Panorama: A database system that annotates its answers to queries with their properties

When responding to queries, humans often volunteer additional information about their answers. Among other things, they may qualify the answer as to its reliability, and they may characterize it abstractly. This paper describes a relational database system that similarly annotates its answers with their properties. The process assumes that various assertions about properties of the data have been stored in the database (meta-information). These assertions are then used to infer properties of each answer provided by the system (meta-answers). Meta-answers are offered to users along with each answer issued, and help them to assess the value and meaning of the information that they receive. The advantages of the method described include: (1) It is extensible in that it allows users to determine the kinds of properties that the system will maintain and manipulate. (2) It has a built-in mechanism for determining the relevance of computed meta-information. (3) It is efficient: the number of operations required for meta-processing a given query can be expressed as a polynomial in the size of the meta-database. (4) It can be implemented externally with any commercial relational database system.     

A knowledge-based process planning system for injection mold

The goal of this study is to develop a knowledge-based process planning system interfaced with design for injection mold. Mold parts are interactively designed by using feature-based design approach, and then a part feature database is created through a data conversion module. Attribute information of the database is used as an input for the expert process design module and the standard time estimation module of the knowledge-based process planning system developed in this study. In this developed system, decision making of process design is performed by rules which were acquired from experienced process planners through interviews, and machining operation time is estimated by using empirical formulas derived from the actual shop floor data. A case example of split cavity plate is used to demonstrate the system performance.     

基于知识系统的不确定性模型

研究基于知识系统（KBS）中知识的不确定性是开发KBS的重要问题,但现有模型化KBS几乎都是基于确定性知识的。以经典的CommonKADS模型为背景,采用模型化工程中的不确定性技术,研究KBS中不确定性知识的表示方法。首先在基于值系统的值集概念上引入假设函数集合的评估函数,定义静态不确定性领域知识;而后采用因果模型描述动态的不确定性推理知识和任务知识;最后将三类不确定性知识映射至CommonKADS模型。由此给出了描述不确定KBS的通用模型,扩展了KBS的可用性。     

A practical algorithm for evaluating database queries

This paper describes an algorithm for evaluating database queries represented as expressions in a logical language. Such a database query expression can be evaluated efficiently by focusing on the variable dependencies. The algorithm recursively computes the values of subexpressions to evaluate the input expression, but it avoids re-evaluation of those subexpressions whose values are not affected by new variable assignments. The input expression is internally structured as a directed acyclic graph. Two additional techniques to improve efficiency of the evaluation are discussed: transformations of the input expression and special primitive database operations. Finally, its implementation in the natural language question-answering system SPICOS is described.     

A knowledge-based vision system for industrial applications

This paper introduces a knowledge-based vision system for industrial environments. It is designed to control a cell in an assembly system. The images from the environment are taken as gray scale images. Based on a single image, the system has to recognize type and position of the recorded parts and to control their placement in the environment for further manipulation. This requires the explicit representation of rich task-specific knowledge. The effort to adapt our system to new tasks is very small. Thus, it is very important that the system is able to support major parts of the activities that are necessary for the acquisition of new knowledge. The system consists of three components-image segmentation, knowledge acquisition, and matching-which are described in detail. All the methods presented were tested using different parts of an electric motor as an example.     

A knowledge-based inspection process planning system for coordinate measuring machines

The coordinate measuring machine (CMM) is one of the most effective geometry inspection facilities used in manufacturing industry. To fully utilize its capabilities in a computer-integrated manufacturing (CIM) environment, we should integrate CMM with other systems and facilities. This paper presents the development of a knowledge-based inspection planner based on the fundamental principles of AI planning to integrate computer-aided design systems and CMMs. The issues involved in CAD-directed inspection process planning are examined; the task of inspection process planning is decomposed into a number of sub-tasks. According to the task decomposition, a knowledge-based planning system was designed with several modules. Each of these modules consists of a knowledge base, a control operator, a context and a communication interface. The knowledge base is the local knowledge source for problem solving; the control operator determines when and where the knowledge is applied; the context contains the initial planning state which is essential input part information, the intermediate planning states which result from the tentative decision made by the modules, and the goal state. The module interfacing was realized by directly calling procedures defined in other modules to pass the planning tasks and decisions. Examples are included to explain the planning knowledge and strategy.     

A cost-reducing question-selection algorithm for propositional knowledge-based systems

In many applications of knowledge-based systems, initially given data are often not sufficient to reach a conclusion and more data are needed. A question-selection algorithm is to identify missing information and select proper questions to ask. We present a question-selection algorithm for propositional knowledge-based systems, which aims at asking more relevant and less expensive questions. Comparing to those algorithms currently used in many expert systems, the new algorithm is capable of reaching a conclusion more economically in our computational experiments. AMS subject classification 03B05, 68T20, 68T15     

A knowledge-based approach to design for manufacturability

In the light of growing global competition, organizations around the world today are constantly under pressure to produce high-quality products at an economical price. The integration of design and manufacturing activities into one common engineering effort has been recognized as a key strategy for survival and growth. Design for manufacturability (DFM) is an approach to design that fosters the simultaneous involvement of product design and process design. The implementation of the DFM approach requires the collaboration of both the design and manufacturing functions within an organization. Many reasons can be cited for the inability to implement the DFM approach effectively, including: lack of interdisciplinary expertise of designers; inflexibility in organizational structure, which hinders interaction between design and manufacturing functions; lack of manufacturing cost information at the design phase; and absence of integrated engineering effort intended to maximize functional and manufacturability objectives. The purpose of this research is to show how expert systems methodology could be used to provide manufacturability expertise during the design phase of a product. An object- and rule-based expert system has been developed that has the capability: (1) to make process selection decisions based on a set of design and production parameters to achieve cost-effective manufacture; and (2) to estimate manufacturing cost based on the identified processes. The expertise for primary process selection is developed for casting and forging processes. The specialized processes considered are die casting, investment casting, sand casting, precision forging, open die forging and conventional die forging. The processes considered for secondary process selection are end milling and drilling. The cost estimation expertise is developed for the die casting process, the milling and drilling operations, and the manual assembly operations. The results obtained from the application of the expert system suggest that the use of expert systems methodology is a feasible method for implementing the DFM approach.     

A hybrid knowledge-based system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.     

CoBase: A scalable and extensible cooperative information system

A new generation of information systems that integrates knowledge base technology with database systems is presented for providing cooperative (approximate, conceptual, and associative) query answering. Based on the database schema and application characteristics, data are organized into Type Abstraction Hierarchies (TAHs). The higher levels of the hierarchy provide a more abstract data representation than the lower levels. Generalization (moving up in the hierarchy), specialization (moving down the hierarchy), and association (moving between hierarchies) are the three key operations in deriving cooperative query answers for the user. Based on the context, the TAHs can be constructed automatically from databases. An intelligent dictionary/directory in the system lists the location and characteristics (e.g., context and user type) of the TAHs. CoBase also has a relaxation manager to provide control for query relaxations. In addition, an explanation system is included to describe the relaxation and association processes and to provide the quality of the relaxed answers. CoBase uses a mediator architecture to provide scalability and extensibility. Each cooperative module, such as relaxation, association, explanation, and TAH management, is implemented as a mediator. Further, an intelligent directory mediator is provided to direct mediator requests to the appropriate service mediators. Mediators communicate with each other via KQML. The GUI includes a map server which allows users to specify queries graphically and incrementally on the map, greatly improving querying capabilities. CoBase has been demonstrated to answer imprecise queries for transportation and logistic planning applications. Currently, we are applying the CoBase methodology to match medical image (X-ray, MRI) features and approximate matching of emitter signals in electronic warfare applications.This work supported by ARPA contract F30602-94-C-0207.     

A query index for continuous queries on RFID streaming data

RFID middleware collects and filters RFID streaming data to process applications＇ requests called continuous queries, because they are executed continuously during tag movement. Several approaches to building an index on queries rather than data records, called a query index, have been proposed to evaluate continuous queries over streaming data. EPCglobal proposed an Event Cycle Specification （ECSpec） model, which is a de facto standard query interface for RFID applications. Continuous queries based on ECSpec consist of a large number of segments that represent the query conditions. The problem when using any of the existing query indexes on these continuous queries is that it takes a long time to build the index, because it is necessary to insert a large number of segments into the index. To solve this problem, we propose a transform method that converts a group of segments into compressed data. We also propose an efficient query index scheme for the transformed space. Comparing with existing query indexes, the performance of proposed index outperforms the others on various datasets.     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

A knowledge-based system for reactive scheduling decision-making in FMS

This paper describes research into the development of an intelligent simulation environment. The environment was used to analyze reactive scheduling scenarios in a specific flexible manufacturing systems (FMS) configuration. Using data from a real FMS, simulation models were created to study the reactive scheduling problem and this work led to the concept of capturing instantaneous FMS status data as snapshot data for analysis. Various intelligent systems were developed and tested to asses their decision-making capabilities. The concepts of History Logging and expert system learning is proposed and these ideas are implemented into the environment to provide decision-making and control across a FMS schedule lifetime. This research proposes an approach for the analysis of reactive scheduling in an FMS. The approach and system that was subsequently developed was based on the principle of automated intelligent decision-making via knowledge elicitation from FMS status data, together with knowledge base augmentation to facilitate a learning ability based on past experiences.     

Virtual Tree: A robust architecture for interval valid queries in dynamic distributed systems

This paper studies the problem of answering aggregation queries, satisfying the interval validity semantics, in a distributed system prone to continuous arrival and departure of participants. The interval validity semantics states that the query answer must be calculated considering contributions of at least all processes that remained in the distributed system for the whole query duration. Satisfying this semantics in systems experiencing unbounded churn is impossible due to the lack of connectivity and path stability between processes. This paper presents a novel architecture, namely Virtual Tree, for building and maintaining a structured overlay network with guaranteed connectivity and path stability in settings characterized by bounded churn rate. The architecture includes a simple query answering algorithm that provides interval valid answers. The overlay network generated by the Virtual Tree architecture is a tree-shaped topology with virtual nodes constituted by clusters of processes and virtual links constituted by multiple communication links connecting processes located in adjacent virtual nodes. We formally prove a bound on the churn rate for interval valid queries in a distributed system where communication latencies are bounded by a constant unknown by processes. Finally, we carry out an extensive experimental evaluation that shows the degree of robustness of the overlay network generated by the virtual tree architecture under different churn rates.     

A language for simple interactive retrieval from a database system

Query interaction with a database system requires, in general, some understanding of the content and structure of the database, and knowledge of a suitable query language to encode the request for data. These factors impose barriers against access to a database on a casual or irregular basis. To overcome such restrictions we have investigated the use of a pseudo-intelligent front-end retrieval system. This system was designed to be independent of any specific database management system, although a relational database structure was considered to be the most appropriate. A prototype version of the system has been set up to run on top of Logica's relational database management system RAPPORT and the IBM relational database system SQL. As a result of this experience we have developed an adaptable language to facilitate intelligent interaction between an end user and a database management system.     

An agent-oriented and knowledge-based system for strategic e-procurement

Abstract: This paper presents an agent‐oriented and knowledge‐based system (AOKBS) for strategic e‐procurement. The AOKBS has been designed to capture and leverage the knowledge of an enterprise to generate dynamic business rules by which an effective procurement strategy can be generated based on enterprise needs and the analysis of relevant market conditions. The performance and practices of the suppliers can also be evaluated and captured continuously, respectively, by the AOKBS. Artificial intelligence technologies such as case‐based reasoning and agent technology are incorporated for decision analysis. A prototype e‐procurement system has been built and implemented successfully with a multinational manufacturer.     

Knowledge-based system for supporting statistical database management

In the development of the expert system described here, knowledge-based information processing has been approached from the point of view of database technology. The outlines and processes of arranging an economic national balances system are provided, and the problems encountered in applying the variables involved in such a process to an expert system are identified. The structure and architecture of the expert system are defined, and the problems of knowledge acquisition are discussed.     

Object-oriented query language access to relational databases: A semantic framework for query translation

This research investigates and approach to query processing in a multidatabase system that uses an objectoriented model to capture the semantics of other data models. The object-oriented model is used to construct a global schema, defining an integrated view of the different schemas in the environment. The model is also used as a self-describing model to build a meta-database for storing information about the global schema. A unique aspect of this work is that the object-oriented model is used to describe the different data models of the multidatabase environment, thereby extending the meta database with semantic information about the local schemas. With the global and local schemas all represented in an object-oriented form, structural mappings between the global schema and each local schema are then easily supported. An object algebra then provides a query language for expressing global queries, using the structural mappings to translate object algebra queries into SQL queries over local relational schema. The advantage of using an object algebra is that the object-oriented database can be viewed as a blackboard for temporary storage of local data and for establishing relationships between different databases. The object algebra can be used to directly retrieve temporarily-stored data from the object-oriented database or to transparently retrieve data from local sources using the translation process described in this paper.