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Knowledge and Data Engineering, IEEE Transactions on

Issue 5 • Date Oct 1995

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Displaying Results 1 - 16 of 16
  • Probabilistic knowledge bases

    Page(s): 691 - 698
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (780 KB)  

    We define a new fixpoint semantics for rule based reasoning in the presence of weighted information. The semantics is illustrated on a real world application requiring such reasoning. Optimizations and approximations of the semantics are shown so as to make the semantics amenable to very large scale real world applications. We finally prove that the semantics is probabilistic and reduces to the usual fixpoint semantics of stratified Datalog if all information is certain. We implemented various knowledge discovery systems which automatically generate such probabilistic decision rules. In collaboration with a bank in Hong Kong we use one such system to forecast currency exchange rates View full abstract»

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  • Critics for knowledge-based design systems

    Page(s): 740 - 750
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    Expert critics have been built to critique human performance in various areas such as engineering design, decision making, etc. We suggest that critics can also be useful in the building and use of knowledge based design systems (KBDSs). Knowledge engineers elicit knowledge from domain experts and build a knowledge based design system. The system generates designs. The amount of knowledge the system possesses and the way it applies the knowledge directly influence the performance of its designs. Therefore, critics are proposed to assist in: acquiring sufficient knowledge for constructing a desirable system; and applying proper knowledge to generating designs. Methodologies of equipping a KBDS with critics are developed. Our practice in building and using a KBDS shows the applicability and capability of these critics View full abstract»

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  • A graph-based data model and its ramifications

    Page(s): 809 - 823
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    Currently, database researchers are investigating new data models in order to remedy the deficiencies of the flat relational model when applied to nonbusiness applications. Herein we concentrate on a recent graph based data model called the hypernode model. The single underlying data structure of this model is the hypernode which is a digraph with a unique defining label. We present in detail the three components of the model, namely its data structure, the hypernode, its query and update language, called HNQL, and its provision for enforcing integrity constraints. We first demonstrate that the said data model is a natural candidate for formalising hypertext. We then compare it with other graph based data models and with set based data models. We also investigate the expressive power of HNQL. Finally, using the hypernode model as a paradigm for graph based data modelling, we show how to bridge the gap between graph based and set based data models, and at what computational cost this can be done View full abstract»

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  • The partitioned synchronization rule for planar extendible partial orders

    Page(s): 797 - 808
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1156 KB)  

    The partitioned synchronization rule is a technique for proving the correctness of concurrency control algorithms. Prior work has shown the applicability of the partitioned synchronization rule to hierarchically decomposed databases whose structure is restricted to semitrees. The principal contribution of the paper is a demonstration that the partitioned synchronization rule also applies to more general structures than semitrees, specifically, to any planar extendible partial order, a partial order which when extended with a least and a greatest element still remains planar. To demonstrate utility, the paper presents two applications of the partitioned synchronization rule. The first application shows correctness of a component based timestamp generation algorithm suitable for implementing a timestamp ordering concurrency control algorithm. The second application shows correctness of a snapshot algorithm for concurrency control in a replicated multilevel secure database; we choose this application to highlight that hierarchically decomposed databases and multilevel secure databases are structurally similar. In both cases, the correctness proofs via the partitioned synchronization rule are substantially simpler than corresponding direct proofs View full abstract»

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  • Genetics-based learning of new heuristics: rational scheduling of experiments and generalization

    Page(s): 763 - 785
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    We present new methods for the automated learning of heuristics in knowledge lean applications and for finding heuristics that can be generalized to unlearned domains. These applications lack domain knowledge for credit assignment; hence, operators for composing new heuristics are generally model free, domain independent, and syntactic in nature. The operators we have used are genetics based; examples of which include mutation and cross over. Learning is based on a generate and test paradigm that maintains a pool of competing heuristics, tests them to a limited extent, creates new ones from those that perform well in the past, and prunes poor ones from the pool. We have studied three important issues in learning better heuristics: anomalies in performance evaluation; rational scheduling of limited computational resources in testing candidate heuristics in single objective as well as multiobjective learning; and finding heuristics that can be generalized to unlearned domains. We show experimental results in learning better heuristics for: process placement for distributed memory multicomputers, node decomposition in a branch and bound search, generation of test patterns in VLSI circuit testing, and VLSI cell placement and routing View full abstract»

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  • Causal knowledge elicitation based on elicitation failures

    Page(s): 725 - 739
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    The paper presents an approach to causal knowledge elicitation supported by a tool directly used by the domain expert. This knowledge elicitation approach is characterized by trying to guess an interpretation of the knowledge entered by the expert. The tool (initially general), as it is used, self customizes its guessing capability, remembers failures in guessing (in order to avoid similar failures in the future) and when they occur elicits their explanations. Even in this case, elicitation is supported by guessing on the basis of previous similar failures. The resulting overall effect is that the tool digs up tenaciously causal knowledge from the expert's mind, playing in this way a cooperative role for model building View full abstract»

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  • Description logics in data management

    Page(s): 671 - 682
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    Description logics and reasoners, which are descendants of the KL-ONE language, have been studied in depth in artificial intelligence. After a brief introduction, we survey their application to the problems of information management, using the framework of an abstract information server equipped with several operations-each involving one or more languages. Specifically, we indicate how one can achieve enhanced access to data and knowledge by using descriptions in languages for schema design and integration, queries, answers, updates, rules, and constraints View full abstract»

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  • On-the-fly reading of entire databases

    Page(s): 834 - 838
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    A common database need is to obtain a global-read, which is a consistent read of an entire database. To avoid terminating normal system activity, and thus improve availability, we propose an on-the-fly algorithm that reads database entities incrementally and allows normal transactions to proceed concurrently. The algorithm assigns each entity a color based on whether the entity has been globally read, and a shade based on how normal transactions have accessed the entity. Serializability of execution histories is ensured by requiring normal transactions to pass both a color test and a shade test before being allowed to commit. Our algorithm improves on a color-only-based scheme from the literature; the color-only scheme does not guarantee serializability View full abstract»

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  • Theoretical and practical considerations of uncertainty and complexity in automated knowledge acquisition

    Page(s): 699 - 712
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    Inductive machine learning has become an important approach to automated knowledge acquisition from databases. The disjunctive normal form (DNF), as the common analytic representation of decision trees and decision tables (rules), provides a basis for formal analysis of uncertainty and complexity in inductive learning. A theory for general decision trees is developed based on C. Shannon's (1949) expansion of the discrete DNF, and a probabilistic induction system PIK is further developed for extracting knowledge from real world data. Then we combine formal and practical approaches to study how data characteristics affect the uncertainty and complexity in inductive learning. Three important data characteristics, namely, disjunctiveness, noise and incompleteness, are studied. The combination of leveled pruning, leveled condensing and resampling estimation turns out to be a very powerful method for dealing with highly disjunctive and inadequate data. Finally the PIK system is compared with other recent inductive learning systems on a number of real world domains View full abstract»

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  • Maintaining temporal consistency: pessimistic vs. optimistic concurrency control

    Page(s): 786 - 796
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    We study the performance of concurrency control algorithms in maintaining temporal consistency of shared data in hard real time systems. In our model, a hard real time system consists of periodic tasks which are either write only, read only or update transactions. Transactions may share data. Data objects are temporally inconsistent when their ages and dispersions are greater than the absolute and relative thresholds allowed by the application. Real time transactions must read temporally consistent data in order to deliver correct results. Based on this model, we have evaluated the performance of two well known classes of concurrency control algorithms that handle multiversion data: the two phase locking and the optimistic algorithms, as well as the rate monotonic and earliest deadline first scheduling algorithms. The effects of using the priority inheritance and stack based protocols with lock based concurrency control are also studied View full abstract»

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  • Quasi-acyclic propositional Horn knowledge bases: optimal compression

    Page(s): 751 - 762
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    Horn knowledge bases are widely used in many applications. The paper is concerned with the optimal compression of propositional Horn production rule bases-one of the most important knowledge bases used in practice. The problem of knowledge compression is interpreted as a problem of Boolean function minimization. It was proved by P.L. Hammer and A. Kogan (1993) that the minimization of Horn functions, i.e., Boolean functions associated with Horn knowledge bases, is NP complete. The paper deals with the minimization of quasi acyclic Horn functions, the class of which properly includes the two practically significant classes of quadratic and of acyclic functions. A procedure is developed for recognizing in quadratic time the quasi acyclicity of a function given by a Horn CNF, and a graph based algorithm is proposed for the quadratic time minimization of quasi acyclic Horn functions View full abstract»

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  • Acquisition of linguistic patterns for knowledge-based information extraction

    Page(s): 713 - 724
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    The paper presents an automatic acquisition of linguistic patterns that can be used for knowledge based information extraction from texts. In knowledge based information extraction, linguistic patterns play a central role in the recognition and classification of input texts. Although the knowledge based approach has been proved effective for information extraction on limited domains, there are difficulties in construction of a large number of domain specific linguistic patterns. Manual creation of patterns is time consuming and error prone, even for a small application domain. To solve the scalability and the portability problem, an automatic acquisition of patterns must be provided. We present the PALKA (Parallel Automatic Linguistic Knowledge Acquisition) system that acquires linguistic patterns from a set of domain specific training texts and their desired outputs. A specialized representation of patterns called FP structures has been defined. Patterns are constructed in the form of FP structures from training texts, and the acquired patterns are tuned further through the generalization of semantic constraints. Inductive learning mechanism is applied in the generalization step. The PALKA system has been used to generate patterns for our information extraction system developed for the fourth Message Understanding Conference (MUC-4) View full abstract»

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  • A nonblocking transaction data flow graph based protocol for replicated databases

    Page(s): 829 - 834
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    Replicated data management systems adopt the 1-copy serializability criteria for processing transactions. In order to achieve this goal, many approaches rely on obtaining votes from other sites for processing update requests. In the proposed approach, a technique for generation of precedence graphs for each transaction execution is analyzed. The transaction data flow graph approach is a fully distributed approach. The proposed technique, is free from deadlocks, and avoids resubmission of transactions View full abstract»

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  • Representing knowledge by neural networks for qualitative analysis and reasoning

    Page(s): 683 - 690
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    A systematic approach has been developed to construct neural networks for qualitative analysis and reasoning. These neural networks are used as specialized parallel distributed processors for solving constraint satisfaction problems. A typical application of such a neural network is to determine a reasonable change of a system after one or more of its variables are changed. A six-node neural network is developed to represent fundamental qualitative relations. A larger neural network can be constructed hierarchically for a system to be modeled by using six-node neural networks as building blocks. The complexity of the neural network building process is thus kept manageable. An example of developing a neural network reasoning model for a transistor equivalent circuit is demonstrated. The use of this neural network model in the equivalent circuit parameter extraction process is also described View full abstract»

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  • Automatic structuring of knowledge bases by conceptual clustering

    Page(s): 824 - 829
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    An important structuring mechanism for knowledge bases is building an inheritance hierarchy of classes based on the content of their knowledge objects. This hierarchy facilitates group-related processing tasks such as answering set queries, discriminating between objects, finding similarities among objects, etc. Building this hierarchy is a difficult task for the knowledge engineer. Conceptual clustering may be used to automate or assist the engineer in the creation of such a classification structure. This article introduces a new conceptual clustering method which addresses the problem of clustering large amounts of structured objects. The conditions under which the method is applicable are discussed View full abstract»

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  • Enriching the expressive power of security labels

    Page(s): 839 - 841
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (304 KB)  

    Common security models such as Bell-LaPadula focus on the control of access to sensitive data but leave some important systems issues unspecified, such as the implementation of read-only objects, garbage collection, and object upgrade and downgrade paths. Consequently, different implementations of the same security model may have conflicting operational and security semantics. We propose the use of more expressive security labels for specifying these system issues within the security model, so that the semantics of a system design are precisely understood and are independent of implementation details View full abstract»

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Aims & Scope

IEEE Transactions on Knowledge and Data Engineering (TKDE) informs researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area.

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Meet Our Editors

Editor-in-Chief
Jian Pei
Simon Fraser University