Mining gene expression data with pattern structures in formal concept analysis

This paper addresses the important problem of efficiently mining numerical data with formal concept analysis (FCA). Classically, the only way to apply FCA is to binarize the data, thanks to a so-called scaling procedure. This may either involve loss of information, or produce large and dense binary data known as hard to process. In the context of gene expression data analysis, we propose and compare two FCA-based methods for mining numerical data and we show that they are equivalent. The first one relies on a particular scaling, encoding all possible intervals of attribute values, and uses standard FCA techniques. The second one relies on pattern structures without a priori transformation, and is shown to be more computationally efficient and to provide more readable results. Experiments with real-world gene expression data are discussed and give a practical basis for the comparison and evaluation of the methods.     

A general purpose computer analysis system for chromatographic data

A manual integration system for the analysis of chromatographic data is described. The analog output produced by an HPLC absorbance monitor is passed to a non-inverting signal amplifier. This amplified signal is sent to an IBM PC where an analog to digital converter is used to digitize the data. A set of six computer programs which collect, store and analyze these data are presented. This system was used to analyze the nucleotide content of the anaerobic organism Clostridium acetobutylicum by strong anion-exchange HPLC.     

Nivel: a metamodelling language with a formal semantics

Much work has been done to clarify the notion of metamodelling and new ideas, such as strict metamodelling, distinction between ontological and linguistic instantiation, unified modelling elements and deep instantiation, have been introduced. However, many of these ideas have not yet been fully developed and integrated into modelling languages with (concrete) syntax, rigorous semantics and tool support. Consequently, applying these ideas in practice and reasoning about their meaning is difficult, if not impossible. In this paper, we strive to add semantic rigour and conceptual clarity to metamodelling through the introduction of Nivel, a novel metamodelling language capable of expressing models spanning an arbitrary number of levels. Nivel is based on a core set of conceptual modelling concepts: class, generalisation, instantiation, attribute, value and association. Nivel adheres to a form of strict metamodelling and supports deep instantiation of classes, associations and attributes. A formal semantics is given for Nivel by translation to weight constraint rule language (WCRL), which enables decidable, automated reasoning about Nivel. The modelling facilities of Nivel and the utility of the formalisation are demonstrated in a case study on feature modelling.

A general framework for adaptive processing of data structures

A structured organization of information is typically required by symbolic processing. On the other hand, most connectionist models assume that data are organized according to relatively poor structures, like arrays or sequences. The framework described in this paper is an attempt to unify adaptive models like artificial neural nets and belief nets for the problem of processing structured information. In particular, relations between data variables are expressed by directed acyclic graphs, where both numerical and categorical values coexist. The general framework proposed in this paper can be regarded as an extension of both recurrent neural networks and hidden Markov models to the case of acyclic graphs. In particular we study the supervised learning problem as the problem of learning transductions from an input structured space to an output structured space, where transductions are assumed to admit a recursive hidden state-space representation. We introduce a graphical formalism for representing this class of adaptive transductions by means of recursive networks, i.e., cyclic graphs where nodes are labeled by variables and edges are labeled by generalized delay elements. This representation makes it possible to incorporate the symbolic and subsymbolic nature of data. Structures are processed by unfolding the recursive network into an acyclic graph called encoding network. In so doing, inference and learning algorithms can be easily inherited from the corresponding algorithms for artificial neural networks or probabilistic graphical model.     

IDL: A machine-independent data language

A Low-level data language, IDL, is presented as a machine-independent medium for representing data structures. It is used to transfer structures generated by SLP, a compact list processing language, across the boundaries imposed by differing machines, operating systems and languages. Translation overheads are minimized and use of the language reduces data and machine dependence in programs. Translation algorithms and their performance statistics are presented. The use of the representation for the formation and manipulation of libraries of data structures in demonstrated.     

A user oriented computer language for the retrieval and manipulation of energy data

The need for timely energy data for testing the implications of decisions on fiscal, economic and/or regulatory proposals was brought into sharp focus by the Arab Oil embargo of 1973 and has been intensified by the continuing shortage of critical energy resources. To satisfy this need, a research program was begun at the University of Houston to develop an energy data bank and information retrieval system suitable for use by the unsophisticated computer user. The results of this research program include a simple to use, conversational language which allows the user to access a time series energy data bank for the data of interest, to manipulate the data, and to document the data in both tabular and graphical form. The conversational user language consists of a retrieve command, twenty-two analysis commands, and two documentation commands. The command structure uses simple language and syntax which are suggestive of the operation requested. In addition, the language is capable of simple extensions, making it very responsive to the user's needs. The object of this paper is to describe the user language, its operational structure, and to examplify the ease of its use.     

面向大数据环境的决策信息需求通用描述语言

制定重大决策需要全方面的数据支持,而大数据时代的到来却让决策信息搜集更加困难。数据多样性和价值密度低的特点,让决策者耗费大量时间在信息需求的表达上。设计了一种通用的信息需求描述语言,及其为数据库、发布/订阅、搜索引擎等常见信息资源解析转换的方法,使得用户可以通过一种统一的方式将相关异构信息全部搜集到。实验表明,其基于本体的语义描述能力能够有效屏蔽数据异构性,提高信息搜集的全面性和准确性。     

G: A functional language with generic abstract data types

A scientific approach to language design involves the exhaustive analysis of the demands of one of the criteria for language design and or assessment. G is designed on the sole criterion of support for data abstraction, which leads to the adoption of the applicative or functional model. As well as a full complement of standard data types and operations, G emphasises tools for: type definition; type checking; function argument and list types; function types; data structure types; generic types; generic functions; and generic function types. Comparative analysis shows that the benefits of our simple and general structure justify the need for dynamic type checking.     

A strict functional language with cyclic recursive data

The paper proposes a strict functional language to program with cyclic recursive data structures. In the language, each recursive datum is represented by a system of equations. Cyclic structures are naturally expressed by this representation, and the language provides a general mechanism that transforms several such systems of equations into a single new system of equations. An operational semantics and a polymorphic type system for the language are given, and a type soundness proof is sketched. Finally, it is also shown that the language can be implemented in a data-parallel fashion.     

Protocol modelling: A modelling approach that supports reusable behavioural abstractions

We describe a behavioural modelling approach based on the concept of a “Protocol Machine”, a machine whose behaviour is governed by rules that determine whether it accepts or refuses events that are presented to it. We show how these machines can be composed in the manner of mixins to model object behaviour and show how the approach provides a basis for defining reusable fine-grained behavioural abstractions. We suggest that this approach provides better encapsulation of object behaviour than traditional object modelling techniques when modelling transactional business systems. We relate the approach to work going on in model driven approaches, specifically the Model Driven Architecture initiative sponsored by the Object Management Group. Communicated by August-Wilhelm Scheer Ashley McNeile is a practitioner with over 25 years of experience in systems development and IT related management consultancy. His main areas of interest are requirements analysis techniques and model execution and in 2001 he founded Metamaxim Ltd. to pioneer new techniques in these areas. He has published and presented widely on object oriented development methodology and systems architecture. Nicholas Simons has been working with formal methods of system specification since their introduction, and has over 20 years experience in building tools for system design, code generation and reverse engineering. In addition, he lectures on systems analysis and design, Web programming and project planning. He is a co-founder and director of Metamaxim Ltd.