On the complementation rule for multivalued dependencies in database relations

Summary We study the interrelation between various versions of the complementation rule and other inference rules for multivalued dependencies in database relations. In particular we settle two open questions of [1] concerning the derivability of inference rules for Boolean operations on the right side of multivalued dependencies. Furthermore we prove that there is a trade-off between the complementation rule and the augmentation rule.     

Parallel algorithms for solving the satisfaction problem of functional and multivalued data dependencies

Parallel algorithms for solving the satisfaction problem of non-trivial functional and multivalued data dependencies (FDs and MVDs) in a relation of N tuples by M processors are developed in this paper. Algorithms performing, in a parallel manner, batch or interactive checking of these data dependencies are also discussed. The M processors are organized as a linear systolic array. The time complexities of the first two algorithms for solving the FD satisfaction problem under M N are both O(N), and that of Algorithm (3) or (4) for solving the FD or MVD satisfaction problem under N M is O(N²/M). The latter complexity reduced to O(N) if N = M and is at least not worse than O(N log N) if N = M (N/log N).     

基于XML Schema的XML强多值依赖的推理规则集

XML强多值依赖的推理规则集问题是解决不完全信息环境下XML数据依赖蕴涵问题的基础,是不完全信息环境下XML模式设计理论的关键问题之一。提出了XML Schema、符合XML Schema的不完全XML文档树等概念;基于子树信息等价和子树信息相容的概念提出了XML强多值依赖的定义及性质;给出了相应的推理规则集,并对其正确性和完备性进行了证明。研究成果为不完全信息环境下存在XSMVD的XML Schema设计奠定了基础。     

XML弱函数依赖及其推理规则

XML函数依赖问题是进行XML数据库后续研究的基础。首先基于M.Arenas等人给定的XML中DTD和XML树的定义,提出空值、不完全树元组、数据值偏序、最小扩展树等概念,在此基础上,给出弱函数依赖及其满足性的定义;其次研究了XML弱函数依赖的逻辑蕴含问题,提出一组适合XML空值模型的函数依赖推理规则集;最后给出推理规则集的正确性和完备性证明。     

An analysis of multivalued and join dependencies based on the entity-relationship approach

In this paper, we show that multivalued dependencies and join dependencies are not very viable for certain cases of relational database design; they are sometimes difficult to be identified; they are relation sensitive; and we are unable to talk about these types of dependencies without referring to some specific relation. We also show that the entity-relationship approach can be used for relational database design without any of the above mentioned undesirable properties of multivalued dependencies and join dependencies.     

Another view of functional and multivalued dependencies in the relational database model

In this paper, pseudo-functional and pseudo-multivalued dependencies are introduced. They are shown to be isomorphic with functional and multivalued dependencies, i.e., they behave in the same way with respect to implication. This formalism leads in a very natural way to a rather efficient algorithm for the inference of functional and multivalued dependencies. Some applications to acyclic join dependencies are discussed.     

An algebraic theory of functional and multivalued dependencies in relational databases

Computation of the dependency basis is the fundamental step in solving the membership problem for functional dependencies (FDs) and multivalued dependencies (MVDs) in relational database theory. We examine this problem from an algebraic perspective. We introduce the notion of the inference basis of a set M of MVDs and show that it contains the maximum information about the logical consequences of M. We propose the notion of a dependency-lattice and develop an algebraic characterization of inference basis using simple notions from lattice theory. We also establish several interesting properties of dependency-lattices related to the implication problem. Founded on our characterization, we synthesize efficient algorithms for (a): computing the inference basis of a given set M of MVDs; (b): computing the dependency basis of a given attribute set w.r.t. M; and (c): solving the membership problem for MVDs. We also show that our results naturally extend to incorporate FDs also in a way that enables the solution of the membership problem for both FDs and MVDs put together. We finally show that our algorithms are more efficient than existing ones, when used to solve what we term the ‘generalized membership problem’.     

Modeling the information completeness of object tracking systems

We propose an analytical model that evaluates the information completeness of object tracking systems based on their size, breadth and reading depth. We showcase that the larger size of a system’s configuration does not ensure better completeness; instead, the effect of reading depth is greater. The completeness of tracking information can characterize the visibility of the inspected objects the tracking systems offer. The model may be used for both ex-ante and ex-post evaluations of the visibility of tracking systems versus their deployment cost, considering that the emergence of real-time tracking and sensing technologies will boost the adoption of such systems.     

A small-gain theorem for motone systems with multivalued input-state characteristics

We provide a small-gain theorem for feedback interconnections of monotone input-output systems with multi-valued input-state characteristics. This extends a small-gain theorem of Angeli and Sontag for monotone systems with singleton-valued characteristics. We prove our theorem using Thieme's convergence theory for asymptotically autonomous systems. We also provide an illustrative example.     

Stability of discrete systems near a multivalued equilibrium

The research was supported by the State Committee of Science and Technology of Ukraine.     

A probabilistic inference model for recommender systems

Recommendation is an important application that is employed on the Web. In this paper, we propose a method for recommending items to a user by extending a probabilistic inference model in information retrieval. We regard the user’s preference as the query, an item as a document, and explicit and implicit factors as index terms. Additional information sources can be added to the probabilistic inference model, particularly belief networks. The proposed method also uses the belief network model to recommend items by combining expert information. Experimental results on real-world data sets show that the proposed method can improve recommendation effectiveness.     

Self-adaptive neuro-fuzzy inference systems for classification applications

This paper presents a self-adaptive neuro-fuzzy inference system (SANFIS) that is capable of self-adapting and self-organizing its internal structure to acquire a parsimonious rule-base for interpreting the embedded knowledge of a system from the given training data set. A connectionist topology of fuzzy basis functions with their universal approximation capability is served as a fundamental SANFIS architecture that provides an elasticity to be extended to all existing fuzzy models whose consequent could be fuzzy term sets, fuzzy singletons, or functions of linear combination of input variables. Without a priori knowledge of the distribution of the training data set, a novel mapping-constrained agglomerative clustering algorithm is devised to reveal the true cluster configuration in a single pass for an initial SANFIS construction, estimating the location and variance of each cluster. Subsequently, a fast recursive linear/nonlinear least-squares algorithm is performed to further accelerate the learning convergence and improve the system performance. Good generalization capability, fast learning convergence and compact comprehensible knowledge representation summarize the strength of SANFIS. Computer simulations for the Iris, Wisconsin breast cancer, and wine classifications show that SANFIS achieves significant improvements in terms of learning convergence, higher accuracy in recognition, and a parsimonious architecture.     

A study of context inference for Web-based information systems

Recently, context-awareness has been a hot topic in the ubiquitous computing field. Numerous methods for capturing, representing and inferring context have been developed and relevant projects have been performed. Existing research has tried to determine user’s contextual information physically by using stereo type cameras, RFID, smart devices, etc. These are heavily focusing on external context such as location, temperature, light, etc. However, cognitive elements of the context are important and need more study. This paper confines its research domain to the Web-based information system (IS) and, first, proposes two algorithms, the context inference algorithm and the service recommendation algorithm, for inferring cognitive context in the IS domain. Second, this paper demonstrates cognitive context-awareness on the Web-based information systems through implementing prototype deploying the proposed algorithms. The proposed system deploying the context inference and service recommendation algorithm can help the IS user to work with an IS conveniently and enable an existing IS to deliver ubiquitous service. In this fashion, we show the direction of existing IS and, ultimately, shows the typical services of a ubiquitous computing-based IS.     

A new method for computing fuzzy functional dependencies in relational database systems

In this paper, we present a new method for computing fuzzy functional dependencies between attributes in fuzzy relational database systems. The method is based on the use of fuzzy implications. A literature analysis has shown that there is no algorithm that would enable the identification of attribute relationships in fuzzy relational schemas. This fact was the motive for development a new methodology in the analysis of fuzzy functional dependencies over a given set of attributes. Solving this, not so new problem, is not only research challenge having theoretical importance, but it also has practical significance. Possible applications of the proposed methodology include GIS, data mining, information retrieval, reducing data redundancy in fuzzy relations through implementation of logical database model, estimation of missing values etc.     

Generalization of adaptive neuro-fuzzy inference systems

The adaptive network-based fuzzy inference systems (ANFIS) of Jang (1993) is extended to the generalized ANFIS (GANFIS) by proposing a generalized fuzzy model (GFM) and considering a generalized radial basis function (GRBF) network. The GFM encompasses both the Takagi-Sugeno (TS)-model and the compositional rule of inference (CRI) model. The conditions by which the proposed GFM converts to TS-model or the CRI-model are presented. The basis function in GRBF is a generalized Gaussian function of three parameters. The architecture of the GRBF network is devised to learn the parameters of GFM, where the GRBF network and GFM have been proved to be functionally equivalent. It Is shown that GRBF network can be reduced to either the standard RBF or the Hunt's RBF network. The issue of the normalized versus the non-normalized GRBF networks is investigated in the context of GANFIS. An interesting property of symmetry on the error surface of GRBF network is investigated. The proposed GANFIS is applied to the modeling of a multivariable system like stock market.     

DeepRT: predictable deep learning inference for cyber-physical systems

Real-Time Systems - Recently, in mobile and embedded devices, deep learning is changing the way computers see, hear, and understand the world. When deep learning is deployed to such systems, they...