• Journal of Electrical Engineering and information Science
• /
• v.1 no.2
• /
• pp.145-156
• /
• 1996

Although knowledge discovery is increasingly important in databases, the discovered knowledge sets may not be effectively used for application domains. It is partly because knowledge discovery does not take user's interests into account, and too many knowledge sets are discovered to handle efficiently. We believe that user's interests are conveyed by a query and if a nested query is concerned it may include a user's thought process. This paper describes a novel concept for discovering knowledge sets based on query processing. Knowledge discovery process is performed by: extracting features from databases, spanning features to generate range features, and constituting a knowledge set. The contributions of this paper include the following: (1) not only simple queries but also nested queries are considered to discover knowledge sets regarding user's interests and user's thought process, (2) not only positive examples (answer to a query) but also negative examples are considered to discover knowledge sets regarding database abstraction and database exceptions, and (3) finally, the discovered knowledge sets are quantified.

• Proceedings of the Korea Society of Design Studies Conference
• /
• 2001.10a
• /
• pp.85.1-85
• /
• 2001

• Journal of Internet Computing and Services
• /
• v.13 no.2
• /
• pp.73-86
• /
• 2012

This paper theoretically derives an algorithm to discover a new type of social networks from workflow models, which is termed workflow-based social network intelligence. In general, workflow intelligence (or business process intelligence) technology consists of four types of techniques that discover, analyze, monitor and control, and predict from workflow models and their execution histories. So, this paper proposes an algorithm, which is termed ICN-based workflow-based social network intelligence discovery algorithm, to be classified into the type of discovery techniques, which are able to discover workflow-based social network intelligence that are formed among workflow performers through a series of workflow models and their executions, In order particularly to prove the correctness and feasibility of the proposed algorithm, this paper tries to apply the algorithm to a specific workflow model and to show that it is able to generate its corresponding workflow-based social network intelligence.

• Journal of Korean Society for Quality Management
• /
• v.45 no.1
• /
• pp.117-128
• /
• 2017

Purpose: This study analyzes automobile quality review data to develop alternative analytical method of informal data. Existing methods to analyze informal data are based mainly on the frequency of informal data, however, this research tries to use correlation information of each informal data. Method: After sentimental analysis to acquire the user information for automobile products, three classification methods, that is, $na{\ddot{i}}ve$ Bayes, random forest, and support vector machine, were employed to accurately classify the informal user opinions with respect to automobile qualities. Additionally, Word2vec was applied to discover correlated information about informal data. Result: As applicative results of three classification methods, random forest method shows most effective results compared to the other classification methods. Word2vec method manages to discover closest relevant data with automobile components. Conclusion: The proposed method shows its effectiveness in terms of accuracy and sensitivity on the analysis of informal quality data, however, only two sentiments (positive or negative) can be categorized due to human errors. Further studies are required to derive more sentiments to accurately classify informal quality data. Word2vec method also shows comparative results to discover the relevance of components precisely.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.2A
• /
• pp.128-137
• /
• 2006

In this paper, we propose a wireless network discovery scheme which support effective device power management by employing battery efficient network scanning procedure. Multi-mode terminals need to discover other wireless systems, above all, to execute an inter-system handover in the environment of heterogeneous wireless networks. The existing methods introduced in some recent research reports have certain shortcomings, such as battery power consumption increased by frequent modem activation, or the multi-mode terminal's inability to promptly discover wireless system. We Propose a scheme in which multi-mode terminals more quickly and accurately discover other wireless systems than previous schemes, while consuming minimum power. It also proves that the scheme has better performance by comparing it with the existing schemes.

• The KIPS Transactions:PartD
• /
• v.19D no.2
• /
• pp.139-146
• /
• 2012

Data Mining is widely used to discover knowledge in many fields. Although there are many methods to discover association rule, most of them are based on frequency-based approaches. Therefore it is not appropriate for stream environment. Because the stream environment has a property that event data are generated continuously. it is expensive to store all data. In this paper, we propose a new method to discover association rules based on stream environment. Our new method is using a variable window for extracting data items. Variable windows have variable size according to the gap of same target event. Our method extracts data using COBJ(Count object) calculation method. FPMDSTN(Frequent pattern Mining over Data Stream using Terminal Node) discovers association rules from the extracted data items. Through experiment, our method is more efficient to apply stream environment than conventional methods.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.42 no.4 s.334
• /
• pp.7-14
• /
• 2005

Earlier researches have typically concentrated on discovering IP network topology, which implies that the connectivity of all Ethernet devices is ignored. But automatic discovery of Physical topology Plays a crucial role in enhancing the manageability of modem Metro Ethernet mesh networks due to the benefits of Ethernet services, including: Ease of use, Cost Effectiveness and flexibility. Because of proprietary solutions targeting specific product families and related algorithm which depends on Layer 2 forwarding table information it is impossible to discover physical topology in the Ethernet mesh networks. To cope with these shortcomings, in this paper we propose a novel and practical algorithmic solution that can discover accurate physical topology in the Ethernet mesh networks. Our algorithm divides the Ethernet mesh networks into bridged networks and host networks and those bridges located in boundary are named edge bridges. Our algorithm uses the standard spanning tree protocol MIB information for the bridged networks and uses the standard Layer 2 forwarding table MIB information for the host networks. As using the standard MIB information to discover physical topology we can offer interoperability guarantee in the Ethernet mesh networks composed of the various vendors' products.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.05a
• /
• pp.97-99
• /
• 2011

As interoperability between systems in distributed environment has been important, it has been possible for various organizations to share resources and exchange relevant information. However, semantic heterogeneity between the systems and organizations causes the problem of making their interoperability impossible. Thereby, in this paper, we propose an ontology matching-based knowledge management system which can automatically discover semantic correspondences between ontologies. Moreover, even though there have been many existing ontology matchers, it is still difficult to directly apply them to the proposed system. To deal with the problems, we want to discover matching patterns (MP) which they discover from two given ontologies.

• Journal of Korean Medical classics
• /
• v.31 no.3
• /
• pp.33-58
• /
• 2018

Objectives : Literatures related to Bianque are studied to discover the path of development and the impact of Bianque school's pulse diagnosis system. Methods : Texts regarding Bianque were searched in history books such as Shiji and Zhanguoce, and medical texts such as the medical books of Mawangdui Han Tomb, Huangdineijng, Maijing, and Qianjinyifang to understand how the Bianque school's pulse diagnosis system was developed. Results : 1. Bianque school's pulse diagnosis system was used to inspect the distribution pattern of blood vessels and discover the location of the disease including the palpatation realm such as only hard or only fall. 2. The system of inspection was created when the diagnosis method that uses the color of the pulse by using the color of blood vessels was added to the diagnostic method of pulse condition. 3. Adding the concept of pulse to the visual information that derives from pulse condition becomes pulsation. This is a diagnostic method that falls under the realm of palpation, and it was used to discover the location of disease. 4. The qi of pulse is motor that induces pulse, and this concept is used in order to understand how normal and abnormal pulsations appear, and to treat the circulation disorder of qi and blood. 5. Cubit skin examination is a method that comprehensively take into account the upper arm skin's cold and heat, slippery and roughness, and relax and tension state. This method was used together with other diagnostic methods. As described above, it seems that the diagnostic method with blood vessels used by Bianque school seems to have developed from Bianque's special inspection ability to the stage where it uses palpation, and then to the stage of cubit skin examination which uses both palpation and inspection.

• Journal of KIISE:Databases
• /
• v.28 no.3
• /
• pp.301-314
• /
• 2001

Data mining refers to a set of techniques for discovering implicit and useful knowledge from large database. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering knowledge from temporal database, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treat problems for discovering temporal pattern from data which are stamped with time points and do not consider problems for discovering knowledge from temporal interval data. For example, there are many examples of temporal interval data that it can discover useful knowledge from. These include patient histories, purchaser histories, web log, and so on. Allen introduces relationships between intervals and operators for reasoning about relations between intervals. We present a new data mining technique that can discover temporal relation rules in temporal interval data by using the Allen's theory. In this paper, we present two new algorithms for discovering algorithm for generating temporal relation rules, discovers rules from temporal interval data. This technique can discover more useful knowledge in compared with conventional data mining techniques.