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Register a new userPrinciples of the Concept-Oriented Data Model
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Added by
Alexandr Savinov
Publication date
2007
fields
Informatics Engineering
and research's language is
English
Authors
Alexandr Savinov
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In the paper a new approach to data representation and manipulation is described, which is called the concept-oriented data model (CODM). It is supposed that items represent data units, which are stored in concepts. A concept is a combination of superconcepts, which determine the concepts dimensionality or properties. An item is a combination of superitems taken by one from all the superconcepts. An item stores a combination of references to its superitems. The references implement inclusion relation or attribute-value relation among items. A concept-oriented database is defined by its concept structure called syntax or schema and its item structure called semantics. The model defines formal transformations of syntax and semantics including the canonical semantics where all concepts are merged and the data semantics is represented by one set of items. The concept-oriented data model treats relations as subconcepts where items are instances of the relations. Multi-valued attributes are defined via subconcepts as a view on the database semantics rather than as a built-in mechanism. The model includes concept-oriented query language, which is based on collection manipulations. It also has such mechanisms as aggregation and inference based on semantics propagation through the database schema.
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We describe a new logical data model, called the concept-oriented model (COM). It uses mathematical functions as first-class constructs for data representation and data processing as opposed to using exclusively sets in conventional set-oriented models. Functions and function composition are used as primary semantic units for describing data connectivity instead of relations and relation composition (join), respectively. Grouping and aggregation are also performed by using (accumulate) functions providing an alternative to group-by and reduce operations. This model was implemented in an open source data processing toolkit examples of which are used to illustrate the model and its operations. The main benefit of this model is that typical data processing tasks become simpler and more natural when using functions in comparison to adopting sets and set operations.
Object-oriented programming (OOP) is aimed at describing the structure and behaviour of objects by hiding the mechanism of their representation and access in primitive references. In this article we describe an approach, called concept-oriented programming (COP), which focuses on modelling references assuming that they also possess application-specific structure and behaviour accounting for a great deal or even most of the overall program complexity. References in COP are completely legalized and get the same status as objects while the functions are distributed among both objects and references. In order to support this design we introduce a new programming construct, called concept, which generalizes conventional classes and concept inclusion relation generalizing class inheritance. The main advantage of COP is that it allows programmers to describe two sides of any program: explicitly used functions of objects and intermediate functionality of references having cross-cutting nature and executed implicitly behind the scenes during object access.
In April 2016, a community of researchers working in the area of Principles of Data Management (PDM) joined in a workshop at the Dagstuhl Castle in Germany. The workshop was organized jointly by the Executive Committee of the ACM Symposium on Principles of Database Systems (PODS) and the Council of the International Conference on Database Theory (ICDT). The mission of this workshop was to identify and explore some of the most important research directions that have high relevance to society and to Computer Science today, and where the PDM community has the potential to make significant contributions. This report describes the family of research directions that the workshop focused on from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.
Class invariants -- consistency constraints preserved by every operation on objects of a given type -- are fundamental to building and understanding object-oriented programs. They should also be a key help in verifying them, but turn out instead to raise major verification challenges which have prompted a significant literature with, until now, no widely accepted solution. The present work introduces a general proof rule meant to address invariant-related issues and allow verification tools benefit from invariants. It first clarifies the notion of invariant and identify the three problems: callbacks, furtive access and reference leak. As an example, the 2016 Ethereum DAO bug, in which $50 million were stolen, resulted from a callback invalidating an invariant. The discussion starts with a Simple Model and an associated proof rule, demonstrating its soundness. It then removes one by one the three assumptions of the Simple Model, each removal bringing up one of the three issues, and introduces the corresponding adaptation to the proof rule. The final version of the rule can tackle tricky examples, including challenge problems listed in the literature.
It is largely believed that complex cognitive phenomena require the perfect orchestrated collaboration of many neurons. However, this is not what converging experimental evidence suggests. Single neurons, the so-called concept cells, may be responsible for complex tasks performed by an individual. Here, starting from a few first principles, we layout physical foundations showing that concept cells are not only possible but highly likely, given that neurons work in a high dimensional space.
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