• Journal of KIISE:Databases
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• v.30 no.3
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• pp.225-236
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• 2003

MOLAP systems store data in a multidimensional away called a 'cube' and access them using way indexes. When a cube is placed into disk, it can be Partitioned into a set of chunks of the same side length. Such a cube storage scheme is called the chunk-based MOLAP cube storage scheme. It gives data clustering effect so that all the dimensions are guaranteed to get a fair chance in terms of the query processing speed. In order to achieve high space utilization, sparse chunks are further compressed. Due to data compression, the relative position of chunks cannot be obtained in constant time without using indexes. In this paper, we propose a bitmap index for chunk-based MOLAP cubes. The index can be constructed along with the corresponding cube generation. The relative position of chunks is retained in the index so that chunk retrieval can be done in constant time. We placed in an index block as many chunks as possible so that the number of index searches is minimized for OLAP operations such as range queries. We showed the proposed index is efficient by comparing it with multidimensional indexes such as UB-tree and grid file in terms of time and space.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.17-35
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• 2011

The collective intelligence is a common-based production by the collaboration and competition of many peer individuals. In other words, it is the aggregation of individual intelligence to lead the wisdom of crowd. Recently, the utilization of the collective intelligence has become one of the emerging research areas, since it has been adopted as an important principle of web 2.0 to aim openness, sharing and participation. This paper introduces an approach to seek the collective intelligence by cognition of the relation and interaction among individual participants. It describes a methodology well-suited to evaluate individual intelligence in information retrieval and classification as an application field. The research investigates how to derive and represent such cognitive intelligence from individuals through the application of fuzzy relational theory to personal construct theory and knowledge grid technique. Crucial to this research is to implement formally and process interpretatively the cognitive knowledge of participants who makes the mutual relation and social interaction. What is needed is a technique to analyze cognitive intelligence structure in the form of Hasse diagram, which is an instantiation of this perceptive intelligence of human beings. The search for the collective intelligence requires a theory of similarity to deal with underlying problems; clustering of social subgroups of individuals through identification of individual intelligence and commonality among intelligence and then elicitation of collective intelligence to aggregate the congruence or sharing of all the participants of the entire group. Unlike standard approaches to similarity based on statistical techniques, the method presented employs a theory of fuzzy relational products with the related computational procedures to cover issues of similarity and dissimilarity.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.1-21
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• 2012

In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 438~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.