Chu# W.W.# #Neighborhood and associative query answering## in Journal of Intel# ligent Information Systems# Kluwer Academic Publishers# Vol. 1# 1992# 355#382  Home/Search   Document Not in Database   Summary   Related Articles   Check

....is for the range of 250k 300k instead of 280k 300k to avoid misunderstanding. 5. 2 Cooperative Query Answering Since an MLDB stores general database information in higher layers, many techniques investigated in previous researches into cooperative query answering in (single layer) databases [17, 3, 2, 10, 16] can be extended effectively to cooperative query answering in MLDBs. The following reasoning may convince us that an MLDB may greatly facilitate cooperative query answering. Many cooperative query answering techniques need certain kinds of generalization [2, 11] whereas different kinds of ....

....databases [17, 3, 2, 10, 16] can be extended effectively to cooperative query answering in MLDBs. The following reasoning may convince us that an MLDB may greatly facilitate cooperative query answering. Many cooperative query answering techniques need certain kinds of generalization [2, 11]; whereas different kinds of frequently used generalizations are performed and stored in the high layers of an MLDB. Also, they often need to compare the neighborhood information [3, 2] The generalized neighborhood tuples are usually stored in the same high layer relations, ready for comparison ....

[Article contains additional citation context not shown here]

W. W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1:355-382, 1992.

....interesting knowledge discovery techniques and systems prototypes, such as INLEN [18] KDW [24] Quest [1] IMACS [3] Datalogic R [31] 49er [32] etc. which demonstrates the promising future of knowledge dis covery. Different from most of the previous studies on cooper ative query answering [26, 25, 15, 7, 6, 30] and querying database knowledge [20] which emphasize on the ap plication or inquiry of deduction rules and integrity constraints in relational or deductive databases, this study extends the domain of study from a deductive database to a knowledge rich database assisted with generalized knowledge ....

.... data regularities at the primitive concept level, their roles in intelligent query answering are similar to that of deduction rules and or integrity con straints defined at the primitive concept level, which have been discussed in intelligent query answering in relational or deductive databases [26, 25, 15, 7, 6]. Therefore, this study will emphasize more on the impact of generalizationbased discovery to intelligent query answering. The techniques studied here are based on one generalization method: attribute oriented induction developed in our previous stud ies [9, 11] with an emphasis on the ....

W. W. Chu and Q. Chcn. Neighborhood and associative query answering. Journal of Intelligent Information Sys- tems, 1:355 382, 1992.

.... : there is a such that (I, co[x a] l: where co: X D is a valuation of variables, co[x a] is a valuation identical to co such that co(x) a. Exact answer to a query (by interpretation ) 3. Approximate answers Many researches have been interested in cooperative and approximate answering [ 1,2]. We explore two techniques, child and neighbor semantics, from a formal point of view, and propose a new approach to define partial ordering (subsumption) on queries (on both they syntax and answers) Further on, we assume that (D, is a partially ordered set of atomic constants, and ....

Chu W.W., Chen Q., Neighborhood and Associative Query Answering, Journal oflntelligent Systems, 1, 1992, pp. 355-382.

....this query answer paradigm is augmented to allow the system to relax a query in some cases (e.g. when a query fails or if access privileges of the user are too low) Then an approximate answer may be provided. Relaxation of a query may consist in rewriting it into a set of more general queries [5, 6, 10]. in [10] we have discussed semantics of approximate answers in the context of relational databases. in this paper, we extend these considerations to semistructured data. Semistructured data is data that has no absolute schema fixed in advance, its structure is irregular, incomplete, ....

.... ) Further on, if o o , we will say that o is more general than o , or that o is more specific than o; similarly for object identifiers and labels. 4 Approximate answers In the context of cooperative and multilevel databases, many researches have been interested in approximate answering [5,6,10]. In [10] we described the application of child and neighbor semantics to approximate answering in relational databases. Now, we discuss this semantics for labeled objects. We will use the partial pre ordering relation specified in Theorem 3. 4.1 Child semantics In the child semantics ....

Chu W.W., Chen Q., Neighborhood and Associative Query Answering, Journal of intelligent Systems, 1, pp. 355-382, 1992.

....allow the user to specify high level operations including query definition (including temporal and evolutionary predicates) data analysis methods, and visualization methods for results. PICQUERY also provides menu options for visualizing knowledge including various type abstraction hierarchies [Chu92b] and rules as the user defines the query. Using PICQUERY we are able to conveniently express, for example, the following high level medical imaging queries: # Show brain cases which demonstrate abnormally shaped ventricles. # Find an image of the proximal phalanx of the fifth finger for patient ....

W.W. Chu and Q. Chen, "Neighborhood and Associative Query Answering", J of Intelligent Information Systems: Integrating Artificial Intelligence and Database Technologies, pp. 355--382, Dec. 1992.

....about the terminology used to set up a database schema. The goal is to construct an assistant which allows to explore a database schema and to get suggestions for formulating exact queries as required by an underlying database system. This approach corresponds to the solutions of class (1) [3] presents an interesting approach for incorporating neighborhood information and associative relationships between objects to answer user requests. The approach which is of type (2) is based on the idea that one can use abstractions (type class hierarchies) from the original data to broaden ....

....project. Prominent among the features of the VML data model, which underlies the VODAK system, are the notions of classes and metaclasses. With their help, it is possible to exploit to a large degree the general knowledge built into semantic hierarchies, such as specialization generalization [3][15] Moreover, classes and metaclasses have been implemented in VODAK in a way that promotes extensibility and modularity, the latter feature being indispensable for efficiently structuring large sets of rules. The text is organized as follows. Section 2 summarizes our basic approach to ....

Chu, W. W., Chen, Q.: Neighborhood and Associative Query Answering. Journal of Intelligent Information Systems, 1, 1992, 355--382 (1992).

....interesting knowledge discovery techniques and systems prototypes, such as INLEN [18] KDW [24] Quest [1] IMACS [3] Datalogic R [31] 49er [32] etc. which demonstrates the promising future of knowledge discovery. Different from most of the previous studies on cooperative query answering [26, 25, 15, 7, 6, 30] and querying database knowledge [20] which emphasize on the application or inquiry of deduction rules and integrity constraints in relational or deductive databases, this study extends the domain of study from a deductive database to a knowledge rich database assisted with generalized knowledge ....

.... data regularities at the primitive concept level, their roles in intelligent query answering are similar to that of deduction rules and or integrity constraints defined at the primitive concept level, which have been discussed in intelligent query answering in relational or deductive databases [26, 25, 15, 7, 6]. Therefore, this study will emphasize more on the impact of generalizationbased discovery to intelligent query answering. The techniques studied here are based on one generalization method: attribute oriented induction developed in our previous studies [9, 11] with an emphasis on the derivation ....

W. W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1:355--382, 1992.

.... Cooperative (or intelligent) query answering refers to a mechanism which answers information system queries cooperatively and intelligently by analyzing the intent of a query and providing some generalized, neighborhood, or associated answers [20, 24, 39, 8] Many interesting techniques [58, 25, 19, 38, 63] have been developed for cooperative query answering, by integration of the methods developed in several related fields, such as semantic data modeling, deductive databases, knowledge discovery in databases, etc. In this chapter, we propose a new technique: the construction and application of a ....

....queries which are not exactly what (s)he wants to know. Such kind of queries are better treated as information probes and answered by providing general or associated information with data distribution statistics, which may help users to better understand the data and form more accurate queries [19, 8, 24]. In a multiple layered database system, probe queries can be mapped to a relatively higher conceptual layer and be processed in such a layer. Such answers may provide associative and summary information and assist users to refine their queries. Thirdly, a multiple layered database may provide a ....

[Article contains additional citation context not shown here]

W. W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1:355--382, 1992.

....is for the range of 250k 300k instead of 280k 300k to avoid misunderstanding. 5. 2 Cooperative Query Answering Since an MLDB stores general database information in higher layers, many techniques investigated in previous researches into cooperative query answering in (single layer) databases [17, 3, 2, 10, 16] can be extended effectively to cooperative query answering in MLDBs. The following reasoning may convince us that an MLDB may greatly facilitate cooperative query answering. Many cooperative query answering techniques need certain kinds of generalization [2, 11] whereas different kinds of ....

....(single layer) databases [17, 3, 2, 10, 16] can be extended effectively to cooperative query answering in MLDBs. The following reasoning may convince us that an MLDB may greatly facilitate cooperative query answering. Many cooperative query answering techniques need certain kinds of generalization [2, 11]; whereas different kinds of frequently used generalizations are performed and stored in the high layers of an MLDB. Also, they often need to compare the neighborhood information [3, 2] The generalized neighborhood tuples are usually stored in the same high layer relations, ready for comparison ....

[Article contains additional citation context not shown here]

W. W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1:355--382, 1992.

....query answering in database systems. 1 Introduction Cooperative (or intelligent) query answering refers to a mechanism which answers queries cooperatively and intelligently by analyzing the intent of a query and providing some generalized, neighborhood, or associated answers [5, 11, 2]. Many interesting techniques [14, 6, 5, 10, 13, 15] have been developed for cooperative query answering, by integration of the methods developed in several related fields, such as semantic data modeling [3] Research partially supported by the Natural Sciences and Engineering Research Council ....

....database systems. 1 Introduction Cooperative (or intelligent) query answering refers to a mechanism which answers queries cooperatively and intelligently by analyzing the intent of a query and providing some generalized, neighborhood, or associated answers [5, 11, 2] Many interesting techniques [14, 6, 5, 10, 13, 15] have been developed for cooperative query answering, by integration of the methods developed in several related fields, such as semantic data modeling [3] Research partially supported by the Natural Sciences and Engineering Research Council of Canada under grant OGP0037230 and the Centre for ....

[Article contains additional citation context not shown here]

W. W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1:355--382, 1992.

....is complementary in many ways to the Carmin system. It covers a different set of cooperative techniques. 10 The focus of CoBase is to find related answers to users queries. The system consists of three parts: 1) approximate and conceptual query answering [8] 2) associative query answering [6, 7]; and 3) explanation systems. CoBase is written in LISP and interfaces with Oracle. A simpler version of CoBase is written in SmallTalk and interfaces with the object oriented database system Gemstone. The CoBase system is currently being rewritten in C . The system is being used in the ....

W. W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1:355--382, 1992.

....the system to obtain a precise description of the actions it took in arriving at answers. Through this, a user may critique the techniques used by the system, so that the system is more cooperative in the future. This paper addresses the use of explanation technology in the CoBase system. CoBase [1][3] is a cooperative information system, providing approximate and associated answers to a user s relational and object oriented queries. This paper focuses on approximate query answering through query relaxation. Relaxation is cooperative when a user is interested in approximate answers, which ....

Chu, Wesley W. and Chen, Q. Neighborhood and Associative Query Answering. Journal of Intelligent Information Systems, 1(3/4), 1992.

....Los Angeles Cooperative Information Systems let users pose imprecise queries and receive approximate or summary answers in return. Yet cooperative answers should be accompanied by an explanation of how they were derived. We present an explanation system for the cooperative information system CoBase[1][3] The architecture and formalism for this explanation system are the main focus of this report. In addition this report touches on the more general problem of how to interactively refine and extend explanations. CoBase s explanation system provides multimedia, interactive, user sensitive and ....

....exact answer sets. In Cooperative Information Systems people pose imprecise queries and receive approximations when answer sets are non existent and summaries when answer sets are large. Yet these 2 cooperative answers should be accompanied by an explanation of how they were derived. CoBase [1][3] is a cooperative information system, providing approximate, associated and summary answers to a user s relational and object oriented queries. CoBase uses the Type Abstraction Hierarchy (TAH) 2] as its principle knowledge structure to guide the query transformation process. This work addresses ....

Chu, Wesley W. and Chen, Q. Neighborhood and Associative Query Answering. Journal of Intelligent Information Systems, 1(3/4), 1992.

.... Approach Although knowledge about the application domain can be expressed as a set of logical rules, such a rule based approach lacks a systematic organization to guide the query transformation process [10, 13] To remedy this problem, we use the notion of a type abstraction hierarchy (TAH) [4, 6, 7, 12] for providing an efficient and organized framework for cooperative query processing. Type abstraction emphasizes the abstract representation of object instances (see Figure 1) For example, runway length range in the domain of range (e.g. 4,000 to 8,000 ft. is a more abstract representation ....

W. W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1(3/4):355--382, 1992.

....morning, afternoon) 3.2 Query Pattern Relaxation The proposed cooperative query answering mechanism can also be viewed as a query pattern relaxation mechanism [12] To show this we introduce the notions of pattern and pattern instance. A pattern is defined on a type by specifying a condition [3]. For example, given the following schema definition, AA flight(departure airport, arrival airport, departure time, arrival time, flight#) conditions such as departure airport = LAX , or departure time = 9am define patterns on the type AA flight. The objects of the type that satisfy the ....

W. W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1(3/4):355--382, 1992.

....databases can be used to abstract the data into high level concepts. The discovered concept, or abstraction, usually implies a certain context, thus providing more information than the raw data. As a result, the abstraction can be used to characterize databases and process queries intelligently [6, 3]. In particular, abstraction can be used to derive approximate answers when the objects requested by a query are not available, the query conditions can be relaxed to their corresponding abstraction where neighborhood objects can be found and returned as the approximate answers. For ....

....up manner until all objects are in a single cluster. In Conceptual clustering, the goodness measures are usually defined for the overall partitioning of objects instead of for pairs of objects. Clustering methods are designed that maximize the goodness measure. For approximate query answering [3, 5], the goal is to minimizes the relaxation error of the approximate answers where the relaxation error is defined based on This work is supported in part by DARPA contract N00174 91 C 0107 y The authors may be reached at fwwc,kuorongg cs.ucla.edu the overall partitioning of objects. This is ....

Wesley W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1(3/4), 1992.

....and receive approximations when answer sets are non existent and summaries when answer sets are large. Yet these cooperative answers should be accompanied by an explanation of how they were derived. This work addresses the use of explanation technology in the cooperative information system CoBase[1][2] 3] 8] CoBase provides approximate and associated answers to a user s relational and object oriented queries. CoBase s explanation system provides multimedia, interactive, user sensitive and context dependent descriptions and explanations. Specifically the system describes CoBase queries, ....

Chu, Wesley W. and Chen, Q. Neighborhood and Associative Query Answering. Journal of Intelligent Information Systems, 1(3/4), 1992.

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Chu# W.W.# #Neighborhood and associative query answering## in Journal of Intel# ligent Information Systems# Kluwer Academic Publishers# Vol. 1# 1992# 355#382

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Chu# W.W.# #Neighborhood and associative query answering## Journal of Intelligent Information Systems# Kluwer Academic Publishers# Vol. 1# 1992# 355#382

No context found.

Wesley W. Chu and Q. Chen. Neighborhood and associative query answering. Journal of Intelligent Information Systems, 1(3/4), 1992.

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W.W. Chu and Q. Chen, "Neighborhood and Associative Query Answering,"; Journal of Intelligent Information System, Vol.1 No.3/4, 1992, pp.355--382.

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W.W. Chu and Q. Chen, "Neighborhood and Associative Query Answering,"; Journal of Intelligent Information System, Vol.1 No.3/4, 1992, pp.355--382.

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