Applications of a categorical framework for conceptual data modeling

For successful information systems development, conceptual data modeling is essential. Nowadays a plethora of techniques for conceptual data modeling exist. Many of these techniques lack a formal foundation and a lot of theory, e.g. concerning updates or schema transformations, is highly data model specific. As such there is a need for a unifying formal framework providing a sufficiently high level of abstraction. In this paper, focus is on the applications of such a framework defined in category theory. Well-known conceptual data modeling concepts, such as relationship types, generalization, specialization, and collection types are defined from a categorical point of view in this framework and an essential advantage is its “configurable semantics”. Features such as null values, uncertainty, and temporal behavior can be added by selecting appropriate instance categories. The addition of these features usually requires a complete redesign of the formalization in traditional set-based approaches to semantics. Applications of the framework in the context of schema transformations and improved automated modeling support are discussed. Received: 10 September 1996 / 19 February 1997     

A framework to improve communication during the requirements elicitation process in GSD projects

Achieving a shared understanding of requirements is difficult in any situation, even more so in global software development projects. In such environments, people must deal not only with the lack of face to face communication, but also with other issues such as time difference, cultural diversity and a large amount of information originating from different sources throughout the world. Obtaining the right requirements therefore implies extra effort. In order to minimize such problems, we propose a framework that focuses on analyzing the factors that may be problematic in global software development and which suggests a set of strategies to improve the requirements elicitation process in such environments. In this paper, we describe the different phases of our framework and present the results of an experiment that test part of this framework. The results indicate that applying some of the strategies proposed in the framework seems to positively affect the stakeholders’ satisfaction with regard to communication. Moreover, the quality of the written software requirements specifications seems to be better as well when using those strategies.     

A scalable framework for mobile real-time group communication services

A scalable framework for mobile real-time group communication services is developed in this paper. Examples for possible applications of this framework are mobile social networks, mobile conference calls, mobile instant messaging services, and mobile multi-player on-line games. A key requirement for enabling a real-time group communication service is the tight constraint imposed on the call delivery delay. Since establishing such communication service for a group of independent mobile users under a tight delay constraint is NP-hard, a two-tier architecture is proposed, that can meet the delay constraint imposed by the real-time service requirement for many independent mobile clients in a scalable manner. This goal is achieved by two dimensional partition of the space, first by organization and then geographically. Both the time and memory complexity associated with the location management of N mobile users are O(N) for the location management provided by the proposed framework, while a distributed scheme requires O(N²) for both time and memory complexity.     

A cost-effective cloud computing framework for accelerating multimedia communication simulations

Multimedia communication research and development often requires computationally intensive simulations in order to develop and investigate the performance of new optimization algorithms. Depending on the simulations, they may require even a few days to test an adequate set of conditions due to the complexity of the algorithms. The traditional approach to speed up this type of relatively small simulations, which require several develop–simulate–reconfigure cycles, is indeed to run them in parallel on a few computers and leaving them idle when developing the technique for the next simulation cycle. This work proposes a new cost-effective framework based on cloud computing for accelerating the development process, in which resources are obtained on demand and paid only for their actual usage. Issues are addressed both analytically and practically running actual test cases, i.e., simulations of video communications on a packet lossy network, using a commercial cloud computing service. A software framework has also been developed to simplify the management of the virtual machines in the cloud. Results show that it is economically convenient to use the considered cloud computing service, especially in terms of reduced development time and costs, with respect to a solution using dedicated computers, when the development time is longer than one hour. If more development time is needed between simulations, the economic advantage progressively reduces as the computational complexity of the simulation increases.     

A framework for feature-based representation of the design process

The design process is very complicated and differs with each engineer. The development of computer-based aids for design must take this into consideration and allow for the nuances of the individual. A generic representation of the information used in design is required for the development of any data base that supports the design aid. A properly designed data base or set of data bases can help aid. A properly designed data base or set of data bases can help integrate CAD, expert system, and ancillary programs, thereby forming an intelligent design system. A model has been developed that allows the information requirements of the design process to be analyzed. A data base for an intelligent design system can be developed based on these results.     

A framework for business process modeling by QoS-based pruning

In this paper, we have proposed a methodology towards improved business process model redesign based on QoS. We have extended an existing framework that generates an exhaustive space of process models from a set of capability library. The solution space is pruned based on goal and constraints considered thereafter. An algebraic framework is deployed that permits integrated multi-dimensional assessments of QoS factors for choosing path from the reduced space towards derivation of an optimal business process model by comparing the QoS values on both quantitative and qualitative scales. The proposed methodology ensures that while deriving a solution, no possible superior business process model is left out. Further, the designs that do not satisfy the given constraints are eliminated. Eventually, the extended and improved framework provides a comprehensive, both syntactically and semantically correct, consistent and improved business process that adheres to the target business goals and constraints specific to a business house. A use case is used to describe our methodology.     

A framework for integrating manufacturing process design and analysis

Product design and evaluation requires a broad and varied set of information and analysis tools. Yet effective design and evaluation of a product during its design phase is critical if production costs are to be minimized. A system is described that integrates product design specifications with material and process databases, and a simulation-based analysis module. The system allows product designs to be evaluated in terms of economic and technical criteria, and to identify the best production environment.     

A smart process controller framework for Industry 4.0 settings

This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module—performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process -diagnosis module—performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module—performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform—an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework’s applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

     

A framework for comparing heterogeneous objects: on the similarity measurements for fuzzy, numerical and categorical attributes

Real-world data collections are often heterogeneous (represented by a set of mixed attributes data types: numerical, categorical and fuzzy); since most available similarity measures can only be applied to one type of data, it becomes essential to construct an appropriate similarity measure for comparing such complex data. In this paper, a framework of new and unified similarity measures is proposed for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes. Examples are used to illustrate, compare and discuss the applications and efficiency of the proposed approach to heterogeneous data comparison and clustering.     

HE-Tree: a framework for detecting changes in clustering structure for categorical data streams

Analyzing clustering structures in data streams can provide critical information for real-time decision making. Most research in this area has focused on clustering algorithms for numerical data streams, and very few have proposed to monitor the change of clustering structure. Most surprisingly, to our knowledge, no work has been proposed on monitoring clustering structure for categorical data streams. In this paper, we present a framework for detecting the change of primary clustering structure in categorical data streams, which is indicated by the change of the best number of clusters (Best K) in the data stream. The framework uses a Hierarchical Entropy Tree structure (HE-Tree) to capture the entropy characteristics of clusters in a data stream, and detects the change of Best K by combining our previously developed BKPlot method. The HE-Tree can efficiently summarize the entropy property of a categorical data stream and allow us to draw precise clustering information from the data stream for generating high-quality BKPlots. We also develop the time-decaying HE-Tree structure to make the monitoring more sensitive to recent changes of clustering structure. The experimental result shows that with the combination of the HE-Tree and the BKPlot method we are able to promptly and precisely detect the change of clustering structure in categorical data streams.     

A Mezei-Wright theorem for categorical algebras

The main result of this paper is a generalization of the Mezei-Wright theorem, a result on solutions of a system of fixed point equations. In the typical setting, one solves a system of fixed point equations in an algebra equipped with a suitable partial order; there is a least element, suprema of ω-chains exist, the operations preserve the ordering and least upper bounds of ω-chains. In this setting, one solution of this kind of system is provided by least fixed points. The Mezei-Wright theorem asserts that such a solution is preserved by a continuous, order preserving algebra homomorphism.In several settings such as (countable) words or synchronization trees there is no well-defined partial order but one can naturally introduce a category by considering morphisms between the elements. The generalization of this paper consists in replacing ordered algebras by “categorical algebras”; the least element is replaced by an initial element, and suprema of ω-chains are replaced by colimits of ω-diagrams. Then the Mezei-Wright theorem for categorical algebras is that initial solutions are preserved by continuous morphisms. We establish this result for initial solutions of parametric fixed point equations.One use of the theorem is to characterize an “algebraic” element as one that can arise as a solution of some system of fixed point equations. In familiar examples, an algebraic element is one that is context-free, regular or rational. Then, if h:A→B is a continuous morphism of categorical algebras, the algebraic objects in B are those isomorphic to h-images of algebraic objects in A.     

A generic framework for deriving process stoichiometry in environmental models

It has been recognized for many decades that the stoichiometry of biological reactions is important for linking ecological and biogeochemical processes. However, only during the past decade has the scientific community become aware that “biological stoichiometry” may also help bridge evolutionary biology and ecosystem ecology. This awareness led to increasing attention to biological process stoichiometry in ecology during the past decade. Despite this trend in ecological data analysis and interpretation, ecological models are still predominantly formulated without sufficient attention to process stoichiometry. To support scientists in formulating stoichiometry in process models based on elemental mass conservation, we transfer techniques from chemical process engineering to achieve the following objectives: (i) develop a generic mathematical framework to formulate and solve stoichiometric equations; (ii) facilitate the extension of currently used process stoichiometries to consider more elements (e.g. adding S and/or Si to C, H, O, N and P); (iii) identify the need for stoichiometric constraints of biogeochemical processes in addition to elemental mass balances; (iv) unify approaches to characterize organic matter by elemental mass fractions, organic carbon, or chemical oxygen demand; (v) provide a small package of functions for the statistics and graphics software R (http://www.r-project.org) to support environmental model building, and (vi) show how to incorporate automatic stoichiometric calculations into next generation environmental simulation software. The small R package “stoichcalc” can be downloaded from http://www.eawag.ch/^～reichert or from the package repository of the R project (http://www.r-project.org).     

A framework for an automotive body assembly process design system

This paper describes a framework for an automotive body assembly process design system. It is a computer-aided intelligent system that can automatically generate the optimal joint types and assembly sequences for the best dimensional quality. The backbone of this system is the Case-Based Reasoning (CBR) methodology, which works by searching through a case library created from previous designs whose identifying features resemble the current case. Algorithms for initial solution generation, dimension chain generation, joint design selections, assembly sequence generation, and tolerance analysis and optimization are developed. Based on the framework, a software tool called Body Build Advisor, or BBA, is developed. The software allows process designers to analyze candidate assembly schemes and achieve the best assembly process design prior to having detailed knowledge of geometry of the parts, and thus is ideal for architectural process design. In addition, the system has the advantage of an open structure that can be easily modified and adapted to accommodate existing assemblies and to suggest areas for improvement.     

Concept convergence process: A framework for improving product concepts

Concept selection is one of the most important decisions in product development, since success of the final product depends on the selected concept. The exploration and evaluation of alternatives early in the product development (PD) process reduces the amount and magnitude of changes in later stages and increases the likelihood of success of new product development (NPD) projects. Though, currently available methods attempt to select the best concept from the available set of initial concepts, they do not help create an improved concept based on the learning and knowledge generated through the evaluation of initial concepts. The paper proposes a framework for selecting and/or evolving improved concepts through a rigorous concept evaluation and convergence process. The concept convergence process allows bringing together the best (desirable) traits from the initial set of concepts and creates a new set of hybrid concepts. The framework uses a fuzzy inference process for evaluating each initial concept against identified decision criteria, thus generating hybrid concepts to select the best feasible concept under given cost and technological constraints. The approach is demonstrated using a steering wheel concept generation example.     

MobileCDP: A mobile framework for the consumer decision process

The consumer decision process is a widely accepted model covering consumer activities, and accordingly contains five interrelated stages: problem recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation. In order to help consumers deal with challenges associated with all these stages, mobile information systems bring significant capabilities, as in other application domains. However, related prior research is mostly restricted to the individual stages of the process. Since the stages are interrelated, and the data collected in one are also valuable for another, we propose a mobile framework designed to provide assistance in all stages of the Consumer Decision Process, named MobileCDP. A prototype is also implemented and evaluated to show the applicability of the framework. Experiments show that the functions provided by the prototype are useful, well integrated, and easy to use. Moreover, statistical analysis of the results proves that the prototype reduces time, costs, and cognitive effort of the user.