J. W. Han, Y. Huang, N. Cercone, and Y. J. Fu. Intelligent query answering by knowledge discovery techniques. In IEEE Trans, pages 373-390, 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....using association rules. The programs developed to identify these rules are based on standard algorithms for identifying quantitative rules [SA96] and rules over interval data [MY97] Semantic knowledge that has been discovered using data mining techniques can be used for query optimization [HHCF96, Hsu96, HK96, Hsu97, HK98, HK00, Kin81b, LS95, MC96, SHKC93, YSP97, YS89] A number of heuristics have been proposed that take advantage of association rules in order to optimize queries semantically [Bel96, CGM90, GGMR97, GGZ01, Kin81a, SSS92] We refer briefly to the heuristics presented by ....

J. Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge discovery techniques. IEEE Transactions on Knowledge and Data Engineering, 8(3):373-- 390, 1996.

....of knowledge. In order to discover various kinds of knowledge, we have to develop one algorithm (or subsystem) for each kind of knowledge. Simple integration of these subsystems will lead to too large a system and too high expense to maintain. With the rising of intelligent query answering [10] [8], there are increasingly pressing calls on general purpose data mining methods that are able to extract a wide spectrum of rules so as to serve the representation and implementation of deductive databases and intelligent query answering. In this paper, we present a novel data mining method ....

J. Han, Y. Huang, N. Cercone and Y. Fu. Intelligent query answering by knowledge discovery Techniques, IEEE Transaction on Knowledge and Data Engineering. Mar. 1995

....is efficient and effective at mining large time series databases. 3. 6 Intelligent query answering with data mining techniques With data mining techniques available, database queries can be answered intelligently using concept hierarchies, data mining results, or on line data mining techniques [17]. For example, instead of presenting bulky answers, one can present a summary of answers and allow users to manipulate such a summary by drilling or dicing. One can present related answers or rules in the form of associations or correlations based on association mining results. Moreover, one may ....

J. Hah, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge discovery techniques. IEEE Trans. Knowledge and Data Engineering 8:373390, 1996.

....is efficient and effective at mining large time series databases. 3. 6 Intelligent query answering with data mining techniques With data mining techniques available, database queries can be answered intelligently using concept hierarchies, data mining results, or on line data mining techniques [17]. For example, instead of presenting bulky answers, one can present a summary of answers and allow users to manipulate such a summary by drilling or dicing. One can present related answers or rules in the form of associations or correlations based on association mining results. Moreover, one may ....

J. Hah, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge discovery techniques. IEEE Trans. Knowledge and Data Engineering, 8:373390, 1996.

....k , we get a sequence of (A 1 ; D 1 ) A 2 ; D 2 ) A k ; D k ) or KC 1 ; KC 2 ; KC k of knowledge concentrates. Refer to (Fig. 3. 3 (b) In principle, The KCs are similar to Generalized Data Base (GDB) component of Knowledge Rich Databases (KRDB) data model proposed in [27]. Extraction of KCs from the incremental database, represents the I O intensive task in Intension Mining and may invoke multiple scans of data. Signi cantly, all the accumulation aggregation activities are performed o line. The user de nes the periodicity of this activity in the schema and the ....

....access can use this component. The strength of the model stems from the last two components which are instrumental for on line knowledge discovery. They map to the Knowledge Discovery Tools (KDT) for intelligent query answering, proposed in Knowledge Rich Databases (KRDB) data model proposed in [27]. The modularity in the design of the components adds to this strength. There is a wide and clear scope for designing the desired accumulation function (P acc , to be precise) and the corresponding operators for experimenting and monitoring. 4.5 Conclusion I MIN process model is a generic, ....

J. Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent Query Answering by Knowledge Discovery Techniques. IEEE Transactions on Knowledge and Data Engineering, 8(3):373-390, 1996.

....A user can supply the threshold for a given key word in the warehouse concept mart and the words with the threshold above the given value can be taken into consideration when answering the query. The query can also be answered using different levels of concept in the warehouse concept mart [3] or can provide approximate answers [4] Another interesting idea is to provide the user some knowledge in framing the global coupling query graph. For example, If a user frame a query graph to find some information about Database system , he can be supplied some related concepts like ORACLE ....

J. Han, Yue Huang, et al. Intelligent Query Answering by Knowledge Discovery Techniques, IEEE TKDE, 1996.

....This is a rapidly developing area where GA techniques are used to establish the criterion when a symbolic reduction is performed. Various traditional mechanisms have been developed by the data mining research community to search for patterns, rules, and important information (Han et al. 1993; Han et al. 1996; Siegel et al. 1992) All of these techniques require the DBA to establish two items: a concept hierarchy and a set of criterion that the data mining algorithm should use to compare and contrast its underlying data elements. A human typically guides the discovery process. There has been some ....

Han, J., Huang, Y., Cerone, N., & Fu, Y. (1996). Intelligent query answering by knowledge discovery techniques. IEEE Transactions on Knowledge and Data Engineering, 8(3), 373-389.

.... systems [32, 2] and GUI generation [17] Deductive database languages support rapid prototyping via rules that express both domain knowledge (either user encoded or discovered) and database searches; these traits make them well suited for data mining and knowledge discovery applications [50, 23, 3, 48, 49, 52, 16]. The main obstacle remains the lack of commercial systems. Indeed, while powerful prototypes are available [11, 36, 54] they have not yet matured into commercial systems. These and other research prototypes are surveyed in [38] ....

J. Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent Query Answering by Knowledge Discovery Techniques, IEEE Transactions on Knowledge and Data Engineering, 8(3):373-390, 1996.

..... Finally, the query processor at mobile host may decide to rephrase the query to use only summary data to get an answer to an alternative query. In this paper, we are interested in the last two strategies listed above. To summarize the main database, we make use of concept hierarchies [HCC, HHCF, R, RR]. Concept hierarchies are generally supplied a priori by the DBA but can also be generated from the domain information available about the attributes and on the functional dependencies that exist in a particular relation. We propose several ways of summarizing the database using concept ....

....hierarchies to answer the queries that may yield sound but not complete approximate answers [R, RCR1] We have also discussed the cost benefit analysis with respect to storage, communication and query processing cost. Our query processing model is different from the model proposed by Han et al. [HHCF]. The model proposed in [HHCF] for intelligent query processing uses concept hierarchies, however, the queries are rewritten using lower level concepts. Our model uses higher level concepts, thus providing approximate answers possibly in fewer steps. More importantly, we have proposed methods for ....

[Article contains additional citation context not shown here]

Han, J., Huang, Y. Cercone, N. and Fu, Y. Intelligent Query Answering by Knowledge Discovery Techniques, IEEE Transactions on Knowledge and Data Engineering, 8(3): 373-390, 1996.

.... study of knowledge discovery in different kinds of databases [14, 12, 17, 13, 20] including knowledge discovery in relational, object oriented, deductive, spatial, and active databases, and global information sys tems, and the application of knowledge discovery for intelligent query answering [11], multiple layered database construction [9] etc. Research is partially supported by the Natural Sciences and Engineering Research Council of Canada under the grant OGP0037230, by the Networks of Centres of Excellence Program (with the participation of PRECARN association) under the grants ....

....have demonstrated some limited success of the approach [20] Knowledge discovery has strong application potential. Besides the use of discovered knowledge for decision making, process control and knowledge base construction, we have investigated its application in intelligent query answering [11] and multiple layered database construction [9] Database queries can be answered intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Knowledge discovery substantially broadens the spectrum of ....

J. Hah, Y. Huang, N. Ccrconc, and Y. Fu. Intelligent query answering by knowledge discovery techniques. In IEEE Trans. Knowledge and Data Engineering (accepted), 1995.

....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 ....

J. Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge discovery techniques. IEEE Trans. Knowledge and Data Engineering, 8:373-390, 1996.

....is efficient and effective at mining large time series databases. 3. 6 Intelligent query answering with data mining techniques With data mining techniques available, database queries can be answered intelligently using concept hierarchies, data mining results, or on line data mining techniques [17]. For example, instead of presenting bulky answers, one can present a summary of answers and allow users to manipulate such a summary by drilling or dicing. One can present related answers or rules in the form of associations or correlations based on association mining results. Moreover, one may ....

J. Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge discovery techniques. IEEE Trans. Knowledge and Data Engineering, 8:373-- 390, 1996.

....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 ....

J. Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge discovery techniques. IEEE Trans. Knowledge and Data Engineering, 8:373--390, 1996.

....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 ....

....range of 250k 300k instead of 280k 300k to avoid misunderstanding. 2 4. 2 Cooperative query answering in an MLDB Since an MLDB stores general database information in higher layers, many techniques investigated in previous researches on cooperative query answering in (single layered) databases [14, 6, 5, 10, 13] can be extended to cooperative query answering in MLDBs, easily, effectively and efficiently. 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 [5, 11] whereas ....

J. Han, Y. Huang, and N. Cercone. Intelligent query answering by knowledge discovery techniques. In submitted to IEEE Trans. Knowledge and Data Engineering, 1993.

.... The study of knowledge discovery in different kinds of databases [14, 12, 17, 13, 20] including knowledge discovery in relational, object oriented, deductive, spatial, and active databases, and global information systems, and the application of knowledge discovery for intelligent query answering [11], multiple layered database construction [9] etc. Research is partially supported by the Natural Sciences and Engineering Research Council of Canada under the grant OGP0037230, by the Networks of Centres of Excellence Program (with the participation of PRECARN association) under the grants ....

....have demonstrated some limited success of the approach [20] Knowledge discovery has strong application potential. Besides the use of discovered knowledge for decision making, process control and knowledge base construction, we have investigated its application in intelligent query answering [11] and multiple layered database construction [9] Database queries can be answered intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Knowledge discovery substantially broadens the spectrum of ....

J. Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge discovery techniques. In IEEE Trans. Knowledge and Data Engineering (accepted), 1995.

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J. W. Han, Y. Huang, N. Cercone, and Y. J. Fu. Intelligent query answering by knowledge discovery techniques. In IEEE Trans, pages 373-390, 1996.

No context found.

Han, J.; Huang, Y.; Cercone, N.; and Fu, Y. 1996. Intelligent Query Answering by Knowledge Discovery Techniques. IEEE Transactions on Knowledge and Data Engineering 8(3):373-389.

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