Intelligent Database System Research Articles

Spatial inference and similarity retrieval of an intelligent image database system based on object's spanning representation

In this paper, we presented a novel image representation method to capture the information about spatial relationships between objects in a picture. Our method is more powerful than all other previous methods in terms of accuracy, flexibility, and capability of discriminating pictures. In addition, our method also provides different degrees of granularity for reasoning about directional relations in both 8- and 16-direction reference frames. In similarity retrieval, our system provides twelve types of similarity measures to support flexible matching between the query picture and the database pictures. By exercising a database containing 3600 pictures, we successfully demonstrated the effectiveness of our image retrieval system. Experiment result showed that 97.8% precision rate can be achieved while maintaining 62.5% recall rate; and 97.9% recall rate can be achieved while maintaining 51.7% precision rate. On an average, 86.1% precision rate and 81.2% recall rate can be achieved simultaneously if the threshold is set to 0.5 or 0.6. This performance is considered to be very good as an information retrieval system.

Development of materials database system for cae system of heat treatment based on data mining technology

Computer simulation for materials processing needs a huge database containing a great deal of various physical properties of materials. In order to employ the accumulated large data on materials heat treatment in the past years, it is significant to develop an intelligent database system. Based on the data mining technology for data analysis, an intelligent database web tool system of computer simulation for heat treatment process named as IndBASEweb-HT was built up. The architecture and the arithmetic of this system as well as its application were introduced.

Towards an intelligent database system founded on the SP theory of computing and cognition

The SP theory of computing and cognition, described in previous publications, is an attractive model for intelligent databases because it provides a simple but versatile format for different kinds of knowledge, it has capabilities in artificial intelligence, it can function effectively in the face of errors in its input data, and it can function like established database models when that is required. This paper first describes the SP theory in outline and the computer models in which it is expressed. The main sections of the paper describe, with examples from the SP62 computer model, how the SP framework can emulate other abstract models used in database applications: the relational model (including retrieval of information in the manner of query-by-example, creating a join between two or more tables, and aggregation), object-oriented models (including class-inclusion hierarchies, part-whole hierarchies and their integration, inheritance of attributes, cross-classification and multiple inheritance), and hierarchical and network models (including discrimination networks). Comparisons are made between the SP model and those other models. The artificial intelligence capabilities of the SP model are briefly reviewed: representation and integration of diverse kinds of knowledge in one versatile format; fuzzy pattern recognition and recognition at multiple levels of abstraction; best-match and semantic forms of information retrieval; various kinds of exact reasoning and probabilistic reasoning; analysis and production of natural language; planning; problem solving; and unsupervised learning. Also considered are ways in which current prototypes may be translated into an ‘industrial strength’ working system.

Image database design based on 9D-SPA representation for spatial relations

Spatial relationships between objects are important features for designing a content-based image retrieval system. We propose a new scheme, called 9D-SPA representation, for encoding the spatial relations in an image. With this representation, important functions of intelligent image database systems such as visualization, browsing, spatial reasoning, iconic indexing, and similarity retrieval can be easily achieved. The capability of discriminating images based on 9D-SPA representation is much more powerful than any spatial representation method based on minimum bounding rectangles or centroids of objects. The similarity measures using 9D-SPA representation provide a wide range of fuzzy matching capability in similarity retrieval to meet different user's requirements. Experimental results showed that our system is very effective in terms of recall and precision. In addition, the 9D-SPA representation can be incorporated into a two-level index structure to help reduce the search space of each query processing. The experimental results also demonstrated that, on average, only 0.1254 percent /spl sim/ 1.6829 percent of symbolic pictures (depending on various degrees of similarity) were accessed per query in an image database containing 50,000 symbolic pictures.

Dynamic learning from multiple examples for semantic object segmentation and search

We present a novel “ dynamic learning” approach for an intelligent image database system to automatically improve object segmentation and labeling without user intervention, as new examples become available, for object-based indexing. The proposed approach is an extension of our earlier work on “ learning by example,” which addressed labeling of similar objects in a set of database images based on a single example. The proposed dynamic learning procedure utilizes multiple example object templates to improve the accuracy of existing object segmentations and labels. Multiple example templates may be images of the same object from different viewing angles, or images of related objects. This paper also introduces a new shape similarity metric called normalized area of symmetric differences (NASD), which has desired properties for use in the proposed “ dynamic learning” scheme, and is more robust against boundary noise that results from automatic image segmentation. Performance of the dynamic learning procedures has been demonstrated by experimental results.

Building Intelligent Learning Database Systems

Induction and deduction are two opposite operations in data-mining applications. Induction extracts knowledge in the form of, say, rules or decision trees from existing data, and deduction applies induction results to interpret new data. An intelligent learning database (ILDB) system integrates machine-learning techniques with database and knowledge base technology. It starts with existing database technology and performs both induction and deduction. The integration of database technology, induction (from machine learning), and deduction (from knowledge-based sys-tems) plays a key role in the construction of ILDB systems, as does the design of efficient induction and deduction algorithms. This article presents a system structure for ILDB systems and discusses practical issues for ILDB applications, such as instance selection and structured induction.

Symbolic picture retrieval by relative-metric spatial relations

Symbolic pictures can be used for iconic indexing, spatial reasoning, and similarity retrieval in the design of intelligent image database systems. [S. K. Chang, C. W. Yan, Donald C. Dimitroff, and Timothy Arndt, IEEE Trans. Software Engineering 1988, 14, 681–688; S.-K. Chang, Principles of Pictorial Information Systems Design, Prentice-Hall, New York, 1989.] However, previous approaches to designing such systems usually ignore relative-metric information on symbolic pictures and cause several deficiencies in indexing, spatial reasoning, and retrieval. In our approach, we extract relative-metric information from symbolic pictures and use such information to help establish indexes based on an improvement from a minimal perfect hashing scheme. As a result, more accurate picture retrieval can be achieved through our indexing mechanism. Capabilities in spatial reasoning and query representation/processing are also improved. By utilizing relative-metric spatial relations, an image database system becomes more flexible and intelligent. ©2000 John Wiley & Sons, Inc.

Application of a neural network for improving the quality of five-axis machining

As many operating parameters are involved in five-axis machining and significantly affect the machining results, use of either an analytical method or an experimental method can only solve the problem to a degree. Therefore, a concept for developing a database system using a neural network as the inference mechanism for the purpose of improving the quality of five-axis machining was addressed in this research. A database system was constructed on the basis of the VERICUT cutting simulation and experimental data from previous literature. The simulation data were put into an intelligent database for automatic generation of the suitable tool inclination angle for each cutting point on the machined surface. The tool inclination angle was the dominating factor for improving the surface quality in five-axis machining. The inclination angle was further entered into a post-processor to modify the cutter location file and to generate the numerical control (NC) program file for practical machining. The input data used in the database required for this system are basic operating parameters, quality requirements and the geometry of the designed sculptured surface. In this research, an intelligent database system for ball mills was successfully established for five-axis machining.

Composite event support in an active database

An intelligent or active database system is able to provide support to engineering applications, which require both data and rules to be managed. CLOSE is an active database, which integrates an object-oriented database, ObjectStore with an expert system, CLIPS. Engineering applications require a broad range of composite events, which are combinations of primitive events such as time events, method events and absolute events. This paper focuses on the design and implementation of composite events. Detection of these composite events is supported by a Rule Manager. The data structures used and the mechanism for composite event detection is described. Example applications are also presented in order to highlight the usefulness of composite events.

Standardizing city tourism statistics

The major problems that city tourism managers face are the availability and comparability of market research information for their own city and their strongest competitors. To overcome this problem in market volume and share studies on European city tourism, an intelligent database system was developed. A comprehensive knowledge acquisition process on the quality and comparability of the statistics led to a prototype version of an expert system approved by leading European city tourism office managers. Additional tests of the system's accuracy showed reasonable results and helped reveal further possibilities for improving its performance. The final outcome of this model represents the largest, standardized longitudinal data set on European city tourism.

Design of large intelligent image database systems

Image knowledge structure and an access method for retrieving images are two of the most important problems in the design of large intelligent image data base systems. In this article, we present a software architecture which utilizes an image knowledge structure, called 2D C+-string, as the theoretical basis for intelligent image data base systems. Our architecture also provides an efficient access method to quickly locate the desired images by pruning a large percentage of nonpromising iconic indices represented by 2D C+-strings. The 2D C+-string spatial knowledge representation makes the image data base system become more intelligent, while the access method supports effective image retrieval from a large image data base without degrading the system's overall performance. Experimental results show that our access method is flexible enough to adapt to the change of image data base and very efficient in reducing the number of searches in similarity retrieval. © 1996 John Wiley & Sons, Inc.

CPIDS: a plant parameter selection program for erosion prediction modeling

Newly developed erosion prediction models require detailed input parameters including those describing cropping systems. Each model exhibits a different level of complexity for crop growth modeling, and many plant parameters for these models are difficult to obtain. Furthermore, numerous parameters require interpretation from other measurable plant characteristics. We developed a database containing measurable plant characteristics to supply users of these new erosion prediction technologies with the necessary parameters. The new database supports the Revised Universal Soil Loss Equation (RUSLE) and the Water Erosion Prediction Project (WEPP) erosion prediction models. The Crop Parameter Intelligent Database System (CPIDS) was developed to assist crop database builders and users of RUSLE and WEPP in interpretation of plant characteristics and selection of plant parameters. CPIDS is a Microsoft® Windows™-based program, currently running on a personal computer (PC) under Windows 3.1™, Windows 95™ and Windows NT 4.0™.

Towards on-line analytical mining in large databases

Great efforts have been paid in the Intelligent Database Systems Research Lab for the research and development of efficient data mining methods and construction of on-line analytical data mining systems.Our work has been focused on the integration of data mining and OLAP technologies and the development of scalable, integrated, and multiple data mining functions. A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, classification, prediction, and clustering. It also builds up a user-friendly, interactive data mining environment and a set of knowledge visualization tools. In-depth research has been performed on the efficiency and scalability of data mining methods. Moreover, the research has been extended to spatial data mining, multimedia data mining, text mining, and Web mining with several new data mining system prototypes constructed or under construction, including GeoMiner, MultiMediaMiner, and WebLogMiner.This article summarizes our research and development activities in the last several years and shares our experiences and lessons with the readers.

Formal reasoning in intelligent database systems

Expert database systems were proposed to solve the difficulties encountered in traditional database systems. Prolog provides a fast prototyping tool for building such database systems. However, an intelligent database system implemented in Prolog faces a major restriction that only Horn rules are allowed in the knowledge base. We propose a theorem prover which can make inference for non-Horn intelligent database systems. Conclusions can be deduced from the facts and rules stored in a knowledge base. For a knowledge base with a finite domain, the prover can provide correct answers to queries, derive logical consequences of the database, and provide help in detecting inconsistencies or locating bugs in the database. The theorem prover is efficient in deriving conclusions from large knowledge bases which might swamp most of the other deductive systems. The theorem prover is also useful in solving heuristically the satisfiability problem related to a database with an infinite domain. A truth maintenance mechanism is provided to help eliminate repetitious work for the same goals.

A framework for join pattern indexing in intelligent database systems

In intelligent database systems, knowledge directed inference often derives large amounts of data, and the efficiency of query processing in these systems depends upon how the derived data is maintained. This paper focuses on situations where the rule is conditional on a join of multiple data objects (relations) and the rule-derived data are materialized to reduce the overall query processing costs. We develop an indexing technique based on a unique construct called join pattern relation. Several pattern redundancy reduction methods are also introduced to minimize the overhead cost of join indexing.

Plant Parameter Database for Erosion Prediction Models

Recently developed erosion prediction models require detailed input parameters including those describing cropping systems. Each model requires a different level of detail for crop growth modeling, and most plant parameters for these models are not readily found. Many parameters require interpretation from other measurable plant characteristics. In order to supply the users of these new erosion prediction technologies with required parameters, a database containing measurable plant characteristics was developed. The two erosion prediction models that the database currently supports are the USDA Revised Universal Soil Loss Equation (RUSLE) and the USDA Water Erosion Prediction Project (WEPP). A Microsoft Windows-based program, the Crop Parameter Intelligent Database System (CPIDS), was developed to assist crop database builders and users of RUSLE and WEPP in interpretation of plant characteristics and selection of plant parameters.

Development of an intelligent pavement management database system

The gradual deterioration of the existing road network with age, and the scarcity of funding available for pavement rehabilitation has stimulated many municipalities to look at strategies to optimize the resources available. This paper describes the development of an intelligent database pavement management system. The system will assist engineers in decision-making and planning in relation to the rehabilitation of pavements. The system integrates the expert system to the database system to provide decision-making support for the selection and costing of the appropriate remedial measures. The system will be of particular benefit to local municipalities lacking access to human expertise to optimize the scarce resources available for pavement rehabilitation.

A Microcomputer Intelligent Database System for Indonesia Marine Geosciences Data: ABSTRACT

The Agency for Assessment and Application of Technology - Indonesia (BPP Teknologi) has collected extensive marine geosciences data in Indonesia's maritime area by using the Baruna Jaya I, II and III research vessels. Types of data include: geological/geophysical data (coring, sampling, dredging, bathymetry, heat flow, magnetic, seismic, etc.), oceanography data (water temperature, salinity, sound velocity, chemical concentrations, etc.) and hydrography/meteorology data (air temperature, pressure, humidity, sea current and direction, etc.). A microcomputer database system has been developed using Prolog language for managing the data and it provides the integrated capabilities of database management system and expert system. The database system provides easy data entry, storage and retrieval in which the user could query the data based on the coordinate ranges, types of survey, name of survey, name of vessel and survey location. The output can be displayed in map format or text format and can be imported by some commercial softwares. The expert system provides intelligent assistance in searching specific geological, geophysical, oceanographical and hydrographical/meteorological information. The Prolog declarative expression of the database itself makes the data accessible for any logic or expert system programming. This can be built to suite specific purposes. The system is especially useful for institutions involved with marine data (i.e.. oil company, oceanographic institutions, etc.). It is designed as an economical and practical microcomputer based system. The advantages of using Prolog language and Intelligent Database System concept is discussed.

An intelligent database system for composite material selection in structural design

This paper describes the development of a prototype intelligent database system for composite material selection in structural design. This intelligent system integrates the expert system with the database system to provide decision-making support systems that exhibit some forms of intelligence. The overall architecture of this system is illustrated. The present capabilities of this system are discussed and demonstrated with an example problem.

A lexical analyser for Arabic

Practical natural language processing (NLP) systems such as database front-ends, deductive databases or object-oriented databases are at the forefront of research into the next-generation intelligent database systems. The research described in this paper has been aimed at integrating front-end paradigms and rule-based deduction to provide a single powerful framework for database systems in Arabic. The lexicon stores only roots of verbs and uses a program intelligent enough to handle all derived forms automatically. This is significant, as these alone represent 70% of the total dictionary. As part of the discussion of this system, its utility in such NLP applications as parsing and machine translation is examined.