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The Anscombe dataset is popular for teaching the importance of graphics in data analysis. It consists of four datasets that have identical summary statistics (e.g., mean, standard deviation, and correlation) but dissimilar data graphics (scatterplots). In this article, we provide a general procedure to generate datasets with identical summary statistics but dissimilar graphics by using a genetic algorithm based approach.

While there are efforts to establish a single international accounting standard, there are strong current and future needs to handle heterogeneous accounting methods and systems. We advocate a context-based approach to dealing with multiple accounting standards and equational ontological conflicts. In this paper we first define what we mean by equational ontological conflicts and then describe a new approach, using Constraint Logic Programming and abductive reasoning, to reconcile such conflicts among disparate information systems. In particular, we focus on the use of Constraint Handling Rules as a simultaneous symbolic equation solver, which is a powerful way to combine, invert and simplify multiple conversion functions that translate between different contexts. Finally, we demonstrate a sample application using our prototype implementation that demonstrates the viability of our approach. IntroductionThe recent accounting scandals are underlining the need for more transparent and accurate access to information in financial statements. A recent survey carried out by McKinsey found that 90 per cent of institutional investors favored a single international accounting standard, but they differed over what it should be 1 . The likelihood of a single international accounting standard coming to dominate anytime soon is quite slim. This is further complicated by the complexities and localities involved in the accounting practices of different countries (e.g. the UK views the proposed standards as actually reducing the quality of their corporate reporting.) Even within a single country, there are good reasons why many investors need access to data in various forms, such as pro forma numbers that offer insights into the performance of companies' core business by excluding one-time events that can skew the financial results.There is, however, a lack of information technology products that can conveniently collect and integrate data from disparate financial statements and present them to the users in the way they are accustomed to see or in one of several accounting standards. In this paper, we present a framework that can gracefully handle the representation of different data semantics and integrate information from diverse sources in the presence of equational ontological conflicts (EOC).In the next sections, we first define what we mean by EOC, and provide specific examples. Then, we explain how we resolve EOC in our extended COntext INterchange (ECOIN) framework by using Constraint Logic Programming techniques, specifically through the use of Constraint Handling Rules (CHR.) Finally, we provide a simple e-business example from our prototype implementation that demonstrates the viability of our approach.

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