THE DEEP STRUCTURE OF AN ENNOVATTVE ACCOUNTING INFORMATION SYSTEM

For centuries accounting was the only formal information system in existence for business enterprises. Now accounting is often only a small part of an integrated information system. While numerous attempts have been made to clarify the exact structure of accounting, they have all been encumbered by past traditions that, while optimal under manual methods, did not do justice to accounting in a database environment. This paper shows how an informal model of accounting as movement of sand in a sandbox can be mapped to an ontologically complete financial accounting information system. Finally, the new model is contrasted with three competing models, and its implications for design and use of accounting information systems are discussed

Scalable Model-Based Management of Correlated Dimensional Time Series in ModelarDB+

To monitor critical infrastructure, high quality sensors sampled at a high frequency are increasingly used. However, as they produce huge amounts of data, only simple aggregates are stored. This removes outliers and fluctuations that could indicate problems. As a remedy, we present a model-based approach for managing time series with dimensions that exploits correlation in and among time series. Specifically, we propose compressing groups of correlated time series using an extensible set of model types within a user-defined error bound (possibly zero). We name this new category of model-based compression methods for time series Multi-Model Group Compression (MMGC). We present the first MMGC method GOLEMM and extend model types to compress time series groups. We propose primitives for users to effectively define groups for differently sized data sets, and based on these, an automated grouping method using only the time series dimensions. We propose algorithms for executing simple and multi-dimensional aggregate queries on models. Last, we implement our methods in the Time Series Management System (TSMS) ModelarDB (ModelarDB+). Our evaluation shows that compared to widely used formats, ModelarDB+ provides up to 13.7 times faster ingestion due to high compression, 113 times better compression due to the adaptivity of GOLEMM, 630 times faster aggregates by using models, and close to linear scalability. It is also extensible and supports online query processing.Comment: 12 Pages, 28 Figures, and 1 Tabl

CONSUMERS FACING SUPRA-COMPLEX CHOICES IN THE MODERN MARKETPLACE

In this paper, we suggest that many of the choice situations confronting consumers in the modern marketplace have become supra-complex. Supra-complex decision-making occurs when the perceived difficulty of transforming product information into knowledge exceeds the expected benefits of doing so, even if decision-making heuristics, or other kind of attribute-related decision rules, are applied. Under conditions of supra-complexity, we propose that consumers instead use mental markers in order to justify their decisions. Mental markers are any mental construct the consumer uses for the purpose of gaining mental justification of overall choices. We argue that the usage of mental markers leads to reductions in cognitive dissonance, reduced usage of mental resources and time. Drawing on the principle of mental justification as well as consumers’ propensity to use goals as blueprints for directing their behaviour, we propose a framework for understanding consumer decisions when faced with supra-complexity