Linked Data adoption and application within financial business processes

This book is the first part of a two book series. It is based on a combination of Finance and IT. More precisely, it applies the concept of Linked Data (hence LD), which originates from the IT landscape, to the specifics of the financial world. LD is a new concept for efficient handling of data, which could be used for dealing with a complex data set and data structures, as well as Big Data. The focus of this book is on the adoption of LD and its application within financial business processes.
First, LD is briefly explained and framed in the context of the financial services domain.
Second, modeling the determinants of LD adoption needed a clear statement over its advantages and disadvantages, amongst others within the financial domain. Despite the high interest towards the LD concept, no such overview existed before this work.
Fourth, the model on LD adoption is applied to business (financial) reporting, illustrated with the XBRL case.
Finally, semi-structured interviews with financial experts reconfirm and extend the findings. The main potentials are described in detail.

Autorentext
Kathrin Kalcheva is a member of the "Linked Data Nederland" Community. She holds a B.Sc. in Business Administration from the TU Munich and graduated cum laude, earning a M.Sc. in Business Administration (Finance) at the University of Twente, Netherlands. The author is among the top 1% international graduates in the Netherlands and has received honours in Change Leadership. Also, she is an e-Fellows Scholar.
For more information please refer to: https://de.linkedin.com/pub/desislava-kalcheva-kathrin/13/693/424.

Leseprobe
Text sample:
Chapter 5 Results from the systematic literature review:
5.1 Linked Data and the Web of Data (Web 3.0):
Nowadays the amount of digital data grows over more than 50% per year. Therefore any means to structure this data, becomes increasingly relevant (IDC, 2010). Next to that knowledge management and decision-making tasks rely on this data (Meijer et al., 2014). This justifies the necessity of a research work that discovers and illustrates the potential of LD, in terms of advantages and disadvantages, in improving current (financial) business processes and eventually even creating new business opportunities.
LD is a "set of best practices for publishing and connecting structured data on the Web" (Bizer et al., 2009) and ist essence consists of semantics and standards. The semantics capture the meaning of the data. The standards allow for interpretation because they imply how the meaning and relations should be set in order to enable digital exchange and processing (Folmer and Verdonk, 2014). This set of best practices has been adopted by a high and constantly increasing number of data providers allowing for the creation of a global data space that contains billions of assertions or the so called Web of Data (Bizer et al., 2009). More elaboration on the number of datasets, triples and ist growth rate will be provided in the next section.
The Web of data, also referred to as the Semantic Web or the Web 3.0, is a "global information space", in which not only the documents but also the data itself is linked (Bizer et al., 2009). It was conceived in 2001 (Berners-Lee et al., 2001). It is a large knowledge-base of sources that delivers (references) information as RDF files or through SPARQL endpoints. The idea behind it is to add machine-understandable, semantic annotation to web-published contents. That way they can be retrieved and effectively processed by humans and machines in a variety of tasks. This is done by attaching semantics to resources, from very simple to very complex annotations depending on the requirements, by using semantic web technologies. They enable a new dimension to data integration by providing a common terminology, standard format for resources (RDF/(S) and OWL ), semantically linked data (Nebot and Berlanga, 2012). The result is more and more (semi)-structured data and knowledge resources, published on the Web, all together creating the Web of Data (Bizer et al., 2009). The main difference between the Web 2.0 and the new Web of data, or Web 3.0 is thus that Web 2.0 mashups work against a fixed set of data sources, whereas the LD applications operate on top of an "unbound, global data space." This then enables answers from new data sources on the Web, expressed in the AAA principle, "Anybody can say Anything about Any topic". Furthermore this can be extended if space and time are added (Hitzler and Janowicz, 2013) to AAAAA. Thus every party that has information at ist disposal is able to share it and/or make it available with LD. Thereby different perspectives come into play. However from businesses' perspective this may not always be beneficial, as they do not want to "play" but to use relevant data from reliable sources. This will be further elaborated on within the disadvantages section.
LD is not relational data, SQL etc. but graph data. The graphs are decentralized, which means that there isn't a single knowledge-base of statements but anyone can contribute with statements to the information space of the Web of Data. Shared identifiers (URIs) and shared terms allow for merging of these statements and therefore providing useful services to human and software clients, (Tummarello et al., 2007) This enables new types of applications, which were not possible until now with the Web 2.0. Semantic search is one of the first applications that makes use of and exploits the Web of Data (Nebot and Berlan