Knowledge Discovery System Research Articles

Knowledge discovery using genetic algorithm for maritime situational awareness

Due to the large volume of data related to vessels, to manually pore through and to analyze the information in a bid to identify potential maritime threat is tedious, if at all possible. This study aims to enhance maritime situational awareness through the use of computational intelligence techniques in detecting anomalies. A knowledge discovery system based on genetic algorithm termed as GeMASS was proposed and investigated in this research. In the development of GeMASS, a machine learning approach was applied to discover knowledge that is applicable in characterizing maritime security threats. Such knowledge is often implicit in datasets and difficult to discover by human analysts. As the knowledge relevant to maritime security may vary from time to time, GeMASS was specified to learn from streaming data and to generate up-to-date knowledge in a dynamic fashion. Based on the knowledge discovered, the system functions to screen vessels for anomalies in real-time. Traditionally in maritime security studies, datasets that are applied as knowledge sources are related to vessels’ geographical and movement information. This study investigated a novel leverage of multiple data sources, including Automatic Identification System, classification societies, and port management and security systems for the enhancement of maritime security. A prototype of GeMASS was developed and employed as a vehicle to study and demonstrate the functions of the proposed methodology.

On Knowledge Based Intelligent Systems

This paper presents a proposal for an architecture that integrates knowledge discovery systems (automatic acquisition) and knowledge based systems (experts systems). This work formulates considerations over the viability of the implementation of this architecture according to the advanceof the technologies involved.

Discovering biological knowledge by integrating high‐throughput data and scientific literature on the cloud

SUMMARYIn this paper, we present a bioinformatics knowledge discovery tool for extracting and validating associations between biological entities. By mining specialized scientific literature, the tool not only generates biological hypotheses in the form of associations between genes, proteins, miRNA and diseases but also validates the plausibility of such associations against high‐throughput biological data (e.g. microarray) and annotated databases (e.g. Gene Ontology). Both the knowledge discovery system and its validation are carried out by exploiting the advantages and the potentialities of the Cloud, which allowed us to derive and check the validity of thousands of biological associations in a reasonable amount of time. The system was tested on a dataset containing more than 1000 gene–disease associations achieving an average recall of about 71%, outperforming existing approaches. The results also showed that porting a data‐intensive application in an Infrastructure as a Service cloud environment boosts significantly the application's efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

ARTIFICIAL ENDOCRINE SYSTEM: A NEW PARADIGM OF KNOWLEDGE DISCOVERY

We propose an artificial endocrine system (AES) for extracting the knowledge from database so that effective and reliable decision rules can be constructed. The proposed AES mimics the functionality of biological endocrine system (BES) to some extent. A mathematical model is proposed for expressive representation of endocrine system as well as for homeostasis. Further, different aspects of our proposed "Artificial endocrine system for knowledge discovery" (AESKD) have been compared with state of art classifiers e.g., support vector machine, neural network, radial basis function (RBF) network and K-NN for some bench mark datasets.

Bayesian analysis of GUHA hypotheses

The LISp-Miner system for data mining and knowledge discovery uses the GUHA method to comb through a large data base and finds 2 × 2 contingency tables that satisfy a certain condition given by generalised quantifiers and thereby suggest the existence of possible relations between attributes. In this paper, we show how a more detailed interpretation of the data in the tables that were found by GUHA can be obtained using Bayesian statistical methods. Using a multinomial sampling model and Dirichlet prior, we derive posterior distributions for parameters that correspond to GUHA generalised quantifiers. Examples are presented illustrating the new Bayesian post-processing tools implemented in LISp-Miner. A statistical model for the analysis of contingency tables for data from two subpopulations is also presented.

TOWARDS A COLLABORATIVE KNOWLEDGE DISCOVERY SYSTEM FOR ENRICHING SEMANTIC INFORMATION ABOUT RISKS OF GEOSPATIAL DATA

Abstract. The aim of this research is to design and implement a knowledge discovery system that facilitates, using a web 2.0 collaborative approach, the identification of new risks of geospatial data misuse based on a contributed knowledge repository fed by application domain experts. [Context/Motivation] This research is motivated by the irregularity of risk analysis efforts and the poor semantic of the collected information about risks. In the context of risk analysis during geospatial database design, the knowledge about risks of geospatial data misuse is typically held by domain application experts. The collection and record of that knowledge are usually considered as optional activities. It is usually performed through face-to-face risk assessment meetings and reports. Such techniques end up by restricting the scope of risk analysis to a set of obvious risks usually already identified. Besides, little consideration is devoted to the storage of risk information in an appropriate format for automatic reasoning and new risk information discovery. As a consequence, many foreseeable risky aspects inherent to the data remain overlooked leading to ill-defined specification and faulty decisions. [Principal ideas/results] In this paper, we present a contributed knowledge discovery system that aims at enriching the semantic information about risks of geospatial data misuse in order to identify foreseeable risks. The proposed web-based system relies on a systematic and more active involvement of users in risk analysis. The approach consists of 1) providing an overview of the related work in the domains of risk analysis within the context of geospatial database design, 2) presenting an ontology-based knowledge discovery system that helps experts in risks identification based on an upper-level risk ontology and on a structured representation of the domain-specific knowledge and, 3) presenting the components of the proposed system architecture and how it may be implemented and used in practice, and finally 4) we conclude by discussing the approach. [Contribution] A major outcome is that the proposed platform can help discovering implicit domain knowledge, and facilitating the identification of foreseeable risks of geospatial data misuse in a way to preventively improve the resulting fitness-for-use.

An ontology based text mining system for knowledge discovery from the diagnosis data in the automotive domain

In automotive domain, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis (FD) process. Here, the aim of knowledge discovery using text mining (KDT) task is to discover the best-practice repair knowledge from millions of repair verbatim enabling accurate FD. However, the complexity of KDT problem is largely due to the fact that a significant amount of relevant knowledge is buried in noisy and unstructured verbatim. In this paper, we propose a novel ontology-based text mining system, which uses the diagnosis ontology for annotating key terms recorded in the repair verbatim. The annotated terms are extracted in different tuples, which are used to identify the field anomalies. The extracted tuples are further used by the frequently co-occurring clustering algorithm to cluster the repair verbatim data such that the best-practice repair actions used to fix commonly observed symptoms associated with the faulty parts can be discovered. The performance of our system has been validated by using the real world data and it has been successfully implemented in a web based distributed architecture in real life industry.

Full-length Transcriptome-based H-InvDB Throws a New Light on Chromosome-centric Proteomics

H-Invitational Database (H-InvDB; http://hinv.jp/ ) is an integrated database of all human genes and transcripts that started in an international collaborative research project for establishing a functional annotation database of human full-length cDNAs. Because H-InvDB contains an abundance of information for human transcripts, including not only well-characterized protein-coding transcripts but also those without experimental evidence at the protein level, this will be a useful information resource for identifying novel and uncharacterized human proteins (so-called missing proteins). By extending predicted protein data in H-InvDB, we developed the H-Inv Extended Protein Database (H-EPD; http://hinv.jp/hinv/h-epd/ ). From now on, we plan to carry out a database-driven proteome research that makes full use of H-EPD to promote discoveries in the current and future C-HPP. Furthermore, we will push forward with the integration of genome, transcriptome, and proteome databases using a unique tool for connecting distributed databases and would like to develop a knowledge discovery system by incorporating data mining tools.

IADRs: towards online adverse drug reaction analysis

Adverse Drug Reaction (ADR) is one of the most important issues in the assessment of drug safety. In fact, many adverse drug reactions are not discovered during limited pre-marketing clinical trials; instead, they are only observed after long term post-marketing surveillance of drug usage. In light of this, the detection of adverse drug reactions, as early as possible, is an important topic of research for the pharmaceutical industry. Recently, large numbers of adverse events and the development of data mining technology have motivated the development of statistical and data mining methods for the detection of ADRs. These stand-alone methods, with no integration into knowledge discovery systems, are tedious and inconvenient for users and the processes for exploration are time-consuming. This paper proposes an interactive system platform for the detection of ADRs. By integrating an ADR data warehouse and innovative data mining techniques, the proposed system not only supports OLAP style multidimensional analysis of ADRs, but also allows the interactive discovery of associations between drugs and symptoms, called a drug-ADR association rule, which can be further developed using other factors of interest to the user, such as demographic information. The experiments indicate that interesting and valuable drug-ADR association rules can be efficiently mined.

Pedestrian navigation using the sense of touch

Haptics is a feedback technology that takes advantage of the human sense of touch by applying forces, vibrations, and/or motions to a haptic-enabled user device such as a mobile phone. Historically, human–computer interaction has been visual, data, or images on a screen. Haptic feedback can be an important modality in Mobile Location-Based Services like – knowledge discovery, pedestrian navigation and notification systems. In this paper we describe a methodology for the implementation of haptics in four distinct prototypes for pedestrian navigation. Prototypes are classified based on the user’s navigation guidance requirements, the user type (based on spatial skills), and overall system complexity. Here haptics is used to convey location, orientation, and distance information to users using pedestrian navigation applications. Initial user trials have elicited positive responses from the users who see benefit in being provided with a “heads up” approach to mobile navigation. We also tested the spatial ability of the user to navigate using haptics and landmark images based navigation. This was followed by a test of memory recall about the area. Users were able to successfully navigate from a given origin to a Destination Point without the use of a visual interface like a map. Results show the users of haptic feedback for navigation prepared better maps (better memory recall) of the region as compared to the users of landmark images based navigation.

Rough Set Approach in Machine Learning: A Review

The Rough Set (RS) theory can be considered as a tool to reduce the input dimensionality and to deal with vagueness and uncertainty in datasets. Over the years, there has been a rapid growth in interest in rough set theory and its applications in artificial intelligence and cognitive sciences, especially in research areas such as machine learning, intelligent systems, inductive reasoning, pattern recognition, data preprocessing, knowledge discovery, decision analysis, and expert systems. This paper discusses the basic concepts of rough set theory and point out some rough set-based research directions and applications. The discussion also includes a review of rough set theory in various machine learning techniques like clustering, feature selection and rule induction. General Terms Information and decision systems, (in)discernibility , approximation spaces, rough sets, rough membership functions, reducts, decision rules, dependencies of attributes. Clustering, Rule Induction, Feature Selection

Structuring the Chinese disjointed literature-based knowledge discovery system: The key technologies to success

The existing evidence shows that Chinese disjointed literature-based knowledge discovery is largely limited to traditional Chinese medical literature instead of the entire medical literature available in Chinese. Some limitations exist in current research when put it into practice, for many reasons, including the data all coming from a single source. During the process of knowledge discovery, the problem of excessively large intermediate and target concept sets still needs attention. This Chinese medical disjointed literature-based knowledge discovery system was constructed by applying research to the concepts of extraction, pruning and sorting algorithms in an endeavour to solve the existing problems associated with disjointed literature-based knowledge discovery systems. The system was tested and verified in accordance with the classic magnesium–migraine hypothesis of Dr Swanson. Future improvements and development of the system are also proposed.

Efficient Iceberg Query Evaluation Using Compressed Bitmap Index

Decision support and knowledge discovery systems often compute aggregate values of interesting attributes by processing a huge amount of data in very large databases and/or warehouses. In particular, iceberg query is a special type of aggregation query that computes aggregate values above a user-provided threshold. Usually, only a small number of results will satisfy the threshold constraint. Yet, the results often carry very important and valuable business insights. Because of the small result set, iceberg queries offer many opportunities for deep query optimization. However, most existing iceberg query processing algorithms do not take advantage of the small-result-set property and rely heavily on the tuple-scan-based approach. This incurs intensive disk accesses and computation, resulting in long processing time especially when data size is large. Bitmap index, which builds one bitmap vector for each attribute value, is gaining popularity in both column-oriented and row-oriented databases in recent years. It occupies less space than the raw data and gives opportunities for more efficient query processing. In this paper, we exploited the property of bitmap index and developed a very effective bitmap pruning strategy for processing iceberg queries. Our index-pruning-based approach eliminates the need of scanning and processing the entire data set (table) and thus speeds up the iceberg query processing significantly. Experiments show that our approach is much more efficient than existing algorithms commonly used in row-oriented and column-oriented databases.

Integration of Clustering and Rule Induction Mining Framework for Evaluation of Web Usage Knowledge Discovery System

Integration of Clustering and Rule Induction Mining Framework for Evaluation of Web Usage Knowledge Discovery System

Identification of significant factors for air pollution levels using a neural network based knowledge discovery system

Artificial neural network (ANN) is a commonly used approach to estimate or forecast air pollution levels, which are usually assessed by the concentrations of air contaminants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, and suspended particulate matters (PMs) in the atmosphere of the concerned areas. Even through ANN can accurately estimate air pollution levels they are numerical enigmas and unable to provide explicit knowledge of air pollution levels by air pollution factors (e.g. traffic and meteorological factors). This paper proposed a neural network based knowledge discovery system aimed at overcoming this limitation in ANN. The system consists of two units: (a) an ANN unit, which is used to estimate the air pollution levels based on relevant air pollution factors; (b) a knowledge discovery unit, which is used to extract explicit knowledge from the ANN unit. To demonstrate the practicability of this neural network based knowledge discovery system, numerical data on mass concentrations of PM2.5 and PM1.0, meteorological and traffic data measured near a busy traffic road in Hangzhou city were applied to investigate the air pollution levels and the potential air pollution factors that may impact on the concentrations of these PMs. Results suggest that the proposed neural network based knowledge discovery system can accurately estimate air pollution levels and identify significant factors that have impact on air pollution levels.

KD4v: comprehensible knowledge discovery system for missense variant

A major challenge in the post-genomic era is a better understanding of how human genetic alterations involved in disease affect the gene products. The KD4v (Comprehensible Knowledge Discovery System for Missense Variant) server allows to characterize and predict the phenotypic effects (deleterious/neutral) of missense variants. The server provides a set of rules learned by Induction Logic Programming (ILP) on a set of missense variants described by conservation, physico-chemical, functional and 3D structure predicates. These rules are interpretable by non-expert humans and are used to accurately predict the deleterious/neutral status of an unknown mutation. The web server is available at http://decrypthon.igbmc.fr/kd4v.

Knowledge discovery system for automated quality abstraction

To encourage the delivery of high quality care with an additional emphasis on the transparency of hospital results and reporting, the Centers for Medicare & Medicaid Services (CMS) and the Joint Commission require each hospital to submit certain inpatient core measures every quarter. In this research, we developed a system that can achieve this automatically with high accuracy for any arbitrary clinical question, in a manner that is easily user configurable/teachable.

텍스트 마이닝 및 자동 추론 기반 생물학 지식 발견 시스템을 위한 확률 기반 필터링

본 논문에서는 텍스트 마이닝을 통해 생물학 문헌에서 분자 수준의 사건(event) 정보를 자동으로 추출하고, 이들 사건 정보를 기반으로 새로운 생물학 지식을 자동 추론하는 텍스트 마이닝 - 추론 통합 구조의 시스템을 다룬다. 이러한 통합 구조의 지식 발견 시스템은 미리 추출되어 데이터베이스에 등록된 정보만을 입력으로 사용하는 시스템들에 비하여 최신 정보를 보다 빨리 사용할 수 있고, 미리 정의된 형식 이외의 다양한 정보를 사용할 수 있다는 장점이 있다. 반면, 텍스트 마이닝 정보 추출 결과를 그대로 사용하기 때문에 텍스트 마이닝 모듈(module)의 성능에 따라 전체 시스템의 효용성이 크게 저하될 수도 있다는 문제가 있다. 본 논문에서는 확률 기반 필터링(filtering) 방법을 제안하여, 텍스트 마이닝 결과 중 양성 오류(false positive)를 효과적으로 제거함으로써 전체 지식 발견 시스템의 정확도 및 효용성을 높이고자 한다. 본 논문에서 제안한 확률 기반 필터링 방법은 기준(baseline) 방법으로 사용된 횟수 기반 필터링 방법보다 높은 성능을 보였다. In this paper, we discuss the structure of biological knowledge discovery system based on text mining and automatic inference. Given a set of biology documents, the system produces a new hypothesis in an integrated manner. The text mining module of the system first extracts the 'event' information of predefined types from the documents. The inference module then produces a new hypothesis based on the extracted results. Such an integrated system can use information more up-to-date and diverse than other automatic knowledge discovery systems use. However, for the success of such an integrated system, the precision of the text mining module becomes crucial, as any hypothesis based on a single piece of false positive information would highly likely be erroneous. In this paper, we propose a probabilistic filtering method that filters out false positives from the extraction results. Our proposed method shows higher performance over an occurrence-based baseline method.

Development of Knowledge Discovery System from Annotated Time Sereis Data

This paper proposes a knowledge discovery system from annotated time series data, they are expressed as sequences of numerical values. They generally have a lot of important information in background, but it is not included in data. Analysis methods without background information have limitations. Several studies propose meta data approaches, which is often expressed as a short text, the techniques are insufficient for our purpose. We therefore develop a method that uses annotations which are compact expressions of back ground information. Subsequences obtained by using domain knowledge are organized into groups based on a distance measure. Among the groups some of them are identified as important features. In order to measure importance, we develop a method that uses global and local frequencies of subsequences. This idea is similar to the TF*IDF method, which is used in text mining. A subsequence that represents an important group is regarded as a feature pattern. In addition, we extract association rules over feature patterns and annotations. We introduce a new concept, called max allowance length, to focus only on influential annotations to a pattern. We demonstrate an effect of the proposed method by using financial data.

Privacy preserving distributed knowledge discovery: survey and future directions

The aim of privacy preserving data mining algorithms is to extract relevant knowledge from large amounts of data while protecting at the same time sensitive information. In the first part of this paper, we discuss privacy preserving distributed data mining techniques and provide a survey on the state–of–the–art methods in this field. We have considered randomisation, k–anonymisation, and cryptographic techniques. In the second part of the paper, we have described our proposed system for privacy preserving knowledge discovery over distributed databases which is still under development phase. The system is designed to perform local operations (local mining) in each site. This produces intermediate data that can be used to obtain the final result without revealing the private information of any site. Our proposal is mainly based on association rule mining and cryptographic techniques.