Design Challenges for GDPR RegTech

The Accountability Principle of the GDPR requires that an organisation can demonstrate compliance with the regulations. A survey of GDPR compliance software solutions shows significant gaps in their ability to demonstrate compliance. In contrast, RegTech has recently brought great success to financial compliance, resulting in reduced risk, cost saving and enhanced financial regulatory compliance. It is shown that many GDPR solutions lack interoperability features such as standard APIs, meta-data or reports and they are not supported by published methodologies or evidence to support their validity or even utility. A proof of concept prototype was explored using a regulator based self-assessment checklist to establish if RegTech best practice could improve the demonstration of GDPR compliance. The application of a RegTech approach provides opportunities for demonstrable and validated GDPR compliance, notwithstanding the risk reductions and cost savings that RegTech can deliver. This paper demonstrates a RegTech approach to GDPR compliance can facilitate an organisation meeting its accountability obligations

Towards an automatic data value analysis method for relational databases

Data is becoming one of the world’s most valuable resources and it is suggested that those who own the data will own the future. However, despite data being an important asset, data owners struggle to assess its value. Some recent pioneer works have led to an increased awareness of the necessity for measuring data value. They have also put forward some simple but engaging survey-based methods to help with the first-level data assessment in an organisation. However, these methods are manual and they depend on the costly input of domain experts. In this paper, we propose to extend the manual survey-based approaches with additional metrics and dimensions derived from the evolving literature on data value dimensions and tailored specifically for our use case study. We also developed an automatic, metric-based data value assessment approach that (i) automatically quantifies the business value of data in Relational Databases (RDB), and (ii) provides a scoring method that facilitates the ranking and extraction of the most valuable RDB tables. We evaluate our proposed approach on a real-world RDB database from a small online retailer (MyVolts) and show in our experimental study that the data value assessments made by our automated system match those expressed by the domain expert approach

An intelligent linked data quality dashboard

This paper describes a new intelligent, data-driven dashboard for linked data quality assessment. The development goal was to assist data quality engineers to interpret data quality problems found when evaluating a dataset us-ing a metrics-based data quality assessment. This required construction of a graph linking the problematic things identified in the data, the assessment metrics and the source data. This context and supporting user interfaces help the user to un-derstand data quality problems. An analysis widget also helped the user identify the root cause multiple problems. This supported the user in identification and prioritization of the problems that need to be fixed and to improve data quality. The dashboard was shown to be useful for users to clean data. A user evaluation was performed with both expert and novice data quality engineers

DELTA-R: a change detection approach for RDF datasets

This paper presents the DELTA-R approach that detects and classifies the changes between two versions of a linked dataset. It contributes to the state of the art firstly: by proposing a more granular classification of the resource level changes, and secondly: by automatically selecting the appropriate resource properties to identify the same resources in different versions of a linked dataset with different URIs and similar representation. The paper also presents the DELTA-R change model to represent the changes detected by the DELTA-R approach. This model bridges the gap between resource-centric and triple-centric views of changes in linked datasets. As a result, a single change detection mechanism will be able to support the use cases like interlink maintenance and dataset or replica synchronization. Additionally, the paper describes an experiment conducted to examine the accuracy of the DELTA-R approach in detecting the changes between two versions of a linked dataset. The result indicates that the accuracy of DELTA-R approach outperforms the state of the art approaches by up to 4%. It is demonstrated that the proposed more granular classification of changes helped to identifyup to 1529 additional updated resources compered to X.By means of a case study, we demonstrate the support of DELTA-R approach and change model for an interlink maintenance use case. The result shows that 100% of the broken interlinks were repaired between DBpedia person snapshot 3.7 and Freebase

Saffron: a data value assessment tool for quantifying the value of data assets

Data has become an indispensable commodity and it is the basis for many products and services. It has become increasingly important to understand the value of this data in order to be able to exploit it and reap the full benefits. Yet, many businesses and entities are simply hoarding data without understanding its true potential. We here present Saffron; a Data Value Assessment Tool that enables the quantification of the value of data assets based on a number of different data value dimensions. Based on the Data Value Vocabulary (DaVe), Saffron enables the extensible representation of the calculated value of data assets, whilst also catering for the subjective and contextual nature of data value. The tool exploits semantic technologies in order to provide traceable explanations of the calculated data value. Saffron therefore provides the first step towards the efficient and effective exploitation of data assets

Understanding information professionals: a survey on the quality of Linked Data sources for digital libraries

In this paper we provide an in-depth analysis of a survey related to Information Professionals (IPs) experiences with Linked Data quality. We discuss and highlight shortcomings in linked data sources following a survey related to the quality issues IPs find when using such sources for their daily tasks such as metadata creation

Semantic data ingestion for intelligent, value-driven big data analytics

In this position paper we describe a conceptual model for intelligent Big Data analytics based on both semantic and machine learning AI techniques (called AI ensembles). These processes are linked to business outcomes by explicitly modelling data value and using semantic technologies as the underlying mode for communication between the diverse processes and organisations creating AI ensembles. Furthermore, we show how data governance can direct and enhance these ensembles by providing recommendations and insights that to ensure the output generated produces the highest possible value for the organisation

Assessing the quality of geospatial linked data – experiences from Ordnance Survey Ireland (OSi)

Ordnance Survey Ireland (OSi) is Ireland’s national mapping agency that is responsible for the digitisation of the island’s infrastructure in terms of mapping. Generating data from various sensors (e.g. spatial sensors), OSi build its knowledge in the Prime2 framework, a subset of which is transformed into geo-Linked Data. In this paper we discuss how the quality of the generated sematic data fares against datasets in the LOD cloud. We set up Luzzu, a scalable Linked Data quality assessment framework, in the OSi pipeline to continuously assess produced data in order to tackle any quality problems prior to publishing

Milan: automatic generation of R2RML mappings

Milan automatically generates R2RML mappings between a source relational database and a target ontology, using a novel multi-level algorithms. It address real world inter-model semantic gap by resolving naming conflicts, structural and semantic heterogeneity, thus enabling high fidelity mapping generation for realistic databases. Despite the importance of mappings for interoperability across relational databases and ontologies, a labour and expertise-intensive task, the current state of the art has achieved only limited automation. The paper describes an experimental evaluation of Milan with respect to the state of the art systems using the RODI benchmarking tool which shows that Milan outperforms all systems in all categorie

A distributed intelligent network based on CORBA and SCTP

The telecommunications services marketplace is undergoing radical change due to the rapid convergence and evolution of telecommunications and computing technologies. Traditionally telecommunications service providers’ ability to deliver network services has been through Intelligent Network (IN) platforms. The IN may be characterised as envisioning centralised processing of distributed service requests from a limited number of quasi-proprietary nodes with inflexible connections to the network management system and third party networks. The nodes are inter-linked by the operator’s highly reliable but expensive SS.7 network. To leverage this technology as the core of new multi-media services several key technical challenges must be overcome. These include: integration of the IN with new technologies for service delivery, enhanced integration with network management services, enabling third party service providers and reducing operating costs by using more general-purpose computing and networking equipment. In this thesis we present a general architecture that defines the framework and techniques required to realise an open, flexible, middleware (CORBA)-based distributed intelligent network (DIN). This extensible architecture naturally encapsulates the full range of traditional service network technologies, for example IN (fixed network), GSM-MAP and CAMEL. Fundamental to this architecture are mechanisms for inter-working with the existing IN infrastructure, to enable gradual migration within a domain and inter-working between IN and DIN domains. The DIN architecture compliments current research on third party service provision, service management and integration Internet-based servers. Given the dependence of such a distributed service platform on the transport network that links computational nodes, this thesis also includes a detailed study of the emergent IP-based telecommunications transport protocol of choice, Stream Control Transmission Protocol (SCTP). In order to comply with the rigorous performance constraints of this domain, prototyping, simulation and analytic modelling of the DIN based on SCTP have been carried out. This includes the first detailed analysis of the operation of SCTP congestion controls under a variety of network conditions leading to a number of suggested improvements in the operation of the protocol. Finally we describe a new analytic framework for dimensioning networks with competing multi-homed SCTP flows in a DIN. This framework can be used for any multi-homed SCTP network e.g. one transporting SIP or HTTP