


Homs City Centre re-imagined by Ammar Azzouz and Ayla Shawish. Credit: Azzouz/Shawish


While projects such as Contested Memories: The Battle of Mount Street Bridge and Rome Reborn exemplify the fascinating perspective and useful applications of reverse engineering virtual worlds, and are certainly worth exploring, I would like to use this blog post to focus on how digital architecture is, or could, shape the cities we live in.

The aim of this blog post is to explore much more than the cost effectiveness of Building Information Modelling (BIM). Projects such as Digital Built Britain are more than willing to extol the financial virtues of the process for those interested. Instead I am more interested in the movement beyond the traditional scale model towards the digital city. According to Bernstein modern consensus suggests that the cost of renovation will ‘outstrip’ new building as constructed cities fill to the brim with apartments, offices and leisure zones (Bernstein). The result of this process will be a heightened emphasis on sustainability, reliability and higher levels of performance and it is digital architecture which is making this achievable. Rather than constructing singular models architects are utilizing BIM software to reproduce digital versions of growing cities. The digitisation of these physical landscapes allows the modern architect to move beyond the one dimensional approach of building design and facilitates the incorporation of a singular building design into a connected city network of combined infrastructure. This move will maximise the capabilities of buildings and increase their productivity as well as their lifespan.  


The Shanghai Tower’s torqueing design based on urban wind flow simulations. Image courtesy of Shanghai Tower Construction and Development Co., Ltd. Rendering by Gensler.

 Taking this process one step further Ammar Azzouz is researching how BIM technology can be used to redesign cities destroyed by conflict. Azzouz’s aim is not to use the existing technology to recreate a lost city marred or lost to conflict, but to design a new one that respects the city’s past by incorporating destroyed sites as memorials within the surrounding multi-functional buildings for ‘Homsians to gather with a collective sense of belonging’ (Azzouz). Initially, after reading the title of Azzouz’s piece, ‘Digital architecture can help rebuild ancient Syrian cities’, I was skeptical of his intentions. Fearing his article would suggest replicating a pre-war version of Homs I had already conceived the idea that Azzouz was an advocate for bypassing the painful yet necessary redesign of a ravaged city. My assumptions led me to believe that he was simply trying to erase the country’s recent past through an uncontrolled expression of technological fetishism, following the Palmyra arch vein of thinking. However, I was delighted to find that Azzouz’s thesis is actually the antithesis of this “sweep it under the rug” assumption. Combining economic reasoning with utility Azzouz explores the multi-functional capacity of BIM. His proposed plans for the reconstruction of Homs which essentially incorporates the financial concerns of construction, particularly costly postwar reconstruction, and the desire for utility and memorialisation, which can facilitate a safe and equal space for all the residents of Homs, highlights the true advantages of BIM.

In conclusion, the true advantage of BIM is not its cost effectiveness or a progressive technological form of planning. While these are two great additional characteristics, in my opinion, the true advantage of BIM is the ability to incorporate the existing and surrounding landscape into architectural design. BIM  facilitates the construction of a community through building and reconstruction. It goes beyond the singular and sees the wider field. The resulting effect of this is to highlight that physical spaces are not just places of utility but significant spaces which have the ability to shape, impact and inform our lives. If Azzouz’s research shows us anything it is that BIM allows us in the present to respect the past while planning for our future.



Rome Reborn. University of Virginia et al. 1997. Web. 10 December 2016.

Contested Memories: The Battle of Mount Street Bridge. Maynooth University. 2015. Web. 10 December 2016.

Azzouz, Amar, ‘Digital architecture can help rebuild ancient Syrian cities.’ PhysOrg. Web. 10 December 2016.

Bernstein, Phil, ‘Why Today’s Architects Build Digital Cities Instead of Scale Models.’ Gizmodo. Web. 10 December 2016.

Turner, Lauren, ‘Palmyra’s Arch of Triumph recreated in London’, BBC News. Web. 10 December 2016.

Seeing What The Eye Can’t See: Computational Analysis versus Curatorial Expertise

New imaging system can beat human eye

Often when a new or growing technology is released we are confronted with questions; traditional or modern, book or computer, digital or analogue? In part these questions result from fear, fear of the new and fear of losing the old, the known. However, in reality, these questions are a milestone, a marker of a technology which has not yet been fully incorporated into practice. One day these technologies will cease to be a question and simply transition to a method, as their predecessors did before them. Computational analysis is such a technology. With the ever enhancing capabilities of computers, followed quickly by mechanical limitations, we are asked to assess the value of two separate methodologies, the known and the new, computational analysis or curatorial expertise. I reject this choice.

Despite the rapid advance in technology and its increasing innovation there is no doubt that a computer cannot do everything a human can do. When it comes to curating images the human eye sees an image and categorises it based on a number of influential factors such as culture, social conditioning and experience. However, humans can also recognise a multiplicity of term, that one word can be synonymous or interchangeable with another is a given of human language. Therefore, for argument’s sake sometimes curatorial expertise is necessary in order to generate a comprehensive catalogue.

However, just as computers can’t do everything a human can do, humans can’t do everything a computer can do. For example, in Oxford Dr  Christoffer Nellaker and his team are developing computer vision algorithms which will analyse photographs of faces for disease-relevant phenotypes (Oxford University Innovation). Categorised under the field of eHealth Dr Nellaker’s project will use these algorithms to help medical professionals make easier and more accurate diagnoses of rare genetic diseases. The programme uses machine learning to create a multidimensional space shaped to account for deceptive variations such as lighting, pose, occlusions, and image quality. Similarly, computational imaging techniques have been applied to patients in order to provide automated decision support for experts when trying to grade the severity of pathologies such as diabetic retinopathy and macular degeneration (Dessauer & Dua 39). Such technologies allow these experts to create pattern recognition which enhance their ability to make informed case decisions based on common characteristics and previous cases in a much more efficient way.

Therefore, both curatorial expertise and computational analysis have their benefits. Just as a computer cannot interpret the various interpretations associated with one image, an optometrist cannot perform fifty eye exams and accurately diagnose the severity of a pathology. This is why I don’t believe it is a choice between one or the other, it is a compromise, it is a discussion but most of all it revolves around implementation. One method does not supercede the other, both have their merits and both have their pitfalls. The real question is how do we or can ever perfectly combine the two?


Dessauer, Michael & Dua, Sumeet, ‘Computational Methods for  Feature Detection in Optical Images.’ Computational Analysis of the Human Eye with Applications. Ed. Duan Semeet et al. Singapore: World Scientific Publishing, 2011. 39 – 88.

Nellaker, Christoffer, ‘Diagnosis of rare diseases with computational analysis of photographs.’ Oxford University Innovation ( Web. 09 December 2016.

Knowledge Vs. Action: Respecting the Transdiscipline

In a recent post on the established presence and practice of digital archaeology Andre Costopoulos laments what he sees as an over-analyzed and excessively theorized field. For Costopoulos the established presence of digital archaeology as a practice has earned the discipline the right to forego the typical extensive discussions which accompany methodologies within the humanities (Costopoulos). While I can appreciate Costopoulos’ restlessness his lament is in many ways a reflection of an old argument, science versus humanities, practice versus theory, knowledge for action versus knowledge for knowledge’s sake. Perhaps this conundrum is a reflectance of Costopoulos’ position in a transdiscipline, one which seeks knowledge through action, however, I believe his desire to simply do digital archaeology in the absence of discussion is to do an injustice to the properties of both fields.

historiographyAs any student of the humanities could probably tell you the study of their subject is never simplistic and always incorporates the theory of that discipline. As a history student I took at least three modules on interpreting history and historiography over the course of my four year degree, this doesn’t even account for the hours spent on the same subject within other modules. Therefore, the humanities is pervaded by its ambition to fully understand itself, it is a discipline which is increasingly self aware and seeks to analyse the methodology and interpretation of every community member in order to understand the practice and results. As a member of this community archaeology, digital or not, will likely always be a contributor to these discussions. Many archaeologists have sought to confirm archaeology as the epitome of transdiscipline, a study which incorporates both scientific methodology and the humanities even before the digital is applied (Huggett 87). However, Costopoulos’ desire to remove this theoretical component, or to at least move on from it, is to ignore a fundamental characteristic of the humanities element of his discipline, and, therefore, will not improve his field but work towards changing it in such a way that it no longer represents its original self.

The first Cornell Electronic Analog Computer (COREAC), circa 1960s
The first Cornell Electronic Analog Computer (COREAC), circa 1960s

Similarly, Costopoulos’ desire to forego the usual discussions which accompany the practice of digital archaeology seems to omit the necessity of these discussions due to the rapid acceleration of technical abilities. While demonstrating himself that multiple technologies have been applied to the field over time Costopoulos fails to acknowledge that with each new technology there are new issues discuss and applications to be decided. Similarly, has Huggett has argued, while all archaeologists may be digital archaeologists the degree to which they incorporate the technology varies across the field. Just as not all archaeologist specialise in bone not all archaeologist specialise in software or digital capturing (Huggett). Therefore, in order for the field to progress and strengthen each specialist must reveal the advantages and complications of their respective specialty so that progress can be made as a whole. As Robert Groves has argued this theorising, gaining knowledge for knowledge’s sake allows the formulation of principles in which specialists will seek to push concepts further and students will strive to comprehend and manipulate to the field.

In conclusion, while sitting, discussing, debating and theorising may not feel like the kind of action that Costopoulos seems to think the field of archaeology deserves it is part of the process. It is simply not enough to do digital archaeology. To do so would undermine the field it originates from and the one which it has long since incorporated.


Costopoulos, Andre, ‘Digital Archeology Is Here (and Has Been for a While)’ Frontiers in Digital Archaeology 3 (2016). Web. 01 December 2016.


Huggett, Jeremy, ‘A manifesto for an introspective digital archaeology’. Open Archaeology, 1/1 (2015): 86-95. Web. 01 December 2016.

Hugget, Jeremy, ‘Let’s talk about digital archaeology’ INTROSPECTIVE DIGITAL ARCHAEOLOGY. Web. 01 December 2016.