Showing papers on "Knowledge extraction published in 1970"
TL;DR: The process of Knowledge Discovery in Databases (KDD) arises as a technology which can help with the knowledge search in the data, which can facilitate the understanding, on the part of human Analysts, of the knowledge extracted from data.
Abstract: The ever larger interest of the companies in accompanying new processing technologies and storage of data, as well as using the information as a large patrimony, has motivated several researches for the study of the process of transformation of this data into knowledge, which provides an intelligent aid to decision-making In this context, the process of Knowledge Discovery in Databases (KDD) arises as a technology which can help with the knowledge search in the data This search can be accomplished with the aid of visualization techniques, which can facilitate the understanding, on the part of human Analysts, of the knowledge extracted from data This can be accomplished by identifying structures, characteristics, tendencies, anomalies and relationships among the data These techniques frequently offer mechanisms that facilitate the search of patterns/models from data bases
6 citations
TL;DR: Based on the results of investigation into the decision- making process, the model of knowledge in the form of hierarchical decision nets was developed and the control mechanism for the processing of arranged knowledge was presented.
Abstract: Based on the results of investigation into the decision- making process, the model of knowledge in the form of hierarchical decision nets was developed. Two principal types of knowledge can be distinguished in this model: control knowledge and classification knowledge. The paper also presents the control mechanism for the processing of such arranged knowledge. The approach was verified through the development of the expert system for machining process planning.
4 citations
TL;DR: This research has been involved in researching and applying such AI techniques as data mining, induction, genetic algorithms, probabilistic nets, machine learning, to a complex non-deterministic engineering problem in the telecommunications domain as a Knowledge Discovery architecture (NetExtract).
Abstract: Applying Artificial Intelligence (A.I.) to complex engineering problems is an increasing trend. The authors have been involved in researching and applying such AI techniques as data mining, induction, genetic algorithms, probabilistic nets, machine learning, to a complex non-deterministic engineering problem in the telecommunications domain. This has been implemented as a Knowledge Discovery architecture (NetExtract) which is generic in nature.
4 citations
TL;DR: An integrated system DECADIS DEscoberta de Conhecimento em Armazens de Dados de DIStribuigao (Knowledge Discovery in Retail Data Warehouses), designed for understanding customer behaviour and consumption patterns in a Portuguese company in the retail industry is proposed.
Abstract: Minute by minute the amount of data in the world databases is increasing inexorably. To support this growth of data the concept of data warehouse (DW) was created. DW when combined with On-Line Analytical Processing (OLAP) Codd[2] and Executive Information Systems (EIS) Buytendijk[l] tools, enable data access and visualization in a very flexible way. Features include very quick data exploration, vertical navigation (drill up/drill down), aggregation and graphical facilities. However, the amount and the complexity of data in data warehouses is so big that it becomes difficult to the business analysts to recognise trends and relations in data even with multidimensional decision support systems. A new generation of tools and techniques for automated intelligent database analysis is needed. These tools and techniques are the subject of the rapidly emerging field of Knowledge Discovery in Data Bases (KDD). In this paper, we propose an integrated system DECADIS DEscoberta de Conhecimento em Armazens de Dados de DIStribuigao (Knowledge Discovery in Retail Data Warehouses), designed for understanding customer behaviour and consumption patterns in a Portuguese company in the retail industry. Transactions on Information and Communications Technologies vol 19 © 1998 WIT Press, www.witpress.com, ISSN 1743-3517
3 citations
TL;DR: A hybrid Machine Learning algorithm developed at the University of South Florida is presented as a knowledge acquisition tool for developing knowledge-based systems.
Abstract: Automation of the knowledge acquisition process in building knowledgebased systems for process design is addressed through Machine Learning techniques. A hybrid Machine Learning algorithm developed at the University of South Florida is presented as a knowledge acquisition tool for developing knowledge-based systems. The learning algorithm addresses the knowledge acquisition problem by developing and maintaining the knowledge base through inductive learning from the examples. The learning algorithm named as Symbolic-Connectionist net (SCnet), overcomes the problems associated with neural and symbolic learning systems by integrating the symbolic information into a neural network representation. The learning system allows for knowledge extraction and background knowledge encoding in the form of rules. Fuzzy logic has been made use of in dealing with uncertainty in the learning domain. The description language for the learning system consists of continuous and discrete variables along with relational and fuzzy comparators. The applicability of the learning system for process design is illustrated through a complex column sequencing example. The performance of the learning system is discussed in terms of the knowledge extracted from example cases and its classification accuracy on the test cases. Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517
2 citations
01 Jan 1970
TL;DR: In this article, the authors describe the process of data integration in LBS and SOLAP using Semantic Web technology, and explain how to distribute the data available from these three systems (LBS, GIS, OLAP) in the web.
Abstract: Location Based Service (LBS) LBS are mobile service that has the capability to provide real time information basedon the user's location. Geographical Information System (GIS) has been the heart of LBS in order to provide all the functionalities in LBS. Although mostly transparent, GIS provides the basis for most functionality, from services like geocoding, routing, location search to map presentation in LBS. In the Knowledge Discovery realm, Spatial Online Analytical Processing (SOLAP) integrates conventional OLAP with GIS data sets .Integration of these two heterogeneous data sources deals with issues such as different data model structures, different schemas and query languages. In the implementation of SOLAP, two different data model must be considered. Geographical Information System (GIS) describes its data model in a hierarchical structure, use to represent spatial features. Online Analytical Processing (OLAP) however describes its data in a multi-dimensional structure, known for fast analytical processing. Although having such differences, it is now possible to distribute the data available from these three systems (LBS, GIS, OLAP) in the web. The internet has become the main transport for data and information exchange, and a proper integration framework should be use. This paper explains the process of data integration in LBS and SOLAP using Semantic Web Technology.
2 citations
TL;DR: An algorithm to mine a SOODB is proposed and after a spatial object query and a mathematical and fuzzy preprocessing, this algorithm applies decision tree based techniques and fuzzy set theory to discover knowledge.
Abstract: In this paper, an approach is presented to search for useful patterns and discover hidden information in Spatial Object-Oriented Databases (SOODB). Although many approaches of knowledge discovery for relational spatial databases exist, there is a growing interest in mining SOODB. Indeed, objectoriented databases are well-suited to represent complex spatial information. Moreover, a very large number of existing spatial databases are ready to be mined. We propose an algorithm to mine a SOODB. After a spatial object query and a mathematical and fuzzy preprocessing, we apply decision tree based techniques and fuzzy set theory to discover knowledge. An experiment on a region of France to discover classification rules related to houses and urban area is conducted with this algorithm to validate the interest of the approach.
2 citations
TL;DR: This work proposes a knowledge based approach to the fragmentation phase of the distributed design of object oriented databases, and shows a rule-based implementation of an analysis algorithm from a previous work and proposes some ideas towards the use of Inductive Logic Programming to perform a knowledge discovery/revision process using the authors' set of rules as background knowledge.
Abstract: The performance of applications on Object Oriented Database Management Systems (OODBMSs) is strongly affected by Distributed Design, which reduces irrelevant data accessed by applications and data exchange among sites. In an OO environment, the Distributed Design is a very complex task, and an open research problem. In this work we propose a knowledge based approach to the fragmentation phase of the distributed design of object oriented databases. In this approach, we will show a rule-based implementation of an analysis algorithm from our previous work and propose some ideas towards the use of Inductive Logic Programming (ILP) to perform a knowledge discovery/revision process using our set of rules as background knowledge. The objective of the work is to uncover some previously unknown issues to be considered in the distributed design process. Our main objective here is to show the viability of performing a revision process in order to obtain better and better fragmentation algorithms. We do not intend to propose the best fragmentation algorithm ever possible. We concentrate here on the process of revising a DDOODB algorithm through Knowledge Discovery techniques, rather than only obtaining a final optimal algorithm. Transactions on Information and Communications Technologies vol 19 © 1998 WIT Press, www.witpress.com, ISSN 1743-3517
1 citations