A Model for Query Decomposition and Answer Construction in Heterogeneous Distributed Database Systems

The problem of retrieving information from a collection of heterogeneous distributed databases has attracted a number of solutions. However, the task of integrating established database systems is complicated not only by the differences between the database systems themselves, but also by the differences in structure and semantics of the information contained within them. The problem is exacerbated when one needs to provide access to such a system for naive end-users.This paper is concerned with a Knowledge-Based Systems approach to solving this problem for clearly bounded situations, in which both the domain and the types of query are constrained. At the user interface, dialogue is conducted in terms of concepts with which the user is familiar, and these are then mapped into appropriate database queries. To achieve this a model for query decomposition and answer construction has been used. This model is based around the development of an Intensional Structure containing information necessary for the recapture of semantic information lost in the query decomposition process and required in the answer construction process. The model has been successfully implemented in combination with an embedded KBS, within a five-layer representation model.     

Decomposition of information systems for production management

This paper approaches the issue of decentralization and decomposition of information systems from two angles, viz. from an organizational and from an infological point of view. Current information systems tend to become more and more integrated. However, this integration causes organizational complexity, which, in turn, becomes prohibitive for organizational change. Thus, there is a need for decomposition of the information system from an organizational point of view. A strategy for such a decomposition in a production environment is given.     

Das Dekompositionsmodell nach Wand und Weber im Kontext der Prozessmodellierung

Whereas the benefits of decomposing process models are obvious, the question what actually characterizes a ??good?? decomposition of a business process model has been given little attention to date. In addition, the process of decomposition itself is considered as being an ??art?? in literature. Our approach for achieving a ??good?? decomposition is Wand and Weber??s decomposition model for information systems. As a first step in our investigation we aim to explore in how far the decomposition model can be adapted for business process modelling at all. The potential this model might bear for evaluating decompositions of process models has been promoted in literature quite often, while a corresponding investigation is still missing. We address this gap by the following research. In the long term, we intend to establish guidelines for decomposing business process models in a structured way.     

Reduced Modeling of Unknown Trajectories

This paper deals with model order reduction of parametrical dynamical systems. We consider the specific setup where the distribution of the system’s trajectories is unknown but the following two sources of information are available: (i) some “rough” prior knowledge on the system’s realisations; (ii) a set of “incomplete” observations of the system’s trajectories. We propose a Bayesian methodological framework to build reduced-order models (ROMs) by exploiting these two sources of information. We emphasise that complementing the prior knowledge with the collected data provably enhances the knowledge of the distribution of the system’s trajectories. We then propose an implementation of the proposed methodology based on Monte-Carlo methods. In this context, we show that standard ROM learning techniques, such e.g., proper orthogonal decomposition or dynamic mode decomposition, can be revisited and recast within the probabilistic framework considered in this paper. We illustrate the performance of the proposed approach by numerical results obtained for a standard geophysical model.     

基于最优模糊聚类的模糊推理系统及其在产品质量估计中的应用

研究以输入空间模糊聚类为基础的模糊推理模 型,通过模型结构奇异值分解与模糊统计信息准则相结合方法实现输入空间的最优模糊聚类 ,从而获得最优模糊推理模型．这一方法成功地应用于加氢裂化装置分馏塔航空煤油干点估 计器的建模．     

An ontological model of an information system

An ontological model of an information system that provides precise definitions of fundamental concepts like system, subsystem, and coupling is proposed. This model is used to analyze some static and dynamic properties of an information system and to examine the question of what constitutes a good decomposition of an information system. Some of the major types of information system formalisms that bear on the authors' goals and their respective strengths and weaknesses relative to the model are briefly reviewed. Also articulated are some of the fundamental notions that underlie the model. Those basic notions are then used to examine the nature and some dynamics of system decomposition. The model's predictive power is discussed     

Performance evaluation and enhancement of multistage manufacturing systems with rework loops

The phenomena of machine failures, defects, multiple rework loops, etc., results in much difficulty in modeling rework systems, and therefore the performance analysis of such systems has been investigated limitedly in the past. We propose an analytical method for the performance evaluation of rework systems with unreliable machines and finite buffers. To characterize the rework flow in the system, a new 3M1B (three-machine and one-buffer) Markov model is first presented. Unlike previous models, it is capable of representing multiple rework loops, and the rework fraction of each loop is calculated based on the quality of material flow in the system. A decomposition method is then developed for multistage rework systems using the proposed 3M1B model as one of the building blocks. The experimental results demonstrate that the decomposition method provides accurate estimates of performance measures such as throughput and Work-In-Process (WIP). We have applied this method to several problems, such as the determination of the optimal inspection location and the identification of bottleneck machines in rework systems.     

Iterative Filtering Decomposition Based on Local Spectral Evolution Kernel

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms.     

A verifiable simulation model for real-world microscopic traffic simulations

This article presents a scientific discussion about the ongoing progress in the development of traffic simulation system platforms. As part of the discussion, the presentation introduces a simulation model that is based on the fully functional, real-world online traffic information system OLSIMv4 which is the updated version of the traffic information platform for the highway network of North Rhine-Westphalia (Germany). The simulation model consists of a simulation engine and a combination of several subject-specific model families such as vehicle models, microscopic traffic models, detector models, and tuning element models. Additionally, it provides a data model for arbitrary road traffic networks in highway and urban environments.The presentation includes a demonstration of how to form and initialize all relevant system components by providing an example for each component. The demonstrations use the declarative programming language Maude to form and initialize the components due to its simplicity and expressive power. The components facilitate their enhancement by a verification and validation management approach. The goal of the enhancement effort is to optimize the further development of the underlying OLSIMv4 system. In addition, the presented methodology stands exemplarily for the design and implementation of a whole class of systems. Additionally, the definitions of the simulation model can be used as a specification for an implementation with sequential, parallel, and distributed operation. Therein, independent entities can be inferred automatically by the simulation engine as part of an automatic domain decomposition. It has been implemented as a sequential and a parallel simulation that exploits CPU thread-level parallelism.     

A Two-Stage Decomposition of High School Timetabling applied to cases in Denmark

Integer Programming (IP) has been used to model educational timetabling problems since the very early days of Operations Research. It is well recognized that these IP models in general are hard to solve, and this area of research is dominated by heuristic solution approaches. In this paper a Two-Stage Decomposition of an IP model for a practical case of high school timetabling is shown. This particular timetabling problem consists of assigning lectures to both a timeslot and a classroom, which is modeled using a very large amount of binary variables. The decomposition splits this model into two separate problems (Stage I and Stage II) with far less variables. These two separate problems are solved in sequence, such that the solution for the Stage I model is given as input to the Stage II model, implying that irreversible decisions are made in Stage I. However, the objective of the Stage II model is partly incorporated in the Stage I model by exploiting that Stage II can be seen as a minimum weight maximum matching problem in a bipartite graph. This theoretically strengthens the decomposition in terms of global optimality. The approach relies on Hall's theorem for the existence of matchings in bipartite graphs, which in its basic form yields an exponential amount of constraints in the Stage I model. However, it is shown that only a small subset of these constraints is needed, making the decomposition tractable in practice for IP solvers. To evaluate the decomposition, 100 real-life problem instances from the database of the high school ERP system Lectio are used. Computational results show that the decomposition performs significantly better than solving the original IP, in terms of both found solutions and bounds.     

电力系统负荷的分解建模及预报方法

本文依据电力系统负荷变化的非平稳性,提出分解建模及预报方法,推导出建模及预报公 式,并成功地应用于黑龙江省电力系统的负荷预报.     

Transputers and switches as objects in OCCAM

This paper introduces a transputer multi-processor architecture that has been developed at Southampton University in collaboration with other partners under an Esprit research contract. It first introduces and characterises the T800 transputer, which is the component used in the construction of this supercomputer. The machine was designed as an OCCAM engine, although other language support will be developed within this collaboration. The strategy for embedding hardware features such as the switches and busses used in the design, into an OCCAM model, is explored and further development to provide an insight into how a such a system may be efficiently programmed using a variety of parallel decomposition techniques. In particular we look at an object-oriented approach to scheduling resources, executing processes and performing operations or methods on whole data structures.     

Decomposition criteria for the design of complex systems

The hierarchical decomposition of a general system is a powerful tool in the reduction of complexity, as has been recognized by various authors. It may be used either to simplify an already existing system, or as an aid to the design of a new system. This paper is concerned with decomposition in the latter sense, being an approach to the modular design of complex systems. The proposed approach involves the estimation of the information transfer between the modules of the system. This necessitates the creation of a multivariate binary probability measure representing the problem, which leads in turn to a general criterion for the decomposition of the system into its most independent subsystems, or modules. As an application, the detection of the modular structure of a computer operating system is discussed.     

STEP-NC compliant process planning as an enabler for adaptive global manufacturing

Manufacturing firms are seeking more efficient methods of CNC manufacture. ISO14649 informally known as STEP-NC has been proposed as a high-level hierarchical manufacturing information model as a replacement for the low-level machining instructions of ISO6983 and RS274D. In this paper, the applicability of STEP-NC as an enabler for creating an adaptive global manufacturing system is examined. The overall framework of the system is presented followed by an outline of its information requirements. Suitability of STEP-NC to support each requirement is then studied with the necessary additions highlighted. Finally, a test component is used in conjunction with a prototype of the advanced global manufacturing system to demonstrate the applicability of the STEP-NC standard to support manufacturing information in such a system.     

基于正负反馈矩阵的SVD推荐模型

矩阵奇异值分解技术已经被广泛应用在个性化推荐系统之中。通过矩阵奇异值分解可以提高个性化推荐的准确度。传统的奇异值分解模型对整个矩阵进行分解,得到 user 和 item 两个特征矩阵,然后进行评分预测,并未考虑不同范围的评分包含的不同信息。通过计算评分中的临界值,把评分矩阵拆分成两个矩阵,称为正反馈矩阵和负反馈矩阵。再基于两个反馈矩阵的特征来完成对评分的预测。在实验数据方面,使用MovieLens的数据集,对传统的奇异值分解模型(SVD)和基于超图的奇异值分解模型(HSVD)进行改进。实验结果表明,引入偏好区分概念的模型PSVD、PHSVD,其推荐效果都优于原模型。     

基于图卷积与外积的协同过滤推荐模型

推荐系统帮助用户主动找到满足其偏好的个性化物品并推荐给用户.协同过滤算法是推荐系统中较为经典的算法,但是其会受到数据冷启动和稀疏性的限制,具有可解释性差和模型泛化能力差等缺点.针对其缺点进行研究,通过将原始的评分矩阵以用户—项目二部图的形式作为输入,将图卷积神经网络设计为一种图自编码器的变体,通过迭代的聚合邻居节点信息得到用户和项目的潜在向量表示,并在其基础上结合卷积神经网络,提出了一种基于卷积矩阵分解的推荐算法,提升了模型的可解释性和泛化能力,同时融合辅助信息也解决了数据的稀疏性问题,并使推荐的性能分别得到了1.4％和1.7％的提升.为今后在基于图神经网络的推荐方向上提供了一种新的思路.     

广义优势多粒度直觉模糊粗糙集的属性约简

证据理论和多粒度粗糙集模型的结合已成为知识挖掘中的热点研究之一,其建立的模型已被应用于不完备、覆盖、模糊等信息系统,但在直觉模糊决策信息系统中还未见相关讨论。首先,在直觉模糊决策信息系统中利用三角模和三角余模定义了3种优势关系,得到了3种优势类,并构造了广义优势关系多粒度直觉模糊粗糙集模型;其次,基于证据理论,讨论了广义多粒度直觉模糊粗糙集的信任结构;然后,通过定义粒度重要性和属性重要性给出了属性约简方法;最后,通过实例说明了该模型在处理直觉模糊决策信息系统时是有效的。     

Application of statistical information criteria for optimal fuzzymodel construction

Theoretical studies have shown that fuzzy models are capable of approximating any continuous function on a compact domain to any degree of accuracy. However, constructing a good fuzzy model requires finding a good tradeoff between fitting the training data and keeping the model simple. A simpler model is not only easily understood, but also less likely to overfit the training data. Even though heuristic approaches to explore such a tradeoff for fuzzy modeling have been developed, few principled approaches exist in the literature due to the lack of a well-defined optimality criterion. In this paper, we propose several information theoretic optimality criteria for fuzzy models construction by extending three statistical information criteria: 1) the Akaike information criterion [AIC] (1974); 2) the Bhansali-Downham information criterion [BDIC] (1977); and 3) the information criterion of Schwarz (1978) and Rissanen (1978) [SRIC]. We then describe a principled approach to explore the fitness-complexity tradeoff using these optimality criteria together with a fuzzy model reduction technique based on the singular value decomposition (SVD). The role of these optimality criteria in fuzzy modeling is discussed and their practical applicability is illustrated using a nonlinear system modeling example     

带有脉冲模的广义控制系统设计

本文对带有脉冲模的广义控制系统给出了一种输入输出可逆变换标准结构分解。利用这种标准结构分解,使得广义控制系统设计变得非常直观和简单,易于工程实现。