BIKAS:Bio-Inspired Knowledge Acquisition and Simulacrum-A Knowledge Database to Support Multifunctional Design Concept Generation

A detailed acquisition,analysis,and representation of biological systems exhibiting different functions is required to develop unique bio-inspired multifunctional conceptual designs and methods.This paper presents BIKAS:Bio-inspired Knowledge Acquisition and Simulacrum,a knowledge database of biological systems exhibiting various functionalities,developed based on case-based bio-inspired examples from literature.The knowledge database represents the biological features,their characteristics,and the function exhibited by the biological feature as a combination of its integrated structure and structural strategy.Furthermore,this knowledge database is utilized by the Expandable Domain Integrated Design(xDID)model that works on classifying,mapping,and representing biological features into their respective geometric designations called Domains.The combination of features from the Domains results in the generation of multifunctional conceptual designs.In addition,Meta-level design factors are proposed to aid designers in filtering the biological features and their respective functions having a similar structural strategy,thus aiding designers in rapidly selecting and emulating biological functions.

Intelligent Information Management and Knowledge Discovery in Large Numeric and Scientific Databases

The present article outlines progress made in designing an intelligent information system for automatic management and knowledge discovery in large numeric and scientific databases, with a validating application to the CAST-NEONS environmental databases used for ocean modeling and prediction. We describe a discovery-learning process (Automatic Data Analysis System) which combines the features of two machine learning techniques to generate sets of production rules that efficiently describe the observational raw data contained in the database. Data clustering allows the system to classify the raw data into meaningful conceptual clusters, which the system learns by induction to build decision trees, from which are automatically deduced the production rules.

A New Approach for Knowledge Discovery in Distributed Databases Using Fragmented Data Storage Model

Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.

A Knowledge Based Approach to Integrated Battle Damage Assessment and Repair Analysis

BDAR analysis is a kind of very complex analysis technology which can be used to determine the battle damage mode of equipment and relevant decision strategy. BDAR analysis is based on the information about FMEA/DMEA, MA(maintenance analysis), FTA(fault tree analysis) and vulnerability analysis. These information and analysis results are obtained on the basis of the domain expert's experience and knowledge. Upon the basis of the summary of BDARA methods, this paper provides applied knowledge database, puts forward BDARA's integrated thinking, implementing methods, and the key technology for IBDARA knowledge database's development.

Human Body Information Database and Method of Application

This paper elaborate the emergence of human information database and the important role it plays in the various industries of economic development. It also interpret the primary human information database of current domestic and abroad and analysis it＇s classification characteristic, Besides, this papers further explains how to make use of human information database and how to make the database to play its due value. In the end, the prospect of our country＇s body information database has been set forth, using relatively mature foreign database to improve Chinese body information database.

A Neuro-genetic Based Short-term Forecasting Framework for Network Intrusion Prediction System

Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by ＂authorized＂ users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system （IDS） is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference （KDD 2009） intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS （NGIDS） involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.

Some Massively Parallel Algorithms from Nature

We introduced the work on parallel problem solvers from physics and biology being developed by the research team at the State Key Laboratory of Software Engineering, Wuhan University. Results on parallel solvers include the following areas: Evolutionary algorithms based on imitating the evolution processes of nature for parallel problem solving, especially for parallel optimization and model-building; Asynchronous parallel algorithms based on domain decomposition which are inspired by physical analogies such as elastic relaxation process and annealing process, for scientific computations, especially for solving nonlinear mathematical physics problems. All these algorithms have the following common characteristics: inherent parallelism, self-adaptation and self-organization, because the basic ideas of these solvers are from imitating the natural evolutionary processes.

Form alizing Incomplete Knowledge in Incomplete Databases

Up to now, no satisfactory theory has been established for formalizing incomplete knowledge in incomplete databases. In this paper, we clarify why existing closed world approaches, such as the CWA, the GCWA, the ECWA, circumscription, predicate completion and the PWA, fail to do so, and propose a new method. The method is an augmentation of both the ECWA and circumscrip- tion with the mechanism to discriminate implicitly expressed positive knowledge, negative knowledge and truly unknown knowledge.

An Experimental Analysis of the Applications of Datamining Methods on Bigdata

Data mining is a procedure of separating covered up,obscure,however possibly valuable data from gigantic data.Huge Data impactsly affects logical disclosures and worth creation.Data mining(DM)with Big Data has been broadly utilized in the lifecycle of electronic items that range from the structure and generation stages to the administration organize.A far reaching examination of DM with Big Data and a survey of its application in the phases of its lifecycle won't just profit scientists to create solid research.As of late huge data have turned into a trendy expression,which constrained the analysts to extend the current data mining methods to adapt to the advanced idea of data and to grow new scientific procedures.In this paper,we build up an exact assessment technique dependent on the standard of Design of Experiment.We apply this technique to assess data mining instruments and AI calculations towards structure huge data examination for media transmission checking data.Two contextual investigations are directed to give bits of knowledge of relations between the necessities of data examination and the decision of an instrument or calculation with regards to data investigation work processes.

An Overview of Data Mining and Knowledge Discovery

With massive amounts of data stored in databases, mining information and knowledge in databases has become an important issue in recent research. Researchers in many different fields have shown great interest in data mining and knowledge discovery in databases. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining and knowledge discovery techniques to understand user behavior better, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a comprehensive survey on the data mining and knowledge discovery techniques developed recently, and introduce some real application systems as well. In conclusion, this article also lists some problems and challenges for further research.

Knowledge Discovery and its Applications in Telecommunications Industry

It is important for telecom companies to make sense of the large number of data they have accumulated over the years. This paper reviews the concepts and the techniques of knowledge discovery in databases (KDD), and surveys applications of this technology in the telecommunications sector all over the world. It also discusses some possible applications of this technology in China, and reports a preliminary result of the first attempt to apply KDD technique in telephone traffic volume prediction. It concludes that KDD is a promising technology that can help to enhance-the competitiveness of China's telecom companies in the face of looming competition in a liberated market.

Rule Extraction from Trained Artificial Neural Network Based on Genetic Algorithm

This paper discusses how to extract symbolic rules from trained artificial neural network (ANN) in domains involving classification using genetic algorithms (GA). Previous methods based on an exhaustive analysis of network connections and output values have already been demonstrated to be intractable in that the scale-up factor increases with the number of nodes and connections in the network. Some experiments explaining effectiveness of the presented method are given as well.