Associative retrieval in spatial big data based on spreading activation with semantic ontology

The opportunities associated with big data have helped generate significant interest, and big data analytics has emerged as an important area of study for both practitioners and researchers. For example, traditional cause–effect analysis and conditional retrieval fall short in dealing with data that are so large and complex. Associative retrieval, on the other hand, has been identified as a potential technique for big data. In this paper, we integrate the spreading activation (SA) algorithm and the ontology model in order to promote the associative retrieval of big data. In our approach, constraints based on variant weights of semantic links are considered with the aim of improving the spreading-activation process and ensuring the accuracy of search results. Semantic inference rules are also introduced to the SA algorithm to find latent spreading path and help obtain results which are more relevant. Our theoretical and experimental analysis demonstrate the utility of this approach.     

Associative diagnosis

Abstract: We present a new architecture of a diagnostic system composed of an associative memory with feedback connections. This new architecture requires limited computer resources, it is fast, and it can run on small computers. The diagnostic process is described by a deduction system that performs an abductive inference. The abductive inference itself is explained by the verbal category theory. We model both processes by an associative memory that performs the inference with the aid of the feedback connections. The represented knowledge is arranged in groups that define taxonomy. An embedded diagnostic system for the determination of a disorder with applications in modern industrial machines is presented.     

LZ trie and dictionary compression

An efficient algorithm for trie compression has already been described. Here we present its practical value and demonstrate its superiority in terms of space savings to other methods of lexicon compression. Apart from simple lexicons, a compressed trie can, with some additional processing, be used as a component in the compact representation of simple static databases. We present the potential of the algorithm in compressing natural language dictionaries. Copyright © 2005 John Wiley & Sons, Ltd.     

Associative Neural Network

An associative neural network (ASNN) is a combination of an ensemble of the feed-forward neural networks and the K-nearest neighbor technique. The introduced network uses correlation between ensemble responses as a measure of distance among the analyzed cases for the nearest neighbor technique and provides an improved prediction by the bias correction of the neural network ensemble both for function approximation and classification. Actually, the proposed method corrects a bias of a global model for a considered data case by analyzing the biases of its nearest neighbors determined in the space of calculated models. An associative neural network has a memory that can coincide with the training set. If new data become available the network can provide a reasonable approximation of such data without a need to retrain the neural network ensemble. Applications of ASNN for prediction of lipophilicity of chemical compounds and classification of UCI letter and satellite data set are presented. The developed algorithm is available on-line at http://www.virtuallaboratory.org/lab/asnn.     

A method of compressing trie structures

A trie structure can immediately determine whether a desired key is in a given key set or not, and can find its longest match easily. Thanks to these attractive properties, a trie structure is frequently used for various fields, such as natural language dictionaries, database systems and compilers. However, the total number of states of a trie becomes large, so space requirements are not good for a huge key set. To resolve this disadvantage a new structure which reduces the total number of states in a traditional trie, called a double-trie, is introduced in this paper. Insertion and deletion operations, as well as key retrieval for this double-trie, are presented. The efficiency of this method is shown by the results of simulations for various key sets.     

An efficient implementation of trie structures

A new internal array structure, called a double-array, implementing a trie structure is presented. The double-array combines the fast access of a matrix form with the compactness of a list form. The algorithms for retrieval, insertion and deletion are introduced through examples. Although insertion is rather slow, it is still practical, and both the deletion and the retrieval time can be improved from the list form. From the comparison with the list for various large sets of keys, it is shown that the size of the double-array can be about 17 per cent smaller than that of the list, and that the retrieval speed of the double-array can be from 3–1 to 5–1 times faster than that of the list.     

Associative processing and processors

Associative memory concerns the concept that one idea may trigger the recall of a different but related idea. Traditional computers, however, rely upon a memory design that stores and retrieves data by its address rather than its content. In such a search, every accessed data word must travel individually between the processing unit and the memory. The simplicity of this retrieval-by-address approach has ensured its success, but has also produced some inherent disadvantages. One is the von Neumann bottleneck, where the memory-access path becomes the limiting factor for system performance. A related disadvantage is the inability to proportionally increase the size of a unit transfer between the memory and the processor as the size of the memory scales up. Associative memory, in contrast, provides a naturally parallel and scalable form of data retrieval for both structured data (e.g. sets, arrays, tables, trees and graphs) and unstructured data (raw text and digitized signals). An associative memory can be easily extended to process the retrieved data in place, thus becoming an associative processor. This extension is merely the capability for writing a value in parallel into selected cells     

Associative tabular data structures

Attention is drawn to a method of implementing data structures in core memory by means of associative links instead of pointers. The properties of associative links are discussed and the way in which they may be exploited in a program for formal differentiation is illustrated. There is a section on microprogramming support for the associative search operations involved.     

Implicative Fuzzy Associative Memories

Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this paper, we introduce implicative fuzzy associative memories (IFAMs), a class of associative neural memories based on fuzzy set theory. An IFAM consists of a network of completely interconnected Pedrycz logic neurons with threshold whose connection weights are determined by the minimum of implications of presynaptic and postsynaptic activations. We present a series of results for autoassociative models including one pass convergence, unlimited storage capacity and tolerance with respect to eroded patterns. Finally, we present some results on fixed points and discuss the relationship between implicative fuzzy associative memories and morphological associative memories     

Vector Associative Memory Models

The Hopfield model effectively stores a comparatively small number of initial patterns, about 15% of the size of the neural network. A greater value can be attained only in the Potts-glass associative memory model, in which neurons may exist in more than two states. Still greater memory capacity is exhibited by a parametric neural network based on the nonlinear optical signal transfer and processing principles. A formalism describing both the Potts-glass associative memory and the parametric neural network within a unified framework is developed. The memory capacity is evaluated by the Chebyshev–Chernov statistical method.     

From Bidirectional Associative Memory to a noise-tolerant, robust Protein Processor Associative Memory

Protein Processor Associative Memory (PPAM) is a novel architecture for learning associations incrementally and online and performing fast, reliable, scalable hetero-associative recall. This paper presents a comparison of the PPAM with the Bidirectional Associative Memory (BAM), both with Kosko's original training algorithm and also with the more popular Pseudo-Relaxation Learning Algorithm for BAM (PRLAB). It also compares the PPAM with a more recent associative memory architecture called SOIAM. Results of training for object-avoidance are presented from simulations using player/stage and are verified by actual implementations on the E-Puck mobile robot. Finally, we show how the PPAM is capable of achieving an increase in performance without using the typical weighted-sum arithmetic operations or indeed any arithmetic operations.     

Time-space trade-off analysis of morphic trie images

The author explores the use of tries to represent tries. A morphic trie image is a trie that represents a set of transformed keywords using an isomorphism h:Σ*→(σ^q)*. Morphic trie images using tenary alphabets achieve near optimal performances but approximation errors degrade their performances. A condition which determines whether tenary or bit tries should be used is found. Even though bit tries have better storage reduction in some cases, tenary tries access faster than bit tries. We show that the morphic trie images use less space than minimal prefix tries. If morphic trie images were compressed to form minimal prefix tries, then the total storage reduction is the product of the two. Approximation errors have no effect on minimal prefix tries     

多值指数式多向联想记忆模型

多向联想记忆MDAM(multidirectional associative memory)模型是Kosko双向联想记忆模型BAM(bidirectional associative memory)的一个直接推广,它可应用于数据融合及维数分裂,使模型能处理大维数输入问题.目前所提出的若干种多向模型均局限于二值输入/输出模式对,但如在图象处理等的实际应用中,所处理的模式均是多值的.本文的目的就是提出一个多值指数式多向联想记忆模型MVeMDAM(multivalued exponential multidi     

核化的自联想反馈神经网络

将M ercer的核思想与自联想反馈神经网络的理论相结合,提出了核化的自联想反馈神经网络KARN,它是对多值自联想反馈神经网络M REM的有效扩展.实验结果证明,相对于自联想反馈神经网络,核化的自联想反馈神经网络的主要优势在于有效扩大了网络的容量.     

Fuzzy Associative Conjuncted Maps Network

The fuzzy associative conjuncted maps (FASCOM) is a fuzzy neural network that associates data of nonlinearly related inputs and outputs. In the network, each input or output dimension is represented by a feature map that is partitioned into fuzzy or crisp sets. These fuzzy sets are then conjuncted to form antecedents and consequences, which are subsequently associated to form if–then rules. The associative memory is encoded through an offline batch mode learning process consisting of three consecutive phases. The initial unsupervised membership function initialization phase takes inspiration from the organization of sensory maps in our brains by allocating membership functions based on uniform information density. Next, supervised Hebbian learning encodes synaptic weights between input and output nodes. Finally, a supervised error reduction phase fine-tunes the network, which allows for the discovery of the varying levels of influence of each input dimension across an output feature space in the encoded memory. In the series of experiments, we show that each phase in the learning process contributes significantly to the final accuracy of prediction. Further experiments using both toy problems and real-world data demonstrate significant superiority in terms of accuracy of nonlinear estimation when benchmarked against other prominent architectures and exhibit the network's suitability to perform analysis and prediction on real-world applications, such as traffic density prediction as shown in this paper.      

Associative memory with dynamic synapses

We have examined a role of dynamic synapses in the stochastic Hopfield-like network behavior. Our results demonstrate an appearance of a novel phase characterized by quick transitions from one memory state to another. The network is able to retrieve memorized patterns corresponding to classical ferromagnetic states but switches between memorized patterns with an intermittent type of behavior. This phenomenon might reflect the flexibility of real neural systems and their readiness to receive and respond to novel and changing external stimuli.     

关联规则概述

对近年来关联规则数据挖掘的主要方法进行了概述,同时介绍了关联规则进一步研究的方向.     

A support-ordered trie for fast frequent itemset discovery

The importance of data mining is apparent with the advent of powerful data collection and storage tools; raw data is so abundant that manual analysis is no longer possible. Unfortunately, data mining problems are difficult to solve and this prompted the introduction of several novel data structures to improve mining efficiency. Here, we critically examine existing preprocessing data structures used in association rule mining for enhancing performance in an attempt to understand their strengths and weaknesses. Our analyses culminate in a practical structure called the SOTrielT (support-ordered trie itemset) and two synergistic algorithms to accompany it for the fast discovery of frequent itemsets. Experiments involving a wide range of synthetic data sets reveal that its algorithms outperform FP-growth, a recent association rule mining algorithm with excellent performance, by up to two orders of magnitude and, thus, verifying its' efficiency and viability.