Maize is a well-studied crop. It has been used as a model plant for C4 studies of photosynthesis, as its leaves possess the Kranz Structure (KS). Unfortunately, only few studies addressed the use of computational models to describe dry maize. In particular, the mechanism of KS formation remains unclear during leaf development. This study aims to develop a computational model to answer the following two questions for leaf development in dry maze: (1) How Auxin inhibits BDL, and (2) How the MP transcription activates BDL in the seed of dry maize in early stages of embryonic leaves. We first analyze dry maize based on the S-systems model and compare it with two different regulatory networks: (1) Auxin inhibits BODENLOS (BDL), and (2) MONOPTEROS (MP) activates BODENLOS (BDL). Our hypotheses are: (1) Auxin does not inhibit BDL, and (2) MP does not activate BDL. In the second stage, we compare the S-systems parameter estimation method (SPEM) and the engineering method to analyze the two regulatory networks. Our result suggests a general mechanism for studying how the transient accumulation of Auxin activates self-sustaining and how, similar to other genetic switches, it results in unequivocal developmental responses of leaves in dry maize. The MP activates BDL are very important to the Auxin signaling mediated by MP and BDL proteins which are essential for cell-fate specification events in early embryogenesis of maize.

Advances in small and low power electronics have created new opportunities for the Internet of Things (IoT), leading to an explosion of physical objects being connected to the Internet. However, there still lacks an indoor localization solution that can answer the needs of various location-based IoT applications with desired simplicity, robustness, accuracy, and responsiveness. We introduce Foglight, a visible light enabled indoor localization system for IoT devices that relies on unique spatial encoding produced when mechanical mirrors inside a projector are flipped based on gray-coded binary images. Foglight employs simple off-the-shelf light sensors that can be easily coupled with existing IoT devices-such as thermometers, gas meters, or light switches-making their location discoverable. Our sensor unit is computation efficient; it can perform highaccuracy localization with minimum signal processing overhead, allowing any low-power IoT device on which it rests to be able to locate itself. Additionally, results from our evaluation reveal that Foglight can locate a target device with an average accuracy of 1.7 mm and average refresh rate of 84 Hz with minimal latency, 31.46 ms on Wi-Fi and 23.2 ms on serial communication. Two example applications are developed to demonstrate possible scenarios as proof of concept. We also discuss limitations, how they could be overcome, and propose next steps.

We present Lift, a visible light-enabled finger tracking and object localization technique that allows users to perform freestyle multi-touch gestures on any object's surface in an everyday environment. By projecting encoded visible patterns onto an object's surface (e.g. paper, display, or table), and localizing the user's fingers with light sensors, Lift offers users a richer interactive space than the device's existing interfaces. Additionally, everyday objects can be augmented by attaching sensor units onto their surface to accept multi-touch gesture input. We also present two applications as proof of concept. Finally, results from our experiments indicate that Lift can localize ten fingers simultaneously with an average accuracy of 1.7 millimeter and an average refresh rate of 84 Hz with 31 milliseconds delay on WiFi and 23 milliseconds delay on serial communication, making gesture recognition on non-instrumented objects possible.

The construction and understanding of Gene Regulatory Networks (GRNs) are among the hardest tasks faced by systems biology. To infer gene regulatory networks from gene expression data has been a vigorous research area. It aims to constitute an intermediate step from exploratory to gene expression analysis. In recent years, many reverse engineering methods have been proposed. In practice, different model approaches will generate different network structures. Therefore, it is very important for users to assess the performance of these algorithms. We present a comparative study with three different reverse engineering methods, including the S-system Parameter Estimation Method (SPEM), the Graphical Gaussian Model (GGM) and the TimeDelay-ARACNE. Our approach consists of the analysis of real gene expression data with the different methods, and the assessment of algorithmic performances by sensitivity, specificity, precision and F-score.

Cardiovascular disease (CVD) is group of diseases of the heart and blood vessels and a major global cause of death, with more people dying every year from CVDs than from any other cause. Therefore, controlling and preventing CVDs and their complex pathogenesis (i.e., influenced by genetic and lifestyle factors) has gained considerable attention. In this study, we use the LSA algorithm to cluster highly related CVD association genes. The LSA can further explore each functional gene cluster including listing of the consensus terms shared by the genes in the cluster. A comprehensive network biology analysis can integrates clustered genes in the CVD genes with pathway information by building a network of interconnected pathways. Based on LSA, the CVD association genes from the GAD database are divided into 5 clusters (k=5). The gene set enrichment analysis reveals 25 significantly pathways. Further analysis to integrate pathway analysis and to combine miRNA and Drugbank data to gain more insights in the interplay can be useful to suggest drug repositioning.

A data processing functions are expected as a key-issue of knowledge-intensive service functions in the Cloud computing environment. Cloud computing is a technology that evolved from technologies of the field of virtual machine and distributed computing. However, these unique technologies brings unique privacy and security problems concerns for customers and service providers due to involvement of expertise (such as knowledge, experience, idea, etc.) in data to be processed. We propose the cryptographic protocols preserving the privacy of users and confidentiality of the problem solving servers.

Massive rules processing has attracted more attention in recently years. Firstly, we propose a rule description language that can express all kind of rules by structured nature language. We design a set of graphical symbols for rule nodes. We also propose a rule traffic flow model and a rule cost model. Thought these models, it is easier to process massive numbers rules and optimize them.

In this paper, the movement of bacteria, i.e. E. coli, is simulated based on the run and tumble model. The random walk parameters, such as the speed, tumbling frequency, run duration, and the turn angle between two successive runs were taken from experimental measurements, and use them to simulate the bacteria movement in cases of three different uniform chemical concentration distributions. The motility coefficient is computed to characterize the migration responses. Furthermore, a case of chemical attractant gradient distribution in the environment is designed to validate the run and tumble model. It is found that bacteria move with higher motility coefficient in higher chemical concentrations. Simulation results suggested that bacterial run and tumble model can be used to describe real bacteria movement.

For real-time applications that consist of massive number of rules, partitioning of the rules to support parallel processing is important. This paper proposes a suite of algorithms called GAPCM for parallel processing of massive number of rules. By considering even distribution, minimal waiting time and minimal inter-processor communication, we propose three algorithms for subnet allocation, and apply these algorithms to association rule mining.

Semantic Capability Description Language (SCDL) plays an important role as a common language and provides a generic format for problem formalization. In this paper we extend SCDL to include typical applications in drug discovery and development that include similarity and substructure searches, quantitative structure activity relationship modeling and in silico docking experiments.

Semantic capability description language (SCDL) plays an important role as a common language and provides a generic format for problem formalization. In this paper we extend SCDL to include dynamic biological systems. A new process description language SPDL (semantic process description language) is presented and several queries as well as their corresponding SPDL descriptions are provided that are relevant to biological research. These include metabolic pathway searches and modeling of enzyme kinetics.

Over the past several years, there has been a great interest in topic detection and tracking (TDT). Recently, analyzing general research trend from the huge amount of history documents also arouses considerable attention. However, existing work on TDT mainly focuses on overall trend analysis, and is unable to address questions such as "what determines the evolution of a topic?" and "when and how does a new topic get formed?".

Cognitive informatics (CI) is the transdisciplinary enquiry of cognitive and information sciences that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications via an interdisciplinary approach. CI develops a coherent set of fundamental theories and denotational mathematics, which form the foundation for most information and knowledge based science and engineering disciplines such as computer science, cognitive science, neuropsychology, systems science, cybernetics, software engineering, knowledge engineering, and computational intelligence. This paper reviews the central doctrine of CI and its applications. The theoretical framework of CI is described on the architecture of CI and its denotational mathematic means. A set of theories and formal models of CI is presented in order to explore the natural and computational intelligence. A wide range of applications of CI are described in the areas of cognitive computers, cognitive properties of knowledge, simulations of human cognitive behaviors, cognitive complexity of software, autonomous agent systems, and computational intelligence.

Although Web service composition has been widely discussed, most approaches did not concern the two important aspects inherent to Web service composition: description of functionality and management of data flows between component Web services. This paper proposes an algorithm based on the Semantic Object Behavior Language (SOBL) combined with the Semantic Capability Description Language (SCDL) to derive execution workflows for relational services.

This paper is a preliminary report on the development of a "declarative" programming language SPL+. It assists non-technical people to write programs. The key idea behind SPL+ is that the "programmer" only needs to solve target problems with a standardized methodology without worrying about how to solve the problem efficiently.

Citation graph analysis has been used to evaluate the significance of documents and authors, or to estimate the impact of publication venues. In this paper, we investigate its new application in topic identification. We first model the communities in the citation graph as related documents on a specific topic. And then, a scientific theme detection algorithm is proposed based on community partition, attempting to identify the emergency of a new theme by tracking the change of the community where the top cited nodes lie in. Experimental results on real dataset show that the proposed method can detect new topic timely with only a subset of data.

This paper addresses the composition problem for relational Web services. The relational service composition problem considers the use of available relational services to answer complex Web services that may not be answered by a single service but may be collectively answered by a set of services. We present a method to reduce the time complexity of service composition based on condition transitive matrices.

The problem of n-connection subgraph discovery (n-CSDP for short) is to find a small sized subgraph that can well capture the relationship among the n given nodes in a large graph. However there have been very few researches directly addressing the CSDP problem. Furthermore the currently available methods, for example, the electricity analogues based algorithm can only be suitable for tackling the 2-keynodes CSDP and does not work any more when n is greater than two. To deal with this problem, we propose an effective approach to discover the subgraph in two stages. In the first stage, we propose a neighbor-growth based method to extract a relatively bigger candidate subgraph compared with that of result subgraph. In the second stage, an evolutionary algorithm for optimizing the result subgraph is proposed. For this purpose, UTM code, a transformed representation of the adjacent matrix of graphs is designed to encode the topology of subgraph as individuals. Then corresponding evolutionary operators able to be directly performed on UTM code are given. Thus the efficiency of the algorithm is largely improved. The experimental results obtained on two real large scale graphs with different topology characteristics demonstrate that our method solves n-connection subgraph discovery problems effectively

This paper proposes a Bayesian classification method to deal with heterogeneous distributed grid data. Based on the classification result, an intelligent-agent can provide an overloading function to process certain classified data sets over a grid. The method assigns a set of agents to coordinate a user job over the grid. It can definitely resolve the main problems existing in grid data management so as to enhance grid data management efficiency

Summary form only given. This article discusses issues, challenges, and future directions in multimedia research, along the following three scale-oriented issues plus the one which is common to them

Although extensive research on Web services has been reported in the last decade, problems still remain due to the lack of natural language query support, the lack of automatic service discovery and composition, and the existence of a large number of existing web sites that are query driven. In this paper we introduce a framework that integrates some novel solutions to the above problems. Specifically we differentiate query-based services (QBS) from the traditional function-based services (FBS) and address the following issues: natural language user interface, automatic service discovery and composition, and semantic UDDI

In general biologists are not accustomed to formulating biological problems in the precise mathematical terms that are required to solve the problems analytically or numerically. Although many computational tools for systems biology have been developed recently, our observations indicate that many of these tools are powerful only in the hands of those who know a lot about how to use them. For most biologists, the tools have a protracted learning curve and unfriendly user interface that often diminish their likelihood of being used. Our long-term goal is to build a knowledge system that allows biologists to synthesize complex biological systems via natural language interactions, and the system is able to generate the corresponding mathematical descriptions so that the often cumbersome communication process between biologists and mathematicians/engineers in formulating complex biological problems in mathematic terms can be performed more easily. To focus, the first goal in this research is to build a knowledge system prototype that focuses on transport related biological problems that occur from the cellular to tissue level. We address specifically two inter-related problems: (1) Provision of an intelligent system that is capable of automatically synthesizing smaller components into more complex systems; Provision of a user-friendly and natural language interface.

In general biologists are not accustomed to formulating biological problems in the precise mathematical terms that are required to solve them analytically or numerically. Although many computational tools for systems biology have been developed recently, our observations indicate that many of these tools are powerful only in the hands of those who know a lot about how to use them. For most biologists, the tools have a protracted learning curve and unfriendly user interface that often diminish their likelihood of being used. Our long term goal is to build a knowledge system that allows biologists to synthesize complex biological systems via natural language interactions, and the system is able to generate the corresponding mathematical descriptions so that the often cumbersome communication process between biologists and mathematicians/engineers in formulating complex biological problems in mathematic terms can be performed more easily. To focus, the first goal in this research is to build a knowledge system prototype that focuses on transport related biological problems that occur from the cellular to tissue level. We address specifically two inter-related problems: (1) provision of an intelligent system that is capable of automatically synthesizing smaller components into more complex systems; (2) provision of a user-friendly and natural language interface.

Most commercially available image retrieval systems are so generic that they are not specialized to handle biological images and the feature domains associated with them. In molecular biology, neurobiology and cellular biology, for example, the recognition, classification and retrieval of distinct cellular features is a critically needed tool representing a computational problem that embodies the central challenges facing biological image database research. It often requires the consideration of expert/conceptual knowledge of images and the objects contained within such images. This paper discusses the feasibility of developing a set of imaging algorithms that allows the user to train an imaging system/database to recognize biological objects of various sorts based on their own criteria. The image software builds a model of the selected objects by reiterative training, evolving the ability (i.e., the underlying rules) to recognize these objects. These objects are in turn archived into a growing database that builds upon the experience of multiple individuals that can be referred to as an object zoo. The user can search these zoos using newly acquired images based on similarly using more narrow or broadened criteria based on a new, semantic database framework called Semantic Objects.

Most of the content-based image retrieval systems focuses on similarity-based retrieval of natural picture images by utilizing color, shape, and texture features. For the neuroscience image databases, we found that retrieving similar images based on global average features is meaningless to pathological researchers. To realize the practical content-based retrieval on images in neuroscience databases, it is essential to represent internal contents or semantics of images in detail. In this paper, we present how to represent image contents and their related concepts to support more useful retrieval on such images. We also describe the operational semantics to support these advanced retrievals by using object-oriented message path expressions. Our schemes are flexible and extensible, enabling users to incrementally add more semantics on image contents for more enhanced content searching.

To assure the quality of software by running test cases and evaluating the results is one of the difficult parts of the entire software development project. The difficulty usually comes from the lack of appropriate supporting tools and the complexity of the software. In the past ad hoc supporting tools were made for each project and test results were usually not used across projects. This conventional way of test and evaluation (T&E) is time consuming, and the most important decision "When is this software ready to ship?" is left to the engineers depending on their experiences. Our objective is to build a knowledge-based T&E environment such that tests cases, test results, object snapshots and other information are accumulated in a database. These longitudinal data can be automatically tracked and analyzed to provide decision support information. As a result, test results can be reviewed repeatedly and software quality can be assured by analyzing these data from various perspectives.

As neuroscience research advances, there is an increasing need to integrate data from a single patient who has undergone multiple levels of analysis. In a biomedical application (such as an Alzheimer's clinic), a patient might have medical data, MRI data, cognitive data and autopsy data all collected at various times. This paper introduces "BioCompose", an object-relational database system that integrates clinical, neuropathological and image data sets into a uniform framework with a simple user interface. It addresses the advantages of taking an object-relational approach for a large and complex application.

BioCompose is an object-relational database query tool that runs on top of any relational database and provides an intelligent and complete object-relational interface to the user. Unlike the traditional approach, which is completely table driven, queries in BioCompose are structured along the lines of natural language and sentences. In BioCompose, the database server continuously monitors the triggers and integrity constraints which are expressed as logical rules and are evaluated based on incoming events which include real time events, value events, transaction events, abstract events, and message events. The paper presents a novel approach that compiles triggers and integrity constraints into an asynchronous network to minimize the rule evaluation effort under different kinds of events.

Neuron count in various brain structures is an important factor in many neurobiological studies. We describe a machine vision system which uses color images for the automated classification and counting of neurons in tissue samples. Samples are sliced into registered sections whose thickness is on the order of the diameter of a neuronal nucleus. Sections are stained so that the spectral transmission functions of the neuronal nuclei differ from the surrounding tissue. Each section is imaged using a light microscope. A Bayesian classifier is used for pixel labeling and a geometric analysis routine is employed to segment neuron regions in each section. The 3-D tissue sample is reconstructed using registered neuron regions from each section. An object-oriented database management system provides an efficient framework for cataloging neuron classes. Experimental results are presented and compared with results obtained by a histologist.

This paper describes a 3-D visualization method based on the concept of characteristic views (CVs). The idea of characteristic views was derived based on the observation that the infinite possible views of a 3-D object can be grouped into a finite number of equivalence classes so that within each class all the views are isomorphic in the sense that they have the same line-junction graphs. To visualize the changes of scenes in real time, the BSP tree algorithm is known to be efficient in a static environment in which the viewpoint can be changed easily. However, if a scene consists of many objects and each object consists of many polygons, the time complexity involved in traversing a BSP tree increases rapidly so that the original BSP tree algorithm may not be efficient. The method proposed in this paper is object-oriented in the sense that, for all viewpoints, at the preprocessing stage the ordering for displaying the objects is determined. At run time, the objects are displayed based on a pre-calculated ordering according to the viewpoint. In addition, a CV is used as a basic 2-D projected image of a 3-D object.

As the demand for virtual reality systems increases, a new approach is needed to meet the following requirements: a world consists of a large number of objects; a camera is allowed to move and a world should be displayed in near real-time depending on the position of the camera as well as the angle of its lens; and minimum effort should be needed when objects are moving. We satisfy these requirements with the following: an object-oriented rendering approach is developed; and an approach based on spatial indexing (specifically R/sup +/-tree) is developed to manage operations on objects (i.e., retrieval, insertion and deletion) effectively.

Neuron count in various brain structures is an important factor in many neurobiological studies. We describe a machine vision system which uses color images for the automated classification and counting of neurons in tissue samples. Samples are sliced into registered sections whose thickness is on the order of the diameter of a neuronal nucleus. Sections are stained so that the spectral transmission functions of the neuronal nuclei differ from the surrounding tissue. Each section is imaged using a light microscope. A Bayesian classifier is used for pixel labeling and a geometric analysis routine is employed to segment neuron regions in each section. The 3D tissue sample is reconstructed using registered neuron regions from each section. An object oriented database management system provides an experimental framework for cataloging neuron classes. Experimental results are presented and compared with results obtained by a histologist.

This paper describes a parallel object-oriented approach to dynamic 3-D visualization. We specify 3D objects and pixels as active objects. It employs a set of ECA rules to describe the behavior of active objects which are are compiled into a state network. A state network consists of a set of processes each of which corresponds to a state of an active object. Our analysis shows that the management cost for the state network is smaller than the pure object-oriented approach.

An object-oriented representation of databases which takes advantage of the structure of logical representation and an object-oriented algorithm that evaluates conjunctive queries in a massively parallel environment are reported and analyzed. The object-oriented representation is used to implement data level parallelism as data is encapsulated as an object. By distributing these objects in a database, parallel evaluation is achieved by a global pattern matching approach which is different from the conventional tuple-by-tuple, operation-by-operation evaluation strategy. This algorithm overcomes the data dependency imposed by shared variables which is a major problem in achieving AND-parallelism. Simulation results for 2 way and 3 way joins are analysed according to the effect of database size and join size. Results tend to show that the scheme proposed performs better for queries with large number of AND conjuncts.>

Taking advantage of the structure of logical representations, we report an algorithm that evaluates conjunctive queries in a massively parallel environment under an object-based representation for deductive databases. By distributing objects in a database, we show that parallel evaluation of a query can be achieved in a cooperative way so that the conventional tuple-by-tuple, operation-by-operation evaluation strategy can be replaced by a global, parallel matching approach. With the proposed scheme, all conjuncts of a given query can be examined at the same time, which enables us to eliminate the need of any temporary relation. On the other hand, compared with the interpretive method, we show that any data dependency imposed by shared variables is no longer a major problem in achieving AND-parallelism by the proposed scheme.>

OASIS, a database programming environment that extends UNIX with the concept of persistent objects, is discussed. The OASIS query languages extend conventional database query languages with procedural methods and general control statements. As the complexity of the languages makes it difficult to device a query optimizer based on a universally applicable algorithm, each query in OASIS is optimized based on a collection of basic patterns for which each pattern is associated with a separation query optimization algorithm. The optimization techniques for a set of basic patterns consisting of iterative statements and a set of nested statements is described. The optimization techniques discussed include an extended decomposition algorithm, evaluation of multiple conditions, data dependence analysis, and optimization of queries with arbitrary nesting.>

The problem of path planning for two planar robot manipulators that cooperate in carrying a rectangular object from an initial position and orientation to a destination position and orientation in a 2-D environment is investigated. The two robot arms, the carried object, and the straight line connecting the two robot bases are modeled as a 6-link closed chain. The problem of path planning for the chain is solved by two major algorithms: a collision-free feasible-configuration-finding algorithm and a collision-free path-finding algorithm. The former maps the free space in the Cartesian world space to the robot's joint space in which all the collision-free feasible configuratiions (CFFCs) for the 6-link closed chain are found. The latter builds a connection graph representing the CFFCs and the transitions between any two groups of CFFCs at adjacent joint intervals. A graph search method is employed to find a collision-free path for each joint of both manipulators.>

A policy for scheduling concurrent transactions in which the preconditions and effects associated with the transactions can be specified is described. Also proposed is a recovery procedure for when transaction failures are detected. The advantages of the scheduling algorithm can be summarized as follows: for conventional transaction models, it is less constrained than the two-phased protocol, particularly for short transactions; and it can perform concurrency control based on semantic dependencies.>

An efficient method for planning a safe trajectory for a Cartesian manipulator doing pick-and-place motion is described. A 3D grid model is used to represent the workspace of the manipulator. An algorithm, called the critical configuration algorithm, is introduced to support the path searching process in an environment cluttered with obstacles. The algorithm decomposes the movement of the manipulator into a departure motion, an intermediate motion, and an approach motion. Based on the information about the object being moved and the manipulator, a temporary intermediate motion is obtained, using the VGRAPH algorithm and the critical configuration algorithm, by transforming the problem into a 2D problem. The intermediate motion found in the first step is optimized according to heuristic information. A departure motion and an approach motion are then computed and optimized, and a global trajectory is determined.>

A set of algorithms is described that can be used to reduce the complexity of evaluating multiple queries of a transaction in a distributed environment. With the consideration of conjunct sharing, it compiles a set of queries into a network based on the concept of semijoins. As some of the queries in a transaction may change the contents of a database, evaluation of the network corresponding to the transaction is synchronized into several phases so that the dependencies among the queries can be properly captured. It is shown how a transaction that includes database updates can be evaluated incrementally in multiple phases such that the states of the evaluation process can be saved and only part of the transaction which is affected by a change needs to be reevaluated. The algorithms described can be applied to relational databases with slight modifications.>

In a software engineering environment, it is essential to describe the structures and behavior of different processes. Once the processes have been described, it would also be desirable to use the description for consistently controlling the execution of the processes. In this paper we describe an extension of the conventional object-oriented paradigm which provides an integrated process description/execution environment that allows a user to describe a process with multiple methodologies and to review a process from multiple views. Once a process has been described in detail, the environment can monitor the behavior of application processes, derive the inter-dependencies among different activities in a process, and check the consistency among the activities.

We investigate the problem of finding collision-free paths for two planar robots which coordinately carry a rectangular object from an initial position and orientation to a destination position and orientation in a cluttered 2-D environment. The robot arms and the carried object construct a 6-link closed chain. The path planning problem for the 6-link closed chain is solved by using two major algorithms: the collision-free feasible configuration finding algorithm and the collision-free path finding algorithm. The collision-free feasible configuration finding algorithm finds all collision-free feasible configurations (CFFCs) of the 6-link closed chain in each discrete interval of two joint angles. The collision-free path finding algorithm builds a connection graph by CFFCs and the transitions between any two groups of CFFCs at adjacent joint intervals. Then a graph search method is used to find a collision-free path for each joint of the robots.

An algorithm is introduced to evaluate queries in a massively parallel computing environment under an object-based representation of a database. By distributing facts into connected networks of processing elements, it is shown that parallel execution of a query can be achieved in a cooperative way, so that the conventional tuple-by-tuple evaluation can be avoided. Furthermore, it is shown that dependency imposed by shared variables is not a major problem in achieving AND parallelism any longer. Although only a single query is considered, the evaluation scheme can be extended to multiple queries with multiple query networks and with replication of counters in the fact network.>

In this paper, we describe a knowledge‐based software environment (KBSE) which supports concurrent programming based on the framework of object‐oriented knowledge base. The object‐oriented knowledge base framework combines an object‐oriented data model with the logic programming paradigm. In KBSE, the user can describe a concurrent process with a set of (sequential) flowcharts and a set of coordination constraints and integrity constraints specified in a high level description language. The description is then synthesized into a Petri net representation. With the Petri net representation, existing analysis tools for Petri net can be fully utilized to assist the design and analysis of concurrent processes.

A logic-oriented object base is defined to be a deductive database based on an object data model. Like conventional databases, logic-orientated object bases can be constructed on top of computer networks in such a way that the distribution of logical and physical components of the system is kept hidden from the users. Objects are organized hierarchically and are retrieved through customized methods. The problem of query optimization in such an environment is investigated.>

The authors propose a novel representation scheme using the concept of a BB (bounding box) to facilitate automatic process preplanning. This preplanning step is necessary because it can reduce human involvement and facilitate detailed process planning, thus making the whole planning process faster, more efficient and more consistent. It is shown that process preplanning can be automated in a non-ad-hoc way.>

An object-oriented knowledge base is a database that is constructed on the object data model. Using mathematical logic as formal representation, an object-oriented knowledge base can be constructed to support classification, aggregation, generalization, and association. It further extends existing databases with procedural semantics. In this article we consider the problems of automatic software reusability, object management, and transaction planning with the aid of knowledge and meta-knowledge. We describe the approaches to automate the processes of classifying objects, instantiating abstract algorithms, and ordering conjunctive operations.

It is generally accepted that object-based systems provide a simple and elegant paradigm for general-purpose programming that meshes well with data models. Such systems are characterized by three core concepts: object, class, and message. As a consequence we are able to define object types with operations, to structure objects according to their properties, and to have objects that share properties. All these features make them suitable to serve as a basis for the next generation computing systems. However, they have been criticized for the lack of declarative semantics which is an essential feature for knowledge-based applications. For this purpose we propose the framework of logic-oriented object bases, i.e. databases that are constructed based on object model and augmented by mathematical logic. Adopting logic as a formal means for knowledge representation, we have developed both algorithmic and knowledge-based approaches to relate objects, to evaluate declarative queries that involve high level concepts, and to schedule declarative update requests such that changes to objects can be made consistently.

At the very abstract level, generative process planning is like any other planning problem. An analogy can be drawn between the process planning problem and the robot planning problem. At the top there are a set of goals to be achieved and a set of operations available, and the computer is asked to, starting from scratch, synthesize a sequence of operations to achieve the given set of goals. It can be shown that conjunctive goal planning problems in general are very diffcult to solve. One remedy that has been proposed to solve the robot programming problem, and apparently could be applied to the process planning problem, is to provide the users with very high level programming languages such that details to achieve a high level operation can be transparent to the users. In other words, the computer is used to fill in low-level details. Another possible remedy would be to generalize the family concept such that families can be defined precisely enough to eliminate human modification and broad enough to include most variations. In this paper we purpose the framework of object-oriented knowledge base to achieve these functions.