Keynote Lectures

Abstract
Digital visualisation is a way of representing data, ranging from a simple form such as a graph or chart to a complex form like animated visualisations that allows user to interact with the underlying data through direct manipulation. There are various approaches in realising data visualisation such as data visualisation and visual analytics. Digital visualisation intertwines with a set of components such as data collection, data processing and transformation and a graphic engine that offers visualisation capabilities. 
Visualisation is always purposeful that will illustrate relationships; discover patterns and interdependencies; or generate some hypothesis or theory. The user’s hypothesis would very much influence on what data would be interested in the analysis and be included in the visualisation. Therefore, there will be always a set of questions highly relevant in any visualisation, such as data availability, access, format (data itself and display format), meaning (i.e. interpretation), purpose of data presented, and effect of visualised data on the recipients. Such questions can be best answered by drawing input from semiotics. 
Semiotics is a formal doctrine of signs introduced by Peirce back in the 1930’s. A special branch is organisational semiotics which has been developed by Stamper and his colleagues to study the effective use of information in business context. Data, under the study through visualisation, are signs. From a semiotic perspective, visualisation is a process of abduction, a key process of scientific inquiry, or a process of generating new knowledge. When encountered with a new phenomenon, prior knowledge will enable us to produce some initial, but often, plausible explanations. Abduction thus allows us to generate hypotheses (which should be plausible) and further to determine which hypothesis or proposition to be tested. Peirce defines abduction as “the process of forming explanatory hypotheses”, and the “only kind of argument which starts a new idea”. 
There are four key steps in the process of abduction in data visualisation: 1) users establish the initial hypothesis based on what they see, 2) users derive some patterns of knowledge, 3) they verify the perceived visual objects based on their prior knowledge and lastly 4) they reaffirm the establish hypothesis. In short, subjectivity and user’s intention have significant impact on the visualisation which can be better examined with semiotics. A set of principles of data visualisation is proposed towards the end of the keynote speech.

Abstract

ERP systems, like other kinds of enterprise information systems, are rarely a real success. The main cause of the many failures in practice is the prevailing wrong understanding of these systems. The prominent current idea is that an enterprise information system (EIS) is a product, like for example a car, which can deliberately be replaced by another one, without much ado. Unfortunately, this view is fundamentally wrong. The proper metaphor for an EIS is the nervous system of a human body. Like the nervous system is intrinsically and intensely connected to the body it supports, an EIS is (or should be) intrinsically and intensely connected to the organisation that it serves. Consequently, like a neurologic surgeon needs appropriate and thorough knowledge of both the nervous system and the human body, an EIS designer must not only have thorough knowledge of information systems. He or she must also, and particularly, have thorough and appropriate knowledge of organisations.

 The needed thorough and appropriate knowledge is provided by the PSI-theory (Performance in Social Interaction), one of the theoretical pillars of the discipline of Enterprise Engineering. The PSI-theory re-establishes people as the ‘pearls’ of every organisation. Equipped with the right authority and bearing the corresponding responsibility, they deliver services to each other and to environmental actors, in universal patterns of social interaction, called transactions. The essential model of an organisation is a network of transactions and actors, fully abstracted from realisation and implementation. Over 20 years of practical application of the PSI-theory, notably through the DEMO methodology (Design and Engineering Methodology for Organisations), has clarified that organisations, like biological systems, can be said to have a genotype and a phenotype. The phenotype is the ever changing ‘outside’, as typically captured in organisational charts, in business process models, and in data models. The genotype is the hidden, very stable, ‘inside’. It can be revealed by the PSI-theory. If the genotype of an organisation is fully respected during the design of a supporting EIS, the EIS will perfectly fit its needs.

 Although ERP systems are built on a common understanding of enterprises in the same domain, this understanding is rather different from an organisation’s genotype. The so-called architecture of an ERP system typically comprises descriptions of the functional areas that are supported, like Operation, Finance, and HRM. The additionally needed constructional knowledge, as contained in the essential model of an enterprise’s organisation, is mostly lacking. Consequently, ERP systems, once implemented, become ‘armours’, which frustrate organisations in their fundamental need to be authentic, i.e. to be compliant with their genotype. The situation can be compared with a human body in which a ‘wrong’ nervous system is implemented. As a consequence, one is for example not able anymore to fully lift one of the arms, and is condemned to ‘live with it’.

Abstract
The nature and the role of conceptual modeling in information systems development has neither in theory nor in practice been established satisfactorily. There are diverse views on what conceptual modeling is and on how to perform it. In one extreme, there is the view that conceptual modeling is an (optional) activity whose main purpose is to improve communication between the parties involved in the development process. In the other extreme, there is the view (shared by us) that conceptual modeling is an activity that is necessarily performed in all cases, and whose purpose is to define the conceptual schema, that is, the general knowledge a system needs to know to perform its functions. The latter has been captured in what we call the principle of necessity of conceptual schemas, which states that "To develop an information system it is necessary to define its conceptual schema". Agile development processes have added even more confusion to conceptual modeling. The value of "Working software over comprehensive documentation", stated in the manifesto for agile software development, seems to undermine conceptual schemas in favor of working code. However, as we explain in the talk, it does not need to be so. In the talk, we present a framework that describes the contents of conceptual schemas, the form they may take and the roles they play in information systems development. Based on that framework, we review the principle of necessity of conceptual schemas. We then apply the framework to the particular case of agile development, and discuss the validity of the principle of necessity in that case. The framework is intended to be useful for inspiring future research, and for improving the practice and teaching of conceptual modeling.

Abstract
Enterprises are dynamical systems, formed by a semantic web of active servers of two kinds: Carbon-based servers, normally called Humans, and silicon-based servers, called Computers.
These active elements change the state of the world through their individual and collective orchestrated networked actions, in real time. All an enterprise "does" is the sum total of the actions of its active servers. No more, no less!
An Enterprise is an entity by itself, whose existence is associated with intentions, missions, goals and purposes that are in some degree shared by its active elements and inform the prescribed organizational elements of its structures. An Enterprise is under permanent change, due to external and internal conditions, and the enacted changes occur either by spontaneous actions of its active servers or by intentional systemic change that propagates top-down and is purposefully adopted by the baseline servers.
This talk will show how relevant is the Engineering Body of Knowledge of Systems Theory and Dynamic Systems Control and the formal principles and methods of Enterprise Engineering to model, design and operate Enterprises and in particular, how to steer top-down strategic transformations and to combine them with adaptive bottom-up emergent adaptive phenomena.

Abstract
Data Fraud is a criminal activity done by at least one person who intentionally acts secretly to deprive other people of something of value, for their own benefit, i.e. profit or prestige.
Data Fraud happened and still happens everywhere in all centuries and in all fields of activities of human mankind: Business, economics, politics, science, health care, religious communities etc.
Data fraud is extensionally characterized by four fields: Data scout, plagiarism, manipulation and fabrication. 
Data scout is spying out data like NSA and others secret services in GB, Russia etc. globally are doing. Data Plagiarism suppresses referring to the source or provenance of data used by the deceiver. Data Manipulation takes existing data and manipulates (“fine-tuning”) the content encapsulated in tables, diagrams or (historical) pictures where mostly numbers of all data types are manipulated. Finally, Data Fabrication generates artificial data in a brute force way thereby avoiding expensive data recording, time-consuming observations or running statistically well-planned experiments.
There is no and will be no omnibus test available to detect data fraud of all kinds. However, a bundle of techniques is available like substring and metadata matching, probability / frequency distribution dependent methods, Benford’s Law, multivariate inliers and outlier tests as well as tests of conformity between a given data set and a fully specified model. The main objective is to give hints to data fraud betrayers with low rates of false positives and negative cases.
Some famous historical and actual cases and a bundle of useful tests are presented.